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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense.

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research.

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words.

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find “food” and “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended text analysis methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other.

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

Content Analysis: It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
Discourse Analysis: Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
Grounded Theory: When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

Fraud: To ensure an actual human being records each response to the survey or the questionnaire
Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
Procedure: To ensure ethical standards were maintained while collecting the data sample
Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

Count, Percent, Frequency
It is used to denote home often a particular event occurs.
Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

Mean, Median, Mode
The method is widely used to demonstrate distribution by various points.
Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

Range, Variance, Standard deviation
Here the field equals high/low points.
Variance standard deviation = difference between the observed score and mean
It is used to identify the spread of scores by stating intervals.
Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

Percentile ranks, Quartile ranks
It relies on standardized scores helping researchers to identify the relationship between different scores.
It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided sample without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected sample to reason that about 80-90% of people like the movie.

Here are two significant areas of inferential statistics.

Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
Cross-tabulation: Also called contingency tables, cross-tabulation is used to analyze the relationship between multiple variables. Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing audience sample il to draw a biased inference.
Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Data Analysis Techniques in Research – Methods, Tools & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

If you want to learn more about this topic and acquire valuable skills that will set you apart in today’s data-driven world, we highly recommend enrolling in the Data Analytics Course by Physics Wallah . And as a special offer for our readers, use the coupon code “READER” to get a discount on this course.

Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

Inspecting : Initial examination of data to understand its structure, quality, and completeness.
Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

Calculate the mean, median, and mode of academic scores for both groups.
Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

Description: An open-source programming language specifically designed for statistical computing and data visualization.
Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

Description: A data mining software application used for building predictive models and conducting advanced analytics.
Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
SQL : A programming language used to manage and manipulate relational databases.
R : An open-source programming language and software environment for statistical computing and graphics.
Python : A general-purpose programming language that is widely used in data analysis and machine learning.
Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
SAS : A statistical analysis software used for data management, analysis, and reporting.
SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
Matlab : A numerical computing software that is widely used in scientific research and engineering.
RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

Defining the question
Collecting the data
Cleaning the data
Analyzing the data
Sharing your results
Embracing failure

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more: What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial . We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

These are the top 9 data analytics tools
10 great places to find free datasets for your next project
How to build a data analytics portfolio

Your Modern Business Guide To Data Analysis Methods And Techniques

Data analysis methods and techniques blog post by datapine

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis.

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include:

Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate.
Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes.
Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic.
Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply.
Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis.

Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step.
Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others. An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario.
Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data.
Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others.
Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them.

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches.

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world:

A. Quantitative Methods

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods.

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

Cohort analysis chart example from google analytics

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist.

Here is an example of how you can use the predictive analysis tool from datapine:

Example on how to use predictive analytics tool from datapine

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge. When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

Example on how to use intelligent alerts from datapine

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result.

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events.

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.

8. Decision Trees

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision.

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely. Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision. In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more.

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments.

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic.

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example.

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of.

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all” and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses. When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all.

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading.

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best.

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Example of multidimensional scaling analysis

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data.

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next.

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context.

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question.

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service.

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore, to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize.

15. Narrative Analysis

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others.

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study.

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on.

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice.

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data.

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes.

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

17 top data analysis techniques by datapine

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical.

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole.

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors.

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

An executive dashboard example showcasing high-level marketing KPIs such as cost per lead, MQL, SQL, and cost per customer.

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations.

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation.
Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving.

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in.

Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low.
External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high.
Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now.
Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps.

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource .

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail.

Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions.
Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective.
Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them.
Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them.
Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.
Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy.
Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way.
Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data.

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers.
Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate.
Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient.
SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis.
Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context.

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
94% of enterprises say that analyzing data is important for their growth and digital transformation.
Companies that exploit the full potential of their data can increase their operating margins by 60% .
We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

Cluster analysis
Cohort analysis
Regression analysis
Factor analysis
Neural Networks
Data Mining
Text analysis
Time series analysis
Decision trees
Conjoint analysis
Correspondence Analysis
Multidimensional Scaling
Content analysis
Thematic analysis
Narrative analysis
Grounded theory analysis
Discourse analysis

Top 17 Data Analysis Techniques:

Collaborate your needs
Establish your questions
Data democratization
Think of data governance
Clean your data
Set your KPIs
Omit useless data
Build a data management roadmap
Integrate technology
Answer your questions
Visualize your data
Interpretation of data
Consider autonomous technology
Build a narrative
Share the load
Data Analysis tools
Refine your process constantly

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

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v.68(4); Jul-Aug 2015

Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study

There are three kinds of lies: lies, damned lies, and statistics. – Mark Twain 1

INTRODUCTION

Statistics represent an essential part of a study because, regardless of the study design, investigators need to summarize the collected information for interpretation and presentation to others. It is therefore important for us to heed Mr Twain’s concern when creating the data analysis plan. In fact, even before data collection begins, we need to have a clear analysis plan that will guide us from the initial stages of summarizing and describing the data through to testing our hypotheses.

The purpose of this article is to help you create a data analysis plan for a quantitative study. For those interested in conducting qualitative research, previous articles in this Research Primer series have provided information on the design and analysis of such studies. 2 , 3 Information in the current article is divided into 3 main sections: an overview of terms and concepts used in data analysis, a review of common methods used to summarize study data, and a process to help identify relevant statistical tests. My intention here is to introduce the main elements of data analysis and provide a place for you to start when planning this part of your study. Biostatistical experts, textbooks, statistical software packages, and other resources can certainly add more breadth and depth to this topic when you need additional information and advice.

TERMS AND CONCEPTS USED IN DATA ANALYSIS

When analyzing information from a quantitative study, we are often dealing with numbers; therefore, it is important to begin with an understanding of the source of the numbers. Let us start with the term variable , which defines a specific item of information collected in a study. Examples of variables include age, sex or gender, ethnicity, exercise frequency, weight, treatment group, and blood glucose. Each variable will have a group of categories, which are referred to as values , to help describe the characteristic of an individual study participant. For example, the variable “sex” would have values of “male” and “female”.

Although variables can be defined or grouped in various ways, I will focus on 2 methods at this introductory stage. First, variables can be defined according to the level of measurement. The categories in a nominal variable are names, for example, male and female for the variable “sex”; white, Aboriginal, black, Latin American, South Asian, and East Asian for the variable “ethnicity”; and intervention and control for the variable “treatment group”. Nominal variables with only 2 categories are also referred to as dichotomous variables because the study group can be divided into 2 subgroups based on information in the variable. For example, a study sample can be split into 2 groups (patients receiving the intervention and controls) using the dichotomous variable “treatment group”. An ordinal variable implies that the categories can be placed in a meaningful order, as would be the case for exercise frequency (never, sometimes, often, or always). Nominal-level and ordinal-level variables are also referred to as categorical variables, because each category in the variable can be completely separated from the others. The categories for an interval variable can be placed in a meaningful order, with the interval between consecutive categories also having meaning. Age, weight, and blood glucose can be considered as interval variables, but also as ratio variables, because the ratio between values has meaning (e.g., a 15-year-old is half the age of a 30-year-old). Interval-level and ratio-level variables are also referred to as continuous variables because of the underlying continuity among categories.

As we progress through the levels of measurement from nominal to ratio variables, we gather more information about the study participant. The amount of information that a variable provides will become important in the analysis stage, because we lose information when variables are reduced or aggregated—a common practice that is not recommended. 4 For example, if age is reduced from a ratio-level variable (measured in years) to an ordinal variable (categories of < 65 and ≥ 65 years) we lose the ability to make comparisons across the entire age range and introduce error into the data analysis. 4

A second method of defining variables is to consider them as either dependent or independent. As the terms imply, the value of a dependent variable depends on the value of other variables, whereas the value of an independent variable does not rely on other variables. In addition, an investigator can influence the value of an independent variable, such as treatment-group assignment. Independent variables are also referred to as predictors because we can use information from these variables to predict the value of a dependent variable. Building on the group of variables listed in the first paragraph of this section, blood glucose could be considered a dependent variable, because its value may depend on values of the independent variables age, sex, ethnicity, exercise frequency, weight, and treatment group.

Statistics are mathematical formulae that are used to organize and interpret the information that is collected through variables. There are 2 general categories of statistics, descriptive and inferential. Descriptive statistics are used to describe the collected information, such as the range of values, their average, and the most common category. Knowledge gained from descriptive statistics helps investigators learn more about the study sample. Inferential statistics are used to make comparisons and draw conclusions from the study data. Knowledge gained from inferential statistics allows investigators to make inferences and generalize beyond their study sample to other groups.

Before we move on to specific descriptive and inferential statistics, there are 2 more definitions to review. Parametric statistics are generally used when values in an interval-level or ratio-level variable are normally distributed (i.e., the entire group of values has a bell-shaped curve when plotted by frequency). These statistics are used because we can define parameters of the data, such as the centre and width of the normally distributed curve. In contrast, interval-level and ratio-level variables with values that are not normally distributed, as well as nominal-level and ordinal-level variables, are generally analyzed using nonparametric statistics.

METHODS FOR SUMMARIZING STUDY DATA: DESCRIPTIVE STATISTICS

The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data.

Selection of an appropriate figure to represent a particular set of data depends on the measurement level of the variable. Data for nominal-level and ordinal-level variables may be interpreted using a pie graph or bar graph . Both options allow us to examine the relative number of participants within each category (by reporting the percentages within each category), whereas a bar graph can also be used to examine absolute numbers. For example, we could create a pie graph to illustrate the proportions of men and women in a study sample and a bar graph to illustrate the number of people who report exercising at each level of frequency (never, sometimes, often, or always).

Interval-level and ratio-level variables may also be interpreted using a pie graph or bar graph; however, these types of variables often have too many categories for such graphs to provide meaningful information. Instead, these variables may be better interpreted using a histogram . Unlike a bar graph, which displays the frequency for each distinct category, a histogram displays the frequency within a range of continuous categories. Information from this type of figure allows us to determine whether the data are normally distributed. In addition to pie graphs, bar graphs, and histograms, many other types of figures are available for the visual representation of data. Interested readers can find additional types of figures in the books recommended in the “Further Readings” section.

Figures are also useful for visualizing comparisons between variables or between subgroups within a variable (for example, the distribution of blood glucose according to sex). Box plots are useful for summarizing information for a variable that does not follow a normal distribution. The lower and upper limits of the box identify the interquartile range (or 25th and 75th percentiles), while the midline indicates the median value (or 50th percentile). Scatter plots provide information on how the categories for one continuous variable relate to categories in a second variable; they are often helpful in the analysis of correlations.

In addition to using figures to present a visual description of the data, investigators can use statistics to provide a numeric description. Regardless of the measurement level, we can find the mode by identifying the most frequent category within a variable. When summarizing nominal-level and ordinal-level variables, the simplest method is to report the proportion of participants within each category.

The choice of the most appropriate descriptive statistic for interval-level and ratio-level variables will depend on how the values are distributed. If the values are normally distributed, we can summarize the information using the parametric statistics of mean and standard deviation. The mean is the arithmetic average of all values within the variable, and the standard deviation tells us how widely the values are dispersed around the mean. When values of interval-level and ratio-level variables are not normally distributed, or we are summarizing information from an ordinal-level variable, it may be more appropriate to use the nonparametric statistics of median and range. The first step in identifying these descriptive statistics is to arrange study participants according to the variable categories from lowest value to highest value. The range is used to report the lowest and highest values. The median or 50th percentile is located by dividing the number of participants into 2 groups, such that half (50%) of the participants have values above the median and the other half (50%) have values below the median. Similarly, the 25th percentile is the value with 25% of the participants having values below and 75% of the participants having values above, and the 75th percentile is the value with 75% of participants having values below and 25% of participants having values above. Together, the 25th and 75th percentiles define the interquartile range .

PROCESS TO IDENTIFY RELEVANT STATISTICAL TESTS: INFERENTIAL STATISTICS

One caveat about the information provided in this section: selecting the most appropriate inferential statistic for a specific study should be a combination of following these suggestions, seeking advice from experts, and discussing with your co-investigators. My intention here is to give you a place to start a conversation with your colleagues about the options available as you develop your data analysis plan.

There are 3 key questions to consider when selecting an appropriate inferential statistic for a study: What is the research question? What is the study design? and What is the level of measurement? It is important for investigators to carefully consider these questions when developing the study protocol and creating the analysis plan. The figures that accompany these questions show decision trees that will help you to narrow down the list of inferential statistics that would be relevant to a particular study. Appendix 1 provides brief definitions of the inferential statistics named in these figures. Additional information, such as the formulae for various inferential statistics, can be obtained from textbooks, statistical software packages, and biostatisticians.

What Is the Research Question?

The first step in identifying relevant inferential statistics for a study is to consider the type of research question being asked. You can find more details about the different types of research questions in a previous article in this Research Primer series that covered questions and hypotheses. 5 A relational question seeks information about the relationship among variables; in this situation, investigators will be interested in determining whether there is an association ( Figure 1 ). A causal question seeks information about the effect of an intervention on an outcome; in this situation, the investigator will be interested in determining whether there is a difference ( Figure 2 ).

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Decision tree to identify inferential statistics for an association.

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Decision tree to identify inferential statistics for measuring a difference.

What Is the Study Design?

When considering a question of association, investigators will be interested in measuring the relationship between variables ( Figure 1 ). A study designed to determine whether there is consensus among different raters will be measuring agreement. For example, an investigator may be interested in determining whether 2 raters, using the same assessment tool, arrive at the same score. Correlation analyses examine the strength of a relationship or connection between 2 variables, like age and blood glucose. Regression analyses also examine the strength of a relationship or connection; however, in this type of analysis, one variable is considered an outcome (or dependent variable) and the other variable is considered a predictor (or independent variable). Regression analyses often consider the influence of multiple predictors on an outcome at the same time. For example, an investigator may be interested in examining the association between a treatment and blood glucose, while also considering other factors, like age, sex, ethnicity, exercise frequency, and weight.

When considering a question of difference, investigators must first determine how many groups they will be comparing. In some cases, investigators may be interested in comparing the characteristic of one group with that of an external reference group. For example, is the mean age of study participants similar to the mean age of all people in the target group? If more than one group is involved, then investigators must also determine whether there is an underlying connection between the sets of values (or samples ) to be compared. Samples are considered independent or unpaired when the information is taken from different groups. For example, we could use an unpaired t test to compare the mean age between 2 independent samples, such as the intervention and control groups in a study. Samples are considered related or paired if the information is taken from the same group of people, for example, measurement of blood glucose at the beginning and end of a study. Because blood glucose is measured in the same people at both time points, we could use a paired t test to determine whether there has been a significant change in blood glucose.

What Is the Level of Measurement?

As described in the first section of this article, variables can be grouped according to the level of measurement (nominal, ordinal, or interval). In most cases, the independent variable in an inferential statistic will be nominal; therefore, investigators need to know the level of measurement for the dependent variable before they can select the relevant inferential statistic. Two exceptions to this consideration are correlation analyses and regression analyses ( Figure 1 ). Because a correlation analysis measures the strength of association between 2 variables, we need to consider the level of measurement for both variables. Regression analyses can consider multiple independent variables, often with a variety of measurement levels. However, for these analyses, investigators still need to consider the level of measurement for the dependent variable.

Selection of inferential statistics to test interval-level variables must include consideration of how the data are distributed. An underlying assumption for parametric tests is that the data approximate a normal distribution. When the data are not normally distributed, information derived from a parametric test may be wrong. 6 When the assumption of normality is violated (for example, when the data are skewed), then investigators should use a nonparametric test. If the data are normally distributed, then investigators can use a parametric test.

ADDITIONAL CONSIDERATIONS

What is the level of significance.

An inferential statistic is used to calculate a p value, the probability of obtaining the observed data by chance. Investigators can then compare this p value against a prespecified level of significance, which is often chosen to be 0.05. This level of significance represents a 1 in 20 chance that the observation is wrong, which is considered an acceptable level of error.

What Are the Most Commonly Used Statistics?

In 1983, Emerson and Colditz 7 reported the first review of statistics used in original research articles published in the New England Journal of Medicine . This review of statistics used in the journal was updated in 1989 and 2005, 8 and this type of analysis has been replicated in many other journals. 9 – 13 Collectively, these reviews have identified 2 important observations. First, the overall sophistication of statistical methodology used and reported in studies has grown over time, with survival analyses and multivariable regression analyses becoming much more common. The second observation is that, despite this trend, 1 in 4 articles describe no statistical methods or report only simple descriptive statistics. When inferential statistics are used, the most common are t tests, contingency table tests (for example, χ 2 test and Fisher exact test), and simple correlation and regression analyses. This information is important for educators, investigators, reviewers, and readers because it suggests that a good foundational knowledge of descriptive statistics and common inferential statistics will enable us to correctly evaluate the majority of research articles. 11 – 13 However, to fully take advantage of all research published in high-impact journals, we need to become acquainted with some of the more complex methods, such as multivariable regression analyses. 8 , 13

What Are Some Additional Resources?

As an investigator and Associate Editor with CJHP , I have often relied on the advice of colleagues to help create my own analysis plans and review the plans of others. Biostatisticians have a wealth of knowledge in the field of statistical analysis and can provide advice on the correct selection, application, and interpretation of these methods. Colleagues who have “been there and done that” with their own data analysis plans are also valuable sources of information. Identify these individuals and consult with them early and often as you develop your analysis plan.

Another important resource to consider when creating your analysis plan is textbooks. Numerous statistical textbooks are available, differing in levels of complexity and scope. The titles listed in the “Further Reading” section are just a few suggestions. I encourage interested readers to look through these and other books to find resources that best fit their needs. However, one crucial book that I highly recommend to anyone wanting to be an investigator or peer reviewer is Lang and Secic’s How to Report Statistics in Medicine (see “Further Reading”). As the title implies, this book covers a wide range of statistics used in medical research and provides numerous examples of how to correctly report the results.

CONCLUSIONS

When it comes to creating an analysis plan for your project, I recommend following the sage advice of Douglas Adams in The Hitchhiker’s Guide to the Galaxy : Don’t panic! 14 Begin with simple methods to summarize and visualize your data, then use the key questions and decision trees provided in this article to identify relevant statistical tests. Information in this article will give you and your co-investigators a place to start discussing the elements necessary for developing an analysis plan. But do not stop there! Use advice from biostatisticians and more experienced colleagues, as well as information in textbooks, to help create your analysis plan and choose the most appropriate statistics for your study. Making careful, informed decisions about the statistics to use in your study should reduce the risk of confirming Mr Twain’s concern.

Appendix 1. Glossary of statistical terms * (part 1 of 2)

1-way ANOVA: Uses 1 variable to define the groups for comparing means. This is similar to the Student t test when comparing the means of 2 groups.
Kruskall–Wallis 1-way ANOVA: Nonparametric alternative for the 1-way ANOVA. Used to determine the difference in medians between 3 or more groups.
n -way ANOVA: Uses 2 or more variables to define groups when comparing means. Also called a “between-subjects factorial ANOVA”.
Repeated-measures ANOVA: A method for analyzing whether the means of 3 or more measures from the same group of participants are different.
Freidman ANOVA: Nonparametric alternative for the repeated-measures ANOVA. It is often used to compare rankings and preferences that are measured 3 or more times.
Fisher exact: Variation of chi-square that accounts for cell counts < 5.
McNemar: Variation of chi-square that tests statistical significance of changes in 2 paired measurements of dichotomous variables.
Cochran Q: An extension of the McNemar test that provides a method for testing for differences between 3 or more matched sets of frequencies or proportions. Often used as a measure of heterogeneity in meta-analyses.
1-sample: Used to determine whether the mean of a sample is significantly different from a known or hypothesized value.
Independent-samples t test (also referred to as the Student t test): Used when the independent variable is a nominal-level variable that identifies 2 groups and the dependent variable is an interval-level variable.
Paired: Used to compare 2 pairs of scores between 2 groups (e.g., baseline and follow-up blood pressure in the intervention and control groups).

Lang TA, Secic M. How to report statistics in medicine: annotated guidelines for authors, editors, and reviewers. 2nd ed. Philadelphia (PA): American College of Physicians; 2006.

Norman GR, Streiner DL. PDQ statistics. 3rd ed. Hamilton (ON): B.C. Decker; 2003.

Plichta SB, Kelvin E. Munro’s statistical methods for health care research . 6th ed. Philadelphia (PA): Wolters Kluwer Health/ Lippincott, Williams & Wilkins; 2013.

This article is the 12th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.
Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.
Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.
Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.
Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.
Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.
Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.
Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.
Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.
Sutton J, Austin Z. Qualitative research: data collection, analysis, and management. Can J Hosp Pharm . 2014;68(3):226–31.
Cadarette SM, Wong L. An introduction to health care administrative data. Can J Hosp Pharm. 2014;68(3):232–7.

Competing interests: None declared.

Research Guide: Data analysis and reporting findings

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Data analysis and findings

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends.

Data Analysis Checklist

Cleaning data

* Did you capture and code your data in the right manner?

*Do you have all data or missing data?

* Do you have enough observations?

* Do you have any outliers? If yes, what is the remedy for outlier?

* Does your data have the potential to answer your questions?

Analyzing data

* Visualize your data, e.g. charts, tables, and graphs, to mention a few.

* Identify patterns, correlations, and trends

* Test your hypotheses

* Let your data tell a story

Reports the results

* Communicate and interpret the results

* Conclude and recommend

* Your targeted audience must understand your results

* Use more datasets and samples

* Use accessible and understandable data analytical tool

* Do not delegate your data analysis

* Clean data to confirm that they are complete and free from errors

* Analyze cleaned data

* Understand your results

* Keep in mind who will be reading your results and present it in a way that they will understand it

* Share the results with the supervisor oftentimes

Past presentations

PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga.

Online Resources

Qualitative analysis of interview data: A step-by-step guide
Qualitative Data Analysis - Coding & Developing Themes

Recommended Quantitative Data Analysis books

Recommended Qualitative Data Analysis books

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

Interview transcripts
Surveys with open-ended questions
Contact center transcripts
Texts and documents
Audio and video recordings
Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

Gathering and collecting your qualitative data
Organizing and connecting into your qualitative data
Coding your qualitative data
Analyzing the qualitative data for insights
Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues. It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope, helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered. Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis. Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them. It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations. The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Methods of qualitative data analysis; approaches and techniques to qualitative data analysis

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend, and review analysis is a great place to start. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

G2.com reviews of the product Airtable. You could pull reviews from G2 for your analysis.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.

The benefits of using computer-assisted qualitative data analysis software:

Assists in the organizing of your data
Opens you up to exploring different interpretations of your data analysis
Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

The user interface of CAQDAS software 'NVivo'

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:

Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Some of qualitative data integrations offered by Thematic

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably. For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
Keep repeating step 2, adding new codes and revising the code description as often as necessary. Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Example of a hierarchical coding frame in qualitative data analysis

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy. Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

Demographic
And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

Calculate overall NPS
Calculate NPS in the subset of responses that do not contain that theme
Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

Visualizing qualitative data: Calculating the impact of a code on your score

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

Analyzing qualitative data: Calculate which codes are linked to increases or decreases in my score

The visualization could look like this:

Visualizing qualitative data trends over time

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Codes displayed by volume within Thematic. You can 'manage themes' to introduce human input.

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs. This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Two examples of qualitative data visualizations within Thematic

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable. Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding. Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places. And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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Data Analysis for Qualitative Research: 6 Step Guide

Data analysis for qualitative research is not intuitive. This is because qualitative data stands in opposition to traditional data analysis methodologies: while data analysis is concerned with quantities, qualitative data is by definition unquantified . But there is an easy, methodical approach that anyone can take use to get reliable results when performing data analysis for qualitative research. The process consists of 6 steps that I’ll break down in this article:

Perform interviews(if necessary )
Gather all documents and transcribe any non-paper records
Decide whether to either code analytical data, analyze word frequencies, or both
Decide what interpretive angle you want to take: content analysis , narrative analysis, discourse analysis, framework analysis, and/or grounded theory
Compile your data in a spreadsheet using document saving techniques (windows and mac)
Identify trends in words, themes, metaphors, natural patterns, and more

To complete these steps, you will need:

Microsoft word
Microsoft excel
Internet access

You can get the free Intro to Data Analysis eBook to cover the fundamentals and ensure strong progression in all your data endeavors.

What is qualitative research?

Qualitative research is not the same as quantitative research. In short, qualitative research is the interpretation of non-numeric data. It usually aims at drawing conclusions that explain why a phenomenon occurs, rather than that one does occur. Here’s a great quote from a nursing magazine about quantitative vs qualitative research:

“A traditional quantitative study… uses a predetermined (and auditable) set of steps to confirm or refute [a] hypothesis. “In contrast, qualitative research often takes the position that an interpretive understanding is only possible by way of uncovering or deconstructing the meanings of a phenomenon. Thus, a distinction between explaining how something operates (explanation) and why it operates in the manner that it does (interpretation) may be [an] effective way to distinguish quantitative from qualitative analytic processes involved in any particular study.” (bold added) (( EBN ))

Learn to Interpret Your Qualitative Data

This article explain what data analysis is and how to do it. To learn how to interpret the results, visualize, and write an insightful report, sign up for our handbook below.

Step 1a: Data collection methods and techniques in qualitative research: interviews and focus groups

Step 1 is collecting the data that you will need for the analysis. If you are not performing any interviews or focus groups to gather data, then you can skip this step. It’s for people who need to go into the field and collect raw information as part of their qualitative analysis.

Since the whole point of an interview and of qualitative analysis in general is to understand a research question better, you should start by making sure you have a specific, refined research question . Whether you’re a researcher by trade or a data analyst working on one-time project, you must know specifically what you want to understand in order to get results.

Good research questions are specific enough to guide action but open enough to leave room for insight and growth. Examples of good research questions include:

Good : To what degree does living in a city impact the quality of a person’s life? (open-ended, complex)
Bad : Does living in a city impact the quality of a person’s life? (closed, simple)

Once you understand the research question, you need to develop a list of interview questions. These questions should likewise be open-ended and provide liberty of expression to the responder. They should support the research question in an active way without prejudicing the response. Examples of good interview questions include:

Good : Tell me what it’s like to live in a city versus in the country. (open, not leading)
Bad : Don’t you prefer the city to the country because there are more people? (closed, leading)

Some additional helpful tips include:

Begin each interview with a neutral question to get the person relaxed
Limit each question to a single idea
If you don’t understand, ask for clarity
Do not pass any judgements
Do not spend more than 15m on an interview, lest the quality of responses drop

Focus groups

The alternative to interviews is focus groups. Focus groups are a great way for you to get an idea for how people communicate their opinions in a group setting, rather than a one-on-one setting as in interviews.

In short, focus groups are gatherings of small groups of people from representative backgrounds who receive instruction, or “facilitation,” from a focus group leader. Typically, the leader will ask questions to stimulate conversation, reformulate questions to bring the discussion back to focus, and prevent the discussion from turning sour or giving way to bad faith.

Focus group questions should be open-ended like their interview neighbors, and they should stimulate some degree of disagreement. Disagreement often leads to valuable information about differing opinions, as people tend to say what they mean if contradicted.

However, focus group leaders must be careful not to let disagreements escalate, as anger can make people lie to be hurtful or simply to win an argument. And lies are not helpful in data analysis for qualitative research.

Step 1b: Tools for qualitative data collection

When it comes to data analysis for qualitative analysis, the tools you use to collect data should align to some degree with the tools you will use to analyze the data.

As mentioned in the intro, you will be focusing on analysis techniques that only require the traditional Microsoft suite programs: Microsoft Excel and Microsoft Word . At the same time, you can source supplementary tools from various websites, like Text Analyzer and WordCounter.

In short, the tools for qualitative data collection that you need are Excel and Word , as well as web-based free tools like Text Analyzer and WordCounter . These online tools are helpful in the quantitative part of your qualitative research.

Step 2: Gather all documents & transcribe non-written docs

Once you have your interviews and/or focus group transcripts, it’s time to decide if you need other documentation. If you do, you’ll need to gather it all into one place first, then develop a strategy for how to transcribe any non-written documents.

When do you need documentation other than interviews and focus groups? Two situations usually call for documentation. First , if you have little funding , then you can’t afford to run expensive interviews and focus groups.

Second , social science researchers typically focus on documents since their research questions are less concerned with subject-oriented data, while hard science and business researchers typically focus on interviews and focus groups because they want to know what people think, and they want to know today.

Non-written records

Other factors at play include the type of research, the field, and specific research goal. For those who need documentation and to describe non-written records, there are some steps to follow:

Put all hard copy source documents into a sealed binder (I use plastic paper holders with elastic seals ).
If you are sourcing directly from printed books or journals, then you will need to digitalize them by scanning them and making them text readable by the computer. To do so, turn all PDFs into Word documents using online tools such as PDF to Word Converter . This process is never full-proof, and it may be a source of error in the data collection, but it’s part of the process.
If you are sourcing online documents, try as often as possible to get computer-readable PDF documents that you can easily copy/paste or convert. Locked PDFs are essentially a lost cause .
Transcribe any audio files into written documents. There are free online tools available to help with this, such as 360converter . If you run a test through the system, you’ll see that the output is not 100%. The best way to use this tool is as a first draft generator. You can then correct and complete it with old fashioned, direct transcription.

Step 3: Decide on the type of qualitative research

Before step 3 you should have collected your data, transcribed it all into written-word documents, and compiled it in one place. Now comes the interesting part. You need to decide what you want to get out of your research by choosing an analytic angle, or type of qualitative research.

The available types of qualitative research are as follows. Each of them takes a unique angle that you must choose to get what information you want from the analysis . In addition, each of them has a different impact on the data analysis for qualitative research (coding vs word frequency) that we use.

Content analysis

Narrative analysis, discourse analysis.

Framework analysis, and/or

Grounded theory

From a high level, content, narrative, and discourse analysis are actionable independent tactics, whereas framework analysis and grounded theory are ways of honing and applying the first three.

Definition : Content analysis is identify and labelling themes of any kind within a text.
Focus : Identifying any kind of pattern in written text, transcribed audio, or transcribed video. This could be thematic, word repetition, idea repetition. Most often, the patterns we find are idea that make up an argument.
Goal : To simplify, standardize, and quickly reference ideas from any given text. Content analysis is a way to pull the main ideas from huge documents for comparison. In this way, it’s more a means to an end.
Pros : The huge advantage of doing content analysis is that you can quickly process huge amounts of texts using simple coding and word frequency techniques we will look at below. To use a metaphore, it is to qualitative analysis documents what Spark notes are to books.
Cons : The downside to content analysis is that it’s quite general. If you have a very specific, narrative research question, then tracing “any and all ideas” will not be very helpful to you.
Definition : Narrative analysis is the reformulation and simplification of interview answers or documentation into small narrative components to identify story-like patterns.
Focus : Understanding the text based on its narrative components as opposed to themes or other qualities.
Goal : To reference the text from an angle closer to the nature of texts in order to obtain further insights.
Pros : Narrative analysis is very useful for getting perspective on a topic in which you’re extremely limited. It can be easy to get tunnel vision when you’re digging for themes and ideas from a reason-centric perspective. Turning to a narrative approach will help you stay grounded. More importantly, it helps reveal different kinds of trends.
Cons : Narrative analysis adds another layer of subjectivity to the instinctive nature of qualitative research. Many see it as too dependent on the researcher to hold any critical value.
Definition : Discourse analysis is the textual analysis of naturally occurring speech. Any oral expression must be transcribed before undergoing legitimate discourse analysis.
Focus : Understanding ideas and themes through language communicated orally rather than pre-processed on paper.
Goal : To obtain insights from an angle outside the traditional content analysis on text.
Pros : Provides a considerable advantage in some areas of study in order to understand how people communicate an idea, versus the idea itself. For example, discourse analysis is important in political campaigning. People rarely vote for the candidate who most closely corresponds to his/her beliefs, but rather for the person they like the most.
Cons : As with narrative analysis, discourse analysis is more subjective in nature than content analysis, which focuses on ideas and patterns. Some do not consider it rigorous enough to be considered a legitimate subset of qualitative analysis, but these people are few.

Framework analysis

Definition : Framework analysis is a kind of qualitative analysis that includes 5 ordered steps: coding, indexing, charting, mapping, and interpreting . In most ways, framework analysis is a synonym for qualitative analysis — the same thing. The significant difference is the importance it places on the perspective used in the analysis.
Focus : Understanding patterns in themes and ideas.
Goal : Creating one specific framework for looking at a text.
Pros : Framework analysis is helpful when the researcher clearly understands what he/she wants from the project, as it’s a limitation approach. Since each of its step has defined parameters, framework analysis is very useful for teamwork.
Cons : It can lead to tunnel vision.
Definition : The use of content, narrative, and discourse analysis to examine a single case, in the hopes that discoveries from that case will lead to a foundational theory used to examine other like cases.
Focus : A vast approach using multiple techniques in order to establish patterns.
Goal : To develop a foundational theory.
Pros : When successful, grounded theories can revolutionize entire fields of study.
Cons : It’s very difficult to establish ground theories, and there’s an enormous amount of risk involved.

Step 4: Coding, word frequency, or both

Coding in data analysis for qualitative research is the process of writing 2-5 word codes that summarize at least 1 paragraphs of text (not writing computer code). This allows researchers to keep track of and analyze those codes. On the other hand, word frequency is the process of counting the presence and orientation of words within a text, which makes it the quantitative element in qualitative data analysis.

Video example of coding for data analysis in qualitative research

In short, coding in the context of data analysis for qualitative research follows 2 steps (video below):

Reading through the text one time
Adding 2-5 word summaries each time a significant theme or idea appears

Let’s look at a brief example of how to code for qualitative research in this video:

Click here for a link to the source text. 1

Example of word frequency processing

And word frequency is the process of finding a specific word or identifying the most common words through 3 steps:

Decide if you want to find 1 word or identify the most common ones
Use word’s “Replace” function to find a word or phrase
Use Text Analyzer to find the most common terms

Here’s another look at word frequency processing and how you to do it. Let’s look at the same example above, but from a quantitative perspective.

Imagine we are already familiar with melanoma and KITs , and we want to analyze the text based on these keywords. One thing we can do is look for these words using the Replace function in word

Locate the search bar
Click replace
Type in the word
See the total results

Here’s a brief video example:

Another option is to use an online Text Analyzer. This methodology won’t help us find a specific word, but it will help us discover the top performing phrases and words. All you need to do it put in a link to a target page or paste a text. I pasted the abstract from our source text, and what turns up is as expected. Here’s a picture:

Step 5: Compile your data in a spreadsheet

After you have some coded data in the word document, you need to get it into excel for analysis. This process requires saving the word doc as an .htm extension, which makes it a website. Once you have the website, it’s as simple as opening that page, scrolling to the bottom, and copying/pasting the comments, or codes, into an excel document.

You will need to wrangle the data slightly in order to make it readable in excel. I’ve made a video to explain this process and places it below.

Step 6: Identify trends & analyze!

There are literally thousands of different ways to analyze qualitative data, and in most situations, the best technique depends on the information you want to get out of the research.

Nevertheless, there are a few go-to techniques. The most important of this is occurrences . In this short video, we finish the example from above by counting the number of times our codes appear. In this way, it’s very similar to word frequency (discussed above).

A few other options include:

Ranking each code on a set of relevant criteria and clustering
Pure cluster analysis
Causal analysis

We cover different types of analysis like this on the website, so be sure to check out other articles on the home page .

How to analyze qualitative data from an interview

To analyze qualitative data from an interview , follow the same 6 steps for quantitative data analysis:

Perform the interviews
Transcribe the interviews onto paper
Decide whether to either code analytical data (open, axial, selective), analyze word frequencies, or both
Compile your data in a spreadsheet using document saving techniques (for windows and mac)
Source text [ ↩ ]

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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6 How to Analyze Data in a Primary Research Study

Melody Denny and Lindsay Clark

This chapter introduces students to the idea of working with primary research data grounded in qualitative inquiry, closed-and open-ended methods, and research ethics (Driscoll; Mackey and Gass; Morse; Scott and Garner). [1] We know this can seem intimidating to students, so we will walk them through the process of analyzing primary research, using information from public datasets including the Pew Research Center. Using sample data on teen social media use, we share our processes for analyzing sample data to demonstrate different approaches for analyzing primary research data (Charmaz; Creswell; Merriam and Tisdale; Saldaña). We also include links to additional public data sets, chapter discussion prompts, and sample activities for students to apply these strategies.

At this point in your education, you are familiar with what is known as secondary research or what many students think of as library research. Secondary research makes use of sources most often found in the library or, these days, online (books, journal articles, magazines, and many others). There’s another kind of research that you may or may not be familiar with: primary research. The Purdue OWL defines primary research as “any type of research you collect yourself” and lists examples as interviews, observations, and surveys (“What is Primary Research”).

Primary research is typically divided into two main types—quantitative and qualitative research. These two methods (or a mix of these) are used by many fields of study, so providing a singular definition for these is a bit tricky. Sheard explains that “quantitative research…deals with data that are numerical or that can be converted into numbers. The basic methods used to investigate numerical data are called ‘statistics’” (429). Guest, et al. explain that qualitative research is “information that is difficult to obtain through more quantitatively-oriented methods of data collection” and is used more “to answer the whys and hows of human behavior, opinion, and experience” (1).

This chapter focuses on qualitative methods that explore peoples’ behaviors, interpretations, and opinions. Rather than being only a reader and reporter of research, primary research allows you to be creators of research. Primary research provides opportunities to collect information based on your specific research questions and generate new knowledge from those questions to share with others. Generally, primary research tends to follow these steps:

Develop a research question. Secondary research often uses this as a starting point as well. With primary research, however, rather than using library research to answer your research question, you’ll also collect data yourself to answer the question you developed. Data, in this case, is the information you collect yourself through methods such as interviews, surveys, and observations.
Decide on a research method. According to Scott and Garner, “A research method is a recognized way of collecting or producing [primary data], such as a survey, interview, or content analysis of documents” (8). In other words, the method is how you obtain the data.
Collect data. Merriam and Tisdale clarify what it means to collect data: “data collection is about asking, watching, and reviewing” (105-106). Primary research might include asking questions via surveys or interviews, watching or observing interactions or events, and examining documents or other texts.
Analyze data. Once data is collected, it must then be analyzed. “Data analysis is the process of making sense out of the data… Basically, data analysis is the process used to answer your research question(s)” (Merriam and Tisdale 202). It’s worth noting that many researchers collect data and analyze at the same time, so while these may seem like different steps in the process, they actually overlap.
Report findings. Once the researcher has spent time understanding and interpreting the data, they are then ready to write about their research, often called “findings.” You may also see this referred to as “results.”

While the entire research process is discussed, this chapter focuses on the analysis stage of the process (step 4). Depending on where you are in the research process, you may need to spend more time on step 1, 2, or 3 and review Driscoll’s “Introduction to Primary Research” (Volume 2 of Writing Spaces ).

Primary research can seem daunting, and some students might think that they can’t do primary research, that this type of research is for professionals and scholars, but that’s simply not true. It’s true that primary research data can be difficult to collect and even more difficult to analyze, but the findings are typically very revealing. This chapter and the examples included break down this research process and demonstrate how general curiosity can lead to exciting chances to learn and share information that is relevant and interesting. The goal of this chapter is to provide you with some information about data analysis and walk you through some activities to prepare you for your own data analysis. The next section discusses analyzing data from closed-ended methods and open-ended methods.

Data from Primary Research

As stated above, this chapter doesn’t focus on methods, but before moving on to analysis, it’s important to clarify a few things related to methods as they are directly connected to analyzing data. As a quick reminder, a research method is how researchers collect their data such as surveys, interviews, or textual analysis. No matter which method used, researchers need to think about the types of questions to ask for answering their overall research question. Generally, there are two types of questions to consider: closed-ended and open-ended. The next section provides examples of the data you might receive from asking closed-ended and open-ended questions and options for analyzing and presenting that data.

Data from Closed-Ended Methods

The data that is generated by closed-ended questions on methods such as surveys and polls is often easier to organize. Because the way respondents could answer those questions is limited to specific answers (Yes/No, numbered scales, multiple choice), the data can be analyzed by each question or by looking at the responses individually or as a whole. Though there are several approaches to analyzing the data that comes from closed-ended questions, this section will introduce you to a few different ways to make sense of this kind of data.

Closed-ended questions are those that have limited answers, like multiple choice or check-all-that-apply questions. These questions mean that respondents can provide only the answers given or they may select an “other” option. An example of a closed-ended question could be “Do you use YouTube? Yes, No, Sometimes.” Closed-ended questions have their perks because they (mostly) keep participants from misinterpreting the question or providing unhelpful responses. They also make data analysis a bit easier.

If you were to ask the “Yes, No, Sometimes” question about YouTube to 20 of your closest friends, you may get responses like Yes = 18, No = 1, and Sometimes = 1. But, if you were to ask a more detailed question like “Which of the following social media platforms do you use?” and provide respondents with a check-all-that-apply option, like “Facebook, YouTube, Twitter, Instagram, Snapchat, Reddit, and Tumblr,” you would get a very different set of data. This data might look like Facebook = 17, YouTube = 18, Twitter = 12, Instagram = 20, Snapchat = 15, Reddit = 8, and Tumblr = 3. The big takeaway here is that how you ask the question determines the type of data you collect.

Analyzing Closed-Ended Data

Now that you have data, it’s time to think about analyzing and presenting that data. Luckily, the Pew Research Center conducted a similar study that can be used as an example. The Pew Research Center is a “nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research” (“About Pew Research Center”). The information provided below comes from their public dataset “Teens, Social Media, and Technology, 2018” (Anderson and Jiang). This example is used to show how you might analyze this type of data once collected and what that data might look like. “Teens, Social Media, and Technology 2018” reported responses to questions related to which online platforms teens use and which they use most often. In figure 1 below, Pew researchers show the final product of their analysis of the data:

Pew analyzed their data and organized the findings by percentages to show what they discovered. They had 743 teens who responded to these questions, so presenting their findings in percentages helps readers better “see” the data overall (rather than saying YouTube = 631 and Instagram = 535). However, results can be represented in different ways. When the Pew researchers were deciding how to present their data, they could have reported the frequency, or the number of people who said they used YouTube, Instagram, and Snapchat.

In the scenario of polling 20 of your closest friends, you, too, would need to decide how to present your data: Facebook = 17, YouTube = 18, Twitter = 12, Instagram = 20, Snapchat = 15, Reddit = 8, and Tumblr = 3. In your case, you might want to present the frequency (number) of responses rather than the percentages of responses like Pew did. You could choose a bar graph like Pew or maybe a simple table to show your data.

Looking again at the Pew data, researchers could use this data to generate further insights or questions about user preferences. For example, one could highlight the fact that 85% of respondents reported using YouTube the most, while only 7% reported using Reddit. Why is that? What conclusions might you be able to make based on these data? Does the data make you wonder if any additional questions might be explored? If you want to learn more about your respondents’ opinions or preference, you might need to ask open-ended questions.

Data from Open-Ended Methods

Whereas closed-ended questions limit how respondents might answer, open-ended questions do not limit respondents’ answers and allow them to answer more freely. An example of an open-ended question, to build off the question above, could be “Why do you use social media? Explain.” This type of question gives respondents more space to fully explain their responses. Open-ended questions can make the data varied because each respondent may answer differently. These questions, which can provide fruitful responses, can also mean unexpected responses or responses that don’t help to answer the overall research question, which can sometimes make data analysis challenging.

In that same Pew Research Center data, respondents were likely limited in how they were able to answer by selecting social media platforms from a list. Pew also shares selected data (Appendix A), and based on these data, it can be assumed they also asked open-ended questions, something about the positive or negative effects of social media platforms. Because their research method included both closed-ended questions about which platforms teens use as well as open-ended questions that invited their thoughts about social media, Pew researchers were able to learn more about these participants’ thoughts and perceptions. To give us, the readers, a clearer idea of how they justified their presentation of the data, Pew offers 15 sample excerpts from those open-ended questions. They explain that these excerpts are what the researchers believe are representative of the larger data set. We explain below how we might analyze those excerpts.

Analyzing Open-Ended Data

As Driscoll reminds us, ethical considerations impact all stages of the research process, and researchers should act ethically throughout the entire research process. You already know a little something about research ethics. For example, you know that ethical writers cite sources used in research papers by giving credit to the person who created that information. When creating primary sources, you have a few different ethical considerations for analyzing data, which will be discussed below.

To demonstrate how to analyze data from open-ended methods, we explain how we (Melody and Lindsay) analyzed the 15 excerpts from the Pew data using open coding. Open coding means analyzing the data without any predetermined categories or themes; researchers are just seeing what emerges or seems significant (Charmaz). Creswell suggests four specific steps when coding qualitative data, though he also stresses that these steps are iterative, meaning that researchers may need to revisit a step anywhere throughout the process. We use these four steps to explain our analysis process, including how we ethically coded the data, interpreted what the coding process revealed, and worked together to identify and explain categories we saw in the data.

Step 1: Organizing and Preparing the Data

The first part of the analysis stage is organizing the data before examining it. When organizing data, researchers must be careful to work with primary data ethically because that data often represents actual peoples’ information and opinions. Therefore, researchers need to carefully organize the data in such a way as to not identify their participants or reveal who they are. This is a key component to The Belmont Report , guidelines published in 1979 meant to guide researchers and help protect participants. Using pseudonyms or assigning numbers or codes (in place of names) to the data is a recommended ethical step to maintain participants’ confidentiality in a study. Anonymizing data, or removing names, has the additional effect of eliminating researcher bias, which can occur when researchers are so familiar with their own data and participants that the researchers may begin to think they already know the answers or see connections prior to analysis (Driscoll). By assigning pseudonyms, researchers can also ensure that they take an objective look at each participant’s answers without being persuaded by participant identity.

The first part of coding is to make notations while reading through the data (Merriam and Tisdale). At this point, researchers are open to many possibilities regarding their data. This is also where researchers begin to construct categories. Offering a simple example to illustrate this decision-making process, Merriam and Tisdale ask us to imagine sorting and categorizing two hundred grocery store items (204). Some items could be sorted into more than one category; for example, ice cream could be categorized as “frozen” or as “dessert.” How you decide to sort that item depends on your research question and what you want to learn.

For this step, we, Melody and Lindsay, each created a separate document that included the 15 excerpts. Melody created a table for the quotes, leaving a column for her coding notes, and Lindsay added spaces between the excerpts for her notes. For our practice analysis, we analyzed the data independently, and then shared what we did to compare, verify, and refine our analysis. This brings a second, objective view to the analysis, reduces the effect of researcher bias, and ensures that your analysis can be verified and supported by the data. To support your analysis, you need to demonstrate how you developed the opinions and conclusions you have about your data. After all, when researchers share their analyses, readers often won’t see all of the raw data, so they need to be able to trust the analysis process.

Step 2: Reading through All the Data

Creswell suggests getting a general sense of the data to understand its overall meaning. As you start reading through your data, you might begin to recognize trends, patterns, or recurring features that give you ideas about how to both analyze and later present the data. When we read through the interview excerpts of these 15 participants’ opinions of social media, we both realized that there were two major types of comments: positive and negative. This might be similar to categorizing the items in the grocery store (mentioned above) into fresh/frozen foods and non-perishable items.

To better organize the data for further analysis, Melody marked each positive comment with a plus sign and each negative comment with a minus sign. Lindsay color-coded the comments (red for negative, indicated by boldface type below; green for positive, indicated by grey type below) and then organized them on the page by type. This approach is in line with Merriam and Tisdale’s explanation of coding: “assigning some sort of shorthand designation to various aspects of your data so that you can easily retrieve specific pieces of the data. The designations can be single words, letters, numbers, phrases, colors, or combinations of these” (199). While we took different approaches, as shown the two sections below, both allowed us to visually recognize the major sections of the data:

Lindsay’s Coding Round 1, which shows her color coding indicated by boldface type

“[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) “It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15) “[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17) “It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15) “I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14) “Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14) “We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

Melody’s Coding Round 1, showing her use of plus and minus signs to classify the comments as positive or negative, respectively

+ “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) – “It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15) – “[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17) + “It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15) + “I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14) – “Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14) + “We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

Step 3: Doing Detailed Coding Analysis of the Data

It’s important to mention that Creswell dedicates pages of description on coding data because there are various ways of approaching detailed analysis. To code our data, we added a descriptive word or phrase that “symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute” to a portion of data (Saldaña 3). From the grocery store example above, that could mean looking at the category of frozen foods and dividing them into entrees, side dishes, desserts, appetizers, etc. We both coded for topics or what the teens were generally talking about in their responses. For example, one excerpt reads “Social media allows us to communicate freely and see what everyone else is doing. It gives us a voice that can reach many people.” To code that piece of data, researchers might assign words like communication, voice, or connection to explain what the data is describing.

In this way, we created the codes from what the data said, describing what we read in those excerpts. Notice in the section below that, even though we coded independently, we described these pieces of data in similar ways using bolded keywords:

Melody’s Coding Round 2, with key words added to summarize the meanings of the different quotes

– “Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13) bullying – “It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15) fake + “Because a lot of things created or made can spread joy.” (Boy, age 17) reaching people + “I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15) connection + “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) reaching people

Lindsay’s Coding Round 2, with key words added in capital letters to summarize the meanings of the quotations

“Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13) OPPORTUNITIES TO COMMUNICATE NEGATIVELY/MORE EASILY “It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15) FAKE, NOT REALITY “Because a lot of things created or made can spread joy.” (Boy, age 17) SPREAD JOY “I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15) INTERACTION, LESS LONELY “[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15) COMMUNICATE, VOICE

Though there are methods that allow for researchers to use predetermined codes (like from previous studies), “the traditional approach…is to allow the codes to emerge during the data analysis” (Creswell 187).

Step 4: Using the Codes to Create a Description Using Categories, Themes, Settings, or People

Our individual coding happened in phases, as we developed keywords and descriptions that could then be defined and relabeled into concise coding categories (Saldaña 11). We shared our work from Steps 1-3 to further define categories and determine which themes were most prominent in the data. A few times, we interpreted something differently and had to discuss and come to an agreement about which category was best.

In our process, one excerpt comment was interpreted as negative by one of us and positive by the other. Together we discussed and confirmed which comments were positive or negative and identified themes that seemed to appear more than once, such as positive feelings towards the interactional element of social media use and the negative impact of social media use on social skills. When two coders compare their results, this allows for qualitative validity, which means “the researcher checks for the accuracy of the findings” (Creswell 190). This could also be referred to as intercoder reliability (Lavrakas). For intercoder reliability, researchers sometimes calculate how often they agree in a percentage. Like many other aspects of primary research, there is no consensus on how best to establish or calculate intercoder reliability, but generally speaking, it’s a good idea to have someone else check your work and ensure you are ethically analyzing and reporting your data.

Interpreting Coded Data

Once we agreed on the common categories and themes in this dataset, we worked together on the final analysis phase of interpreting the data, asking “what does it mean?” Data interpretation includes “trying to give sense to the data by creatively producing insights about it” (Gibson and Brown 6). Though we acknowledge that this sample of only 15 excerpts is small, and it might be difficult to make claims about teens and social media from just this data, we can share a few insights we had as part of this practice activity.

Overall, we could report the frequency counts and percentages that came from our analysis. For example, we counted 8 positive comments and 7 negative comments about social media. Presented differently, those 8 positive comments represent 53% of the responses, so slightly over half. If we focus on just the positive comments, we are able to identify two common themes among those 8 responses: Interaction and Expression. People who felt positively about social media use identified the ability to connect with people and voice their feelings and opinions as the main reasons. When analyzing only the 7 negative responses, we identified themes of Bullying and Social Skills as recurring reasons people are critical of social media use among teens. Identifying these topics and themes in the data allows us to begin thinking about what we can learn and share with others about this data.

How we represent what we have learned from our data can demonstrate our ethical approach to data analysis. In short, we only want to make claims we can support, and we want to make those claims ethically, being careful to not exaggerate or be misleading.

To better understand a few common ethical dilemmas regarding the presentation of data, think about this example: A few years ago, Lindsay taught a class that had only four students. On her course evaluations, those four students rated the class experience as “Excellent.” If she reports that 100% of her students answered “Excellent,” is she being truthful? Yes. Do you see any potential ethical considerations here? If she said that 4/4 gave that rating, does that change how her data might be perceived by others? While Lindsay could show the raw data to support her claims, important contextual information could be missing if she just says 100%. Perhaps others would assume this was a regular class of 20-30 students, which would make that claim seem more meaningful and impressive than it might be.

Another word for this is cherry picking. Cherry picking refers to making conclusions based on thin (or not enough) data or focusing on data that’s not necessarily representative of the larger dataset (Morse). For example, if Lindsay reported the comment that one of her students made about this being the “best class ever,” she would be telling the truth but really only focusing on the reported opinion of 25% of the class (1 out of 4). Ideally, researchers want to make claims about the data based on ideas that are prominent, trending, or repeated. Less prominent pieces of data, like the opinion of that one student, are known as outliers, or data that seem to “be atypical of the rest of the dataset” (Mackey and Gass 257). Focusing on those less-representative portions might misrepresent or overshadow the aspects of the data that are prominent or meaningful, which could create ethical problems for your study. With these ethical considerations in mind, the last step of conducting primary research would be to write about the analysis and interpretation to share your process with others.

This chapter has introduced you to ethically analyzing data within the primary research tradition by focusing on close-ended and open-ended data. We’ve provided you with examples of how data might be analyzed, interpreted, and presented to help you understand the process of making sense of your data. This is just one way to approach data analysis, but no matter your research method, having a systematic approach is recommended. Data analysis is a key component in the overall primary research process, and we hope that you are now excited and curious to participate in a primary research project.

Works Cited

“About Pew Research Center.” Pew Research Center, 2020. www.pewresearch.org/about/ . Accessed 28 Dec 2020. Anderson, Monica, and Jingjing Jiang.

“Teens, Social Media & Technology 2018.” Pew Research Center, May 2018, www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/ .

The Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research, Office for Human Research Protections, www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html . 18 Apr. 1979.

Charmaz, Kathy. “Grounded Theory.” Approaches to Qualitative Research: A Reader on Theory and Practice , edited by Sharlene Nagy Hesse-Biber and Patricia Leavy, Oxford UP, 2004, pp. 496-521.

Corpus of Contemporary American English (COCA) . (n.d.). Retrieved April 11, 2021, from https://www.english-corpora.org/coca/

Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 3rd edition, Sage, 2009.

Data.gov . (2020). Retrieved April 11, 2021, from https://www.data.gov/

Driscoll, Dana Lynn. “Introduction to Primary Research: Observations, Surveys, and Interviews.” Writing Spaces: Readings on Writing , Volume 2, Parlor Press, 2011, pp. 153-174.

Explore Census Data . (n.d.). United States Census Bureau. Retrieved April 11, 2021, from https://data.census.gov/cedsci/

Gibson, William J., and Andrew Brown. Working with Qualitative Data . London, Sage, 2009.

Google Trends. (n.d.). Retrieved April 11, 2021, from https://trends.google.com/trends/explore

Guest, Greg, et al. Collecting Qualitative Data: A Field Manual for Applied Research . Sage, 2013.

HealthData.gov . (n.d.). Retrieved April 11, 2021, from https://healthdata.gov/

Lavrakas, Paul J. Encyclopedia of Survey Research Methods . Sage, 2008.

Mackey, Allison, and Sue M. Gass. Second Language Research: Methodology and Design . Lawrence Erlbaum Associates, 2005.

Merriam, Sharan B., and Elizabeth J. Tisdell. Qualitative Research: A Guide to Design and Implementation , John Wiley & Sons, Incorporated, 2015. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/unco/detail.action?docID=2089475 .

Michigan Corpus of Academic Spoken English. (n.d.). Retrieved April 11, 2021, from https://quod.lib.umich.edu/cgi/c/corpus/corpus?c=micase;page=simple

Morse, Janice. M. “‘Cherry Picking’: Writing from Thin Data.” Qualitative Health Research , vol. 20, no. 1, 2009, p. 3.

Pew Research Center . (2021). Retrieved April 11, 2021, from https://www.pewresearch.org/

Saldaña, Johnny. The Coding Manual for Qualitative Researchers , 2nd edition, Sage, 2013.

Scott, Greg, and Roberta Garner. Doing Qualitative Research: Designs, Methods, and Techniques , 1st edition, Pearson, 2012.

Sheard, Judithe. “Quantitative Data Analysis.” Research Methods Information, Systems, and Contexts , edited by Kirsty Williamson and Graeme Johanson, Elsevier, 2018, pp. 429-452.

Teens and Social Media , Google Trends, trends.google.com/trends/explore?-date=all&q=teens%20and%20social%20media . Accessed 15 Jul. 2020.

“What is Primary Research and How Do I Get Started?” The Writing Lab and OWL at Purdue and Purdue U , 2020. owl.purdue.edu/owl . Accessed 21 Dec. 2020.

Zhao, Alice. “How Text Messages Change from Dating to Marriage.” Huffington Post , 21 Oct. 2014, www.huffpost.com .

“My mom had to get a ride to the library to get what I have in my hand all the time. She reminds me of that a lot.” (Girl, age 14)

“Gives people a bigger audience to speak and teach hate and belittle each other.” (Boy, age 13)

“It provides a fake image of someone’s life. It sometimes makes me feel that their life is perfect when it is not.” (Girl, age 15)

“Because a lot of things created or made can spread joy.” (Boy, age 17)

“I feel that social media can make people my age feel less lonely or alone. It creates a space where you can interact with people.” (Girl, age 15)

“[Social media] allows us to communicate freely and see what everyone else is doing. [It] gives us a voice that can reach many people.” (Boy, age 15)

“It makes it harder for people to socialize in real life, because they become accustomed to not interacting with people in person.” (Girl, age 15)

“[Teens] would rather go scrolling on their phones instead of doing their homework, and it’s so easy to do so. It’s just a huge distraction.” (Boy, age 17)

“It enables people to connect with friends easily and be able to make new friends as well.” (Boy, age 15)

“I think social media have a positive effect because it lets you talk to family members far away.” (Girl, age 14)

“Because teens are killing people all because of the things they see on social media or because of the things that happened on social media.” (Girl, age 14)

“We can connect easier with people from different places and we are more likely to ask for help through social media which can save people.” (Girl, age 15)

“It has given many kids my age an outlet to express their opinions and emotions, and connect with people who feel the same way.” (Girl, age 15)

“People can say whatever they want with anonymity and I think that has a negative impact.” (Boy, age 15)

“It has a negative impact on social (in-person) interactions.” (Boy, age 17)

Teacher Resources for How to Analyze Data in a Primary Research Study

Overview and teaching strategies.

This chapter is intended as an overview of analyzing qualitative research data and was written as a follow-up piece to Dana Lynn Driscoll’s “Introduction to Primary Research: Observations, Surveys, and Interviews” in Volume 2 of this collection. This chapter could work well for leading students through their own data analysis of a primary research project or for introducing students to the idea of primary research by using outside data sources, those in the chapter and provided in the activities below, or data you have access to.

From our experiences, students usually have limited experience with primary research methods outside of conducting a small survey for other courses, like sociology. We have found that few of our students have been formally introduced to primary research and analysis. Therefore, this chapter strives to briefly introduce students to primary research while focusing on analysis. We’ve presented analysis by categorizing data as open-ended and closed-ended without getting into too many details about qualitative versus quantitative. Our students tend to produce data collection tools with a mix of these types of questions, so we feel it’s important to cover the analysis of both.

In this chapter, we bring students real examples of primary data and lead them through analysis by showing examples. Any of these exercises and the activities below may be easily supplemented with additional outside data. One way that teachers can bring in outside data is through the use of public datasets.

Public Data Sets

There are many public data sets that teachers can use to acquaint their students with analyzing data. Be aware that some of these datasets are for experienced researchers and provide the data in CSV files or include metadata, all of which is probably too advanced for most of our students. But if you are comfortable converting this data, it could be valuable for a data analysis activity.

In the chapter, we pulled from Pew Research, and their website contains many free and downloadable data sets (Pew Research Center).
The site Data.gov provides searchable datasets, but you can also explore their data by clicking on “data” and seeing what kinds of reports they offer.
The U.S. Census Bureau offers some datasets as well (Explore Census Data): Much of this data is presented in reports, but teachers could pull information from reports and have students analyze the data and compare their results to those in the report, much like we did with the Pew Research data in the chapter.
Similarly, HealthData.gov offers research-based reports packed with data for students to analyze.
In one of the activities below, we used Google Trends to look at searches over a period of time. There are some interesting data and visuals provided on the homepage to help students get started.
If you’re looking for something a bit more academic, the Michigan Corpus of Academic Spoken English is a great database of transcripts from academic interactions and situations.
Similarly, the Corpus of Contemporary American English allows users to search for words or word strings to see their frequency and in which genre and when these occur.

Before moving on to student activities, we’d like to offer one additional suggestion for teachers to consider.

Class Google Form

One thing that Melody does at the beginning of almost all of her research-based writing courses is ask students to complete a Google Form at the beginning of the semester. Sometimes, these forms are about their experiences with research. Other times, they revolve around a class topic (recently, she’s been interested in Generation Z or iGeneration and has asked students questions related to that). Then, when it’s time to start thinking about primary research, she uses that Google Form to help students understand more about the primary research process. Here are some ways that teachers can employ the data gathered from Google Form given to students.

Ask students to look at the questions asked on the survey and deduce the overall research question.
• Ask students to look at the types of questions asked (open- and closed-ended) and consider why they were constructed that way.
Ask students to evaluate the wording of the questions asked.
Ask students to examine the results of a few (or more) or the questions on the survey. This can be done in groups with each group looking at 1-3 questions, depending on the size of your Google Form.
Ask students to think about how they might present that data in visual form. Yes, Google provides some visuals, but you can give them the raw data and see what they come up with.
Ask students to come up with 1-3 major takeaways based on all the data.

This exercise allows students to work with real data and data that’s directly related to them and their classmates. It’s also completely within ethical boundaries because it’s data collected in the classroom, for educational purposes, and it stays within the classroom.

Below we offer some guiding questions to help move students through the chapter and the activities as well as some additional activities.

Discussion Questions

In the opening of this chapter, we introduced you to primary research , or “any type of research you collect yourself” (“What is Primary Research”). Have you completed primary research before? How did you decide on your research method, based on your research question? If you have not worked on primary research before, brainstorm a potential research question for a topic you want to know more about. Discuss what research method you might use, including closed- or open-ended methods and why.
Looking at the chart from the Pew Research dataset, “Teens, Social Media, and Technology 2018,” would you agree that the distributions among online platforms remain similar, or have trends changed?
What do you make of the “none of the above” category on the Pew table? Do you think teens are using online platforms that aren’t listed, or do you think those respondents don’t use any online platforms?

When analyzing data from open-ended questions, which step seems most challenging to you? Explain.

Activity #1: TurnItIn and Infographics

Infographics can be a great way to help you see and understand data, while also giving you a way to think about presenting your own data. Multiple infographics are available on TurnItIn, downloadable for free, that provide information about plagiarism.

Figure 3, titled “The Plagiarism Spectrum,” provides you with the “severity” and “frequency” based on survey findings of nearly 900 high school and college instructors from around the world. TurnItIn encourages educators to print this infographic and hang in their classroom:

This infographic provides some great data analysis examples: specific categories with definitions (and visual representation of their categories), frequency counts with bar graphs, and color gradient bars to show higher vs. lower numbers.

Write a summary of how this infographic presents data.
How do you think they analyzed the data based on this visual?

Activity #2: How Text Messages Change from Dating to Marriage

In Alice Zhao’s Huffington Post piece, she analyzes text messages that she collected during her relationship with her boyfriend, turned fiancé, turned husband to answer the question of how text messages (or communication) change over the course of a relationship. While Zhao offers some insight into her data, she also provides readers with some really cool graphics that you can use to practice your analysis skills.

These first graphics are word clouds. In figure 4, Zhao put her textual data into a program that creates these images based on the most frequently occurring words. Word clouds are another option for analyzing your data. If you have a lot of textual data and want to know what participants said the most, placing your data into a word cloud program is an easy way to “see” the data in a new way. This is usually one of the first steps of analysis, and additional analysis is almost always needed.

What do you notice about the texts from 2008 to 2014?
What do you notice between her texts (me) and his texts (him)?

Zhao also provided this graphic (figure 5), a comparative look at what she saw as the most frequently occurring words from the word clouds. This could be another step in your data analysis procedure: zooming in on a few key aspects and digging a bit deeper.

What do you make of this data? Why might the word “hey” occur more frequently in the dating time frame and the word “ok” occur more frequently in the married time frame?

As part of her research, Zhao also looked at the time of day text messages were sent, shown below in figure 6:

Here, Zhao looked at messages sent a month after their first date, a month after their engagement, and a month after their wedding.

She offers her own interpretation in her piece in figure 6, but what do you think of this?
Also make note of this graphic. It’s a great way to look at the data another way. If your data may be time sensitive, this type of graphic may help you better analyze and understand your data.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) and is subject to the Writing Spaces Terms of Use. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ , email [email protected] , or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. To view the Writing Spaces Terms of Use, visit http://writingspaces.org/terms-of-use . ↵

How to Analyze Data in a Primary Research Study Copyright © 2021 by Melody Denny and Lindsay Clark is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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How to Write Data Analysis Reports in 9 Easy Steps

To see what Databox can do for you, including how it helps you track and visualize your performance data in real-time, check out our home page. Click here .

Imagine a bunch of bricks. They don’t have a purpose until you put them together into a house, do they?

In business intelligence, data is your building material, and a quality data analysis report is what you want to see as the result.

But if you’ve ever tried to use the collected data and assemble it into an insightful report, you know it’s not an easy job to do. Data is supposed to tell a story about your performance, but there’s a long way from unprocessed, raw data to a meaningful narrative that you can use to create an actionable plan for making steady progress towards your goals.

This article will help you improve the quality of your data analysis reports and build them effortlessly and fast. Let’s jump right in.

What Is a Data Analysis Report?

Why is data analysis reporting important, how to write a data analysis report 9 simple steps, data analysis report examples.

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A data analysis report is a type of business report in which you present quantitative and qualitative data to evaluate your strategies and performance. Based on this data, you give recommendations for further steps and business decisions while using the data as evidence that backs up your evaluation.

Today, data analysis is one of the most important elements of business intelligence strategies as companies have realized the potential of having data-driven insights at hand to help them make data-driven decisions.

Just like you’ll look at your car’s dashboard if something’s wrong, you’ll pull your data to see what’s causing drops in website traffic, conversions, or sales – or any other business metric you may be following. This unprocessed data still doesn’t give you a diagnosis – it’s the first step towards a quality analysis. Once you’ve extracted and organized your data, it’s important to use graphs and charts to visualize it and make it easier to draw conclusions.

Once you add meaning to your data and create suggestions based on it, you have a data analysis report.

A vital detail everyone should know about data analysis reports is their accessibility for everyone in your team, and the ability to innovate. Your analysis report will contain your vital KPIs, so you can see where you’re reaching your targets and achieving goals, and where you need to speed up your activities or optimize your strategy. If you can uncover trends or patterns in your data, you can use it to innovate and stand out by offering even more valuable content, services, or products to your audience.

Data analysis is vital for companies for several reasons.

A reliable source of information

Trusting your intuition is fine, but relying on data is safer. When you can base your action plan on data that clearly shows that something is working or failing, you won’t only justify your decisions in front of the management, clients, or investors, but you’ll also be sure that you’ve taken appropriate steps to fix an issue or seize an important opportunity.

A better understanding of your business

According to Databox’s State of Business Reporting , most companies stated that regular monitoring and reporting improved progress monitoring, increased team effectiveness, allowed them to identify trends more easily, and improved financial performance. Data analysis makes it easier to understand your business as a whole, and each aspect individually. You can see how different departments analyze their workflow and how each step impacts their results in the end, by following their KPIs over time. Then, you can easily conclude what your business needs to grow – to boost your sales strategy, optimize your finances, or up your SEO game, for example.

An additional way to understand your business better is to compare your most important metrics and KPIs against companies that are just like yours. With Databox Benchmarks , you will need only one spot to see how all of your teams stack up against your peers and competitors.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

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The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

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It makes data accessible to everyone

Data doesn’t represent a magical creature reserved for data scientists only anymore. Now that you have streamlined and easy-to-follow data visualizations and tools that automatically show the latest figures, you can include everyone in the decision-making process as they’ll understand what means what in the charts and tables. The data may be complex, but it becomes easy to read when combined with proper illustrations. And when your teams gain such useful and accessible insight, they will feel motivated to act on it immediately.

Better collaboration

Data analysis reports help teams collaborate better, as well. You can apply the SMART technique to your KPIs and goals, because your KPIs become assignable. When they’re easy to interpret for your whole team, you can assign each person with one or multiple KPIs that they’ll be in charge of. That means taking a lot off a team leader’s plate so they can focus more on making other improvements in the business. At the same time, removing inaccurate data from your day-to-day operations will improve friction between different departments, like marketing and sales, for instance.

More productivity

You can also expect increased productivity, since you’ll be saving time you’d otherwise spend on waiting for specialists to translate data for other departments, etc. This means your internal procedures will also be on a top level.

Want to give value with your data analysis report? It’s critical to master the skill of writing a quality data analytics report. Want to know how to report on data efficiently? We’ll share our secret in the following section.

Start with an Outline
Make a Selection of Vital KPIs
Pick the Right Charts for Appealing Design
Use a Narrative
Organize the Information
Include a Summary
Careful with Your Recommendations
Double-Check Everything
Use Interactive Dashboards

1. Start with an Outline

If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first. Plan the structure and contents of each section first to make sure you’ve covered everything, and only then start crafting the report.

2. Make a Selection of Vital KPIs

Don’t overwhelm the audience by including every single metric there is. You can discuss your whole dashboard in a meeting with your team, but if you’re creating data analytics reports or marketing reports for other departments or the executives, it’s best to focus on the most relevant KPIs that demonstrate the data important for the overall business performance.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
New Contacts from Sessions . How well is your campaign driving new contacts and customers?
Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

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marketing_overview_hubspot_ga_dashboard_preview

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To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template

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Step 3: Watch your dashboard populate in seconds.

3. Pick the Right Charts for Appealing Design

If you’re showing historical data – for instance, how you’ve performed now compared to last month – it’s best to use timelines or graphs. For other data, pie charts or tables may be more suitable. Make sure you use the right data visualization to display your data accurately and in an easy-to-understand manner.

4. Use a Narrative

Do you work on analytics and reporting ? Just exporting your data into a spreadsheet doesn’t qualify as either of them. The fact that you’re dealing with data may sound too technical, but actually, your report should tell a story about your performance. What happened on a specific day? Did your organic traffic increase or suddenly drop? Why? And more. There are a lot of questions to answer and you can put all the responses together in a coherent, understandable narrative.

5. Organize the Information

Before you start writing or building your dashboard, choose how you’re going to organize your data. Are you going to talk about the most relevant and general ones first? It may be the best way to start the report – the best practices typically involve starting with more general information and then diving into details if necessary.

6. Include a Summary

Some people in your audience won’t have the time to read the whole report, but they’ll want to know about your findings. Besides, a summary at the beginning of your data analytics report will help the reader get familiar with the topic and the goal of the report. And a quick note: although the summary should be placed at the beginning, you usually write it when you’re done with the report. When you have the whole picture, it’s easier to extract the key points that you’ll include in the summary.

7. Careful with Your Recommendations

Your communication skills may be critical in data analytics reports. Know that some of the results probably won’t be satisfactory, which means that someone’s strategy failed. Make sure you’re objective in your recommendations and that you’re not looking for someone to blame. Don’t criticize, but give suggestions on how things can be improved. Being solution-oriented is much more important and helpful for the business.

8. Double-Check Everything

The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything’s in place.

9. Use Interactive Dashboards

Using the right tools is just as important as the contents of your data analysis. The way you present it can make or break a good report, regardless of how valuable the data is. That said, choose a great reporting tool that can automatically update your data and display it in a visually appealing manner. Make sure it offers streamlined interactive dashboards that you can also customize depending on the purpose of the report.

To wrap up the guide, we decided to share nine excellent examples of what awesome data analysis reports can look like. You’ll learn what metrics you should include and how to organize them in logical sections to make your report beautiful and effective.

Marketing Data Analysis Report Example

SEO Data Analysis Report Example

Sales data analysis report example.

Customer Support Data Analysis Report Example

Help Desk Data Analysis Report Example

Ecommerce data analysis report example, project management data analysis report example, social media data analysis report example, financial kpi data analysis report example, marketing data report example.

If you need an intuitive dashboard that allows you to track your website performance effortlessly and monitor all the relevant metrics such as website sessions, pageviews, or CTA engagement, you’ll love this free HubSpot Marketing Website Overview dashboard template .

Tracking the performance of your SEO efforts is important. You can easily monitor relevant SEO KPIs like clicks by page, engaged sessions, or views by session medium by downloading this Google Organic SEO Dashboard .

How successful is your sales team? It’s easy to analyze their performance and predict future growth if you choose this HubSpot CRM Sales Analytics Overview dashboard template and track metrics such as average time to close the deal, new deals amount, or average revenue per new client.

Customer Support Analysis Data Report Example

Customer support is one of the essential factors that impact your business growth. You can use this streamlined, customizable Customer Success dashboard template . In a single dashboard, you can monitor metrics such as customer satisfaction score, new MRR, or time to first response time.

Other than being free and intuitive, this HelpScout for Customer Support dashboard template is also customizable and enables you to track the most vital metrics that indicate your customer support agents’ performance: handle time, happiness score, interactions per resolution, and more.

Is your online store improving or failing? You can easily collect relevant data about your store and monitor the most important metrics like total sales, orders placed, and new customers by downloading this WooCommerce Shop Overview dashboard template .

Does your IT department need feedback on their project management performance? Download this Jira dashboard template to track vital metrics such as issues created or resolved, issues by status, etc. Jira enables you to gain valuable insights into your teams’ productivity.

Project Management Data Analysis Report Example

Need to know if your social media strategy is successful? You can find that out by using this easy-to-understand Social Media Awareness & Engagement dashboard template . Here you can monitor and analyze metrics like sessions by social source, track the number of likes and followers, and measure the traffic from each source.

Social Media Data Analysis Report Example

Tracking your finances is critical for keeping your business profitable. If you want to monitor metrics such as the number of open invoices, open deals amount by stage by pipeline, or closed-won deals, use this free QuickBooks + HubSpot CRM Financial Performance dashboard template .

Financial KPI Data Analysis Report Example

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You can skip the setup and get your first dashboard for free in just 24 hours, with our fantastic customer support team on the line to assist you with the metrics you should track and the structure you should use.

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Stefana Zarić is a freelance writer & content marketer. Other than writing for SaaS and fintech clients, she educates future writers who want to build a career in marketing. When not working, Stefana loves to read books, play with her kid, travel, and dance.

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Methodology

Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

The aim of the research
The type of data that you will collect
The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

Experimental research is primarily a quantitative method.
Interviews , focus groups , and ethnographies are qualitative methods.
Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

Data collection methods
Method	When to use	How to collect data
Experiment	To test a causal relationship.	Manipulate variables and measure their effects on others.
Survey	To understand the general characteristics or opinions of a group of people.	Distribute a list of questions to a sample online, in person or over-the-phone.
Interview/focus group	To gain an in-depth understanding of perceptions or opinions on a topic.	Verbally ask participants open-ended questions in individual interviews or focus group discussions.
Observation	To understand something in its natural setting.	Measure or survey a sample without trying to affect them.
Ethnography	To study the culture of a community or organization first-hand.	Join and participate in a community and record your observations and reflections.
Archival research	To understand current or historical events, conditions or practices.	Access manuscripts, documents or records from libraries, depositories or the internet.
Secondary data collection	To analyze data from populations that you can’t access first-hand.	Find existing datasets that have already been collected, from sources such as government agencies or research organizations.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
Double-check manual data entry for errors.
If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Likert scale

Research bias

Implicit bias
Framing effect
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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What is data analysis? Examples and how to get started

A hero image with an icon of a line graph / chart

Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals.

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Why is data analysis important?

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

Types of data analysis (with examples)

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose.

Types of data analysis including text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.

Text analysis: What is happening?

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text:

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience.

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent.

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant.

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Here are a few methods used to perform descriptive analysis:

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected.

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field.

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large.

Here are a few methods used when performing inferential analysis:

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign.

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews.

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results.

Here are a few methods used to perform diagnostic analysis:

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year.

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales.

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.

Here are a few methods used to perform predictive analysis:

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand.

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis:

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected.

Data analysis process: How to get started

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions.

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis.

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?"

Data collection

Next, collect the required data from both internal and external sources.

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers.

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected. Depending on the type of data you have, cleanup will look different, but it might include:

Removing unnecessary information

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends.

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data.

Structured query language (SQL) tools manage and extract data in relational databases.

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another.

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data.

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation.

Data visualization

Automate your data collection, frequently asked questions.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

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Credit card interest rate margins at all-time high

By some measures, credit cards have never been this expensive. For cardholders who carry a balance without paying it off in full each month, issuers generally charge interest based on annual percentage rates (APRs). In 2022 alone, major credit card companies charged over $105 billion in interest, the primary cost of credit cards to consumers. While the effects of increases to the target federal funds rate have received considerable attention, the average APR margin (the difference between the average APR and the prime rate) has reached an all-time high.

In this analysis, we show that higher APR margin drove about half of the increase in credit card rates over the last decade. In 2023, excess APR margin may have cost the average cardholder over $250. Major credit card companies earned an estimated $25 billion in additional interest revenue by raising APR margin. Increases to the average APR margin - despite lower charge-off rates and a relatively stable share of subprime borrowers - have fueled issuers’ profitability for the past decade. Higher APR margins have allowed credit card companies to generate returns that are significantly higher than other bank activities.

Credit card average APR margin is the highest on record.

Over the last 10 years, average APR on credit cards assessed interest have almost doubled from 12.9 percent in late 2013 to 22.8 percent in 2023 — the highest level recorded since the Federal Reserve began collecting this data in 1994. The APR on most credit card accounts can be viewed as being composed of the prime rate and the APR margin. The prime rate (a benchmark most banks use to set rates) represents a good proxy for banks’ funding costs, which have increased in recent years. But credit card issuers have also sharply increased average APRs beyond changes in the prime rate.

Nearly half of the increase in average APR over the last 10 years has been driven by issuers raising their APR margin. APR margin for revolving accounts is now at 14.3 percent, the highest point in recent history. More than half of issuers sent offers by direct mail with a higher APR margin in the third quarter of 2023 than on the same product the year before, according to our analysis of Competiscan data.

Figure 1: Average APRs on Accounts Assessed Interest and Average Prime Rate at Year End

Figure 1 is a bar graph that shows the average APRs at year end from 2013 to 2023. Each year has the average APR broken down by the prime rate and APR margin. Generally, from 2013 to 2023 the APR margin has increased from 9.6 percent to 14.3 percent.

Source: Federal Reserve

Higher APR margin has fueled the profitability of revolving balances.

Typically, card issuers set an APR margin to generate a profit that is at least commensurate with the risk of lending money to consumers. In the eight years after the Great Recession, the average APR margin stayed around 10 percent, as issuers adapted to reforms in the Credit Card Accountability Responsibility and Disclosure Act of 2009 (CARD Act) that restricted harmful back-end and hidden pricing practices. But issuers began to gradually increase APR margin in 2016. The trend accelerated in 2018, and it continued through the pandemic.

Over the past decade, card issuers increased APR margin despite lower charge-off rates and a relatively stable share of cardholders with subprime credit scores. The average APR margin increased 4.3 percentage points from 2013 to 2023 (while the prime rate was nearly 5 percentage points higher). As such, the profitability of revolving balances excluding loan loss provisions (the money that banks set aside for expected charge-offs) has been increasing over this time period.

Figure 2: Average APR Margin and Charge-Off Rate (Federal Reserve)

Figure 2 is a line graph that shows the quarterly average APR margin and charge off rate from 1995 through 2023. Since 2013, the APR margin has generally increased while the charge off rate decreased.

Excess APR margin costs consumers billions of dollars a year.

In 2023, major credit card issuers, with around $590 billion in revolving balances, charged an estimated $25 billion in additional interest fees by raising the average APR margin by 4.3 percentage points over the last ten years. For an average consumer with a $5,300 balance across credit cards, the excess APR margin cost them over $250 in 2023. Since finance charges are typically part of the minimum amount due, this additional interest burden may push consumers into persistent debt, accruing more in interest and fees than they pay towards the principal each year — or even delinquency.

The increase in APR margin has occurred across all credit tiers. Even consumers with the highest credit scores are incurring higher costs. The average APR margin for accounts with credit scores at 800 or above grew 1.6 percentage points from 2015 to 2022 without a corresponding increase in late payments.

Credit card interest rates are a core driver of profits.

Credit card issuers are reliant on revenue from interest charged to borrowers who revolve on their balances to drive overall profits, as reflected in increasing APR margins. The return on assets on general purpose cards , one measure of profitability, was higher in 2022 (at 5.9 percent) than in 2019 (at 4.5 percent), and far greater than the returns banks received on other lines of business. Even when excluding the impact of loan loss provisions, the profitability of credit cards has been increasing.

CFPB research has found high levels of concentration in the consumer credit card market and evidence of practices that inhibit consumers’ ability to find alternatives to expensive credit card products. These practices may help explain why credit card issuers have been able to prop up high interest rates to fuel profits. Our recent research has shown that while the top credit card companies dominate the market, smaller issuers many times offer credit cards with significantly lower APRs. The CFPB will continue to take steps to ensure that the consumer credit card market is fair, competitive, and transparent and to help consumers avoid debt spirals that can be difficult to escape.

Join the conversation. Follow CFPB on X (formerly Twitter) and Facebook .

The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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A market analysis can help you identify how to better position your business to be competitive and serve your customers.

A market analysis is a thorough assessment of a market within a specific industry. These analyses have many benefits, such as reducing risk for your business and better informing your business decisions. A market analysis can be a time-intensive process, but it is straightforward and easy to do on your own in seven steps.

To perform a market analysis for your business, follow the steps outlined in this guide.

What does a market analysis include?

In a market analysis, you will study the dynamics of your market, such as volume and value, potential customer segments , buying patterns, competition, and other important factors. A thorough marketing analysis should answer the following questions:

Who are my potential customers?
What are my customers’ buying habits?
How large is my target market ?
How much are customers willing to pay for my product?
Who are my main competitors?
What are my competitors’ strengths and weaknesses ?

What are the benefits of running a marketing analysis?

A marketing analysis can reduce risk, identify emerging trends, and help project revenue. You can use a marketing analysis at several stages of your business, and it can even be beneficial to conduct one every year to keep up to date with any major changes in the market.

A detailed market analysis will usually be part of your business plan , since it gives you a greater understanding of your audience and competition. This will help you build a more targeted marketing strategy.

These are some other major benefits of conducting a market analysis:

Risk reduction: Knowing your market can reduce risks in your business, since you’ll have an understanding of major market trends, the main players in your industry, and what it takes to be successful, all of which will inform your business decisions. To help you further protect your business, you can also conduct a SWOT analysis , which identifies the strengths, weaknesses, opportunities and threats for your business.
Targeted products or services: You are in a much better position to serve your customers when you have a firm grasp on what they are looking for from you. When you know who your customers are, you can use that information to tailor your business’s offerings to your customers’ needs.
Emerging trends: Staying ahead in business is often about being the first to spot a new opportunity or trend, and using a marketing analysis to stay on top of industry trends is a great way to position yourself to take advantage of this information.
Revenue projections: A market forecast is a key component of most marketing analyses, as it projects the future numbers, characteristics and trends in your target market. This gives you an idea of the profits you can expect, allowing you to adjust your business plan and budget accordingly.
Evaluation benchmarks: It can be difficult to gauge your business’s success outside of pure numbers. A market analysis provides benchmarks or key performance indicators (KPIs) against which you can judge your company and how well you are doing compared to others in your industry.
Context for past mistakes: Marketing analytics can explain your business’s past mistakes or industry anomalies. For example, in-depth analytics can explain what impacted the sale of a specific product, or why a certain metric performed the way it did. This can help you avoid making those mistakes again or experiencing similar anomalies, because you’ll be able to analyze and describe what went wrong and why.
Marketing optimization: This is where an annual marketing analysis comes in handy – regular analysis can inform your ongoing marketing efforts and show you which aspects of your marketing need work, and which are performing well in comparison to the other companies in your industry.

What are the drawbacks of running a marketing analysis?

The below drawbacks of running a market analysis pertain less to the method itself than the resources it requires.

Market analysis can be expensive. If you’re not as familiar with marketing concepts such as market volume and customer segmentation, you might want to outsource your market analysis. Doing so can be great for your analysis’s quality, but it can also leave a big dent in your budget. Narrow your market analysis to a certain group – perhaps current customers – to lower your costs.
Market analysis can be time-consuming. Market analysis can take precious time away from more directly business-related tasks. You can analyze one area at a time – say, buying patterns or competition – to free up your day-to-day schedule.
Market analysis can require extra staff. Some larger companies retain in-house market analysis staff, and you can follow their lead. Doing so, though, comes with all the usual costs of hiring a new employee . The question then becomes: Do you conduct your market analysis yourself, outsource it, or hire in-house? The more expensive options can often yield more meaningful insights.
Market analysis can be narrow. The most successful market analyses use actual customer feedback, which analysts often get through customer surveys. These surveys may reach only a portion of your entire customer base, leading to an inaccurate sample size. The result is that market analysis may not fully detail your customers and what you should know about them.

Market analysis vs. conjoint analysis vs. sentiment analysis

Where market analysis is broad and comprehensive, conjoint analysis focuses on how customers value what you offer. Surveys are often the backbone of conjoint analysis – they’re a great way for customers to share what drives their purchases. Product testing is an especially common application of conjoint analysis. This method can yield insights into pricing and product features and configurations.

Sentiment analysis goes beyond number-driven market and conjoint analysis to identify how customers qualitatively feel about your offerings. It can show you what customers are happy and unhappy about with your offerings or buying process. You can also wade into deeper emotional territory such as anger, urgency and intention, or you can dig up descriptive feedback. It’s a great tool to use alongside market analysis, whereas conjoint analysis is all but included in market analysis.

How to conduct a market analysis

While conducting a marketing analysis is not a complicated process, it does take a lot of dedicated research, so be prepared to devote significant time to the process.

These are the seven steps of conducting a market analysis:

1. Determine your purpose.

There are many reasons you may be conducting a market analysis, such as to gauge your competition or to understand a new market. Whatever your reason, it’s important to define it right away to keep you on track throughout the process. Start by deciding whether your purpose is internal – like improving your cash flow or business operations – or external, like seeking a business loan. Your purpose will dictate the type and amount of research you will do.

2. Research the state of the industry.

Map a detailed outline of the current state of your industry. Include where the industry seems to be heading, using metrics such as size, trends and projected growth, with plenty of data to support your findings. You can also conduct a comparative market analysis to help you find your competitive advantage within your specific market.

3. Identify your target customer.

Not everyone in the world will be your customer , and it would be a waste of your time to try to get everyone interested in your product. Instead, use a target market analysis to decide who is most likely to want your product and focus your efforts there. You want to understand your market size, who your customers are, where they come from, and what might influence their buying decisions. To do so, look at demographic factors like these:

During your research, you might consider creating a customer profile or persona that reflects your ideal customer to serve as a model for your marketing efforts.

4. Understand your competition.

To be successful, you need a good understanding of your competitors, including their market saturation, what they do differently than you, and their strengths, weaknesses and advantages in the market. Start by listing all your main competitors, then go through that list and conduct a SWOT analysis of each competitor. What does that business have that you don’t? What would lead a customer to choose that business over yours? Put yourself in the customer’s shoes.

Then, rank your list of competitors from most to least threatening, and decide on a timeline to conduct regular SWOT analyses on your most threatening competitors.

5. Gather additional data.

When conducting marketing analyses, information is your friend – you can never have too much data. It is important that the data you use is credible and factual, so be cautious of where you get your numbers. These are some reputable business data resources:

U.S. Bureau of Labor Statistics
U.S. Census Bureau
State and local commerce sites
Trade journals
Your own SWOT analyses
Market surveys or questionnaires

6. Analyze your data.

After you collect all the information you can and verify that it is accurate, you need to analyze the data to make it useful to you. Organize your research into sections that make sense to you, but try to include ones for your purpose, target market and competition.

These are the main elements your research should include:

An overview of your industry’s size and growth rate
Your business’s projected market share percentage
An industry outlook
Customer buying trends
Your forecasted growth
How much customers are willing to pay for your product or service

7. Put your analysis to work.

Once you’ve created a market analysis, it’s time to actually make it work for you. Internally, look for where you can use your research and findings to improve your business. Have you seen other businesses doing things that you’d like to implement in your own organization? Are there ways to make your marketing strategies more effective?

If you conducted your analysis for external purposes, organize your research and data into an easily readable and digestible document to make it easier to share with lenders.

Retain all of your information and research for your next analysis, and consider making a calendar reminder each year so that you stay on top of your market.

Making market analysis easy

If you have the time to conduct a market analysis yourself, go for it – this guide will help. If you don’t have the time, hiring an in-house expert or outsourcing your analysis is often worth the cost. Your analysis will help you figure out who to target and how – and that’s a huge part of business success.

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Israeli Views of the Israel-Hamas War

Jewish Israelis and Arab Israelis see the war very differently

Table of contents.

Views of the Israeli military response against Hamas
Attitudes toward Israel’s war cabinet
Current concerns about the war
Confidence in Biden
Views of how Biden is handling the Israel-Hamas war
Who is Biden favoring in the conflict, or is he striking the right balance?
Views of the U.S.
Who Israelis want to play a role in diplomatically resolving the war
Success against Hamas
Israel’s future national security
The future of Gaza
Views of Palestinian leaders
Palestinian statehood and coexistence
Acknowledgments
Methodology

This Pew Research Center analysis covers Israeli attitudes on the Israel-Hamas war, including opinions on how it’s being conducted, the country’s future, Israeli political leaders and the United States’ role in the conflict.

The data is from a survey of 1,001 Israeli adults conducted face-to-face from March 3 to April 4, 2024. Interviews were conducted in Hebrew and Arabic, and the survey is representative of the adult population ages 18 and older, excluding those in East Jerusalem and non-sanctioned outposts. (The survey also did not cover the West Bank or Gaza.) The survey included an oversample of Arabs in Israel. It was subsequently weighted to be representative of the Israeli adult population with the following variables: gender by ethnicity, age by ethnicity, education, region, urbanicity and probability of selection of respondent.

Here are the questions used for the report, along with responses, and the survey methodology .

A bar chart showing that Israelis are divided over the country’s military response against Hamas in Gaza

A new Pew Research Center survey finds that 39% of Israelis say Israel’s military response against Hamas in Gaza has been about right, while 34% say it has not gone far enough and 19% think it has gone too far.

According to the survey, conducted in March and early April, roughly two-thirds of Israelis are also confident that Israel will either probably (27%) or definitely (40%) achieve its goals in the war against Hamas. Still, majorities of Israeli adults are worried about aspects of the ongoing war:

61% say they are extremely or very concerned about the war expanding into other countries in the region.
68% say they are extremely or very concerned about the war going on for a long time.

When it comes to what should happen after the war, there is less consensus. A 40% plurality of Israelis think Israel should govern the Gaza Strip. Smaller shares think Gazans should decide who governs (14%) or would like to see a Palestinian Authority national unity government either with (6%) or without (12%) President Mahmoud Abbas (also known as Abu Mazen) in leadership.

Separately, 26% of Israelis think a way can be found for Israel and an independent Palestinian state to coexist peacefully with each other – down from 35% who said the same last year , prior to the war, and about half as many as took that position when the question was first asked in 2013.

Research in the West Bank and Gaza

Pew Research Center has polled the Palestinian territories in previous years, but we were unable to conduct fieldwork in Gaza or the West Bank for our March/April 2024 survey due to security concerns. We are actively investigating possibilities for both qualitative and quantitative research on public opinion in the region and hope to be able to provide more data in the coming months.

These are among the key findings of a new survey of 1,001 Israelis, conducted via face-to-face interviews from March 3 to April 4, 2024.

The survey also asked Israelis about the U.S. role in the conflict. (It was conducted before U.S. President Joe Biden took a tougher stance toward Israel in the wake of an Israeli airstrike that killed seven World Central Kitchen aid workers. And it predates Biden’s declaration that the U.S. would not provide offensive weapons to Israel in the event of a Rafah invasion as well as the subsequent Israeli strikes in Rafah .)

The survey shows:

60% of Israelis disapprove of the way Biden is handling the Israel-Hamas war.
41% think Biden is striking the right balance between Israelis and Palestinians. Still, 27% of Israelis say he is favoring Israelis too much, while roughly the same share (25%) say he favors Palestinians too much.
Most Israelis express confidence in Biden to handle world affairs and have a favorable view of the U.S. But ratings of both Biden and the U.S. have fallen at least 10 percentage points since last year. (For more on this, read “How Israelis and Americans view one another and the U.S. role in the Israel-Hamas war.” )

A bar chart showing that a Majority of Israelis want the U.S. to play a major role in diplomatically ending the war

Nonetheless, a large majority (72%) still want the U.S. to play a major role in diplomatically resolving the war – more than say the same about any of the other countries or organizations asked about, including Egypt (45%), Saudi Arabia (29%), Qatar (27%) and the United Nations (24%).

Arab and Jewish Israelis

A dot plot showing that Israeli Arabs and Jews diverge sharply over views of the U.S., Israel-Hamas war and Biden’s handling of it

People across Israeli society perceive the war in vastly different ways, depending on their views of the current leadership, how they identify ideologically, their religious backgrounds and other factors. One of the starkest divides is between Arab and Jewish Israelis:

Arab Israelis are less likely than Jewish Israelis to think Israel will succeed in achieving its war aims (38% vs. 76%) and less optimistic when thinking about the future of the country’s national security (21% vs. 63%).
Israeli Arabs are much more likely than Jews to say the country’s military response has gone too far (74% vs. 4%).
Almost no Israeli Arabs (3%) want Israel to govern the Gaza Strip after the war, while half of Israeli Jews think it should do so. A plurality of Arabs would like the people who live in Gaza to decide who governs (37%), while only 8% of Jews prefer this outcome.
Arab Israelis have much less favorable views of the U.S. than Jewish Israelis do (29% vs. 90%), as well as less confidence in Biden (21% vs. 66%). They are also much more likely to disapprove of Biden’s handling of the war (86% vs. 53%) and to think he favors Israelis too much (86% vs. 11%).
Although a majority of Arabs (63%) want the U.S. to play a major role in diplomatically resolving the war between Israel and Hamas, an even greater share of Jewish Israelis (74%) want this. And roughly two-thirds of Arabs are open to Qatar and Egypt playing a major role, while only about four-in-ten Jews or fewer say the same.
Roughly nine-in-ten Arabs (92%) have a negative view of Israeli Prime Minister Benjamin Netanyahu, compared with around half of Jews (48%). Views of the two other war cabinet members , Benny Gantz and Yoav Gallant, are also divided along ethnic lines. (The survey was conducted before Gantz threatened to leave the war cabinet .)

In many cases, there are also large ideological differences, with Israelis who describe themselves as being on the left generally more critical of Israel’s war response, less optimistic about its success and more critical of the U.S. than those on the right. There also tend to be differences among Israeli Jews based on how religiously observant they are. For more on how we looked at these differences, refer to the box below.

Jewish religious groups in Israel: Haredim, Datiim, Masortim and Hilonim

Nearly all Israeli Jews identify as either Haredi (commonly translated as “ultra-Orthodox”), Dati (“religious”), Masorti (“traditional”) or Hiloni (“secular”). The spectrum of religious observance in Israel – on which Haredim are generally the most religious and Hilonim the least – does not always line up perfectly with Israel’s political spectrum. On some issues, including those pertaining to religion in public life, there is a clear overlap: Haredim are furthest to the right, and Hilonim are furthest to the left, with Datiim and Masortim in between. But on other political issues, including those related to the Israeli-Palestinian conflict and views of the United States, differences between religious groups do not always mirror those between people at different points on the ideological spectrum. Because of sample size considerations, we combine Haredim and Datiim for analysis in this report.

For more information on the different views of these religious groups, read the Center’s 2016 deep dive on the topic, “Israel’s Religiously Divided Society.”

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Data Analysis in Research
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Data Analysis in Research: Types & Methods
Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. Three essential things occur during the data ...
Data Analysis Techniques In Research
Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives. Data Analysis Techniques in Research: While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition ...
Data Analysis
Data Analysis. Definition: Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets.
A Step-by-Step Guide to the Data Analysis Process
1. Step one: Defining the question. The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the 'problem statement'. Defining your objective means coming up with a hypothesis and figuring how to test it.
The Beginner's Guide to Statistical Analysis
Step 1: Write your hypotheses and plan your research design. To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design. Writing statistical hypotheses. The goal of research is often to investigate a relationship between variables within a population. You start with a prediction ...
What is data analysis? Methods, techniques, types & how-to
A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.
What Is Data Analysis? (With Examples)
Written by Coursera Staff • Updated on Apr 19, 2024. Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. "It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock ...
A Really Simple Guide to Quantitative Data Analysis
It is important to know w hat kind of data you are planning to collect or analyse as this w ill. affect your analysis method. A 12 step approach to quantitative data analysis. Step 1: Start with ...
How to Analyze Data in 2023
Now that you're familiar with the fundamentals, let's move on to the exact step-by-step guide you can follow to analyze your data properly. Step 1: Define your goals and the question you need to answer. Step 2: Determine how to measure set goals. Step 3: Collect your data. Step 4: Clean the data.
PDF The SAGE Handbook of Qualitative Data Analysis
The SAGE Handbook of. Qualitative Data Analysis. Uwe Flick. 00-Flick-Prelims.indd 5 29-Oct-13 2:00:39 PM. Data analysis is the central step in qualitative research. Whatever the data are, it is their analysis that, in a decisive way, forms the outcomes of the research. Sometimes, data collection is limited to recording and docu- menting ...
Creating a Data Analysis Plan: What to Consider When Choosing
For those interested in conducting qualitative research, previous articles in this Research Primer series have provided information on the design and analysis of such studies. 2, 3 Information in the current article is divided into 3 main sections: an overview of terms and concepts used in data analysis, a review of common methods used to ...
Learning to Do Qualitative Data Analysis: A Starting Point
For many researchers unfamiliar with qualitative research, determining how to conduct qualitative analyses is often quite challenging. Part of this challenge is due to the seemingly limitless approaches that a qualitative researcher might leverage, as well as simply learning to think like a qualitative researcher when analyzing data. From framework analysis (Ritchie & Spencer, 1994) to content ...
Research Guide: Data analysis and reporting findings
Publication Date: 1995. Analyzing Quantitative Data by Norman W. Blaikie. ISBN: 9780761967590. Publication Date: 2003. Quantitative Analysis of Questionnaires by Steve Humble. ISBN: 9780429400469. Publication Date: 2020-01-08. Making Sense of Multivariate Data Analysis by John Spicer. ISBN: 9781412904018.
Qualitative Data Analysis: Step-by-Step Guide (Manual vs ...
Step 1: Gather your qualitative data and conduct research (Conduct qualitative research) The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.
How to Analyze Research Data: A Step-by-Step Guide
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Data Analysis for Qualitative Research: 6 Step Guide
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How to Analyze Data in a Primary Research Study
Step 2: Reading through All the Data. Creswell suggests getting a general sense of the data to understand its overall meaning. As you start reading through your data, you might begin to recognize trends, patterns, or recurring features that give you ideas about how to both analyze and later present the data.
How to Write Data Analysis Reports in 9 Easy Steps
1. Start with an Outline. If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first.
Data Collection
Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem. While methods and aims may differ between fields, the overall process of ...
What is data analysis? Examples and how to start
Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative ...
What Is Data Analysis? (With Examples)
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PDF Chapter 4: Analysis and Interpretation of Results
from this study. The analysis and interpretation of data is carried out in two phases. The. first part, which is based on the results of the questionnaire, deals with a quantitative. analysis of data. The second, which is based on the results of the interview and focus group. discussions, is a qualitative interpretation.
What Does a Data Analyst Do? Your 2024 Career Guide
A data analyst gathers, cleans, and studies data sets to help solve problems. Here's how you can start on a path to become one. A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem. They work in many industries, including business, finance, criminal justice, science, medicine, and government.
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How To Become a Data Analyst. Follow these steps to land your first job as a data analyst: Understand the fundamentals. Consider a course. Meet the education requirements. Learn a programming language. Get familiar with data analytics tools. Develop your skills. Pursue volunteer, open-source, or freelance projects.
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Data Analysis in Research: Types & Methods

Why analyze data in research?

Data analysis in qualitative research

Data analysis in quantitative research

Phase I: Data Validation

Phase II: Data Editing

Phase III: Data Coding

Descriptive statistics

Measures of Frequency

Measures of Central Tendency

Measures of Dispersion or Variation

Measures of Position

Inferential statistics

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Data Analysis Techniques in Research – Methods, Tools & Examples

What is Data Analysis?

Types of Data Analysis Techniques in Research

1) Qualitative Analysis:

2) Quantitative Analysis:

Data Analysis Techniques in Research Examples

Research Objective:

Data Collection:

Data Analysis Techniques Applied:

Data Analysis Techniques in Quantitative Research

1) Descriptive Statistics:

2) Inferential Statistics:

3) Regression Analysis:

4) Correlation Analysis:

5) Factor Analysis:

6) Time Series Analysis:

7) ANOVA (Analysis of Variance):

8) Chi-Square Tests:

Data Analysis Methods

3) Exploratory Data Analysis (EDA):

4) Predictive Analytics:

5) Prescriptive Analytics:

6) Qualitative Data Analysis:

7) Big Data Analytics:

8) Text Analytics:

Data Analysis Tools

1) Microsoft Excel:

2) R Programming Language:

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

4) SPSS (Statistical Package for the Social Sciences):

5) SAS (Statistical Analysis System):

6) Tableau:

7) Power BI:

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

9) Apache Spark:

10) IBM SPSS Modeler:

Importance of Data Analysis in Research

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What is Advanced Analytics: Types, Tools, Examples, Jobs, Salary

Google Data Analytics Professional Certificate Review, Cost, Eligibility 2023

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Data Analysis – Process, Methods and Types

Data Analysis

Data Analysis Process

Define the Problem

Collect the Data

Clean and Organize the Data

Analyze the Data

Interpret the Results

Communicate the Findings

Take Action

Types of Data Analysis

Descriptive Analysis

Inferential Analysis

Diagnostic Analysis

Predictive Analysis

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Exploratory Analysis