methods for data analysis in quantitative research

Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Quantitative Data Analysis: A Comprehensive Guide

By: Ofem Eteng | Published: May 18, 2022

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A healthcare giant successfully introduces the most effective drug dosage through rigorous statistical modeling, saving countless lives. A marketing team predicts consumer trends with uncanny accuracy, tailoring campaigns for maximum impact.

Table of Contents

These trends and dosages are not just any numbers but are a result of meticulous quantitative data analysis. Quantitative data analysis offers a robust framework for understanding complex phenomena, evaluating hypotheses, and predicting future outcomes.

In this blog, we’ll walk through the concept of quantitative data analysis, the steps required, its advantages, and the methods and techniques that are used in this analysis. Read on!

What is Quantitative Data Analysis?

Quantitative data analysis is a systematic process of examining, interpreting, and drawing meaningful conclusions from numerical data. It involves the application of statistical methods, mathematical models, and computational techniques to understand patterns, relationships, and trends within datasets.

Quantitative data analysis methods typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually collected from close-ended surveys, questionnaires, polls, etc. The data can also be obtained from sales figures, email click-through rates, number of website visitors, and percentage revenue increase. 

Quantitative Data Analysis vs Qualitative Data Analysis

When we talk about data, we directly think about the pattern, the relationship, and the connection between the datasets – analyzing the data in short. Therefore when it comes to data analysis, there are broadly two types – Quantitative Data Analysis and Qualitative Data Analysis.

Quantitative data analysis revolves around numerical data and statistics, which are suitable for functions that can be counted or measured. In contrast, qualitative data analysis includes description and subjective information – for things that can be observed but not measured.

Let us differentiate between Quantitative Data Analysis and Quantitative Data Analysis for a better understanding.

Data Preparation Steps for Quantitative Data Analysis

Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. Below are the steps to prepare a data before quantitative research analysis:

• Step 1: Data Collection

Before beginning the analysis process, you need data. Data can be collected through rigorous quantitative research, which includes methods such as interviews, focus groups, surveys, and questionnaires.

• Step 2: Data Cleaning

Once the data is collected, begin the data cleaning process by scanning through the entire data for duplicates, errors, and omissions. Keep a close eye for outliers (data points that are significantly different from the majority of the dataset) because they can skew your analysis results if they are not removed.

This data-cleaning process ensures data accuracy, consistency and relevancy before analysis.

• Step 3: Data Analysis and Interpretation

Now that you have collected and cleaned your data, it is now time to carry out the quantitative analysis. There are two methods of quantitative data analysis, which we will discuss in the next section.

However, if you have data from multiple sources, collecting and cleaning it can be a cumbersome task. This is where Hevo Data steps in. With Hevo, extracting, transforming, and loading data from source to destination becomes a seamless task, eliminating the need for manual coding. This not only saves valuable time but also enhances the overall efficiency of data analysis and visualization, empowering users to derive insights quickly and with precision

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Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article, which is to describe the methods and techniques of quantitative data analysis.

Methods and Techniques of Quantitative Data Analysis

Quantitative data analysis employs two techniques to extract meaningful insights from datasets, broadly. The first method is descriptive statistics, which summarizes and portrays essential features of a dataset, such as mean, median, and standard deviation.

Inferential statistics, the second method, extrapolates insights and predictions from a sample dataset to make broader inferences about an entire population, such as hypothesis testing and regression analysis.

An in-depth explanation of both the methods is provided below:

• Descriptive Statistics
• Inferential Statistics

1) Descriptive Statistics

Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers obtained from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include: 

• Mean:   This calculates the numerical average of a set of values.
• Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
• Mode: This is used to find the most commonly occurring value in a dataset.
• Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
• Frequency: This indicates the number of times a value is found.
• Range: This shows the highest and lowest values in a dataset.
• Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
• Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.

2) Inferential Statistics

In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values, and descriptive statistics is all about explaining details of a specific dataset using numbers, but it does not explain the motives behind the numbers; hence, a need for further analysis using inferential statistics.

Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.

There are various statistical analysis methods used within inferential statistics; a few are discussed below.

• Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs help understand the nuances of a dataset and factors that may influence a data point.
• Regression Analysis: Regression analysis estimates the relationship between a set of variables. It shows the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may impact the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might affect a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis, and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
• Monte Carlo Simulation: Monte Carlo simulation, also known as the Monte Carlo method, is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. Data analysts use it to perform advanced risk analyses to help forecast future events and make decisions accordingly.
• Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
• Factor Analysis:   A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
• Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis, where these groups or cohorts usually have common characteristics or similarities within a defined period.
• MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank higher than the others in the process. 
• Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different; that is, data points within a cluster will look like each other and different from data points in other clusters.
• Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different times, like weekly and monthly email sign-ups, to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future. 
• SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies

How to Choose the Right Method for your Analysis?

Choosing between Descriptive Statistics or Inferential Statistics can be often confusing. You should consider the following factors before choosing the right method for your quantitative data analysis:

1. Type of Data

The first consideration in data analysis is understanding the type of data you have. Different statistical methods have specific requirements based on these data types, and using the wrong method can render results meaningless. The choice of statistical method should align with the nature and distribution of your data to ensure meaningful and accurate analysis.

2. Your Research Questions

When deciding on statistical methods, it’s crucial to align them with your specific research questions and hypotheses. The nature of your questions will influence whether descriptive statistics alone, which reveal sample attributes, are sufficient or if you need both descriptive and inferential statistics to understand group differences or relationships between variables and make population inferences.

Pros and Cons of Quantitative Data Analysis

1. Objectivity and Generalizability:

• Quantitative data analysis offers objective, numerical measurements, minimizing bias and personal interpretation.
• Results can often be generalized to larger populations, making them applicable to broader contexts.

Example: A study using quantitative data analysis to measure student test scores can objectively compare performance across different schools and demographics, leading to generalizable insights about educational strategies.

2. Precision and Efficiency:

• Statistical methods provide precise numerical results, allowing for accurate comparisons and prediction.
• Large datasets can be analyzed efficiently with the help of computer software, saving time and resources.

Example: A marketing team can use quantitative data analysis to precisely track click-through rates and conversion rates on different ad campaigns, quickly identifying the most effective strategies for maximizing customer engagement.

3. Identification of Patterns and Relationships:

• Statistical techniques reveal hidden patterns and relationships between variables that might not be apparent through observation alone.
• This can lead to new insights and understanding of complex phenomena.

Example: A medical researcher can use quantitative analysis to pinpoint correlations between lifestyle factors and disease risk, aiding in the development of prevention strategies.

1. Limited Scope:

• Quantitative analysis focuses on quantifiable aspects of a phenomenon ,  potentially overlooking important qualitative nuances, such as emotions, motivations, or cultural contexts.

Example: A survey measuring customer satisfaction with numerical ratings might miss key insights about the underlying reasons for their satisfaction or dissatisfaction, which could be better captured through open-ended feedback.

2. Oversimplification:

• Reducing complex phenomena to numerical data can lead to oversimplification and a loss of richness in understanding.

Example: Analyzing employee productivity solely through quantitative metrics like hours worked or tasks completed might not account for factors like creativity, collaboration, or problem-solving skills, which are crucial for overall performance.

3. Potential for Misinterpretation:

• Statistical results can be misinterpreted if not analyzed carefully and with appropriate expertise.
• The choice of statistical methods and assumptions can significantly influence results.

This blog discusses the steps, methods, and techniques of quantitative data analysis. It also gives insights into the methods of data collection, the type of data one should work with, and the pros and cons of such analysis.

Gain a better understanding of data analysis with these essential reads:

• Data Analysis and Modeling: 4 Critical Differences
• Exploratory Data Analysis Simplified 101
• 25 Best Data Analysis Tools in 2024

Carrying out successful data analysis requires prepping the data and making it analysis-ready. That is where Hevo steps in.

Want to give Hevo a try? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing Hevo price , which will assist you in selecting the best plan for your requirements.

Share your experience of understanding Quantitative Data Analysis in the comment section below! We would love to hear your thoughts.

Ofem Eteng is a seasoned technical content writer with over 12 years of experience. He has held pivotal roles such as System Analyst (DevOps) at Dagbs Nigeria Limited and Full-Stack Developer at Pedoquasphere International Limited. He specializes in data science, data analytics and cutting-edge technologies, making him an expert in the data industry.

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• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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.

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

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.

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.

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.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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8 quantitative data analysis methods to turn numbers into insights

Setting up a few new customer surveys or creating a fresh Google Analytics dashboard feels exciting…until the numbers start rolling in. You want to turn responses into a plan to present to your team and leaders—but which quantitative data analysis method do you use to make sense of the facts and figures?

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This guide lists eight quantitative research data analysis techniques to help you turn numeric feedback into actionable insights to share with your team and make customer-centric decisions. 

To pick the right technique that helps you bridge the gap between data and decision-making, you first need to collect quantitative data from sources like:

Google Analytics  

Survey results

On-page feedback scores

Fuel your quantitative analysis with real-time data

Use Hotjar’s tools to collect quantitative data that helps you stay close to customers.

Then, choose an analysis method based on the type of data and how you want to use it.

Descriptive data analysis summarizes results—like measuring website traffic—that help you learn about a problem or opportunity. The descriptive analysis methods we’ll review are:

Multiple choice response rates

Response volume over time

Net Promoter Score®

Inferential data analyzes the relationship between data—like which customer segment has the highest average order value—to help you make hypotheses about product decisions. Inferential analysis methods include:

Cross-tabulation

Weighted customer feedback

You don’t need to worry too much about these specific terms since each quantitative data analysis method listed below explains when and how to use them. Let’s dive in!

1. Compare multiple-choice response rates 

The simplest way to analyze survey data is by comparing the percentage of your users who chose each response, which summarizes opinions within your audience. 

To do this, divide the number of people who chose a specific response by the total respondents for your multiple-choice survey. Imagine 100 customers respond to a survey about what product category they want to see. If 25 people said ‘snacks’, 25% of your audience favors that category, so you know that adding a snacks category to your list of filters or drop-down menu will make the purchasing process easier for them.

💡Pro tip: ask open-ended survey questions to dig deeper into customer motivations.

A multiple-choice survey measures your audience’s opinions, but numbers don’t tell you why they think the way they do—you need to combine quantitative and qualitative data to learn that. 

One research method to learn about customer motivations is through an open-ended survey question. Giving customers space to express their thoughts in their own words—unrestricted by your pre-written multiple-choice questions—prevents you from making assumptions.

Hotjar’s open-ended surveys have a text box for customers to type a response

2. Cross-tabulate to compare responses between groups

To understand how responses and behavior vary within your audience, compare your quantitative data by group. Use raw numbers, like the number of website visitors, or percentages, like questionnaire responses, across categories like traffic sources or customer segments.

Let’s say you ask your audience what their most-used feature is because you want to know what to highlight on your pricing page. Comparing the most common response for free trial users vs. established customers lets you strategically introduce features at the right point in the customer journey . 

💡Pro tip: get some face-to-face time to discover nuances in customer feedback.

Rather than treating your customers as a monolith, use Hotjar to conduct interviews to learn about individuals and subgroups. If you aren’t sure what to ask, start with your quantitative data results. If you notice competing trends between customer segments, have a few conversations with individuals from each group to dig into their unique motivations.

Hotjar Engage lets you identify specific customer segments you want to talk to

Mode is the most common answer in a data set, which means you use it to discover the most popular response for questions with numeric answer options. Mode and median (that's next on the list) are useful to compare to the average in case responses on extreme ends of the scale (outliers) skew the outcome.

Let’s say you want to know how most customers feel about your website, so you use an on-page feedback widget to collect ratings on a scale of one to five.

If the mode, or most common response, is a three, you can assume most people feel somewhat positive. But suppose the second-most common response is a one (which would bring the average down). In that case, you need to investigate why so many customers are unhappy. 

💡Pro tip: watch recordings to understand how customers interact with your website.

So you used on-page feedback to learn how customers feel about your website, and the mode was two out of five. Ouch. Use Hotjar Recordings to see how customers move around on and interact with your pages to find the source of frustration.

Hotjar Recordings lets you watch individual visitors interact with your site, like how they scroll, hover, and click

Median reveals the middle of the road of your quantitative data by lining up all numeric values in ascending order and then looking at the data point in the middle. Use the median method when you notice a few outliers that bring the average up or down and compare the analysis outcomes.

For example, if your price sensitivity survey has outlandish responses and you want to identify a reasonable middle ground of what customers are willing to pay—calculate the median.

💡Pro-tip: review and clean your data before analysis. 

Take a few minutes to familiarize yourself with quantitative data results before you push them through analysis methods. Inaccurate or missing information can complicate your calculations, and it’s less frustrating to resolve issues at the start instead of problem-solving later. 

Here are a few data-cleaning tips to keep in mind:

Remove or separate irrelevant data, like responses from a customer segment or time frame you aren’t reviewing right now 

Standardize data from multiple sources, like a survey that let customers indicate they use your product ‘daily’ vs. on-page feedback that used the phrasing ‘more than once a week’

Acknowledge missing data, like some customers not answering every question. Just note that your totals between research questions might not match.

Ensure you have enough responses to have a statistically significant result

Decide if you want to keep or remove outlying data. For example, maybe there’s evidence to support a high-price tier, and you shouldn’t dismiss less price-sensitive respondents. Other times, you might want to get rid of obviously trolling responses.

5. Mean (AKA average)

Finding the average of a dataset is an essential quantitative data analysis method and an easy task. First, add all your quantitative data points, like numeric survey responses or daily sales revenue. Then, divide the sum of your data points by the number of responses to get a single number representing the entire dataset. 

Use the average of your quant data when you want a summary, like the average order value of your transactions between different sales pages. Then, use your average to benchmark performance, compare over time, or uncover winners across segments—like which sales page design produces the most value.

💡Pro tip: use heatmaps to find attention-catching details numbers can’t give you.

Calculating the average of your quant data set reveals the outcome of customer interactions. However, you need qualitative data like a heatmap to learn about everything that led to that moment. A heatmap uses colors to illustrate where most customers look and click on a page to reveal what drives (or drops) momentum.

Hotjar Heatmaps uses color to visualize what most visitors see, ignore, and click on

6. Measure the volume of responses over time

Some quantitative data analysis methods are an ongoing project, like comparing top website referral sources by month to gauge the effectiveness of new channels. Analyzing the same metric at regular intervals lets you compare trends and changes. 

Look at quantitative survey results, website sessions, sales, cart abandons, or clicks regularly to spot trouble early or monitor the impact of a new initiative.

Here are a few areas you can measure over time (and how to use qualitative research methods listed above to add context to your results):

7. Net Promoter Score®

Net Promoter Score® ( NPS ®) is a popular customer loyalty and satisfaction measurement that also serves as a quantitative data analysis method. 

NPS surveys ask customers to rate how likely they are to recommend you on a scale of zero to ten. Calculate it by subtracting the percentage of customers who answer the NPS question with a six or lower (known as ‘detractors’) from those who respond with a nine or ten (known as ‘promoters’). Your NPS score will fall between -100 and 100, and you want a positive number indicating more promoters than detractors. 

💡Pro tip : like other quantitative data analysis methods, you can review NPS scores over time as a satisfaction benchmark. You can also use it to understand which customer segment is most satisfied or which customers may be willing to share their stories for promotional materials.

Review NPS score trends with Hotjar to spot any sudden spikes and benchmark performance over time

8. Weight customer feedback 

So far, the quantitative data analysis methods on this list have leveraged numeric data only. However, there are ways to turn qualitative data into quantifiable feedback and to mix and match data sources. For example, you might need to analyze user feedback from multiple surveys.

To leverage multiple data points, create a prioritization matrix that assigns ‘weight’ to customer feedback data and company priorities and then multiply them to reveal the highest-scoring option. 

Let’s say you identify the top four responses to your churn survey . Rate the most common issue as a four and work down the list until one—these are your customer priorities. Then, rate the ease of fixing each problem with a maximum score of four for the easy wins down to one for difficult tasks—these are your company priorities. Finally, multiply the score of each customer priority with its coordinating company priority scores and lead with the highest scoring idea. 

💡Pro-tip: use a product prioritization framework to make decisions.

Try a product prioritization framework when the pressure is on to make high-impact decisions with limited time and budget. These repeatable decision-making tools take the guesswork out of balancing goals, customer priorities, and team resources. Four popular frameworks are:

RICE: weighs four factors—reach, impact, confidence, and effort—to weigh initiatives differently

MoSCoW: considers stakeholder opinions on 'must-have', 'should-have', 'could-have', and 'won't-have' criteria

Kano: ranks ideas based on how likely they are to satisfy customer needs

Cost of delay analysis: determines potential revenue loss by not working on a product or initiative

Share what you learn with data visuals

Data visualization through charts and graphs gives you a new perspective on your results. Plus, removing the clutter of the analysis process helps you and stakeholders focus on the insight over the method.

Data visualization helps you:

Get buy-in with impactful charts that summarize your results

Increase customer empathy and awareness across your company with digestible insights

Use these four data visualization types to illustrate what you learned from your quantitative data analysis: 

Bar charts reveal response distribution across multiple options

Line graphs compare data points over time

Scatter plots showcase how two variables interact

Matrices contrast data between categories like customer segments, product types, or traffic source

Use a variety of customer feedback types to get the whole picture

Quantitative data analysis pulls the story out of raw numbers—but you shouldn’t take a single result from your data collection and run with it. Instead, combine numbers-based quantitative data with descriptive qualitative research to learn the what, why, and how of customer experiences. 

Looking at an opportunity from multiple angles helps you make more customer-centric decisions with less guesswork.

Stay close to customers with Hotjar

Hotjar’s tools offer quantitative and qualitative insights you can use to make customer-centric decisions, get buy-in, and highlight your team’s impact.

Frequently asked questions about quantitative data analysis

What is quantitative data.

Quantitative data is numeric feedback and information that you can count and measure. For example, you can calculate multiple-choice response rates, but you can’t tally a customer’s open-ended product feedback response. You have to use qualitative data analysis methods for non-numeric feedback.

What are quantitative data analysis methods?

Quantitative data analysis either summarizes or finds connections between numerical data feedback. Here are eight ways to analyze your online business’s quantitative data:

Compare multiple-choice response rates

Cross-tabulate to compare responses between groups

Measure the volume of response over time

Net Promoter Score

Weight customer feedback

How do you visualize quantitative data?

Data visualization makes it easier to spot trends and share your analysis with stakeholders. Bar charts, line graphs, scatter plots, and matrices are ways to visualize quantitative data.

What are the two types of statistical analysis for online businesses?

Quantitative data analysis is broken down into two analysis technique types:

Descriptive statistics summarize your collected data, like the number of website visitors this month

Inferential statistics compare relationships between multiple types of quantitative data, like survey responses between different customer segments

Quantitative data analysis process

Previous chapter

Quantitative data analysis software

Next chapter

Part II: Data Analysis Methods in Quantitative Research

Data analysis methods in quantitative research.

We started this module with levels of measurement as a way to categorize our data. Data analysis is directed toward answering the original research question and achieving the study purpose (or aim). Now, we are going to delve into two main statistical analyses to describe our data and make inferences about our data:

Descriptive Statistics and Inferential Statistics.

Descriptive Statistics:

Before you panic, we will not be going into statistical analyses very deeply. We want to simply get a good overview of some of the types of general statistical analyses so that it makes some sense to us when we read results in published research articles.

Descriptive statistics   summarize or describe the characteristics of a data set. This is a method of simply organizing and describing our data. Why? Because data that are not organized in some fashion are super difficult to interpret.

Let’s say our sample is golden retrievers (population “canines”). Our descriptive statistics  tell us more about the same.

• 37% of our sample is male, 43% female
• The mean age is 4 years
• Mode is 6 years
• Median age is 5.5 years

Let’s explore some of the types of descriptive statistics.

Frequency Distributions : A frequency distribution describes the number of observations for each possible value of a measured variable. The numbers are arranged from lowest to highest and features a count of how many times each value occurred.

For example, if 18 students have pet dogs, dog ownership has a frequency of 18.

We might see what other types of pets that students have. Maybe cats, fish, and hamsters. We find that 2 students have hamsters, 9 have fish, 1 has a cat.

You can see that it is very difficult to interpret the various pets into any meaningful interpretation, yes?

Now, let’s take those same pets and place them in a frequency distribution table.                          

As we can now see, this is much easier to interpret.

Let’s say that we want to know how many books our sample population of  students have read in the last year. We collect our data and find this:

We can then take that table and plot it out on a frequency distribution graph. This makes it much easier to see how the numbers are disbursed. Easier on the eyes, yes?

Here’s another example of symmetrical, positive skew, and negative skew:

Correlation : Relationships between two research variables are called correlations . Remember, correlation is not cause-and-effect. Correlations  simply measure the extent of relationship between two variables. To measure correlation in descriptive statistics, the statistical analysis called Pearson’s correlation coefficient I is often used.  You do not need to know how to calculate this for this course. But, do remember that analysis test because you will often see this in published research articles. There really are no set guidelines on what measurement constitutes a “strong” or “weak” correlation, as it really depends on the variables being measured.

However, possible values for correlation coefficients range from -1.00 through .00 to +1.00. A value of +1 means that the two variables are positively correlated, as one variable goes up, the other goes up. A value of r = 0 means that the two variables are not linearly related.

Often, the data will be presented on a scatter plot. Here, we can view the data and there appears to be a straight line (linear) trend between height and weight. The association (or correlation) is positive. That means, that there is a weight increase with height. The Pearson correlation coefficient in this case was r = 0.56.

A type I error is made by rejecting a null hypothesis that is true. This means that there was no difference but the researcher concluded that the hypothesis was true.

A type II error is made by accepting that the null hypothesis is true when, in fact, it was false. Meaning there was actually a difference but the researcher did not think their hypothesis was supported.

Hypothesis Testing Procedures : In a general sense, the overall testing of a hypothesis has a systematic methodology. Remember, a hypothesis is an educated guess about the outcome. If we guess wrong, we might set up the tests incorrectly and might get results that are invalid. Sometimes, this is super difficult to get right. The main purpose of statistics is to test a hypothesis.

• Selecting a statistical test. Lots of factors go into this, including levels of measurement of the variables.
• Specifying the level of significance. Usually 0.05 is chosen.
• Computing a test statistic. Lots of software programs to help with this.
• Determining degrees of freedom ( df ). This refers to the number of observations free to vary about a parameter. Computing this is easy (but you don’t need to know how for this course).
• Comparing the test statistic to a theoretical value. Theoretical values exist for all test statistics, which is compared to the study statistics to help establish significance.

Some of the common inferential statistics you will see include:

Comparison tests: Comparison tests look for differences among group means. They can be used to test the effect of a categorical variable on the mean value of some other characteristic.

T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults).

• t -tests (compares differences in two groups) – either paired t-test (example: What is the effect of two different test prep programs on the average exam scores for students from the same class?) or independent t-test (example: What is the difference in average exam scores for students from two different schools?)
• analysis of variance (ANOVA, which compares differences in three or more groups) (example: What is the difference in average pain levels among post-surgical patients given three different painkillers?) or MANOVA (compares differences in three or more groups, and 2 or more outcomes) (example: What is the effect of flower species on petal length, petal width, and stem length?)

Correlation tests: Correlation tests check whether variables are related without hypothesizing a cause-and-effect relationship.

• Pearson r (measures the strength and direction of the relationship between two variables) (example: How are latitude and temperature related?)

Nonparametric tests: Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.

• chi-squared ( X 2 ) test (measures differences in proportions). Chi-square tests are often used to test hypotheses. The chi-square statistic compares the size of any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship. For example, the results of tossing a fair coin meet these criteria. We can apply a chi-square test to determine which type of candy is most popular and make sure that our shelves are well stocked. Or maybe you’re a scientist studying the offspring of cats to determine the likelihood of certain genetic traits being passed to a litter of kittens.

Inferential Versus Descriptive Statistics Summary Table

Statistical Significance Versus Clinical Significance

Finally, when it comes to statistical significance  in hypothesis testing, the normal probability value in nursing is <0.05. A p=value (probability) is a statistical measurement used to validate a hypothesis against measured data in the study. Meaning, it measures the likelihood that the results were actually observed due to the intervention, or if the results were just due by chance. The p-value, in measuring the probability of obtaining the observed results, assumes the null hypothesis is true.

The lower the p-value, the greater the statistical significance of the observed difference.

In the example earlier about our diabetic patients receiving online diet education, let’s say we had p = 0.05. Would that be a statistically significant result?

If you answered yes, you are correct!

What if our result was p = 0.8?

Not significant. Good job!

That’s pretty straightforward, right? Below 0.05, significant. Over 0.05 not   significant.

Could we have significance clinically even if we do not have statistically significant results? Yes. Let’s explore this a bit.

Statistical hypothesis testing provides little information for interpretation purposes. It’s pretty mathematical and we can still get it wrong. Additionally, attaining statistical significance does not really state whether a finding is clinically meaningful. With a large enough sample, even a small very tiny relationship may be statistically significant. But, clinical significance  is the practical importance of research. Meaning, we need to ask what the palpable effects may be on the lives of patients or healthcare decisions.

Remember, hypothesis testing cannot prove. It also cannot tell us much other than “yeah, it’s probably likely that there would be some change with this intervention”. Hypothesis testing tells us the likelihood that the outcome was due to an intervention or influence and not just by chance. Also, as nurses and clinicians, we are not concerned with a group of people – we are concerned at the individual, holistic level. The goal of evidence-based practice is to use best evidence for decisions about specific individual needs.

Additionally, begin your Discussion section. What are the implications to practice? Is there little evidence or a lot? Would you recommend additional studies? If so, what type of study would you recommend, and why?

• Were all the important results discussed?
• Did the researchers discuss any study limitations and their possible effects on the credibility of the findings? In discussing limitations, were key threats to the study’s validity and possible biases reviewed? Did the interpretations take limitations into account?
• What types of evidence were offered in support of the interpretation, and was that evidence persuasive? Were results interpreted in light of findings from other studies?
• Did the researchers make any unjustifiable causal inferences? Were alternative explanations for the findings considered? Were the rationales for rejecting these alternatives convincing?
• Did the interpretation consider the precision of the results and/or the magnitude of effects?
• Did the researchers draw any unwarranted conclusions about the generalizability of the results?
• Did the researchers discuss the study’s implications for clinical practice or future nursing research? Did they make specific recommendations?
• If yes, are the stated implications appropriate, given the study’s limitations and the magnitude of the effects as well as evidence from other studies? Are there important implications that the report neglected to include?
• Did the researchers mention or assess clinical significance? Did they make a distinction between statistical and clinical significance?
• If clinical significance was examined, was it assessed in terms of group-level information (e.g., effect sizes) or individual-level results? How was clinical significance operationalized?

References & Attribution

“ Green check mark ” by rawpixel licensed CC0 .

“ Magnifying glass ” by rawpixel licensed CC0

“ Orange flame ” by rawpixel licensed CC0 .

Polit, D. & Beck, C. (2021).  Lippincott CoursePoint Enhanced for Polit’s Essentials of Nursing Research  (10th ed.). Wolters Kluwer Health 

Vaid, N. K. (2019) Statistical performance measures. Medium. https://neeraj-kumar-vaid.medium.com/statistical-performance-measures-12bad66694b7

Evidence-Based Practice & Research Methodologies Copyright © by Tracy Fawns is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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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QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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Home » Quantitative Data – Types, Methods and Examples

Quantitative Data – Types, Methods and Examples

Table of Contents

Quantitative Data

Definition:

Quantitative data refers to numerical data that can be measured or counted. This type of data is often used in scientific research and is typically collected through methods such as surveys, experiments, and statistical analysis.

Quantitative Data Types

There are two main types of quantitative data: discrete and continuous.

• Discrete data: Discrete data refers to numerical values that can only take on specific, distinct values. This type of data is typically represented as whole numbers and cannot be broken down into smaller units. Examples of discrete data include the number of students in a class, the number of cars in a parking lot, and the number of children in a family.
• Continuous data: Continuous data refers to numerical values that can take on any value within a certain range or interval. This type of data is typically represented as decimal or fractional values and can be broken down into smaller units. Examples of continuous data include measurements of height, weight, temperature, and time.

Quantitative Data Collection Methods

There are several common methods for collecting quantitative data. Some of these methods include:

• Surveys : Surveys involve asking a set of standardized questions to a large number of people. Surveys can be conducted in person, over the phone, via email or online, and can be used to collect data on a wide range of topics.
• Experiments : Experiments involve manipulating one or more variables and observing the effects on a specific outcome. Experiments can be conducted in a controlled laboratory setting or in the real world.
• Observational studies : Observational studies involve observing and collecting data on a specific phenomenon without intervening or manipulating any variables. Observational studies can be conducted in a natural setting or in a laboratory.
• Secondary data analysis : Secondary data analysis involves using existing data that was collected for a different purpose to answer a new research question. This method can be cost-effective and efficient, but it is important to ensure that the data is appropriate for the research question being studied.
• Physiological measures: Physiological measures involve collecting data on biological or physiological processes, such as heart rate, blood pressure, or brain activity.
• Computerized tracking: Computerized tracking involves collecting data automatically from electronic sources, such as social media, online purchases, or website analytics.

Quantitative Data Analysis Methods

There are several methods for analyzing quantitative data, including:

• Descriptive statistics: Descriptive statistics are used to summarize and describe the basic features of the data, such as the mean, median, mode, standard deviation, and range.
• Inferential statistics : Inferential statistics are used to make generalizations about a population based on a sample of data. These methods include hypothesis testing, confidence intervals, and regression analysis.
• Data visualization: Data visualization involves creating charts, graphs, and other visual representations of the data to help identify patterns and trends. Common types of data visualization include histograms, scatterplots, and bar charts.
• Time series analysis: Time series analysis involves analyzing data that is collected over time to identify patterns and trends in the data.
• Multivariate analysis : Multivariate analysis involves analyzing data with multiple variables to identify relationships between the variables.
• Factor analysis : Factor analysis involves identifying underlying factors or dimensions that explain the variation in the data.
• Cluster analysis: Cluster analysis involves identifying groups or clusters of observations that are similar to each other based on multiple variables.

Quantitative Data Formats

Quantitative data can be represented in different formats, depending on the nature of the data and the purpose of the analysis. Here are some common formats:

• Tables : Tables are a common way to present quantitative data, particularly when the data involves multiple variables. Tables can be used to show the frequency or percentage of data in different categories or to display summary statistics.
• Charts and graphs: Charts and graphs are useful for visualizing quantitative data and can be used to highlight patterns and trends in the data. Some common types of charts and graphs include line charts, bar charts, scatterplots, and pie charts.
• Databases : Quantitative data can be stored in databases, which allow for easy sorting, filtering, and analysis of large amounts of data.
• Spreadsheets : Spreadsheets can be used to organize and analyze quantitative data, particularly when the data is relatively small in size. Spreadsheets allow for calculations and data manipulation, as well as the creation of charts and graphs.
• Statistical software : Statistical software, such as SPSS, R, and SAS, can be used to analyze quantitative data. These programs allow for more advanced statistical analyses and data modeling, as well as the creation of charts and graphs.

Quantitative Data Gathering Guide

Here is a basic guide for gathering quantitative data:

• Define the research question: The first step in gathering quantitative data is to clearly define the research question. This will help determine the type of data to be collected, the sample size, and the methods of data analysis.
• Choose the data collection method: Select the appropriate method for collecting data based on the research question and available resources. This could include surveys, experiments, observational studies, or other methods.
• Determine the sample size: Determine the appropriate sample size for the research question. This will depend on the level of precision needed and the variability of the population being studied.
• Develop the data collection instrument: Develop a questionnaire or survey instrument that will be used to collect the data. The instrument should be designed to gather the specific information needed to answer the research question.
• Pilot test the data collection instrument : Before collecting data from the entire sample, pilot test the instrument on a small group to identify any potential problems or issues.
• Collect the data: Collect the data from the selected sample using the chosen data collection method.
• Clean and organize the data : Organize the data into a format that can be easily analyzed. This may involve checking for missing data, outliers, or errors.
• Analyze the data: Analyze the data using appropriate statistical methods. This may involve descriptive statistics, inferential statistics, or other types of analysis.
• Interpret the results: Interpret the results of the analysis in the context of the research question. Identify any patterns, trends, or relationships in the data and draw conclusions based on the findings.
• Communicate the findings: Communicate the findings of the analysis in a clear and concise manner, using appropriate tables, graphs, and other visual aids as necessary. The results should be presented in a way that is accessible to the intended audience.

Examples of Quantitative Data

Here are some examples of quantitative data:

• Height of a person (measured in inches or centimeters)
• Weight of a person (measured in pounds or kilograms)
• Temperature (measured in Fahrenheit or Celsius)
• Age of a person (measured in years)
• Number of cars sold in a month
• Amount of rainfall in a specific area (measured in inches or millimeters)
• Number of hours worked in a week
• GPA (grade point average) of a student
• Sales figures for a product
• Time taken to complete a task.
• Distance traveled (measured in miles or kilometers)
• Speed of an object (measured in miles per hour or kilometers per hour)
• Number of people attending an event
• Price of a product (measured in dollars or other currency)
• Blood pressure (measured in millimeters of mercury)
• Amount of sugar in a food item (measured in grams)
• Test scores (measured on a numerical scale)
• Number of website visitors per day
• Stock prices (measured in dollars)
• Crime rates (measured by the number of crimes per 100,000 people)

Applications of Quantitative Data

Quantitative data has a wide range of applications across various fields, including:

• Scientific research: Quantitative data is used extensively in scientific research to test hypotheses and draw conclusions. For example, in biology, researchers might use quantitative data to measure the growth rate of cells or the effectiveness of a drug treatment.
• Business and economics: Quantitative data is used to analyze business and economic trends, forecast future performance, and make data-driven decisions. For example, a company might use quantitative data to analyze sales figures and customer demographics to determine which products are most popular among which segments of their customer base.
• Education: Quantitative data is used in education to measure student performance, evaluate teaching methods, and identify areas where improvement is needed. For example, a teacher might use quantitative data to track the progress of their students over the course of a semester and adjust their teaching methods accordingly.
• Public policy: Quantitative data is used in public policy to evaluate the effectiveness of policies and programs, identify areas where improvement is needed, and develop evidence-based solutions. For example, a government agency might use quantitative data to evaluate the impact of a social welfare program on poverty rates.
• Healthcare : Quantitative data is used in healthcare to evaluate the effectiveness of medical treatments, track the spread of diseases, and identify risk factors for various health conditions. For example, a doctor might use quantitative data to monitor the blood pressure levels of their patients over time and adjust their treatment plan accordingly.

Purpose of Quantitative Data

The purpose of quantitative data is to provide a numerical representation of a phenomenon or observation. Quantitative data is used to measure and describe the characteristics of a population or sample, and to test hypotheses and draw conclusions based on statistical analysis. Some of the key purposes of quantitative data include:

• Measuring and describing : Quantitative data is used to measure and describe the characteristics of a population or sample, such as age, income, or education level. This allows researchers to better understand the population they are studying.
• Testing hypotheses: Quantitative data is often used to test hypotheses and theories by collecting numerical data and analyzing it using statistical methods. This can help researchers determine whether there is a statistically significant relationship between variables or whether there is support for a particular theory.
• Making predictions : Quantitative data can be used to make predictions about future events or trends based on past data. This is often done through statistical modeling or time series analysis.
• Evaluating programs and policies: Quantitative data is often used to evaluate the effectiveness of programs and policies. This can help policymakers and program managers identify areas where improvements can be made and make evidence-based decisions about future programs and policies.

When to use Quantitative Data

Quantitative data is appropriate to use when you want to collect and analyze numerical data that can be measured and analyzed using statistical methods. Here are some situations where quantitative data is typically used:

• When you want to measure a characteristic or behavior : If you want to measure something like the height or weight of a population or the number of people who smoke, you would use quantitative data to collect this information.
• When you want to compare groups: If you want to compare two or more groups, such as comparing the effectiveness of two different medical treatments, you would use quantitative data to collect and analyze the data.
• When you want to test a hypothesis : If you have a hypothesis or theory that you want to test, you would use quantitative data to collect data that can be analyzed statistically to determine whether your hypothesis is supported by the data.
• When you want to make predictions: If you want to make predictions about future trends or events, such as predicting sales for a new product, you would use quantitative data to collect and analyze data from past trends to make your prediction.
• When you want to evaluate a program or policy : If you want to evaluate the effectiveness of a program or policy, you would use quantitative data to collect data about the program or policy and analyze it statistically to determine whether it has had the intended effect.

Characteristics of Quantitative Data

Quantitative data is characterized by several key features, including:

• Numerical values : Quantitative data consists of numerical values that can be measured and counted. These values are often expressed in terms of units, such as dollars, centimeters, or kilograms.
• Continuous or discrete : Quantitative data can be either continuous or discrete. Continuous data can take on any value within a certain range, while discrete data can only take on certain values.
• Objective: Quantitative data is objective, meaning that it is not influenced by personal biases or opinions. It is based on empirical evidence that can be measured and analyzed using statistical methods.
• Large sample size: Quantitative data is often collected from a large sample size in order to ensure that the results are statistically significant and representative of the population being studied.
• Statistical analysis: Quantitative data is typically analyzed using statistical methods to determine patterns, relationships, and other characteristics of the data. This allows researchers to make more objective conclusions based on empirical evidence.
• Precision : Quantitative data is often very precise, with measurements taken to multiple decimal points or significant figures. This precision allows for more accurate analysis and interpretation of the data.

Advantages of Quantitative Data

Some advantages of quantitative data are:

• Objectivity : Quantitative data is usually objective because it is based on measurable and observable variables. This means that different people who collect the same data will generally get the same results.
• Precision : Quantitative data provides precise measurements of variables. This means that it is easier to make comparisons and draw conclusions from quantitative data.
• Replicability : Since quantitative data is based on objective measurements, it is often easier to replicate research studies using the same or similar data.
• Generalizability : Quantitative data allows researchers to generalize findings to a larger population. This is because quantitative data is often collected using random sampling methods, which help to ensure that the data is representative of the population being studied.
• Statistical analysis : Quantitative data can be analyzed using statistical methods, which allows researchers to test hypotheses and draw conclusions about the relationships between variables.
• Efficiency : Quantitative data can often be collected quickly and efficiently using surveys or other standardized instruments, which makes it a cost-effective way to gather large amounts of data.

Limitations of Quantitative Data

Some Limitations of Quantitative Data are as follows:

• Limited context: Quantitative data does not provide information about the context in which the data was collected. This can make it difficult to understand the meaning behind the numbers.
• Limited depth: Quantitative data is often limited to predetermined variables and questions, which may not capture the complexity of the phenomenon being studied.
• Difficulty in capturing qualitative aspects: Quantitative data is unable to capture the subjective experiences and qualitative aspects of human behavior, such as emotions, attitudes, and motivations.
• Possibility of bias: The collection and interpretation of quantitative data can be influenced by biases, such as sampling bias, measurement bias, or researcher bias.
• Simplification of complex phenomena: Quantitative data may oversimplify complex phenomena by reducing them to numerical measurements and statistical analyses.
• Lack of flexibility: Quantitative data collection methods may not allow for changes or adaptations in the research process, which can limit the ability to respond to unexpected findings or new insights.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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3.6 Quantitative Data Analysis

Remember that quantitative research explains phenomena by collecting numerical data that are analysed using statistics. 1 Statistics is a scientific method of collecting, processing, analysing, presenting and interpreting data in numerical form. 44 This section discusses how quantitative data is analysed and the choice of test statistics based on the variables (data). First, it is important to understand the different types of variables before delving into the statistical tests.

Types of variables

A variable is an item (data) that can be quantified or measured. There are two main types of variables – numerical variables and categorical variables. 45 Numerical variables describe a measurable quantity and are subdivided into two groups – discrete and continuous data. Discrete variables are finite and are based on a set of whole values or numbers such as 0, 1, 2, 3,… (integer). These data cannot be broken into fractions or decimals. 45 Examples of discrete variables include the number of students in a class and the total number of states in Australia. Continuous variables can assume any value between a certain set of real numbers e.g. height and serum glucose levels. In other words, these are variables that are in between points (101.01 to 101.99 is between 101 and 102) and can be broken down into different parts, fractions and decimals. 45

On the other hand, categorical variables are qualitative and describe characteristics or properties of the data. This type of data may be represented by a name, symbol or number code. 46 There are two types- nominal and ordinal variables. Nominal data are variables having two or more categories without any intrinsic order to the categories. 46 For example, the colour of eyes (blue, brown, and black) and gender (male, female) have no specific order and are nominal categorical variables. Ordinal variables are similar to nominal variables with regard to describing characteristics or properties, but these variables have a clear, logical order or rank in the data. 46 The level of education (primary, secondary and tertiary) is an example of ordinal data.

Now that you understand the different types of variables, identify the variables in the scenario in the Padlet below.

Statistics can be broadly classified into descriptive and inferential statistics.  Descriptive statistics explain how different variables in a sample or population relate to one another. 60 Inferential statistics draw conclusions or inferences about a whole population from a random sample of data. 45

Descriptive statistics

This is a summary description of measurements (variables) from a given data set, e.g., a group of study participants. It provides a meaningful interpretation of the data. It has two main measures – central tendency and dispersion measures. 45

The measures of central tendency describe the centre of data and provide a summary of data in the form of mean, median and mode. Mean is the average distribution, the median is the middle value (skewed distribution), and mode is the most frequently occurring variable. 4

• Descriptive statistics for continuous variables

An example is a study conducted among 145 students where their height and weight were obtained. The summary statistics (a measure of central tendency and dispersion) have been presented below in table 3.2.

Table 3.2 Descriptive statistics for continuous variables

• Descriptive statistics for categorical variables

Categorical variables are presented using frequencies and percentages or proportions. For example, a hypothetical scenario is a study on smoking history by gender in a population of 4609 people. Below is the summary statistic of the study (Table 3.3).

Table 3.3 Descriptive statistics for categorical variables

Normality of data

Before proceeding to inferential statistics, it is important to assess the normality of the data. A normality test evaluates whether or not a sample was selected from a normally distributed population. 47 It is typically used to determine if the data used in the study has a normal distribution. Many statistical techniques, notably parametric tests, such as correlation, regression, t-tests, and ANOVA, are predicated on normal data distribution. 47 There are several methods for assessing whether data are normally distributed. They include graphical or visual tests such as histograms and Q-Q probability plots and analytical tests such as the Shapiro-Wilk test and the Kolmogrorov-Smirnov test. 47 The most useful visual method is visualizing the normality distribution via a histogram, as shown in Figure 3.12. On the other hand, the analytical tests (Shapiro-Wilk test and the Kolmogrorov-Smirnov) determine if the data distribution deviates considerably from the normal distribution by using criteria such as the p-value. 47 If the p-value is < 0.05, the data is not normally distributed. 47 These analytical tests can be conducted using statistical software like SPSS and R. However, when the sample size is > 30, the violation of the normality test is not an issue and the sample is considered to be normally distributed. According to the central limit theorem, in large samples of > 30 or 40, the sampling distribution is normal regardless of the shape of the data. 47, 48   Normally distributed data are also known as parametric data, while non-normally distributed data are known as non-parametric data.

Table 3.4  Tests of normality for height by gender

Inferential statistics

This statistical analysis involves the analysis of data from a sample to make inferences about the target population. 45 The goal is to test hypotheses. Statistical techniques include parametric and non-parametric tests depending on the normality of the data. 45 Conducting a statistical analysis requires choosing the right test to answer the research question.

Steps in a statistical test

The choice of the statistical test is based on the research question to be answered and the data. There are steps to take before choosing a test and conducting an analysis. 49

• State the research question/aim
• State the null and alternative hypothesis

The null hypothesis states that there is no statistical difference exists between two variables or in a set of given observations. The alternative hypothesis contradicts the null and states that there is a statistical difference between the variables.

• Decide on a suitable statistical test based on the type of variables.

Is the data normally distributed? Are the variables continuous, discrete or categorical data? The identification of the data type will aid the appropriate selection of the right test.

• Specify the level of significance (α -for example, 0.05). The level of significance is the probability of rejecting the null hypothesis when the null is true. The hypothesis is tested by calculating the probability (P value) of observing a difference between the variables, and the value of p ranges from zero to one. The more common cut-off for statistical significance is 0.05. 50
• Conduct the statistical test analysis- calculate the p-value
• p<0.05 leads to rejection of the null hypothesis
• p>0.05 leads to retention of the null hypothesis
• Interpret the results

In the next section, we have provided an overview of the statistical tests. The step-by-step conduct of the test using statistical software is beyond the scope of this book. We have provided the theoretical basis for the test. Other books, like Pallant’s SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS, is a good resource if you wish to learn how to run the different tests. 48

Types of Statistical tests

A distinction is always made based on the data type (categorical or numerical) and if the data is paired or unpaired. Paired data refers to data arising from the same individual at different time points, such as before and after or pre and post-test designs. In contrast, unpaired data are data from separate individuals. Inferential statistics can be grouped into the following categories:

Comparing two categorical variables

• o Two sample groups (one numerical variable and one categorical variable in two groups)
• o Three sample groups (one numerical variable and one categorical variable in three groups)

Comparing two numerical variables

Deciding on the choice of test with two categorical variables involves checking if the data is nominal or ordinal and paired versus unpaired. The figure below (Figure 3.13) shows a decision tree for categorical variables.

• Chi-square test of independence

The chi-square test of independence compares the distribution of two or more independent data sets. 44 The chi-square value increases when the distributions are found to be increasingly similar, indicating a stronger relationship between them. A value of χ2  =  0 means that there is no relationship between the variables. 44 There are preconditions for the Chi-square test, which include a sample size > 60, and the expected number in each field should not be less than 5. Fisher’s exact test is used if the conditions are not met.

• McNemar’s test

Unlike the Chi-square test, Mcnemar’s test is designed to assess if there is a difference between two related or paired groups (categorical variables). 51

• Chi-square for trend

The chi-square test for trend tests the relationship between one variable that is binary and the other is ordered categorical. 52 The test assesses whether the association between the variables follows a trend. For example, the association between the frequency of mouth rinse (once a week, twice a week and seven days a week) and the presence of dental gingivitis (yes vs no) can be assessed to observe a dose-response effect between mouth rinse usage and dental gingivitis. 52

Tests involving one numerical and one categorical variable

The variables involved in this group of tests are one numerical variable and one categorical variable. These tests have two broad groups – two sample groups and three or more sample groups, as shown in Figures 3.14 and 3.15.

Two sample groups

The parametric two-sample group of tests (independent samples t-test and paired t-test) compare the means of the two samples. On the other hand, the non-parametric tests (Mann-Whitney U test and Wilcoxon Signed Rank test) compare medians of the samples.

• Parametric: Independent samples T-test and Paired Samples t-test

The independent or unpaired t-test is used when the participants in both groups are independent of one another (those in the first group are distinct from those in the second group) and when the parameters are typically distributed and continuous. 44 On the other hand, the paired t-test is used to test two paired sets of normally distributed continuous data. A paired test involves measuring the same item twice on each subject. 44 For instance, you would wish to compare the differences in each subject’s heart rates before and after an exercise. The tests compare the mean values between the groups.

• Non-parametric: Mann-Whitney U test and Wilcoxon Signed Rank test

The nonparametric equivalents of the paired and independent sample t-tests are the Wilcoxon signed-rank test and the Mann-Whitney U test. 44 These tests examine if the two data sets’ medians are equal and whether the sample sets are representative of the same population. 44 They have less power than their parametric counterparts, as is the case with all nonparametric tests but can be applied to data that is not normally distributed or small samples. 44

Three samples group

The t-tests and their non-parametric counterparts cannot be used for comparing three or more groups. Thus, three or more sample groups of the test are used. The parametric three samples group of tests are one-way ANOVA (Analysis of variance) and repeated measures ANOVA. In contrast, the non-parametric tests are the Kruskal-Wallis test and the Friedman test.

• Parametric: One-way ANOVA  and Repeated measures ANOVA

ANOVA is used to determine whether there are appreciable differences between the means of three or more groups. 45 Within-group and between-group variability are the two variances examined in a one-way ANOVA test. The repeated measures ANOVA examines whether the means of three or more groups are identical. 45 When all the variables in a sample are tested under various circumstances or at various times, a repeated measure ANOVA is utilized. 45 The dependent variable is measured repeatedly as the variables are determined from samples at various times. The data don’t conform to the ANOVA premise of independence; thus, using a typical ANOVA in this situation is inappropriate. 45

• Non-parametric: Kuskal Wallis test and Friedman test

The non-parametric test to analyse variance is the Kruskal-Wallis test. It examines if the median values of three or more independent samples differ in any way. 45 The test statistic is produced after the rank sums of the data values, which are ranked in ascending order. On the other hand, the Friedman test is the non-parametric test for comparing the differences between related samples. When the same parameter is assessed repeatedly or under different conditions on the same participants, the Friedman test can be used as an alternative to repeated measures ANOVA. 45

Pearson’s correlation and regression tests are used to compare two numerical variables.

• Pearson’s Correlation and Regression

Pearson’s correlation (r) indicates a relationship between two numerical variables assuming that the relationship is linear. 53 This implies that for every unit rise or reduction in one variable, the other increases or decreases by a constant amount.  The values of the correlation coefficient vary from -1 to + 1. Negative correlation coefficient values suggest a rise in one variable will lead to a fall in the other variable and vice versa. 53 Positive correlation coefficient values indicate a propensity for one variable to rise or decrease in tandem with another. Pearson’s correlation also quantifies the strength of the relationship between the two variables. Correlation coefficient values close to zero suggest a weak linear relationship between two variables, whereas those close to -1 or +1 indicate a robust linear relationship between two variables. 53   It is important to note that correlation does not imply causation. The Spearman rank correlation coefficient test (rs) is the nonparametric equivalent of the Pearson coefficient. It is useful when the conditions for calculating a meaningful r value cannot be satisfied and numerical data is being analysed. 44

Regression measures the connection between two correlated variables. The variables are usually labelled as dependent or independent. An independent variable is a factor that influences a dependent variable (which can also be called an outcome). 54 Regression analyses describe, estimate, predict and control the effect of one or more independent variables while investigating the relationship between the independent and dependent variables.  54 There are three common types of regression analyses – linear, logistic and multiple regression. 54

• Linear regression examines the relationship between one continuous dependent and one continuous independent variable. For example, the effect of age on shoe size can be analysed using linear regression. 54
• Logistic regression estimates an event’s likelihood with binary outcomes (present or absent). It involves one categorical dependent variable and two or more continuous or categorical predictor (independent) variables. 54
• Multiple regression is an extension of simple linear regression and investigates one continuous dependent and two or more continuous independent variables. 54

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Quantitative Data Analysis

In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process .

The same figure within data set can be interpreted in many different ways; therefore it is important to apply fair and careful judgement.

For example, questionnaire findings of a research titled “A study into the impacts of informal management-employee communication on the levels of employee motivation: a case study of Agro Bravo Enterprise” may indicate that the majority 52% of respondents assess communication skills of their immediate supervisors as inadequate.

This specific piece of primary data findings needs to be critically analyzed and objectively interpreted through comparing it to other findings within the framework of the same research. For example, organizational culture of Agro Bravo Enterprise, leadership style, the levels of frequency of management-employee communications need to be taken into account during the data analysis.

Moreover, literature review findings conducted at the earlier stages of the research process need to be referred to in order to reflect the viewpoints of other authors regarding the causes of employee dissatisfaction with management communication. Also, secondary data needs to be integrated in data analysis in a logical and unbiased manner.

Let’s take another example. You are writing a dissertation exploring the impacts of foreign direct investment (FDI) on the levels of economic growth in Vietnam using correlation quantitative data analysis method . You have specified FDI and GDP as variables for your research and correlation tests produced correlation coefficient of 0.9.

In this case simply stating that there is a strong positive correlation between FDI and GDP would not suffice; you have to provide explanation about the manners in which the growth on the levels of FDI may contribute to the growth of GDP by referring to the findings of the literature review and applying your own critical and rational reasoning skills.

A set of analytical software can be used to assist with analysis of quantitative data. The following table  illustrates the advantages and disadvantages of three popular quantitative data analysis software: Microsoft Excel, Microsoft Access and SPSS.

Advantages and disadvantages of popular quantitative analytical software

Quantitative data analysis with the application of statistical software consists of the following stages [1] :

• Preparing and checking the data. Input of data into computer.
• Selecting the most appropriate tables and diagrams to use according to your research objectives.
• Selecting the most appropriate statistics to describe your data.
• Selecting the most appropriate statistics to examine relationships and trends in your data.

It is important to note that while the application of various statistical software and programs are invaluable to avoid drawing charts by hand or undertake calculations manually, it is easy to use them incorrectly. In other words, quantitative data analysis is “a field where it is not at all difficult to carry out an analysis which is simply wrong, or inappropriate for your data or purposes. And the negative side of readily available specialist statistical software is that it becomes that much easier to generate elegantly presented rubbish” [2] .

Therefore, it is important for you to seek advice from your dissertation supervisor regarding statistical analyses in general and the choice and application of statistical software in particular.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of quantitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited.

[2] Robson, C. (2011) Real World Research: A Resource for Users of Social Research Methods in Applied Settings (3rd edn). Chichester: John Wiley.

Data Analysis Techniques in Research – Methods, Tools & Examples

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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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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Quantitative Market Research: Fundamentals, Methods, and Applications

• by Alice Ananian
• August 16, 2024

Did you know that 99% of successful businesses use data to drive their decisions? In our increasingly digital world, quantitative market research has become an essential tool. It doesn’t just provide random facts; it offers precise insights into consumer behavior, market trends, and competitive landscapes, giving businesses the edge they need to storm ahead.

This article explores the fundamentals, methods, and applications of quantitative market research, helping business owners, marketing professionals, and entrepreneurs improve their decision-making and drive their businesses forward.

What is Quantitative Market Research?

Quantitative market research is a methodical approach to gather and analyze numerical data, offering businesses a practical understanding of customer behavior and market trends.

This can be part of both primary and secondary market research. Quantitative market research predominantly relies on structured tools like surveys, polls, and questionnaires to collect quantifiable pieces of information such as percentages, frequencies, and ratings. This research is carried out on a large, representative sample of the target audience to ensure accurate reflection of widespread attitudes and behaviors.

Following the data collection, statistical techniques are applied to reveal patterns, track trends, and identify relationships, effectively converting raw data into actionable insights to guide marketing strategies.

Quantitative vs. Qualitative Research

To fully appreciate quantitative research, it’s essential to understand how it differs from qualitative market research:

While quantitative research provides broad, generalizable insights, qualitative research offers deeper, context-rich understanding. Many successful market research strategies combine both approaches to gain a comprehensive view of the market.

Applications of Quantitative Market Research

Quantitative market research finds applications across various business functions and industries. Here are some key areas where this research method proves invaluable:

Product Development

• Measuring consumer preferences for product features: This involves surveying potential customers to rank or rate different product features, helping companies prioritize which features to include or improve.
• Assessing market demand for new products: Researchers can use quantitative methods to estimate the potential market size and gauge consumer interest in a new product concept before investing in development .
• Evaluating pricing strategies: Through techniques like conjoint analysis or price sensitivity meters, companies can determine optimal price points that maximize both sales and profitability.

Brand Management

• Tracking brand awareness and perception: Regular surveys can measure how many consumers recognize a brand and what associations they have with it, allowing companies to monitor their brand’s health over time.
• Measuring brand loyalty and customer satisfaction: Quantitative research can assess how likely customers are to repurchase or recommend a brand, providing insights into customer retention strategies.
• Comparing brand performance against competitors: Competitive benchmarking surveys can reveal a brand’s strengths and weaknesses relative to competitors in various attributes.

Customer Segmentation

• Identifying distinct customer groups: By analyzing survey data on demographics, behaviors, and preferences, researchers can use cluster analysis to group customers with similar characteristics.
• Determining the size and value of different market segments: Once segments are identified, quantitative research can estimate the size of each segment and its potential value to the business.

Advertising Effectiveness

• Measuring ad recall and recognition: Surveys conducted after ad campaigns can quantify how many people remember seeing an ad and can correctly identify the brand associated with it.
• Assessing the impact of advertising on purchase intent: Researchers can measure how exposure to ads influences consumers’ likelihood to buy a product, helping to justify advertising spend.
• Evaluating return on investment for marketing campaigns: By linking advertising exposure data with sales data, companies can calculate the ROI of their marketing efforts.

Market Sizing and Forecasting

• Estimating market size and growth potential: Using survey data and secondary sources, researchers can quantify the current market size and project future growth based on trends and economic factors.
• Projecting future sales and market share: Time series analysis and regression models can be used to forecast a company’s sales and market share based on historical data and market conditions.

Customer Experience

• Measuring customer satisfaction and loyalty: Regular surveys can track customer satisfaction scores and Net Promoter Scores (NPS) to gauge overall customer sentiment and loyalty.
• Identifying pain points in the customer journey: Quantitative analysis of customer feedback can highlight common issues or areas of dissatisfaction in the customer experience.
• Quantifying the impact of service improvements: By measuring customer satisfaction before and after implementing changes, companies can assess the effectiveness of their improvement initiatives.

Competitive Analysis

• Benchmarking product or service performance: Surveys can compare how a company’s offerings stack up against competitors on various attributes, helping identify areas for improvement.
• Assessing market share and competitive positioning: Regular tracking studies can monitor changes in market share and brand positioning relative to competitors, informing strategic decisions.

Benefits and Challenges of Quantitative Market Research

Quantitative market research offers a range of advantages that make it a valuable tool for businesses seeking data-driven insights. Understanding these benefits can help organizations leverage this research method effectively to inform their strategies and decision-making processes.

Objectivity: Quantitative research provides unbiased, numerical data that can be statistically analyzed. This objectivity ensures that the findings are not influenced by the researcher’s personal biases or perspectives.

Generalizability: Results derived from large sample sizes can be extrapolated to represent the broader population. This means that the findings are more likely to be valid for all individuals within the target group, enhancing the reliability of the study.

Comparability: Standardized data collection methods allow for easy comparison across different time periods or market segments. This comparability is crucial for tracking changes and trends over time, as well as for identifying differences between various subgroups.

Scalability: Quantitative research methods can efficiently gather data from large sample sizes. This scalability makes it possible to conduct studies on a much larger scale, providing more comprehensive insights into the research question.

Hypothesis testing: Quantitative research enables researchers to test specific theories or assumptions about market behavior. By confirming or disproving these hypotheses, researchers can gain a deeper understanding of the factors driving market trends and consumer behaviors.

Decision support: The concrete data obtained from quantitative research provides a solid foundation to support strategic decision-making. This evidence-based approach facilitates more informed and effective decisions, reducing the risk of error and improving outcomes.

While quantitative market research provides numerous advantages, it’s important to recognize that this approach also comes with its own set of limitations and potential pitfalls. Being aware of these challenges can help researchers and businesses plan more effectively and interpret results with appropriate caution.

Limited depth: Quantitative research methods may not capture the nuanced reasons behind consumer behavior or attitudes, often resulting in a superficial understanding of complex issues.

Inflexibility: Structured surveys and experiments may miss unexpected insights that could emerge in more open-ended research methods, limiting the scope of discovery.

Response bias: Respondents may not always provide honest or accurate answers, particularly on sensitive or personal topics, leading to skewed data and unreliable conclusions.

Cost: Conducting large-scale surveys or experiments can be expensive, often requiring significant financial resources for data collection, participant incentives, and analysis.

Time-consuming: The proper design, implementation, and analysis of quantitative research can be time-intensive, potentially delaying the results and impacting project timelines.

Expertise required: Quantitative research requires extensive knowledge of statistical analysis and research methodologies, necessitating skilled professionals to ensure accurate and reliable outcomes.

Examples of Quantitative Market Research

To illustrate the practical applications of quantitative market research, let’s explore some real-world examples:

Netflix A/B Testing Titles

Ever noticed how Netflix displays different titles or artwork for the same movie or show depending on your profile? This is A/B testing, a form of quantitative research. Netflix uses surveys and click-through rates to determine which title or artwork generates the most clicks and engagement.

Spotify Optimizing Playlists

How does Spotify create those eerily perfect playlists that seem to know exactly what you’re in the mood for? Quantitative research plays a role! Spotify analyzes user listening habits, including skip rates, play time, and song popularity, to curate playlists that resonate with different user preferences.

Coca-Cola Testing New Flavors

Developing a new beverage flavor requires understanding consumer preferences. Coca-Cola uses surveys and taste tests to gather quantitative data on sweetness levels, flavor combinations, and overall appeal. This data helps them refine new flavors before a full-scale launch.

Apple gauging iPhone Screen Size Preferences

Before increasing iPhone screen sizes, Apple likely conducted quantitative research. Online surveys and focus groups could have gathered data on user preferences for screen size, one-handed usability, and content viewing experience. This data likely helped Apple determine the optimal screen size for future iPhones.

Dominos Revamping its Pizza Recipe

In 2009, Domino ‘s faced declining sales. Quantitative research came to the rescue. Domino’s conducted customer surveys and taste tests to understand customer dissatisfaction with its pizza crust and sauce. Based on the findings, they revamped the recipe, leading to a significant turnaround in customer satisfaction and sales.

These are just a few examples, but they showcase the power of quantitative research in helping businesses make data-driven decisions that resonate with their target audiences.

Tools and Resources for Quantitative Research

To conduct effective quantitative market research, consider utilizing these tools and resources :

Survey Platforms

Qualtrics : Comprehensive survey software with advanced analytics

Prelaunch : Lets you gather data via a landing page that concisely presents your product 

SurveyMonkey : User-friendly platform for creating and distributing surveys

Google Forms : Free tool for basic surveys and data collection

Statistical Analysis Software

SPSS : Powerful software for complex statistical analysis

R : Open-source programming language for statistical computing

Prelaunch : The platform is a comprehensive concept-validating tool that complies and presents the data you gather via your product’s landing page into insightful section that make it easier to make data-driven decisions.

Excel : Suitable for basic data analysis and visualization

Online Panel Providers

Dynata : Large global panel for diverse respondent recruitment

Amazon Mechanical Turk : Platform for crowdsourcing survey participants

Data Visualization Tools

Tableau : Creates interactive data visualizations and dashboards

Power BI : Microsoft’s business analytics tool for data visualization

Datawrapper : User-friendly tool for creating charts and maps

Market Research Associations

ESOMAR : Global voice of the data, research, and insights community

Insights Association : Leading voice, resource, and network of the marketing research and data analytics community

Academic Resources

Journal of Marketing Research : Scholarly journal featuring cutting-edge research methodologies

Market Research Society (MRS) : Provides training, qualifications, and resources for market researchers

Remember to choose tools that align with your research objectives, budget, and level of expertise. Many of these platforms offer free trials or basic versions, allowing you to experiment before committing to a paid solution.

Quantitative market research is a powerful tool for making data-driven decisions. By providing objective, measurable insights into consumer behavior and market trends, it helps businesses develop targeted strategies and stay ahead of the competition.

While it has its limitations, combining quantitative methods with qualitative approaches can offer a comprehensive market understanding. Careful planning, rigorous methodology, and thoughtful interpretation of results are key to successful quantitative research.

Embrace the power of numbers and let data guide your business success.

Alice Ananian

Alice has over 8 years experience as a strong communicator and creative thinker. She enjoys helping companies refine their branding, deepen their values, and reach their intended audiences through language.

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Top data analysis methods in qualitative research.

Home » Top Data Analysis Methods in Qualitative Research

Qualitative analysis techniques provide a framework for understanding complex human experiences and social phenomena. By delving deeper into people's perceptions, beliefs, and emotions, researchers can uncover rich insights that quantitative data often overlooks. This exploration is essential in many fields, including social sciences, marketing, and user experience research.

In this section, we will introduce various qualitative analysis techniques that facilitate data interpretation. These methods, such as thematic analysis, content analysis, and grounded theory, help researchers analyze interview transcripts and focus group discussions effectively. Understanding these techniques will empower researchers to extract meaningful patterns and themes from qualitative data, ultimately leading to richer, more actionable insights.

Understanding Qualitative Analysis Techniques

Qualitative analysis techniques allow researchers to explore complex phenomena by interpreting non-numeric data, such as text and images. Understanding these techniques involves grasping their flexibility and depth, enabling researchers to uncover deeper meanings. One common technique is thematic analysis, where patterns and themes are identified within qualitative data, leading to insightful interpretations. Another fundamental method is grounded theory, which develops theories based on data collected, creating a dynamic correlation between data and theory.

In addition, narrative analysis examines the stories people tell, revealing how individuals construct meaning in their lives. Ethnographic methods emphasize understanding different cultures by observing and interacting with participants in their environment. By employing qualitative analysis techniques, researchers can not only enhance their findings but also communicate rich, contextual insights. These techniques empower researchers to tell compelling stories that reflect the realities of the subjects studied.

The Essence of Qualitative Data

Qualitative analysis techniques are essential for understanding human behaviors, thoughts, and motivations. They capture rich, nuanced data through interviews, focus groups, and other interactive methods, moving beyond mere numbers to the stories behind them. By immersing researchers in participants' experiences, qualitative data reveals themes and patterns that quantitative methods may overlook.

At its core, qualitative data embodies a comprehensive narrative that informs decision-making. This type of data allows for exploration and a deeper insight into the context in which people act. Researchers can draw meaningful conclusions, as qualitative analysis techniques prioritize participants' perspectives, ensuring their voices shape the analysis. Understanding the essence of qualitative data fosters empathetic interpretation and adds substantial value to research efforts, driving innovation and growth. Ultimately, qualitative insights bridge the gap between data and human experience, enabling a clearer view of the underlying motivations that shape actions within various contexts.

Key Benefits of Qualitative Analysis Techniques

Qualitative analysis techniques offer several distinct advantages that enhance research quality and depth of understanding. Firstly, these techniques facilitate in-depth exploration of participant perspectives, allowing researchers to uncover rich insights that quantitative methods might overlook. By focusing on the meaning behind responses, researchers can develop a nuanced understanding of complex issues and social phenomena.

Secondly, qualitative analysis techniques encourage adaptability throughout the research process. Researchers can modify questions based on participant feedback and emerging themes, enabling dynamic exploration of topics. This flexibility helps to capture the evolving nature of human experience, providing a more accurate representation of participants' viewpoints. Lastly, qualitative analysis promotes engagement with the data, allowing researchers to connect on a personal level with the narratives shared by participants, ultimately leading to more impactful findings.

Top Qualitative Analysis Methods

Qualitative Analysis Techniques play a crucial role in interpreting complex data gathered from interviews, focus groups, and open-ended surveys. One of the most widely used methods is thematic analysis, which identifies patterns and themes within qualitative data. This approach allows researchers to capture rich insights and create a structured understanding of participant perspectives. Another powerful technique is grounded theory, which develops a theory based on the collected data. This fosters an in-depth exploration of phenomena, enabling researchers to construct meaning from participant experiences.

Content analysis is also essential for quantifying and interpreting qualitative information. By categorizing verbal data into manageable themes, researchers can reveal trends and patterns. Finally, narrative analysis focuses on the stories participants share, delving into the context and meaning behind their experiences. These diverse qualitative analysis techniques empower researchers to glean deeper insights, enriching their understanding of human behavior and societal trends.

Thematic Analysis: Unveiling Patterns and Themes

Thematic analysis serves as a powerful tool in qualitative analysis techniques, helping researchers identify patterns and themes within their data. This method involves systematically coding and analyzing participants’ responses to uncover significant insights. By evaluating recurring topics, researchers can better understand the underlying sentiments and motivations expressed in qualitative data.

A crucial aspect of thematic analysis is the organization of data into manageable segments. First, researchers immerse themselves in the transcripts, gaining familiarity. Next, they generate initial codes that represent key features of the data. After this, the coded data is reviewed to identify broader themes. Lastly, themes are defined and refined, ensuring they accurately reflect the core ideas present in the data. Ultimately, thematic analysis not only enhances understanding but also informs practical recommendations based on qualitative research findings. Exploring these themes can significantly advance knowledge in various fields.

Grounded Theory: Building Theory from Data

Grounded theory is a qualitative analysis technique that focuses on developing theories based on data collected during research. Unlike traditional methods that start with a hypothesis, grounded theory evolves as researchers gather and analyze data. This approach allows for a deeper understanding of the participants’ perspectives and experiences, ultimately leading to more refined theories.

Researchers commence grounded theory by open coding their data, identifying key themes and patterns. Next, axial coding organizes these themes into coherent categories, allowing researchers to explore relationships among them. Finally, selective coding integrates these categories to construct a comprehensive theoretical framework. This iterative process not only enhances the depth of analysis but also ensures that the theory generated is closely tied to the data, making it relevant and applicable. By utilizing grounded theory, researchers can derive meaningful insights that resonate with participants' realities, providing a robust foundation for further research and application.

Conclusion: Embracing Qualitative Analysis Techniques for Rich Insights

Qualitative analysis techniques play a vital role in uncovering nuanced insights that quantitative methods may overlook. By embracing these techniques, researchers can explore the depth of human experience, revealing underlying motivations and feelings. This approach enables a richer understanding of participant perspectives, fostering more informed decisions.

To maximize the benefits of qualitative analysis, practitioners must prioritize the context and meaning behind gathered data. Encouraging open dialogue and employing various analytical methods, such as thematic analysis or content analysis, paves the way for profound discoveries. In conclusion, adopting qualitative analysis techniques can significantly enhance research outcomes, leading to a more comprehensive grasp of complex phenomena.

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• Research article
• Open access
• Published: 15 August 2024

The impact of adverse childhood experiences on multimorbidity: a systematic review and meta-analysis

• Dhaneesha N. S. Senaratne 1 ,
• Bhushan Thakkar 1 ,
• Blair H. Smith 1 ,
• Tim G. Hales 2 ,
• Louise Marryat 3 &
• Lesley A. Colvin 1  

BMC Medicine volume  22 , Article number:  315 ( 2024 ) Cite this article

658 Accesses

17 Altmetric

Metrics details

Adverse childhood experiences (ACEs) have been implicated in the aetiology of a range of health outcomes, including multimorbidity. In this systematic review and meta-analysis, we aimed to identify, synthesise, and quantify the current evidence linking ACEs and multimorbidity.

We searched seven databases from inception to 20 July 2023: APA PsycNET, CINAHL Plus, Cochrane CENTRAL, Embase, MEDLINE, Scopus, and Web of Science. We selected studies investigating adverse events occurring during childhood (< 18 years) and an assessment of multimorbidity in adulthood (≥ 18 years). Studies that only assessed adverse events in adulthood or health outcomes in children were excluded. Risk of bias was assessed using the ROBINS-E tool. Meta-analysis of prevalence and dose–response meta-analysis methods were used for quantitative data synthesis. This review was pre-registered with PROSPERO (CRD42023389528).

From 15,586 records, 25 studies were eligible for inclusion (total participants = 372,162). The prevalence of exposure to ≥ 1 ACEs was 48.1% (95% CI 33.4 to 63.1%). The prevalence of multimorbidity was 34.5% (95% CI 23.4 to 47.5%). Eight studies provided sufficient data for dose–response meta-analysis (total participants = 197,981). There was a significant dose-dependent relationship between ACE exposure and multimorbidity ( p  < 0.001), with every additional ACE exposure contributing to a 12.9% (95% CI 7.9 to 17.9%) increase in the odds for multimorbidity. However, there was heterogeneity among the included studies ( I 2  = 76.9%, Cochran Q  = 102, p  < 0.001).

Conclusions

This is the first systematic review and meta-analysis to synthesise the literature on ACEs and multimorbidity, showing a dose-dependent relationship across a large number of participants. It consolidates and enhances an extensive body of literature that shows an association between ACEs and individual long-term health conditions, risky health behaviours, and other poor health outcomes.

Peer Review reports

In recent years, adverse childhood experiences (ACEs) have been identified as factors of interest in the aetiology of many conditions [ 1 ]. ACEs are potentially stressful events or environments that occur before the age of 18. They have typically been considered in terms of abuse (e.g. physical, emotional, sexual), neglect (e.g. physical, emotional), and household dysfunction (e.g. parental separation, household member incarceration, household member mental illness) but could also include other forms of stress, such as bullying, famine, and war. ACEs are common: estimates suggest that 47% of the UK population have experienced at least one form, with 12% experiencing four or more [ 2 ]. ACEs are associated with poor outcomes in a range of physical health, mental health, and social parameters in adulthood, with greater ACE burden being associated with worse outcomes [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ].

Over a similar timescale, multimorbidity has emerged as a significant heath challenge. It is commonly defined as the co-occurrence of two or more long-term conditions (LTCs), with a long-term condition defined as any physical or mental health condition lasting, or expected to last, longer than 1 year [ 9 ]. Multimorbidity is both common and age-dependent, with a global adult prevalence of 37% that rises to 51% in adults over 60 [ 10 , 11 ]. Individuals living with multimorbidity face additional challenges in managing their health, such as multiple appointments, polypharmacy, and the lack of continuity of care [ 12 , 13 , 14 ]. Meanwhile, many healthcare systems struggle to manage the additional cost and complexity of people with multimorbidity as they have often evolved to address the single disease model [ 15 , 16 ]. As global populations continue to age, with an estimated 2.1 billion adults over 60 by 2050, the pressures facing already strained healthcare systems will continue to grow [ 17 ]. Identifying factors early in the aetiology of multimorbidity may help to mitigate the consequences of this developing healthcare crisis.

Many mechanisms have been suggested for how ACEs might influence later life health outcomes, including the risk of developing individual LTCs. Collectively, they contribute to the idea of ‘toxic stress’; cumulative stress during key developmental phases may affect development [ 18 ]. ACEs are associated with measures of accelerated cellular ageing, including changes in DNA methylation and telomere length [ 19 , 20 ]. ACEs may lead to alterations in stress-signalling pathways, including changes to the immune, endocrine, and cardiovascular systems [ 21 , 22 , 23 ]. ACEs are also associated with both structural and functional differences in the brain [ 24 , 25 , 26 , 27 ]. These diverse biological changes underpin psychological and behavioural changes, predisposing individuals to poorer self-esteem and risky health behaviours, which may in turn lead to increased risk of developing individual LTCs [ 1 , 2 , 28 , 29 , 30 , 31 , 32 ]. A growing body of evidence has therefore led to an increased focus on developing trauma-informed models of healthcare, in which the impact of negative life experiences is incorporated into the assessment and management of LTCs [ 33 ].

Given the contributory role of ACEs in the aetiology of individual LTCs, it is reasonable to suspect that ACEs may also be an important factor in the development of multimorbidity. Several studies have implicated ACEs in the aetiology of multimorbidity, across different cohorts and populations, but to date no meta-analyses have been performed to aggregate this evidence. In this review, we aim to summarise the state of the evidence linking ACEs and multimorbidity, to quantify the strength of any associations through meta-analysis, and to highlight the challenges of research in this area.

Search strategy and selection criteria

We conducted a systematic review and meta-analysis that was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) on 25 January 2023 (ID: CRD42023389528) and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

We developed a search strategy based on previously published literature reviews and refined it following input from subject experts, an academic librarian, and patient and public partners (Additional File 1: Table S1). We searched the following seven databases from inception to 20 July 2023: APA PsycNET, CINAHL Plus, Cochrane CENTRAL, Embase, MEDLINE, Scopus, and Web of Science. The search results were imported into Covidence (Veritas Health Innovation, Melbourne, Australia), which automatically identified and removed duplicate entries. Two reviewers (DS and BT) independently performed title and abstract screening and full text review. Discrepancies were resolved by a third reviewer (LC).

Reports were eligible for review if they included adults (≥ 18 years), adverse events occurring during childhood (< 18 years), and an assessment of multimorbidity or health status based on LTCs. Reports that only assessed adverse events in adulthood or health outcomes in children were excluded.

The following study designs were eligible for review: randomised controlled trials, cohort studies, case–control studies, cross-sectional studies, and review articles with meta-analysis. Editorials, case reports, and conference abstracts were excluded. Systematic reviews without a meta-analysis and narrative synthesis review articles were also excluded; however, their reference lists were screened for relevant citations.

Data analysis

Two reviewers (DS and BT) independently performed data extraction into Microsoft Excel (Microsoft Corporation, Redmond, USA) using a pre-agreed template. Discrepancies were resolved by consensus discussion with a third reviewer (LC). Data extracted from each report included study details (author, year, study design, sample cohort, sample size, sample country of origin), patient characteristics (age, sex), ACE information (definition, childhood cut-off age, ACE assessment tool, number of ACEs, list of ACEs, prevalence), multimorbidity information (definition, multimorbidity assessment tool, number of LTCs, list of LTCs, prevalence), and analysis parameters (effect size, model adjustments). For meta-analysis, we extracted ACE groups, number of ACE cases, number of multimorbidity cases, number of participants, odds ratios or regression beta coefficients, and 95% confidence intervals (95% CI). Where data were partially reported or missing, we contacted the study authors directly for further information.

Two reviewers (DS and BT) independently performed risk of bias assessments of each included study using the Risk Of Bias In Non-randomized Studies of Exposures (ROBINS-E) tool [ 34 ]. The ROBINS-E tool assesses the risk of bias for the study outcome relevant to the systematic review question, which may not be the primary study outcome. It assesses risk of bias across seven domains; confounding, measurement of the exposure, participant selection, post-exposure interventions, missing data, measurement of the outcome, and selection of the reported result. The overall risk of bias for each study was determined using the ROBINS-E algorithm. Discrepancies were resolved by consensus discussion.

All statistical analyses were performed in R version 4.2.2 using the RStudio integrated development environment (RStudio Team, Boston, USA). To avoid repetition of participant data, where multiple studies analysed the same patient cohort, we selected the study with the best reporting of raw data for meta-analysis and the largest sample size. Meta-analysis of prevalence was performed with the meta package [ 35 ], using logit transformations within a generalised linear mixed model, and reporting the random-effects model [ 36 ]. Inter-study heterogeneity was assessed and reported using the I 2 statistic, Cochran Q statistic, and Cochran Q p -value. Dose–response meta-analysis was performed using the dosresmeta package [ 37 ] following the method outlined by Greenland and Longnecker (1992) [ 38 , 39 ]. Log-linear and non-linear (restricted cubic spline, with knots at 5%, 35%, 65%, and 95%) random effects models were generated, and goodness of fit was evaluated using a Wald-type test (denoted by X 2 ) and the Akaike information criterion (AIC) [ 39 ].

Patient and public involvement

The Consortium Against Pain Inequality (CAPE) Chronic Pain Advisory Group (CPAG) consists of individuals with lived experiences of ACEs, chronic pain, and multimorbidity. CPAG was involved in developing the research question. The group has experience in systematic review co-production (in progress).

The search identified 15,586 records, of which 25 met inclusion criteria for the systematic review (Fig.  1 ) [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. The summary characteristics can be found in Additional File 1: Table S2. Most studies examined European ( n  = 11) or North American ( n  = 9) populations, with a few looking at Asian ( n  = 3) or South American ( n  = 1) populations and one study examining a mixed cohort (European and North American populations). The total participant count (excluding studies performed on the same cohort) was 372,162. Most studies had a female predominance (median 53.8%, interquartile range (IQR) 50.9 to 57.4%).

Flow chart of selection of studies into the systematic review and meta-analysis. Flow chart of selection of studies into the systematic review and meta-analysis. ACE, adverse childhood experience; MM, multimorbidity; DRMA, dose–response meta-analysis

All studies were observational in design, and so risk of bias assessments were performed using the ROBINS-E tool (Additional File 1: Table S3) [ 34 ]. There were some consistent risks observed across the studies, especially in domain 1 (risk of bias due to confounding) and domain 3 (risk of bias due to participant selection). In domain 1, most studies were ‘high risk’ ( n  = 24) as they controlled for variables that could have been affected by ACE exposure (e.g. smoking status) [ 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. In domain 3, some studies were ‘high risk’ ( n  = 7) as participant selection was based on participant characteristics that could have been influenced by ACE exposure (e.g. through recruitment at an outpatient clinic) [ 45 , 48 , 49 , 51 , 53 , 54 , 58 ]. The remaining studies were deemed as having ‘some concerns’ ( n  = 18) as participant selection occurred at a time after ACE exposure, introducing a risk of survivorship bias [ 40 , 41 , 42 , 43 , 44 , 46 , 47 , 50 , 52 , 55 , 56 , 57 , 59 , 60 , 61 , 62 , 63 , 64 ].

Key differences in risk of bias were seen in domain 2 (risk of bias due to exposure measurement) and domain 5 (risk of bias due to missing data). In domain 2, some studies were ‘high risk’ as they used a narrow or atypical measure of ACEs ( n  = 8) [ 40 , 42 , 44 , 46 , 55 , 56 , 60 , 64 ]; others were graded as having ‘some concerns’ as they used a broader but still incomplete measure of ACEs ( n  = 8) [ 43 , 45 , 48 , 49 , 50 , 52 , 54 , 62 ]; the remainder were ‘low risk’ as they used an established or comprehensive list of ACE questions [ 41 , 47 , 51 , 53 , 57 , 58 , 59 , 61 , 63 ]. In domain 5, some studies were ‘high risk’ as they failed to acknowledge or appropriately address missing data ( n  = 7) [ 40 , 42 , 43 , 45 , 51 , 53 , 60 ]; others were graded as having ‘some concerns’ as they had a significant amount of missing data (> 10% for exposure, outcome, or confounders) but mitigated for this with appropriate strategies ( n  = 6) [ 41 , 50 , 56 , 57 , 62 , 64 ]; the remainder were ‘low risk’ as they reported low levels of missing data ( n  = 12) [ 44 , 46 , 47 , 48 , 49 , 52 , 54 , 55 , 58 , 59 , 61 , 63 ].

Most studies assessed an exposure that was ‘adverse childhood experiences’ ( n  = 10) [ 41 , 42 , 50 , 51 , 53 , 57 , 58 , 61 , 63 , 64 ], ‘childhood maltreatment’ ( n  = 6) [ 44 , 45 , 46 , 48 , 49 , 59 ], or ‘childhood adversity’ ( n  = 3) [ 47 , 54 , 62 ]. The other exposures studied were ‘birth phase relative to World War Two’ [ 40 ], ‘childhood abuse’ [ 43 ], ‘childhood disadvantage’ [ 56 ], ‘childhood racial discrimination’ [ 55 ], ‘childhood trauma’ [ 52 ], and ‘quality of childhood’ (all n  = 1) [ 60 ]. More than half of studies ( n  = 13) did not provide a formal definition of their exposure of choice [ 42 , 43 , 44 , 45 , 49 , 52 , 53 , 54 , 57 , 58 , 60 , 61 , 64 ]. The upper age limit for childhood ranged from < 15 to < 18 years with the most common cut-off being < 18 years ( n  = 9). The median number of ACEs measured in each study was 7 (IQR 4–10). In total, 58 different ACEs were reported; 17 ACEs were reported by at least three studies, whilst 33 ACEs were reported by only one study. The most frequently reported ACEs were physical abuse ( n  = 19) and sexual abuse ( n  = 16) (Table  1 ). The exposure details for each study can be found in Additional File 1: Table S4.

Thirteen studies provided sufficient data to allow for a meta-analysis of the prevalence of exposure to ≥ 1 ACE; the pooled prevalence was 48.1% (95% CI 33.4 to 63.1%, I 2  = 99.9%, Cochran Q  = 18,092, p  < 0.001) (Fig.  2 ) [ 41 , 43 , 44 , 46 , 47 , 49 , 50 , 52 , 53 , 57 , 59 , 61 , 63 ]. Six studies provided sufficient data to allow for a meta-analysis of the prevalence of exposure to ≥ 4 ACEs; the pooled prevalence was 12.3% (95% CI 3.5 to 35.4%, I 2  = 99.9%, Cochran Q  = 9071, p  < 0.001) (Additional File 1: Fig. S1) [ 46 , 50 , 51 , 53 , 59 , 63 ].

Meta-analysis of prevalence of exposure to ≥ 1 adverse childhood experiences. Meta-analysis of prevalence of exposure to ≥ 1 adverse childhood experience. ACE, adverse childhood experience; CI, confidence interval

Thirteen studies explicitly assessed multimorbidity as an outcome, and all of these defined the threshold for multimorbidity as the presence of two or more LTCs [ 40 , 41 , 42 , 44 , 46 , 47 , 50 , 55 , 57 , 60 , 61 , 62 , 64 ]. The remaining studies assessed comorbidities, morbidity, or disease counts [ 43 , 45 , 48 , 49 , 51 , 52 , 53 , 54 , 56 , 58 , 59 , 63 ]. The median number of LTCs measured in each study was 14 (IQR 12–21). In total, 115 different LTCs were reported; 36 LTCs were reported by at least three studies, whilst 63 LTCs were reported by only one study. Two studies did not report the specific LTCs that they measured [ 51 , 53 ]. The most frequently reported LTCs were hypertension ( n  = 22) and diabetes ( n  = 19) (Table  2 ). Fourteen studies included at least one mental health LTC. The outcome details for each study can be found in Additional File 1: Table S5.

Fifteen studies provided sufficient data to allow for a meta-analysis of the prevalence of multimorbidity; the pooled prevalence was 34.5% (95% CI 23.4 to 47.5%, I 2  = 99.9%, Cochran Q  = 24,072, p  < 0.001) (Fig.  3 ) [ 40 , 41 , 44 , 46 , 47 , 49 , 50 , 51 , 52 , 55 , 57 , 58 , 59 , 60 , 63 ].

Meta-analysis of prevalence of multimorbidity. Meta-analysis of prevalence of multimorbidity. CI, confidence interval; LTC, long-term condition; MM, multimorbidity

All studies reported significant positive associations between measures of ACE and multimorbidity, though they varied in their means of analysis and reporting of the relationship. Nine studies reported an association between the number of ACEs (variably considered as a continuous or categorical parameter) and multimorbidity [ 41 , 43 , 46 , 47 , 50 , 56 , 57 , 61 , 64 ]. Eight studies reported an association between the number of ACEs and comorbidity counts in specific patient populations [ 45 , 48 , 49 , 51 , 53 , 58 , 59 , 63 ]. Six studies reported an association between individual ACEs or ACE subgroups and multimorbidity [ 42 , 43 , 44 , 47 , 55 , 62 ]. Two studies incorporated a measure of frequency within their ACE measurement tool and reported an association between this ACE score and multimorbidity [ 52 , 54 ]. Two studies reported an association between proxy measures for ACEs and multimorbidity; one reported ‘birth phase relative to World War Two’, and the other reported a self-report on the overall quality of childhood [ 40 , 60 ].

Eight studies, involving a total of 197,981 participants, provided sufficient data (either in the primary text, or following author correspondence) for quantitative synthesis [ 41 , 46 , 47 , 49 , 50 , 51 , 57 , 58 ]. Log-linear (Fig.  4 ) and non-linear (Additional File 1: Fig. S2) random effects models were compared for goodness of fit: the Wald-type test for linearity was non-significant ( χ 2  = 3.7, p  = 0.16) and the AIC was lower for the linear model (− 7.82 vs 15.86) indicating that the log-linear assumption was valid. There was a significant dose-dependent relationship between ACE exposure and multimorbidity ( p  < 0.001), with every additional ACE exposure contributing to a 12.9% (95% CI 7.9 to 17.9%) increase in the odds for multimorbidity ( I 2  = 76.9%, Cochran Q  = 102, p  < 0.001).

Dose–response meta-analysis of the relationship between adverse childhood experiences and multimorbidity. Dose–response meta-analysis of the relationship between adverse childhood experiences and multimorbidity. Solid black line represents the estimated relationship; dotted black lines represent the 95% confidence intervals for this estimate. ACE, adverse childhood experience

This systematic review and meta-analysis synthesised the literature on ACEs and multimorbidity and showed a dose-dependent relationship across a large number of participants. Each additional ACE exposure contributed to a 12.9% (95% CI 7.9 to 17.9%) increase in the odds for multimorbidity. This adds to previous meta-analyses that have shown an association between ACEs and individual LTCs, health behaviours, and other health outcomes [ 1 , 28 , 31 , 65 , 66 ]. However, we also identified substantial inter-study heterogeneity that is likely to have arisen due to variation in the definitions, methodology, and analysis of the included studies, and so our results should be interpreted with these limitations in mind.

Although 25 years have passed since the landmark Adverse Childhood Experiences Study by Felitti et al. [ 3 ], there is still no consistent approach to determining what constitutes an ACE. This is reflected in this review, where fewer than half of the 58 different ACEs ( n  = 25, 43.1%) were reported by more than one study and no study reported more than 15 ACEs. Even ACE types that are commonly included are not always assessed in the same way [ 67 ], and furthermore, the same question can be interpreted differently in different contexts (e.g. physical punishment for bad behaviour was socially acceptable 50 years ago but is now considered physical abuse in the UK). Although a few validated questionnaires exist, they often focus on a narrow range of ACEs; for example, the childhood trauma questionnaire demonstrates good reliability and validity but focuses on interpersonal ACEs, missing out on household factors (e.g. parental separation), and community factors (e.g. bullying) [ 68 ]. Many studies were performed on pre-existing research cohorts or historic healthcare data, where the study authors had limited or no influence on the data collected. As a result, very few individual studies reported on the full breadth of potential ACEs.

ACE research is often based on ACE counts, where the types of ACEs experienced are summed into a single score that is taken as a proxy measure of the burden of childhood stress. The original Adverse Childhood Experiences Study by Felitti et al. took this approach [ 3 ], as did 17 of the studies included in this review and our own quantitative synthesis. At the population level, there are benefits to this: ACE counts provide quantifiable and comparable metrics, they are easy to collect and analyse, and in many datasets, they are the only means by which an assessment of childhood stress can be derived. However, there are clear limitations to this method when considering experiences at the individual level, not least the inherent assumptions that different ACEs in the same person are of equal weight or that the same ACE in different people carries the same burden of childhood stress. This limitation was strongly reinforced by our patient and public involvement group (CPAG). Two studies in this review incorporated frequency within their ACE scoring system [ 52 , 54 ], which adds another dimension to the assessment, but this is insufficient to understand and quantify the ‘impact’ of an ACE within an epidemiological framework.

The definitions of multimorbidity were consistent across the relevant studies but the contributory long-term conditions varied. Fewer than half of the 115 different LTCs ( n  = 52, 45.2%) were reported by more than one study. Part of the challenge is the classification of healthcare conditions. For example, myocardial infarction is commonly caused by coronary heart disease, and both are a form of heart disease. All three were reported as LTCs in the included studies, but which level of pathology should be reported? Mental health LTCs were under-represented within the condition list, with just over half of the included studies assessing at least one ( n  = 14, 56.0%). Given the strong links between ACEs and mental health, and the impact of mental health on quality of life, this is an area for improvement in future research [ 31 , 32 ]. A recent Delphi consensus study by Ho et al. may help to address these issues: following input from professionals and members of the public they identified 24 LTCs to ‘always include’ and 35 LTCs to ‘usually include’ in multimorbidity research, including nine mental health conditions [ 9 ].

As outlined in the introduction, there is a strong evidence base supporting the link between ACEs and long-term health outcomes, including specific LTCs. It is not unreasonable to extrapolate this association to ACEs and multimorbidity, though to our knowledge, the pathophysiological processes that link the two have not been precisely identified. However, similar lines of research are being independently followed in both fields and these areas of overlap may suggest possible mechanisms for a relationship. For example, both ACEs and multimorbidity have been associated with markers of accelerated epigenetic ageing [ 69 , 70 ], mitochondrial dysfunction [ 71 , 72 ], and inflammation [ 22 , 73 ]. More work is required to better understand how these concepts might be linked.

This review used data from a large participant base, with information from 372,162 people contributing to the systematic review and information from 197,981 people contributing to the dose–response meta-analysis. Data from the included studies originated from a range of sources, including healthcare settings and dedicated research cohorts. We believe this is of a sufficient scale and variety to demonstrate the nature and magnitude of the association between ACEs and multimorbidity in these populations.

However, there are some limitations. Firstly, although data came from 11 different countries, only two of those were from outside Europe and North America, and all were from either high- or middle-income countries. Data on ACEs from low-income countries have indicated a higher prevalence of any ACE exposure (consistently > 70%) [ 74 , 75 ], though how well this predicts health outcomes in these populations is unknown.

Secondly, studies in this review utilised retrospective participant-reported ACE data and so are at risk of recall and reporting bias. Studies utilising prospective assessments are rare and much of the wider ACE literature is open to a similar risk of bias. To date, two studies have compared prospective and retrospective ACE measurements, demonstrating inconsistent results [ 76 , 77 ]. However, these studies were performed in New Zealand and South Africa, two countries not represented by studies in our review, and had relatively small sample sizes (1037 and 1595 respectively). It is unclear whether these are generalisable to other population groups.

Thirdly, previous research has indicated a close relationship between ACEs and childhood socio-economic status (SES) [ 78 ] and between SES and multimorbidity [ 10 , 79 ]. However, the limitations of the included studies meant we were unable to separate the effect of ACEs from the effect of childhood SES on multimorbidity in this review. Whilst two studies included childhood SES as covariates in their models, others used measures from adulthood (such as adulthood SES, income level, and education level) that are potentially influenced by ACEs and therefore increase the risk of bias due to confounding (Additional File 1: Table S3). Furthermore, as for ACEs and multimorbidity, there is no consistently applied definition of SES and different measures of SES may produce different apparent effects [ 80 ]. The complex relationships between ACEs, childhood SES, and multimorbidity remain a challenge for research in this field.

Fourthly, there was a high degree of heterogeneity within included studies, especially relating to the definition and measurement of ACEs and multimorbidity. Whilst this suggests that our results should be interpreted with caution, it is reassuring to see that our meta-analysis of prevalence estimates for exposure to any ACE (48.1%) and multimorbidity (34.5%) are in line with previous estimates in similar populations [ 2 , 11 ]. Furthermore, we believe that the quantitative synthesis of these relatively heterogenous studies provides important benefit by demonstrating a strong dose–response relationship across a range of contexts.

Our results strengthen the evidence supporting the lasting influence of childhood conditions on adult health and wellbeing. How this understanding is best incorporated into routine practice is still not clear. Currently, the lack of consistency in assessing ACEs limits our ability to understand their impact at both the individual and population level and poses challenges for those looking to incorporate a formalised assessment. Whilst most risk factors for disease (e.g. blood pressure) are usually only relevant within healthcare settings, ACEs are relevant to many other sectors (e.g. social care, education, policing) [ 81 , 82 , 83 , 84 ], and so consistency of assessment across society is both more important and more challenging to achieve.

Some have suggested that the evidence for the impact of ACEs is strong enough to warrant screening, which would allow early identification of potential harms to children and interventions to prevent them. This approach has been implemented in California, USA [ 85 , 86 , 87 ]. However, this is controversial, and others argue that screening is premature with the current evidence base [ 88 , 89 , 90 ]. Firstly, not everyone who is exposed to ACEs develops poor health outcomes, and it is not clear how to identify those who are at highest risk. Many people appear to be vulnerable, with more adverse health outcomes following ACE exposure than those who are not exposed, whilst others appear to be more resilient, with good health in later life despite multiple ACE exposures [ 91 ] It may be that supportive environments can mitigate the long-term effects of ACE exposure and promote resilience [ 92 , 93 ]. Secondly, there are no accepted interventions for managing the impact of an identified ACE. As identified above, different ACEs may require input from different sectors (e.g. healthcare, social care, education, police), and so collating this evidence may be challenging. At present, ACEs screening does not meet the Wilson-Jungner criteria for a screening programme [ 94 ].

Existing healthcare systems are poorly designed to deal with the complexities of addressing ACEs and multimorbidity. Possibly, ways to improve this might be allocating more time per patient, prioritising continuity of care to foster long-term relationships, and greater integration between different healthcare providers (most notably primary vs secondary care teams, or physical vs mental health teams). However, such changes often demand additional resources (e.g. staff, infrastructure, processes), which are challenging to source when existing healthcare systems are already stretched [ 95 , 96 ]. Nevertheless, increasing the spotlight on ACEs and multimorbidity may help to focus attention and ultimately bring improvements to patient care and experience.

ACEs are associated with a range of poor long-term health outcomes, including harmful health behaviours and individual long-term conditions. Multimorbidity is becoming more common as global populations age, and it increases the complexity and cost of healthcare provision. This is the first systematic review and meta-analysis to synthesise the literature on ACEs and multimorbidity, showing a statistically significant dose-dependent relationship across a large number of participants, albeit with a high degree of inter-study heterogeneity. This consolidates and enhances an increasing body of data supporting the role of ACEs in determining long-term health outcomes. Whilst these observational studies do not confirm causality, the weight and consistency of evidence is such that we can be confident in the link. The challenge for healthcare practitioners, managers, policymakers, and governments is incorporating this body of evidence into routine practice to improve the health and wellbeing of our societies.

Availability of data and materials

No additional data was generated for this review. The data used were found in the referenced papers or provided through correspondence with the study authors.

Abbreviations

Adverse childhood experience

Akaike information criterion

CONSORTIUM Against pain inequality

Confidence interval

Chronic pain advisory group

Interquartile range

Long-term condition

International prospective register of systematic reviews

Preferred reporting items for systematic reviews and meta-analyses

Risk of bias in non-randomised studies of exposures

Socio-economic status

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Acknowledgements

The authors thank the members of the CAPE CPAG patient and public involvement group for providing insights gained from relevant lived experiences.

The authors are members of the Advanced Pain Discovery Platform (APDP) supported by UK Research & Innovation (UKRI), Versus Arthritis, and Eli Lilly. DS is a fellow on the Multimorbidity Doctoral Training Programme for Health Professionals, which is supported by the Wellcome Trust [223499/Z/21/Z]. BT, BS, and LC are supported by an APDP grant as part of the Partnership for Assessment and Investigation of Neuropathic Pain: Studies Tracking Outcomes, Risks and Mechanisms (PAINSTORM) consortium [MR/W002388/1]. TH and LC are supported by an APDP grant as part of the Consortium Against Pain Inequality [MR/W002566/1]. The funding bodies had no role in study design, data collection/analysis/interpretation, report writing, or the decision to submit the manuscript for publication.

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Contributions

DS and LC contributed to review conception and design. DC, BT, BS, TH, LM, and LC contributed to search strategy design. DS and BT contributed to study selection and data extraction, with input from LC. DS and BT accessed and verified the underlying data. DS conducted the meta-analyses, with input from BT, BS, TH, LM, and LC. DS drafted the manuscript, with input from DC, BT, BS, TH, LM, and LC. DC, BT, BS, TH, LM, and LC read and approved the final manuscript.

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12916_2024_3505_moesm1_esm.docx.

Additional File 1: Tables S1-S5 and Figures S1-S2. Table S1: Search strategy, Table S2: Characteristics of studies included in the systematic review, Table S3: Risk of bias assessment (ROBINS-E), Table S4: Exposure details (adverse childhood experiences), Table S5: Outcome details (multimorbidity), Figure S1: Meta-analysis of prevalence of exposure to ≥4 adverse childhood experiences, Figure S2: Dose-response meta-analysis of the relationship between adverse childhood experiences and multimorbidity (using a non-linear/restricted cubic spline model).

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Senaratne, D.N.S., Thakkar, B., Smith, B.H. et al. The impact of adverse childhood experiences on multimorbidity: a systematic review and meta-analysis. BMC Med 22 , 315 (2024). https://doi.org/10.1186/s12916-024-03505-w

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Progress in remote sensing and gis-based fdi research based on quantitative and qualitative analysis.

1. Introduction

2. research methods and data, 2.1. research methods, 2.2. data sources and screening, 2.3. data processing, 3. subject categories and publication trends, 3.1. subject evolution, 3.2. trends in the number and cited times of published papers, 4. the intellectual structure, 4.1. quantitative analysis, 4.2. qualitative analysis, 4.2.1. macro-environmental research at national, regional, and city scales, 4.2.2. global industrial development and layout, 4.2.3. research on global value chains, 4.2.4. micro-information geography of tncs, 4.2.5. internationalization and commercialization of geo-information industry, 4.2.6. multiple data and interdisciplinary approaches, 5. discussions and conclusions, data availability statement, acknowledgments, conflicts of interest.

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Li, Z. Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis. Land 2024 , 13 , 1313. https://doi.org/10.3390/land13081313

Li Z. Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis. Land . 2024; 13(8):1313. https://doi.org/10.3390/land13081313

Li, Zifeng. 2024. "Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis" Land 13, no. 8: 1313. https://doi.org/10.3390/land13081313

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• Published: 16 August 2024

Examining the perception of undergraduate health professional students of their learning environment, learning experience and professional identity development: a mixed-methods study

• Banan Mukhalalati 1 ,
• Aaliah Aly 1 ,
• Ola Yakti 1 ,
• Sara Elshami 1 ,
• Alaa Daud 2 ,
• Ahmed Awaisu 1 ,
• Ahsan Sethi 3 ,
• Alla El-Awaisi 1 ,
• Derek Stewart 1 ,
• Marwan Farouk Abu-Hijleh 4 &
• Zubin Austin 5  

BMC Medical Education volume  24 , Article number:  886 ( 2024 ) Cite this article

185 Accesses

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The quality of the learning environment significantly impacts student engagement and professional identity formation in health professions education. Despite global recognition of its importance, research on student perceptions of learning environments across different health education programs is scarce. This study aimed to explore how health professional students perceive their learning environment and its influence on their professional identity development.

An explanatory mixed-methods approach was employed. In the quantitative phase, the Dundee Ready Education Environment Measure [Minimum–Maximum possible scores = 0–200] and Macleod Clark Professional Identity Scale [Minimum–Maximum possible scores = 1–45] were administered to Qatar University-Health students ( N  = 908), with a minimum required sample size of 271 students. Data were analyzed using SPSS, including descriptive statistics and inferential analysis. In the qualitative phase, seven focus groups (FGs) were conducted online via Microsoft Teams. FGs were guided by a topic guide developed from the quantitative results and the framework proposed by Gruppen et al. (Acad Med 94:969-74, 2019), transcribed verbatim, and thematically analyzed using NVIVO®.

The questionnaire response rate was 57.8% (525 responses out of 908), with a usability rate of 74.3% (390 responses out of 525) after excluding students who only completed the demographic section. The study indicated a “more positive than negative” perception of the learning environment (Median [IQR] = 132 [116–174], Minimum–Maximum obtained scores = 43–185), and a “good” perception of their professional identity (Median [IQR] = 24 [22–27], Minimum–Maximum obtained scores = 3–36). Qualitative data confirmed that the learning environment was supportive in developing competence, interpersonal skills, and professional identity, though opinions on emotional support adequacy were mixed. Key attributes of an ideal learning environment included mentorship programs, a reward system, and measures to address fatigue and boredom.

Conclusions

The learning environment at QU-Health was effective in developing competence and interpersonal skills. Students' perceptions of their learning environment positively correlated with their professional identity. Ideal environments should include mentorship programs, a reward system, and strategies to address fatigue and boredom, emphasizing the need for ongoing improvements in learning environments to enhance student satisfaction, professional identity development, and high-quality patient care.

Peer Review reports

The learning environment is fundamental to higher education and has a profound impact on student outcomes. As conceptualized by Gruppen et al. [ 1 ], it comprises a complex interplay of physical, social, and virtual factors that shape student engagement, perception, and overall development. Over the last decade, there has been a growing global emphasis on the quality of the learning environment in higher education [ 2 , 3 , 4 ]. This focus stems from the recognition that a well-designed learning environment that includes good facilities, effective teaching methods, strong social interactions, and adherence to cultural and administrative standards can greatly improve student development [ 2 , 5 , 6 , 7 ]. Learning environments impact not only knowledge acquisition and skill development but also value formation and the cultivation of professional attitudes [ 5 ].

Professional identity is defined as the “attitudes, values, knowledge, beliefs, and skills shared with others within a professional group” [ 8 ]. The existing research identified a significant positive association between the development of professional identity and the quality of the learning environment, and this association is characterized by being multifaceted and dynamic [ 9 ]. According to Hendelman and Byszewski [ 10 ] a supportive learning environment, characterized by positive role models, effective feedback mechanisms, and opportunities for reflective practice, fosters the development of a strong professional identity among medical students. Similarly, Jarvis-Selinger et al. [ 11 ] argue that a nurturing learning environment facilitates the socialization process which enables students to adopt and integrate the professional behaviors and attitudes expected in their field. Furthermore, Sarraf-Yazdi et al. [ 12 ] highlighted that professional identity formation is a continuous and multifactorial process involving the interplay of individual values, beliefs, and environmental factors. This dynamic process is shaped by both clinical and non-clinical experiences within the learning environment [ 12 ].

Various learning theories, such as the Communities of Practice (CoP) theory [ 13 ], emphasize the link between learning environments and learning outcomes, including professional identity development. The CoP theory describes communities of professionals with a shared knowledge interest who learn through regular interaction [ 13 , 14 ]. Within the CoP, students transition from being peripheral observers to central members [ 15 ]. Therefore, the CoP theory suggests that a positive learning environment is crucial for fostering learning, professional identity formation, and a sense of community [ 16 ].

Undoubtedly, health professional education programs (e.g., Medicine, Dental Medicine, Pharmacy, and Health Sciences) play a vital role not only in shaping the knowledge, expertise, and abilities of health professional students but also in equipping them with the necessary competencies for implementing healthcare initiatives and strategies and responding to evolving healthcare demands [ 17 ]. Within the field of health professions education, international organizations like the United Nations Educational, Scientific, and Cultural Organization (UNESCO), European Union (EU), American Council on Education (ACE), and World Federation for Medical Education (WFME) have emphasized the importance of high-quality learning environments in fostering the development of future healthcare professionals and called for considerations of the enhancement of the quality of the learning environment of health profession education programs [ 18 , 19 ]. These environments are pivotal for nurturing both the academic and professional growth necessary to navigate an increasingly globalized healthcare landscape [ 18 , 19 ].

Professional identity development is integral to health professions education which evolves continuously from early university years until later stages of the professional life as a healthcare practitioner [ 20 , 21 ]. This ongoing development helps students establish clear professional roles and boundaries, thereby reducing role ambiguity within multidisciplinary teams [ 9 ]. It is expected that as students advance in their professional education, their perception of the quality of the learning environment changes, which influences their learning experiences, the development of their professional identity, and their sense of community [ 22 ]. Cruess et al. [ 23 ] asserted that medical schools foster professional identity through impactful learning experiences, effective role models, clear curricula, and assessments. A well-designed learning environment that incorporates these elements supports medical students' socialization and professional identity formation through structured learning, reflective practices, and constructive feedback in both preclinical and clinical stages [ 23 ].

Despite the recognized importance of the quality of learning environments and their influence on student-related outcomes, this topic has been overlooked regionally and globally [ 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. There is a significant knowledge gap in understanding how different components of the learning environment specifically contribute to professional identity formation. Most existing studies focus on general educational outcomes without exploring the detailed ways in which the learning environment shapes professional attitudes, values, and identity. Moreover, there is a global scarcity of research exploring how students’ perceptions of the quality of the learning environment and professional identity vary across various health profession education programs at different stages of their undergraduate education. This lack of comparative studies makes it challenging to identify best practices that can be adapted across different educational contexts. Furthermore, most research tends to focus on single-discipline studies, neglecting the interdisciplinary nature of modern healthcare education, which is essential for preparing students for collaborative practice in real-world healthcare settings. Considering the complex and demanding nature of health profession education programs and the increased emphasis on the quality of learning environments by accreditation bodies, examining the perceived quality of the educational learning environment by students is crucial [ 19 ]. Understanding students’ perspectives can provide valuable insights into areas needing improvement and highlight successful strategies that enhance both learning environment and experiences and professional identity development.

This research addresses this gap by focusing on the interdisciplinary health profession education programs to understand the impact of the learning environment on the development of the professional identity of students and its overall influence on their learning experiences. The objectives of this study are to 1) examine the perception of health professional students of the quality of their learning environment and their professional identity, 2) identify the association between health professional students’ perception of the quality of their learning environment and the development of their professional identity, and 3) explore the expectations of health professional students of the ideal educational learning environment. This research is essential in providing insights to inform educational practices globally to develop strategies to enhance the quality of health profession education.

Study setting and design

This study was conducted at Qatar University Health (QU Health) Cluster which is an interdisciplinary health profession education program that was introduced as the national provider of higher education in health and medicine in the state of Qatar. QU Health incorporates five colleges: Health Sciences (CHS), Pharmacy (CPH), Medicine (CMED), Dental Medicine (CDEM) and Nursing (CNUR) [ 31 ]. QU Health is dedicated to advancing inter-professional education (IPE) through its comprehensive interdisciplinary programs. By integrating IPE principles into the curriculum and fostering collaboration across various healthcare disciplines, the cluster prepares students to become skilled and collaborative professionals. Its holistic approach to teaching, research, and community engagement not only enhances the educational experience but also addresses local and regional healthcare challenges, thereby making a significant contribution to the advancement of population health in Qatar [ 32 ]. This study was conducted from November 2022 to July 2023. An explanatory sequential mixed methods triangulation approach was used for an in-depth exploration and validation of the quantitative results qualitatively [ 33 , 34 ]. Ethical approval for the study was obtained from the Qatar University Institutional Review Board (approval number: QU-IRB 1734-EA/22).

For the quantitative phase, a questionnaire was administered via SurveyMonkey® incorporating two previously validated questionnaires: the Dundee Ready Educational Environment Measure (DREEM), developed by Roff et al. in 1997 [ 35 ], and the Macleod Clark Professional Identity Scale-9 (MCPIS-9), developed by Adam et al. in 2006 [ 8 ]. Integrating DREEM and MCPIS-9 into a single questionnaire was undertaken to facilitate a comprehensive evaluation of two distinct yet complementary dimensions—namely, the educational environment and professional identity—that collectively influence the learning experience and outcomes of students, as no single instrument effectively assesses both aspects simultaneously [ 36 ]. The survey comprised three sections—Section A: sociodemographic characteristics, Section B: the DREEM scoring scale for assessing the quality of the learning environment, and Section C: the MCPIS-9 scoring scale for assessing professional identity. For the qualitative phase, seven focus groups (FGs) were arranged with a sample of QU-Health students. The qualitative and quantitative data obtained were integrated at the interpretation and reporting level using a narrative, contiguous approach [ 37 , 38 ].

Quantitative phase

Population and sampling.

The total population sampling approach in which all undergraduate QU-Health students who had declared their majors (i.e., the primary field of study that an undergraduate student has chosen during their academic program) at the time of conducting the study in any of the four health colleges under QU-Health ( N  = 908), namely, CPH, CMED, CDEM, and CHS, such as Human Nutrition (Nut), Biomedical Science (Biomed), Public Health (PH), and Physiotherapy (PS), were invited to participate in the study. Nursing students were excluded from this study because the college was just established in 2022; therefore, students were in their general year and had yet to declare their majors at the time of the study. The minimum sample size required for the study was determined to be 271 students based on a margin error of 5%, a confidence level of 95%, and a response distribution of 50%.

Data collection

Data was collected in a cross-sectional design. After obtaining the approval of the head of each department, contact information for eligible students was extracted from the QU-Health student databases for each college, and invitations were sent via email. The distribution of these invitations was done by the administrators of the respective colleges. The invitation included a link to a self-administered questionnaire on SurveyMonkey® (Survey Monkey Inc., San Mateo, California, USA), along with informed consent information. All 908 students were informed about the study’s purpose, data collection process, anonymity and confidentiality assurance, and the voluntary nature of participation. The participants were sent regular reminders to complete the survey to increase the response rate.

A focused literature review identified the DREEM as the most suitable validated tool for this study. The DREEM is considered the gold standard for assessing undergraduate students' perceptions of their learning environment [ 35 ]. Its validity and reliability have been consistently demonstrated across various settings (i.e., clinical and non-clinical) and health professions (e.g., nursing, medicine, dentistry, and pharmacy), in multiple countries worldwide, including the Gulf Cooperation Council countries [ 24 , 35 , 39 , 40 , 41 , 42 ]. The DREEM is a 50-item inventory divided into 5 subscales and developed to measure the academic climate of educational institutions using a five-point Likert scale from 0 “strongly disagree” to 4 “strongly agree”. The total score ranges from 0 to 200, with higher scores reflecting better perceptions of the learning environment [ 35 , 39 , 43 ]. The interpretation includes very poor (0–50), plenty of problems (51–100), more positive than negative (101–151), and excellent (151–200).

The first subscale, Perception to Learning (SpoL), with 12 items scoring 0–48. Interpretation includes very poor (0–12), teaching is viewed negatively (13–24), a more positive approach (25–36), and teaching is highly thought of (37–48). The second domain, Perception to Teachers (SpoT), with 11 items scoring 0–44. Interpretation includes abysmal (0–11), in need of some retraining (12–22), moving in the right direction (23–33), and model teachers (34–44). The third domain, academic self-perception (SASP), with 8 items scoring 0–32. Interpretation includes a feeling of total failure (0–8), many negative aspects (9–16), feeling more on the positive side (17–24), and confident (25–32). The fourth domain, Perception of the atmosphere (SPoA), with 12 items scoring 0–48. Interpretation includes a terrible environment (0–12); many issues need to be changed (13–24), a more positive atmosphere (25–36), and a good feeling overall (37–48). Lastly, the fifth domain, social self-perception (SSSP), with 7 items scoring 0–28. Interpretation includes Miserable (0–7), Not a nice place (8–14), Not very bad (15–21), and very good socially (22–28).

Several tools have been developed to explore professional identity in health professions [ 44 ], but there is limited research on their psychometric qualities [ 45 ]. The MCPIS-9 is notable for its robust psychometric validation and was chosen for this study due to its effectiveness in a multidisciplinary context as opposed to other questionnaires that were initially developed for the nursing profession [ 8 , 46 , 47 ]. MCPIS-9 is a validated 9-item instrument, which uses a 5-point Likert response scale, with scores ranging from 1 “strongly disagree” to 5 “strongly agree”. Previous studies that utilized the MCPIS-9 had no universal guidance for interpreting the MCPIS-9 score; however, the higher the score, the stronger the sense of professional identity [ 46 , 48 ].

Data analysis

The quantitative data were analyzed using SPSS software (IBM SPSS Statistics for Windows, version 27.0; IBM Corp., Armonk, NY, USA). The original developers of the DREEM inventory identified nine negative items: items 11, 12, 19, 20, 21, 23, 42, 43, and 46 – these items were reverse-coded. Additionally, in the MCPIS-9 tool, the original developers identified three negative items: items 3, 4, and 5. Descriptive and inferential analyses were also conducted. Descriptive statistics including number (frequencies [%]), mean ± SD, and median (IQR), were used to summarize the demographics and responses to the DREEM and MCPIS-9 scoring scales. In the inferential analysis, to test for significant differences between demographic subgroups in the DREEM and MCPIS-9 scores, Kruskal–Wallis tests were used for variables with more than two categories, and Mann–Whitney U-tests were used for variables with two categories. Spearman's rank correlation analysis was used to investigate the association between perceived learning environment and professional identity development. The level of statistical significance was set a priori at p  < 0.05. The internal consistency of the DREEM and MCPIS-9 tools was tested against the acceptable Cronbach's alpha value of 0.7.

Qualitative phase

A purposive sampling approach was employed to select students who were most likely to provide valuable insights to gain a deeper understanding of the topic. The inclusion criteria required that participants should have declared their major in one of the following programs: CPH, CMED, CDEM, CHS: Nut, Biomed, PS, and PH. This selection criterion aimed to ensure that participants had sufficient knowledge and experience related to their chosen fields of study within QU-Health. Students were included if they were available and willing to share their experiences and thoughts. Students who did not meet these criteria were excluded from participation. To ensure a representative sample, seven FGs were conducted, one with each health professional education program. After obtaining the approval of the head of each department, participants were recruited by contacting the class representative of each professional year to ask for volunteers to join and provide their insights. Each FG involved students from different professional years to ensure a diverse representation of experiences and perspectives.

The topic guide (Supplementary Material 1) was developed and conceptualized based on the research objectives, selected results from the quantitative phase, and the Gruppen et. al. framework [ 1 ]. FGs were conducted online using Microsoft Teams® through synchronous meetings. Before initiating the FGs, participants were informed of their rights and returned signed consent forms to the researchers. FGs were facilitated by two research assistants (AA and OY), each facilitating separate sessions. The facilitators, who had prior experience with conducting FGs and who were former pharmacy students from the CPH, were familiar with some of the participants, and hence were able to encourage open discussion, making it easier for students to share their perceptions of the learning environment within the QU Health Cluster. Participants engaged in concurrent discussions were encouraged to use the "raise hand" feature on Microsoft Teams to mimic face-to-face interactions. Each FG lasted 45–60 min, was conducted in English, and was recorded and transcribed verbatim and double-checked for accuracy. After the seventh FG, the researchers were confident that a saturation point had been reached where no new ideas emerged, and any further data collection through FGs was unnecessary. Peer and supervisory audits were conducted throughout the research process.

The NVIVO ® software (version 12) was utilized to perform a thematic analysis incorporating both deductive and inductive approaches. The deductive approach involved organizing the data into pre-determined categories based on the Gruppen et al. framework, which outlines key components of the learning environment. This framework enabled a systematic analysis of how each component of the learning environment contributes to students' professional development and highlighted areas for potential improvement. Concurrently, the inductive approach was applied to explore students' perceptions of an ideal learning environment, facilitating the emergence of new themes and insights directly from the data, independent of pre-existing categories. This dual approach provided a comprehensive understanding of the data by validating the existing theory while also exploring new findings [ 49 ]. Two coders were involved in coding the transcripts (AA and BM) and in cases of disagreements between researchers, consensus was achieved through discussion.

The response rate was 57.8% (525 responses out of 908), while the usability rate was 74.3% (390 responses out of 525) after excluding students who only completed the demographic section. The demographic and professional characteristics of the participants are presented in Table  1 . The majority were Qataris (37.0% [ n  = 142]), females (85.1% [ n  = 332]), and of the age group of 21–23 years (51.7% [ n  = 201]). The students were predominantly studying at the CHS (36.9%[ n  = 144]), in their second professional year (37.4% [ n  = 146]), and had yet to be exposed to experiential learning, that is, clinical rotations (70.2% [ n  = 273]).

Perceptions of students of their learning environment

The overall median DREEM score for study participants indicated that QU Health students perceive their learning environment to be "more positive than negative" (132 [IQR = 116–174]). The reliability analysis for this sample of participants indicated a Cronbach's alpha for the total DREEM score of 0.94, and Cronbach's alpha scores for each domain of the DREEM tool, SPoL, SPoT, SASP, SPoA, and SSSP of 0.85, 0.74, 0.81, 0.85, and 0.65, respectively.

Individual item responses representing each domain of the DREEM tool are presented in Table  2 . For Domain I, QU Health students perceived the teaching approach in QU Health to be "more positive" (32 [IQR = 27–36]). Numerous participants agreed that the teaching was well-focused (70.7% [ n  = 274]), student-focused (66.1% [ n  = 254]) and aimed to develop the competencies of students (72.0% [ n  = 278]). The analysis of students’ perceptions related to Domain II revealed that faculty members were perceived to be “moving in the right direction” (30 [IQR = 26–34]). Most students agreed that faculty members were knowledgeable (90.7%[ n  = 345]) and provided students with clear examples and constructive feedback (77.6% [ n  = 294] and 63.8% [ n  = 224], respectively. Furthermore, the analysis of Domain III demonstrated that QU Health students were shown to have a "positive academic self-perception" (22 [IQR = 19–25]). In this regard, most students believed that they were developing their problem-solving skills (78% [ n  = 292]) and that what they learned was relevant to their professional careers (76% [ n  = 288]). Furthermore, approximately 80% ( n  = 306) of students agreed that they had learned empathy in their profession. For Domain IV, students perceived the atmosphere of their learning environment to be "more positive" (32 [IQR = 14–19]). A substantial number of students asserted that there were opportunities for them to develop interpersonal skills (77.7% [ n  = 293]), and that the atmosphere motivated them as learners (63.0% [ n  = 235]). Approximately one-third of students believed that the enjoyment did not outweigh the stress of studying (32.3% [ n  = 174]). Finally, analysis of Domain V indicates that students’ social self-perception was “not very bad” (17 [IQR = 27–36]). Most students agreed that they had good friends at their colleges (83% [ n  = 314]) and that their social lives were good (68% [ n  = 254]).

Table 3 illustrates the differences in the perception of students of their overall learning environment according to their demographic and professional characteristics. No significant differences were noted in the perception of the learning environment among the subgroups with selected demographic and professional characteristics, except for the health profession program in which they were enrolled ( p -value < 0.001), whether they had relatives who studied or had studied the same profession ( p -value < 0.002), and whether they started their experiential learning ( p -value = 0.043). Further analyses comparing the DREEM subscale scores according to their demographic and professional characteristics are presented in Supplementary Material 1.

Students’ perceptions of their professional identities

The students provided positive responses relating to their perceptions of their professional identity (24.00 IQR = [22–27]). The reliability analysis of this sample indicated a Cronbach's alpha of 0.605. The individual item responses representing the MCPIS-9 tool are presented in Table  2 . Most students (85% [ n  = 297]) expressed pleasant feelings about belonging to their own profession, and 81% ( n  = 280) identified positively with members of their profession. No significant differences were noted in the perception of students of their professional identity when analyzed against selected demographic subgroups, except for whether they had relatives who had studied or were studying the same profession ( p -value = 0.027). Students who had relatives studying or had studied the same profession tended to perceive their professional identity better (25 IQR = [22–27] and 24 IQR = [21–26], respectively) (Table  3 ).

Association between MCPIS-9 and DREEM

Spearman's rank correlation between the DREEM and MCPIS-9 total scores indicated an intermediate positive correlation between perceptions of students toward their learning environment and their professional identity development (r = 0.442, p -value < 0.001). The DREEM questionnaire, with its 50 items divided into five subscales, comprehensively assessed various dimensions of the learning environment. Each subscale evaluated a distinct aspect of the educational experience, such as the effectiveness of teaching, teacher behavior and attitudes, academic confidence, the overall learning atmosphere, and social integration. The MCPIS-9 questionnaire specifically assessed professional identity through nine items that measure attitudes, values, and self-perceived competence in the professional domain. The positive correlation demonstrated between the DREEM and MCPIS-9 scores indicated that as students perceive their learning environment more positively, their professional identity is also enhanced.

Thirty-seven students from the QU Health colleges were interviewed: eleven from CPH, eight from CMED, four from CDEM, and fourteen from CHS (six from Nut, three from PS, three from Biomed, and three from PH). Four conventional themes were generated deductively using Gruppen et al.’s conceptual framework, while one theme was derived through inductive analysis. The themes and sub-themes generated are demonstrated in Table  4 .

Theme 1. The personal component of the learning environment

This theme focused on student interactions and experiences within their learning environment and their impact on perceptions of learning, processes, growth, and professional development.

Sub-theme 1.1. Experiences influencing professional identity formation

Students classified their experiences into positive and negative. Positive experiences included hands-on activities such as on-campus practical courses and pre-clinical activities, which built their confidence and professional identity. In this regard, one student mentioned:

“Practical courses are one of the most important courses to help us develop into pharmacists. They make you feel confident in your knowledge and more willing to share what you know.” [CPH-5]

Many students claimed that interprofessional education (IPE) activities enhanced their self-perception, clarified their roles, and boosted their professional identity and confidence. An interviewee stated:

"I believe that the IPE activity,…., is an opportunity for us to explore our role. It has made me know where my profession stands in the health sector and how we all depend on each other through interprofessional thinking and discussions." [CHS-Nut-32]

However, several participants reported that an extensive workload hindered their professional identity development. A participant stated:

“The excessive workload prevents us from joining activities that would contribute to our professional identity development. Also, it restricts our networking opportunities and makes us always feel burnt out.” [CHS-Nut-31]

Sub-theme 1.2. Strategies used by students to pursue their goals

QU Health students employed various academic and non-academic strategies to achieve their objectives, with many emphasizing list-making and identifying effective study methods as key approaches:

“Documentation. I like to see tasks that I need to do on paper. Also, I like to classify my tasks based on their urgency. I mean, deadlines.” [CHS-Nut-31]

“I always try to be as efficient as possible when studying and this can be by knowing what studying method best suits me.” [CHS-Biomed-35]

Nearly all students agreed that seeking feedback from faculty was crucial for improving their work and performance. In this context, a student said:

“We must take advantage of the provided opportunity to discuss our assignments, projects, and exams, like what we did correctly, and what we did wrongly. They always discuss with us how to improve our work on these things.” [CHS-Nut-32]

Moreover, many students also believed that developing communication skills was vital for achieving their goals, given their future roles in interprofessional teams. A student mentioned:

“Improving your communication skills is a must because inshallah (with God’s will) in the future we will not only work with biomedical scientists, but also with nurses, pharmacists, and doctors. So, you must have good communication abilities.” [CHS-Biomed-34]

Finally, students believe that networking is crucial for achieving their goals because it opens new opportunities for them as stated by a student:

“Networking with different physicians or professors can help you to know about research or training opportunities that you could potentially join.” [CMED-15]

Subtheme 1.3. Students’ mental and physical well-being

Students agreed that while emotional well-being is crucial for good learning experiences and professional identity development, colleges offered insufficient support. An interviewee stated:

“We simply don't have the optimal support we need to take care of our emotional well-being as of now, despite how important it is and how it truly reflects on our learning and professional development” [CDEM-20]

Another student added:

“…being in an optimal mental state provides us with the opportunity to acquire all required skills that would aid in our professional identity development. I mean, interpersonal skills, adaptability, self-reflection” [CPH-9]

Students mentioned some emotional support provided by colleges, such as progress tracking and stress-relief activities. Students said:

“During P2 [professional year 2], I missed a quiz, and I was late for several lectures. Our learning support specialist contacted me … She was like, are you doing fine? I explained everything to her, and she contacted the professors for their consideration and support.” [CPH-7]

“There are important events that are done to make students take a break and recharge, but they are not consistent” [CHS-PS-27]

On the physical well-being front, students felt that their colleges ensured safety, especially in lab settings, with proper protocols to avoid harm. A student mentioned:

“The professors and staff duly ensure our safety, especially during lab work. They make sure that we don't go near any harmful substances and that we abide by the lab safety rules” [CHS-Biomed -35]

Theme 2. Social component of the learning environment

This theme focused on how social interactions shape students’ perceptions of learning environments and learning experiences.

Sub-theme 2.1. Opportunities for community engagement

Participants identified various opportunities for social interactions through curricular and extracurricular activities. Project-based learning (PBL) helped them build connections, improve teamwork and enhance critical thinking and responsibility as stated by one student:

“I believe that having PBL as a big part of our learning process improves our teamwork and interpersonal skills and makes us take responsibility in learning, thinking critically, and going beyond what we would have received in class to prepare very well and deep into the topic.” [CMED-12]

Extracurricular activities, including campaigns and events, helped students expand their social relationships and manage emotional stress. A student stated:

“I think that the extracurricular activities that we do, like the campaigns or other things that we hold in the college with other students from other colleges, have been helpful for me in developing my personality and widening my social circle. Also, it dilutes the emotional stress we are experiencing in class” [CDEM-22]

Sub-theme 2.2. Opportunities for learner-to-patient interactions

Students noted several approaches their colleges used to enhance patient-centered education and prepare them for real-world patient interactions. These approaches include communication skills classes, simulated patient scenarios, and field trips. Students mentioned:

“We took a class called Foundation of Health, which mainly focused on how to communicate our message to patients to ensure that they were getting optimal care. This course made us appreciate the term ‘patient care’ more.” [CHS-PH-38]

“We began to appreciate patient care when we started to take a professional skills course that entailed the implementation of a simulated patient scenario. We started to realize that communication with patients didn’t go as smoothly as when we did it with a colleague in the classroom.” [CPH-1]

“We went on a field trip to ‘Shafallah Center for Persons with Disability’ and that helped us to realize that there were a variety of patients that we had to care for, and we should be physically and mentally prepared to meet their needs.” [CDEM-21]

Theme 3. Organizational component of the learning environment

This theme explored students' perceptions of how the college administration, policies, culture, coordination, and curriculum design impact their learning experiences.

Sub-theme 3.1. Curriculum and study plan

Students valued clinical placements for their role in preparing them for the workplace and developing professional identity. A student stated:

“Clinical placements are very crucial for our professional identity development; we get the opportunity to be familiarized with and prepared for the work environment.” [CHS-PS-27]

However, students criticized their curriculum for not equipping them with adequate knowledge and skills. For example, a student said:

“… Not having a well-designed curriculum is of concern. We started very late in studying dentistry stuff and that led to us cramming all the necessary information that we should have learned.” [CDEM-20]

Furthermore, students reported that demanding schedules and limited course availability hindered learning and delayed progress:

“Last semester, I had classes from Sunday to Thursday from 8:00 AM till 3:00 PM in the same classroom, back-to-back, without any break. I was unable to focus in the second half of the day.” [CHS-Nut-38]

“Some courses are only offered once a year, and they are sometimes prerequisites for other courses. This can delay our clinical internship or graduation by one year.” [CHS-Biomed-36]

Additionally, the outdated curriculum was seen as misaligned with advancements in artificial intelligence (AI). One student stated:

“… What we learn in our labs is old-fashioned techniques, while Hamad Medical Corporation (HMC) is following a new protocol that uses automation and AI. So, I believe that we need to get on track with HMC as most of us will be working there after graduation.” [CHS-Biomed-35]

Sub-theme 3.2. Organizational climate and policies

Students generally appreciated the positive university climate and effective communication with the college administration which improves course quality:

“Faculty members and the college administration usually listen to our comments about courses or anything that we want to improve, and by providing a course evaluation at the end of the semester, things get better eventually.” [CPH-2]

Students also valued faculty flexibility with scheduling exams and assignments, and praised the new makeup exam policy which enhances focus on learning:

“Faculty members are very lenient with us. If we want to change the date of the exam or the deadline for any assignment, they agree if everyone in the class agrees. They prioritize the quality of our work over just getting an assignment done.” [CHS-PS-37]

“I am happy with the introduction of makeup exams. Now, we are not afraid of failing and losing a whole year because of a course. I believe that this will help us to focus on topics, not just cramming the knowledge to pass.” [CPH-9]

However, students expressed concerns about the lack of communication between colleges and clinical placements and criticized the lengthy approval process for extracurricular activities:

“There is a contract between QU and HMC, but the lack of communication between them puts students in a grey area. I wish there would be better communication between them.” [CMED-15]

“To get a club approved by QU, you must go through various barriers, and it doesn't work every time. A lot of times you won't get approved.” [CMED-14]

Theme 4. Materialistic component of the learning environment

This theme discussed how physical and virtual learning spaces affect students' learning experiences and professional identity.

Sub-theme 4.1. The physical space for learning

Students explained that the interior design of buildings and the fully equipped laboratory facilities in their programs enhanced focus and learning:

“The design has a calming effect, all walls are simple and isolate the noise, the classrooms are big with big windows, so that the sunlight enters easily, and we can see the green grass. This is very important for focusing and optimal learning outcomes.” [CPH-5]

“In our labs, we have beds and all the required machines for physiotherapy exercises and practical training, and we can practice with each other freely.” [CHS-PS-27]

Students from different emphasized the need for dedicated lecture rooms for each batch and highlighted the importance of having on-site cafeterias to avoid disruptions during the day:

“We don't have lecture rooms devoted to each batch. Sometimes we don't even find a room to attend lectures and we end up taking the lectures in the lab, which makes it hard for us to focus and study later.” [CDEM-23]

“Not having a cafeteria in this building is a negative point. Sometimes we miss the next lecture or part of it if we go to another building to buy breakfast.” [CHS-Nut-29]

Sub-theme 4.2. The virtual space for online learning

Students appreciated the university library's extensive online resources and free access to platforms like Microsoft Teams and Webex for efficient learning and meetings. They valued recorded lectures for flexible study and appreciated virtual webinars and workshops for global connectivity.

“QU Library provides us with a great diversity and a good number of resources, like journals or books, as well as access medicine, massive open online courses, and other platforms that are very useful for studying.” [CMED-16].

“Having your lectures recorded through virtual platforms made it easier to take notes efficiently and to study at my own pace.” [CHS-PS-38]

"I hold a genuine appreciation for the provided opportunities to register in online conferences. I remember during the COVID-19 pandemic, I got the chance to attend an online workshop. This experience allowed me to connect with so many people from around the world." [CMED-15]

Theme 5. Characteristics of an ideal learning environment

This theme explored students’ perceptions of an ideal learning environment and its impact on their professional development and identity.

Sub-theme 5.1. Active learning and professional development supporting environment

Students highlighted that an ideal learning environment should incorporate active learning methods and a supportive atmosphere. They suggested using simulated patients in case-based learning and the use of game-based learning platforms:

“I think if we have, like in ITQAN [a Clinical Simulation and Innovation Center located on the Hamad Bin Khalifa Medical City (HBKMC) campus of Hamad Medical Corporation (HMC)], simulated patients, I think that will be perfect like in an “Integrated Case-Based Learning” case or professional skills or patient assessment labs where we can go and intervene with simulated patients and see what happens as a consequence. This will facilitate our learning.” [CPH-4]

“I feel that ‘Kahoot’ activities add a lot to the session. We get motivated and excited to solve questions and win. We keep laughing, and I honestly feel that the answers to these questions get stuck in my head.” [CHS-PH-38].

Students emphasized the need for more opportunities for research, career planning, and equity in terms of providing resources and opportunities for students:

“Students should be provided with more opportunities to do research, publish, and practice.” [CMED-16]

“We need better career planning and workshops or advice regarding what we do after graduation or what opportunities we have.” [CHS-PS-25]

“I think that opportunities are disproportionate, and this is not ideal. I believe all students should have the same access to opportunities like having the chance to participate in conferences and receiving research opportunities, especially if one fulfills the requirements.” [CHS-Biomed-35]

Furthermore, the students proposed the implementation of mentorship programs and a reward system to enable a better learning experience:

“Something that could enable our personal development is a mentorship program, which our college started to implement this year, and I hope they continue to because it’s an attribute of an ideal learning environment.” [CPH-11]

“There has to be some form of reward or acknowledgments to students, especially those who, for example, have papers published or belong to leading clubs, not just those who are, for example, on a dean’s list because education is much more than just academics.” [CHS-PS-26]

Subtheme 5.2. Supportive physical environment

Participants emphasized that the physical environment of the college significantly influences their learning attitudes. A student said:

“The first thing that we encounter when we arrive at the university is the campus. I mean, our early thoughts toward our learning environment are formed before we even know anything about our faculty members or the provided facilities. So, ideally, it starts here.” [CPH-10]

Therefore, students identified key characteristics of an optimal physical environment which included: having a walkable campus, designated study and social areas, and accessible food and coffee.

“I think that learning in what they refer to as a walkable campus, which entails having the colleges and facilities within walking distance from each other, without restrictions of high temperature and slow transportation, is ideal.” [CPH-8]

“The classrooms and library should be conducive to studying and focusing, and there should also be other places where one can actually socialize and sit with one’s friends.” [CDEM-22]

“It is really important to have a food court or café in each building, as our schedules are already packed, and we have no time to go get anything for nearby buildings.” [CHS-Biomed-34]

Data integration

Table 5 represents the integration of data from the quantitative and qualitative phases. It demonstrates how the quantitative findings informed and complemented the qualitative analysis and explains how quantitative data guided the selection of themes in the qualitative phase. The integration of quantitative and qualitative data revealed both convergences and divergences in students' views of their learning environment. Both data sources consistently indicated that the learning environment supported the development of interpersonal skills, fostered strong relationships with faculty, and promoted an active, student-centered learning approach. This environment was credited with enhancing critical thinking, independence, and responsibility, as well as boosting students' confidence and competence through clear role definitions and constructive faculty feedback.

However, discrepancies emerged between the two phases. Quantitative data suggested general satisfaction with timetables and support systems, while qualitative data uncovered significant dissatisfaction. Although quantitative results indicated that students felt well-prepared and able to memorize necessary material, qualitative findings revealed challenges with concentration and focus. Furthermore, while quantitative data showed contentment with institutional support, qualitative responses pointed to shortcomings in emotional and physical support.

This study examined the perceptions of QU Health students regarding the quality of their learning environment and the characteristics of an ideal learning environment. Moreover, this study offered insights into the development of professional identity, emphasizing the multifaceted nature of learning environments and their substantial impact on professional identity formation.

Perceptions of the learning environment

The findings revealed predominantly positive perceptions among students regarding the quality of the overall learning environment at QU Health and generally favorable perception of all five DREEM subscales, which is consistent with the international studies using the DREEM tool [ 43 , 50 , 51 , 52 , 53 , 54 ]. Specifically, participants engaged in experiential learning expressed heightened satisfaction, which aligns with existing research indicating that practical educational approaches enhance student engagement and satisfaction [ 55 , 56 ]. Additionally, despite limited literature, students without relatives in the same profession demonstrated higher perceptions of their learning environment, possibly due to fewer preconceived expectations. A 2023 systematic review highlighted how students’ expectations influence their satisfaction and academic achievement [ 57 ]. However, specific concerns arose regarding the learning environment, including overemphasis on factual learning in teaching, student fatigue, and occasional boredom. These issues were closely linked to the overwhelming workload and conventional teaching methods, as identified in the qualitative phase.

Association between learning environment and professional identity

This study uniquely integrated the perceptions of the learning environment with insights into professional identity formation in the context of healthcare education which is a relatively underexplored area in quantitative studies [ 44 , 58 , 59 , 60 ]. This study demonstrated a positive correlation between students' perceptions of the learning environment (DREEM) and their professional identity development (MCPIS-9) which suggested that a more positive learning environment is associated with enhanced professional identity formation. For example, a supportive and comfortable learning atmosphere (i.e., high SPoA scores) can enhance students' confidence and professional self-perception (i.e., high MCPIS-9 scores). The relationship between these questionnaires is fundamental to this study. The DREEM subscales, particularly Perception of Learning (SpoL) and Academic Self-Perception (SASP), relate to how the learning environment supports or hinders the development of a professional identity, as measured by MCPIS-9. Furthermore, the Perception of Teachers (SpoT) subscale examines how teacher behaviors and attitudes impact students, which can influence their professional identity development. The Perception of Atmosphere (SPoA) and Social Self-Perception (SSSP) subscales evaluate the broader environment and social interactions, which are crucial for professional identity formation as they foster a sense of community and belonging.

Employing a mixed methods approach and analyzing both questionnaires and FGs through the framework outlined by Gruppen et al. highlighted key aspects across four dimensions of the learning environment: personal development, social dimension, organizational setting, and materialistic dimension [ 1 ]. First, the study underscored the significance of both personal development and constructive feedback. IPE activities emerged as a key factor that promotes professional identity by cultivating collaboration and role identification which is consistent with Bendowska and Baum's findings [ 61 ]. Similarly, the positive impact of constructive faculty feedback on student learning outcomes aligned with the work of Gan et al. which revealed that feedback from faculty members positively influences course satisfaction and knowledge retention, which are usually reflected in course results [ 62 ]. Importantly, the research also emphasized the need for workload management strategies to mitigate negative impacts on student well-being, a crucial factor for academic performance and professional identity development [ 63 , 64 ]. The inclusion of community events and support services could play a significant role in fostering student well-being and reducing stress, as suggested by Hoferichter et al. [ 65 ]. Second, the importance of the social dimension of the learning environment was further highlighted by the study. Extracurricular activities were identified as opportunities to develop essential interpersonal skills needed for professional identity, mirroring the conclusions drawn by Achar Fujii et al. who argued that extracurricular activities lead to the development of fundamental skills and attitudes to build and refine their professional identity and facilitate the learning process, such as leadership, commitment, and responsibility [ 66 ]. Furthermore, Magpantay-Monroe et al. concluded that community and social engagement led to professional identity development in nursing students through the expansion of their knowledge and communication with other nursing professionals [ 67 ]. PBL activities were another key element that promoted critical thinking, learning, and ultimately, professional identity development in this study similar to what was reported by Zhou et al. and Du et al. [ 68 , 69 ]. Third, the organizational setting, particularly the curriculum and clinical experiences, emerged as crucial factors. Clinical placements and field trips were found to be instrumental in cultivating empathy and professional identity [ 70 , 71 ]. However, maintaining an up-to-date curriculum that reflects advancements in AI healthcare education is equally important, as highlighted by Randhawa and Jackson in 2019 [ 72 ]. Finally, the study underlined the role of the materialistic dimension of the learning environment. Physical learning environments with natural light and managed noise levels were found to contribute to improved academic performance [ 73 , 74 ]. Additionally, the value of online educational resources, such as online library resources and massive open online course, as tools facilitating learning by providing easy access to materials, was emphasized, which is consistent with the observations of Haleem et al. [ 75 ].

The above collectively contribute to shaping students' professional identities through appreciating their roles, developing confidence, and understanding the interdependence of different health professions. These indicate that a supportive and engaging learning environment is crucial for fostering a strong sense of professional identity. Incorporating these student-informed strategies can assist educational institutions in cultivating well-rounded healthcare professionals equipped with the knowledge, skills, and emotional resilience needed to thrive in the dynamic healthcare landscape. Compared to existing quantitative data, this study reported a lower median MCPIS-9 score of 24.0, in contrast to previously reported scores of 39.0, 38.0, 38.0, respectively. [ 76 , 77 , 78 ]. This discrepancy may be influenced by the fact that the participants were in their second professional year, known for weaker identity development [ 79 ]. Students with relatives in the same profession perceived their identity more positively, which is likely due to role model influences [ 22 ].

Expectations of the ideal educational learning environment

This study also sought to identify the key attributes of an ideal learning environment from the perspective of students at QU-Health. The findings revealed a strong emphasis on active learning strategies, aligning with Kolb's experiential learning theory [ 80 ]. This preference suggests a desire to move beyond traditional lecture formats and engage in activities that promote experimentation and reflection, potentially mitigating issues of student boredom. Furthermore, students valued the implementation of simple reward systems such as public recognition, mirroring the positive impact such practices have on academic achievement reported by Dannan in 2020 [ 81 ]. The perceived importance of mentorship programs resonates with the work of Guhan et al. who demonstrated improved academic performance, particularly for struggling students [ 82 ]. Finally, the study highlighted the significance of a walkable campus with accessible facilities. This aligns with Rohana et al. who argued that readily available and useable facilities contribute to effective teaching and learning processes, ultimately resulting in improved student outcomes [ 83 ]. Understanding these student perceptions, health professions education programs can inform strategic planning for curricular and extracurricular modifications alongside infrastructural development.

The complementary nature of qualitative and quantitative methods in understanding student experiences

This study underscored the benefits of employing mixed methods to comprehensively explore the interplay between the learning environment and professional identity formation as complex phenomena. The qualitative component provided nuanced insights that complemented the baseline data provided by DREEM and MCPIS-9 questionnaires. While DREEM scores generally indicated positive perceptions, qualitative findings highlighted the significant impact of experiential learning on students' perceptions of the learning environment and professional identity development. Conversely, discrepancies emerged between questionnaire responses and FG interviews, revealing deeper issues such as fatigue and boredom associated with traditional teaching methods and heavy workloads, potentially influenced by cultural factors. In FGs, students revealed cultural pressures to conform and stigma against expressing dissatisfaction, which questionnaire responses may not capture. Qualitative data allowed students to openly discuss culturally sensitive issues, indicating that interviews complement surveys by revealing insights overlooked in quantitative assessments alone. These insights can inform the design of learning environments that support holistic student development. The study also suggested that cultural factors can influence student perceptions and should be considered in educational research and practice.

Application of findings

The findings from this study can be directly applied to inform and enhance educational practices, as well as to influence policy and practice sectors. Educational institutions should prioritize integrating active learning strategies and mentorship programs to combat issues such as student fatigue and boredom. Furthermore, practical opportunities, including experiential learning and IPE activities, should be emphasized to strengthen professional identity and engagement. To address these challenges comprehensively, policymakers should consider developing policies that support effective workload management and community support services, which are essential for improving student well-being and academic performance. Collaboration between educational institutions and practice sectors can greatly improve students' satisfaction with their learning environment and experience. This partnership enhances the relevance and engagement of their education, leading to a stronger professional identity and better preparation for successful careers.

Limitations

As with all research, this study has several limitations. For instance, there was a higher percentage of female participants compared to males; however, it is noteworthy to highlight the demographic composition of QU Health population, where students are majority female. Furthermore, the CHS, which is one of the participating colleges in this study, enrolls only female students. Another limitation is the potentially underpowered statistical comparisons among the sociodemographic characteristics in relation to the total DREEM and MCPIS-9 scores. Thus, the findings of this study should be interpreted with caution.

The findings of this study reveal that QU Health students generally hold a positive view of their learning environment and professional identity, with a significant positive correlation exists between students’ perceptions of their learning environment and their professional identity. Specifically, students who engaged in experiential learning or enrolled in practical programs rated their learning environment more favorably, and those with relatives in the same profession had a more positive view of their professional identity. The participants of this study also identified several key attributes that contribute to a positive learning environment, including active learning approaches and mentorship programs. Furthermore, addressing issues like fatigue and boredom is crucial for enhancing student satisfaction and professional development.

To build on these findings, future research should focus on longitudinal studies that monitor changes in the perceptions of students over time and identify the long-term impact of implementing the proposed attributes of an ideal learning environment on the learning process and professional identity development of students. Additionally, exploring the intricate dynamics of learning environments and their impact on professional identity can allow educators to better support students in their professional journey. Future research should also continue to explore these relationships, particularly on diverse cultural settings, in order to develop more inclusive and effective educational strategies. This approach will ensure that health professional students are well-prepared to meet the demands of their profession and provide high-quality care to their patients.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

United Nations Educational, Scientific, and Cultural Organization

European Union

American Council on Education

World Federation for Medical Education

Communities of Practice

Qatar University Health

College of Health Sciences

College of Pharmacy

College of Medicine

Dental Medicine

College of Nursing

Human Nutrition

Biomedical Science

Public Health

Physiotherapy

Dundee Ready Education Environment Measure

Perception to Learning

Perception to Teachers

Academic Self-Perception

Perception of the Atmosphere

Social Self-Perception

Macleod Clark Professional Identity Scale

Focus Group

InterProfessional Education

Project-Based Learning

Hamad Medical Corporation

Hamad Bin Khalifa Medical City

Artificial Intelligence

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This work was supported by the Qatar University Internal Collaborative Grant: QUCG-CPH-22/23–565.

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Assessment of work safety analysis performance among rural hospitals of Chirumanzu district of midlands province, Zimbabwe

• Tapiwa Shabani 1 ,
• Steven Jerie 1 &
• Takunda Shabani 1  

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Ensuring workplace safety for healthcare workers is vital considering the important role they play in various societies which is to save life. Healthcare workers face different risks when performing tasks in various departments within hospitals, hence there is a need to assess work safety analysis procedures among healthcare workers. As a result, this study aims to assess the effectiveness of work safety analysis procedures among healthcare workers at Muvonde and Driefontein Sanatorium rural hospitals in Chirumanzu district. The research applied the descriptive cross-sectional design, combining quantitative and qualitative data collection methods. A questionnaire with both closed and open ended questionnaire was used for data collection among 109 healthcare workers at Muvonde hospital and 68 healthcare workers at Driefontein Sanatorium hospital. Secondary data sources, observations and interviews were also included as data collection methods. Quantitative data collected during the study was analysed using SPSS version 25. Braun and Clarke (2006)’s six phase framework was applied for qualitative data analysis. Ethical approval form was obtained from the District Medical Officer and Midlands State University. Findings of the study indicated that risks identified at Muvonde and Driefontein Sanatorium rural hospitals are classified as ergonomic, physical, chemical, psychosocial and biological risks. Respondents specified that these risks occur as a result of inadequate equipment, poor training, negative safety behaviour, poor management and pressure due to high workload. Safety inspection, safety workshops and monitoring of worker’s safety behaviour were mentioned as measures to manage risks. However, the strengths and weaknesses of the current safety procedures need to be assessed to highlight areas for improvement to reduce occurrence of risks within the hospitals.

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Introduction

Professionals in hospitals save the lives of people in various communities as a result, this job requires a high level of commitment [ 1 , 2 ]. However, the community must not forget that healthcare workers who save their lives are exposed to various risks during work. [ 3 ] opined that the nature of tasks performed by healthcare workers in hospitals expose them to risks. Healthcare facilities, including rural hospitals, are work environments where workers are exposed to different work-related risks regularly [ 4 , 5 ]. Duties performed by healthcare workers involve lifting and transferring patients, dealing with patients with unpredictable behaviour, and handling infectious materials and exposure to chemicals [ 6 , 7 , 8 ]. This implies that healthcare workers are affected with both ergonomic, biological, chemical, psychosocial and physical risks. However, the protection of healthcare workers from risks depends on the effectiveness of work safety analysis procedures within the healthcare facility [ 9 , 10 ]. Therefore, guaranteeing the efficiency of work safety analysis procedures is important for reducing risks affecting healthcare works and creating a safe work environment. In the healthcare industry, where employees are exposed to various risks, it is essential to have effective work safety analysis procedures in place to minimize the risks of accidents and injuries [ 2 , 11 ].

Insufficient work safety measures can prompt a scope of negative results in the healthcare facilities [ 12 , 13 ]. According to [ 14 ] and [ 15 ] results of inadequate work safety analysis procedures within the workplace include work-related illness, injuries, accidents, stress and reduced productivity. Ergonomic risks affecting healthcare workers as a result of tasks they perform expose them to upper and lower back pain, muscular strain and neck pain [ 16 , 17 ]. In healthcare facilities healthcare workers always report high levels of musculoskeletal injuries related to ergonomic risks. Musculoskeletal disorders expose healthcare workers to acute and chronic injuries [ 8 , 18 , 19 ]. According to [ 20 ] and [ 21 ] healthcare workers are also affected with work-related pressure which expose them to stress, fatigue and anxiety which are categorised as psychosocial risks. Work-related stress is common among healthcare employees as a result of long working hours, shift work and dealing with patients who are critically ill [ 22 , 23 ]. Biological risks affecting healthcare workers globally expose them to tuberculosis, HIV/AIDS and hepatitis B/C [ 24 , 25 ]. In healthcare facilities, piercing materials such as needles are used; however, they result in sharp injuries among healthcare workers [ 26 , 27 ]. Nurses are mostly affected with physical risks such needle stick injuries and pricks/cuts as well as ionizing and non-ionizing radiation [ 28 , 29 ]. Allergies, eye and skin irritation are affecting hospital workers as a result of different chemicals used during hospital procedures [ 30 , 31 ]. This denotes that healthcare workers are exposed to chemical risks when performing their duties.

In sub-Saharan Africa the issue of work-related risks affecting healthcare workers increase as a result of ineffective safety procedures [ 32 , 33 ]. This means poor work safety analysis, negative safety behaviour, inadequate resources and poor safety training expose healthcare workers to risks. In less developed countries hospital employees are affected with occupational risks due to shortage of labour [ 4 , 5 ]. Due to shortage of labour in African countries during the outbreak of Covid-19 they increased the time of shifts for healthcare workers to cope with the high rate of hospitalization [ 34 , 35 ]. However, this increases mental and physical exhaustion among healthcare workers. In developed countries measures used to manage risks affecting workers in healthcare facilities are effective compared to developing countries [ 6 , 10 , 16 ]. This occurs because in developing countries such as Zimbabwe the use of effective work safety analysis procedures is commonly applied in timber, mining and manufacturing companies neglecting healthcare institutions.

Health workers are usually vulnerable to work related risks since issues of safety are always neglected in the health sector, specifically in hospitals located in marginal areas [ 36 , 37 ]. Therefore, this study assesses the effectiveness of work safety analysis procedures among healthcare workers at Muvonde and Driefontein Sanatorium rural hospitals in Chirumanzu district. As rural healthcare facilities Muvonde and Driefontein Sanatorium hospitals, they face unique challenges related to staffing constraints, resource limitations and infrastructure deficiencies. As a result, assessing the effectiveness of work safety analysis procedures at Driefontein Sanatorium and Muvonde rural hospitals is significant for understanding the existing safety protocols and identifying areas for improvement that are tailored to their operational context.

Through a thorough evaluation of the work safety analysis performance in these particular healthcare facilities, the research pinpoint important areas that require improvement and create focused recommendations to improve workplace safety procedures. This will result in the use of cutting-edge technologies for risk assessment and hazard identification, the creation of a culture of continuous improvement in work safety procedures, the introduction of new safety protocols, and the execution of customized training programs. Ultimately, the research findings may improve the health and safety of medical staff in remote hospitals while also acting as a template for raising occupational health and safety standards in similar environments around the world. The findings of the study would help the rural healthcare centres to achieve the demands of Sustainable Development Goal number 3 which focus on good health and well-being.

Materials and methods

Descriptive cross-sectional design was used during the study. The study was conducted at Muvonde and Driefontein Sanatorium rural hospitals. The two hospitals serve as referrals for clinics and other hospitals in Chirumanzu district and outside Chirumanzu district. The study population were medical and paramedical staff within the hospitals. The sample size was calculated using Yamane (1967) formula shown below:

Where: n is the sample size, N is the total population and e is the margin of error.

After calculations a sample of 68 healthcare workers were selected as questionnaire respondents at Driefontein Sanatorium hospital and 109 healthcare workers were selected as questionnaire respondents at Muvonde hospital as indicated by Table  1 . Healthcare workers who participated as questionnaire respondents were selected randomly from every strata. Key informants interviewees were selected purposively. A questionnaire with both closed and open ended questionnaires was prepared and self-administered during data collection to reduce margin of error. The questionnaire is shown in Appendix 1 . The pilot study of the questionnaire consisted of 10% of the participants from each of the two rural hospitals which were considered during the study. This conformed to [ 38 ] that 10% of the target population is used for pilot study before the main survey is done. At Muvonde hospital 10% of 109 questionnaire respondents were considered during the pilot study. This entails that 11 healthcare workers participated during the pilot study of the questionnaire at Muvonde hospital. At Driefontein Sanatorium hospital 10% of 68 healthcare workers were selected for pilot study. This clearly means 7 healthcare workers were taken as participants during the pilot study at Driefontein Sanatorium hospital. The participants who take part during the pilot study provide their suggestions and recommendations on how to improve the drafting of questionnaire items. Test-retest reliability has also been used to assess response stability over time, making sure that the questionnaire produced consistent answers when it was administered again. Experts in healthcare management and work safety analysis examine the questionnaire items to make sure they comply with industry best practices and standards in order to improve validity. To further increase validity and reliability, a pilot test including a sample of rural hospitals in Chirumanzu District was conducted to evaluate the questions’ clarity and relevance.

Semi-structured interviews were prepared to conduct interviews with the Nurse in Charge, Hospital Manager (Matron), Medical Superintendent, Head of Environmental Health department and Human Resource Manager at each rural hospital participating during the interviews. The District Medical Officer and National Social Security Officer were also taken as interviewees to collect the information regarding the objectives of the study. Observations were carried out using an observation checklist focusing much on work environment, equipment, duties performed by healthcare workers and safety procedures used within the hospitals. Rural hospitals’ weekly inspection reports, monthly reports, annual reports and incidents reports as well as review articles and journals were used as secondary data sources.

Quantitative data collected using a questionnaire was analysed using Statistical Package for Social Sciences version 25.0. Quantitative data was presented in the form of tables, pie-charts and graphs which were produced by the SPSS. Braun and Clarke (2006)’s six phase framework for doing thematic analysis was applied during qualitative data analysis. Ethical approval forms were obtained from the District Medical Officer and Midlands State University before data collection starts. All participants participated voluntarily. Every participant was enlightened that participating during the study was voluntary.

Socio-demographic characteristics

Demographic characteristic according to gender, age and marital status.

Table  2 presents the demographic characteristics of the respondents according to gender, age and marital status. From the results, the majority (70%) of the healthcare workers who participated during the study at Muvonde hospital were females. At Driefontein Sanatorium hospital most (69%) of the questionnaire respondents were females. At Muvonde hospital (26.6%) of the participants identified 34–41 years as their age group while at Driefontein Sanatorium hospital revealed that they were aged 34–41 years as indicated in Table  2 . The majority (47.7%) respondents at Muvonde hospital are married and at Driefontein Sanatorium hospital majority (61.8%) of the healthcare workers who participated during the study were married.

Demographic characteristics according to level of education and work experience

Table  3 shows demographic characteristics of the healthcare workers according to level of education and work experience. Based on the feedback provided by the questionnaire, the respondent majority (52.3%) of the participants at Muvonde hospital are holders of diplomas. At Driefontein Sanatorium hospital most (54.4%) designated diplomas as their highest level of education as indicated in Table  3 . Results in Table  3 demonstrate that the majority (42.2%) of the questionnaire respondents at Muvonde hospital specified their work experience between 5 and 10 years. Most (41.2%) of the healthcare workers who participated as questionnaire respondents at Driefontein Sanatorium hospital indicated their work experience between 5 and 10 years.

Risks identified at muvonde and driefontein sanatorium rural hospitals

Different types of risks were identified at Muvonde and Driefontein Sanatorium rural hospitals notably ergonomic, chemical, biological, physical and psychosocial risks as illustrated by Fig.  1 . Majority (44%) of the questionnaire respondents at Muvonde hospital identified ergonomic risks as the main risks affecting them. However, at Driefontein Sanatorium hospital most (30.9%) of the healthcare workers who participated as questionnaire respondents reported biological risks as the risk which mainly affect them at work. At Driefontein Sanatorium hospital psychosocial risks recorded the least percentage (10.3%) while at Muvonde hospital psychosocial risks recorded (11.9%) as designated by the results shown in Fig.  1 .

Types of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Distribution of risks identified at muvonde and driefontein sanatorium hospital

Distribution of ergonomic risks.

The distribution of ergonomic risks is illustrated by Fig.  2 . Based on the findings of the study on distribution of ergonomic risks, the majority (33.9) reported standing for a long time while (20.2%) specified repetitive work at Muvonde hospital as shown in Fig.  2 . Some (17.4%) of the participants indicated lifting patients, manual therapy (11.1%), uncomfortable position (10.1%) and lifting of medical devices was reported by (6.2%) of questionnaire respondents at Muvonde hospital. Based on the results of distribution of ergonomic risks obtained at Driefontein Sanatorium hospital majority (27.9%) reported standing for a long time, lifting of patients (25%), repetitive work (17.6%), manual therapy (11.8%) and (5.9%) indicated lifting of medical devices as a concern as illustrated by Fig.  2 .

Distribution of physical risks

Regarding the distribution of physical risks at Muvonde hospital the majority (28.4%) reported sharp injuries, extreme temperatures (cold/hot) (24.8%), noise (22%), slips and falls (15.6%), radiation (5.5%) and electric shock (3.7%). Results obtained at Driefontein Sanatorium hospital indicated that the majority (30.9%) of the study respondents specified sharp objects, extreme temperatures (cold/hot) (26.5%), noise (20.6%), slips and falls (13.2%), radiation (5.9%) and electric shock (2.9%). The distribution of physical risks is indicated in Fig.  2 .

Distribution of biological risks

Majority (39.4%) of the healthcare workers who participated as questionnaire respondents at Muvonde hospital reported blood spillage while at Driefontein Sanatorium hospital the majority (31%) regarded breathing contaminated as the major biological risk Fig.  2 . Findings at Muvonde hospital shows that (17.4%) of the study participants specified breathing contaminated air, vomitus, sputum or urine of patients (16.5%), contact with wounds (15.6%) and viral infection (11%) was considered as the least biological risk among the biological risks. Furthermore, at Driefontein Sanatorium hospital (25%) of questionnaire respondents stated blood spillage, viral infections (16%), vomitus, sputum or urine of patients (15%) and contact with wounds (13%) were reported as biological risks.

Distribution of chemical risks

The study (Fig.  2 ) provides data about the distribution of chemical risks at Muvonde hospital as sanitizers (29%), cleaning detergents (24%), latex gloves (20%), anaesthetic gases and sterilizing agents (14%) and mercury (13%). Sanitizers (28%), cleaning detergents (21%), latex gloves (19%), anaesthetic gases and sterilizing agents (19%) and mercury (13%) were reported by questionnaire respondents as chemical risks prevailing at Driefontein Sanatorium hospital as shown in Fig.  2 .

Distribution of psychosocial risks

Regarding the distribution of psychosocial risks at Muvonde hospital majority (33%) reported dealing with very ill patients, overwork (26.6%), verbal abuse (16.5%), fatigue (11.9%), physical abuse (6.4%) and problems with the top management was specified by (5.5%) of participants Fig.  2 . During the study at Driefontein Sanatorium hospital the majority (39.7%) of the questionnaire respondents indicated dealing with severely ill patients however, (26.5%) specified overwork, and (13.2%) indicated verbal abuse, (11.8%) reveals fatigue, and (4.4%) reported physical abuse and (4.4%) stated problems with the top management as a risk among psychosocial risks.

Distribution of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Causes of risks identified at muvonde and driefontein sanatorium hospital

Study participants were asked to indicate causes of risks identified at Driefontein Sanatorium hospital and Muvonde hospital. Based on the findings shown in Fig.  3 majority (20.2%) of the healthcare workers who participated as questionnaire respondents at Muvonde hospital reported that risks occur as a result of pressure due to high workload, followed by (17.4%) who specified shortage of labour, (14.7%) indicated inadequate equipment, (3.7%) designated age, gender (10.1%), poor trainings (6.4%) and negative safety behaviour (6.4%). However, findings of the study at Muvonde hospital indicated that (8.3%) of the respondents reported poor management, department the worker is allocated (5.5%) and (7.3%) of the healthcare workers reported that use of personal protective equipment/cloth for a long time exposes them to risks Fig.  3 .

At Driefontein Sanatorium hospital the majority (25%) specified that they are exposed to risks as a result of pressure due to high workload, followed by (16.2%) who stated shortage of labour, (11.8%) indicated inadequate equipment and (2.9%) and (4.4%) pointed out age and gender as the factors which expose them to risks respectively Fig.  3 . Results obtained at Driefontein Sanatorium hospital shows that (8.8%) of the study participants indicated that they are exposed to risks as a result of poor training and this was similar to (8.8%) healthcare workers who indicated negative safety behaviour as a factor which expose healthcare workers to risks. Poor management was designated by (7.4%), the department the worker is allocated was specified by (5.9%) and (8.8%) use of personal protective equipment/cloth for a long time were reported as a factors which expose healthcare workers to occupational risks in hospitals as indicated by Fig.  3 .

Causes of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Effects of risks identified at muvonde and driefontein sanatorium hospital

Effects of ergonomic risks.

Study findings at Muvonde hospital shows that the majority (51.4%) specified back injuries regarding effects of ergonomic risks Fig.  4 . Based on the results (21.1%) reported neck pain while (16.5%) indicated shoulder discomfort followed by muscular strain which was designated by (11%) of the questionnaire respondents at Muvonde hospital. Regarding effects of ergonomic risks at Driefontein Sanatorium hospital most (57.4%) indicated back injuries while neck pain and shoulder discomfort was reported by (19.1%) and (13.2%) respectively Fig.  4 . However, at Driefontein Sanatorium hospital muscular strain was designated as an effect of ergonomic risks by (10.3%) of the study participants.

Effects of biological risks

Regarding effects of biological risks at Muvonde hospital (29.4%) of the respondents reported Covid-19 virus while (6.4%) specified tuberculosis, (3.7%) indicated hepatitis B/C and very few (1.8%) designated HIV/AIDS Fig.  4 . However, the majority (58.7%) of the study participants at Muvonde hospital specified that none of the infections related to ergonomic risks. Based on the findings obtained at Driefontein Sanatorium hospital pertaining effects of ergonomic risks, the majority (33.8%) specified Covid-19 virus whereas (25%) reported tuberculosis, (13.2%) specified hepatitis B/C and (1.5%) indicated HIV/AIDS. Nonetheless, (26.5%) designated that they never experienced any infections related to biological risks at work as shown by Fig.  4 .

Effects of physical risks

Effects of physical risks were examined at Muvonde hospital and Driefontein Sanatorium hospital. Results shows that more than half (57.8%) of the questionnaire respondents at Muvonde hospital reported needlestick injuries. Figure  4 also indicated that (19.3%) of the healthcare workers at Muvonde hospital specified cuts/pricks, (14.7%) indicated influenza and (8.3%) of the study participants designated crumps. Based on the findings regarding effects of physical risks at Driefontein Sanatorium hospital most (52.9%) stated needle stick injuries. Cuts/pricks were reported by (27.9%) healthcare workers, (11.8%) specified influenza while (7.4%) designated crumps among effects of physical risks at Driefontein Sanatorium rural hospital.

Effects of chemical risks

Considering effects of chemical risks at Muvonde hospital most (39.4%) of the study participants reported skin irritation, followed by allergies specified by (33.9%) respondents, (11.9%) stated pulmonary irritation while (2.8%) of the participants identified asthma. However, (10.1%) of the questionnaire respondents indicated they never experienced effects of chemical risks related to tasks they perform at Muvonde hospital and the least (1.8%) indicated birth defects as effects of chemical risks Fig.  4 . At Driefontein Sanatorium hospital the majority (48.5%) of the healthcare workers who participated as questionnaire respondents specified skin irritation regarding effects of chemical risks. Allergies were reported by (26.5%) study participants, (17.6%) indicated pulmonary irritation and (4.4%) stated asthma among the effects of chemical risks they experienced at Driefontein Sanatorium hospital Fig.  4 . Nonetheless, very few (2.9%) did not report any effect of chemical risks at Driefontein Sanatorium hospital.

Effects of psychosocial risks

Majority (47.7%) of the questionnaire respondents at Muvonde hospital indicated stress as an effect of psychosocial risks Fig.  4 . However, some (20.2%) of the respondents reported fatigue, (15.6%) specified anxiety, (3.7%) stated insomnia, and (2.8%) of the participants pointed out persistent tiredness as effects of psychosocial risks they experienced at Muvonde hospital. Blood pressure was specified by the least (0.9%) of the healthcare workers at Muvonde hospital while (9.2%) of the healthcare employees specified that they never experienced any challenges related to psychosocial risks. Figure  4 also indicates effects of psychosocial risks reported by healthcare workers at Driefontein Sanatorium hospital. Most (45.6%) of the study participants at Driefontein Sanatorium hospital indicated that they experienced stress as a result of psychosocial risks. At Driefontein Sanatorium hospital fatigue was reported by (23.5%) respondents, anxiety was specified by (16.2%) participants, insomnia (5.9%) and (2.9%) participants indicated persistent tiredness. Nevertheless, minority (1.5%) of the questionnaire respondents indicated that they are affected with blood pressure as a result of psychosocial risks and some (4.4%) specified that they never experienced effects of psychosocial risks.

Effects of risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Work safety measures used to manage risks identified at muvonde and driefontein sanatorium hospital

Study participants at Driefontein Sanatorium hospital and Muvonde hospital were requested to indicate work safety measures used for coping with work-related risks. Regarding work safety measures at Muvonde hospital the majority (38%) of the questionnaire respondents indicated personal protective equipment/cloth Fig.  5 . However, at Muvonde hospital (13%) of the study participants specified safety inspection, (12%) of healthcare workers reported proper waste disposal, (11%) designated monitoring of workers’ safety behaviour, (9%) safety training and (9%) of the respondents stated safety workshops as methods used to manage risks. Other (8%) of the healthcare workers who participated as questionnaire respondents indicated other measures that can be used to manage risks for example screening health workers for diseases such as hepatitis B/C virus, Covid-19 and tuberculosis as indicated by Fig.  5 .

Based on the results obtained at Driefontein Sanatorium hospital pertaining safety measures the majority (40%) stated personal protective equipment/cloth as indicated by Fig.  5 . At Driefontein Sanatorium hospital safety inspection was reported by (10%) of the questionnaire respondents, proper disposal of waste (9%), monitoring of workers’ safety behaviour (7%), safety training (6%) and safety workshops was specified by (6%) of the healthcare workers. However, Fig.  5 indicated that (22%) of the questionnaire respondents at Driefontein Sanatorium hospital stated other safety measures such as screening healthcare workers for diseases for example Covid-19, tuberculosis and hepatitis B/C.

During the study survey at Driefontein Sanatorium hospital the Matron indicated that, As a hospital which is focusing on maintaining high standard of sterility using available resources to promote quality healthcare service in Chirumanzu district and beyond we put safety informative charts for the benefit of both patients, visitors and workers. The Matron go on to indicate that we also provide safety facilities such as washing hand facilities. This was supported by observations results. During observations informative charts and washing hand facilities were observed at Driefontein Sanatorium hospital as shown by Plate 1 and Plate 2 respectively.

Informative chart shown at driefontein sanatorium hospital. Source Field Survey (2023)

Washing hand facility (bucket) observed at driefontein sanatorium hospital. Source Field Survey (2023)

Work safety measures used to manage risks identified at muvonde and driefontein sanatorium hospital. Source Field Survey (2023)

Safety policies at muvonde and driefontein sanatorium hospital

The majority (47.7%) of the questionnaire respondents agree that at Muvonde hospital there are clear safety policies while (33.9%) strongly agree, (10.1%) disagree and (8.3%) strongly disagree Fig.  6 . However, at Driefontein Sanatorium hospital most (54.4%) of the healthcare workers agree and (27.9%) strongly agree about the availability of clear safety policies at Driefontein Sanatorium hospital. During the study at Driefontein Sanatorium hospital the researcher was given access to some of the hospital’s documents to use them as secondary data sources. The researcher discovered a safety policy manual and went through it and it was showing clear objectives. The objectives of the policy manual include 1 ) To provide continued guidance to health workers and students on infection prevention control measures and policies. 2 ) To promote an educational strategy for healthcare workers with a broader aim in mind. 3 ) To promote participation in infection prevention and control by healthcare workers, patients, relatives and visitors on how to reduce hospital acquired infection. 4 ) To allay unnecessary anxiety by providing fundamental information on infection prevention and control measures. 5 ) To promote, maintain and strengthen the high standard of cleanliness in the hospital and its environment. Appendix 2 only presents the cover page, preface, table of contents and objectives of hospital rules, regulations and policies related to infection prevention and control at Driefontein Sanatorium hospital. At Driefontein Sanatorium hospital few (10.3%) disagree and very few (7.4%) strongly disagree about the availability of clear safety policies at Driefontein Sanatorium hospital Fig.  6 .

Availability of clear safety policies at muvonde and driefontein sanatorium hospitals. Source Field Survey (2023)

Effectiveness of work safety measures used to manage risks at muvonde hospital and driefontein sanatorium hospital

Figure  7 shows that while a small percentage of survey respondents (7.3%) said that institutional measures used to manage risks at Muvonde hospital are poor, more than half (56%) said the measures are good, (21.1%) indicated that the measures are very good and (15.6%) specified that the measures are excellent. As seen in Fig.  7 , the majority of the study participants (51.5%) stated that the institutional measures in place at Driefontein Sanatorium hospital to manage risks are effective because they are good. This is followed by (30.9%) who said the measures are very good and (13.2%) stated that the measures are excellent. A small percentage (4.4%) of the questionnaire respondents at Driefontein Sanatorium hospital indicated that the safety measures are poor.

Effectiveness of work safety measures used to manage risks at muvonde and driefontein sanatorium hospitals. Source Field Survey (2023)

Association between Work Experience (years) and effectiveness of Work Safety measures used to manage risks

During data analysis Chi-Square test was employed to test the association between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

The following hypotheses were tested:

Null hypothesis (H 0 ) – There is no association between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

Alternative hypothesis (H 1 ) - There is an association between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

0.05 was set as the probability value.

If \(\:\:x\) 2 is above 0.05 accept H 0 and reject H 1 . There is no relationship between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

If \(\:\:x\) 2 is below 0.05 accept H 1 and reject H 0 . There is a relationship between work experience and rating the effectiveness of work safety measures used to manage risks at Muvonde hospital and Driefontein Sanatorium hospital.

Table  4 shows that the Chi-Square test results were 0.000, which is less than the significance level 0.05. In light of the findings, we accept H 1 and reject H 0 . Based on the analysis, the findings show that evaluating the effectiveness of work safety measures at Muvonde and Driefontein Sanatorium hospital is associated with work experience.

Females made up the majority of the healthcare workers who took part during the study conducted at Driefontein Sanatorium hospital and Muvonde hospital. The findings of the research conducted at Muvonde and Driefontein Sanatorium hospitals align with the findings concur with [ 39 ] that females constitute the majority of healthcare workers in the United States. A study carried by [ 40 ] also indicated that females constitute 70% of the healthcare workers working in healthcare facilities of Pakistan. This implies that healthcare workers who are men are less than women. The explanations for the gender gap in hospital employment are varied. For example, women are typically drawn to caregiving-related fields due to their nurturing disposition and empathy for others. The purpose of asking the gender of the respondents was to discover risks that are associated with gender.

Overall, data gathered for the study suggests that healthcare staff at Driefontein Sanatorium hospital and Muvonde hospital have a different age range, which offers a variety of perspectives and experiences about dangers impacting healthcare workers. Because this is the traditional age range for people to start their careers in medical institutions, the majority of healthcare workers are between the ages 26–33 and 34–41. Regarding the findings at Muvonde and Driefontein Sanatorium hospitals most of the healthcare personnel who took part during the study are in the active group. This concurs with a study conducted by [ 41 ] from healthcare facilities in Oriental Mindoro, which shows that the majority of hospital employees who took part in the study were between the ages of 26 and 33 which is the active group.

Most of the healthcare workers who participated as questionnaire respondents at Driefontein Sanatorium hospital and Muvonde hospital are married. Similar findings about married status were found at Muvonde hospital and Driefontein Sanatorium hospital. This could be due to the fact that hospital’ staff members prioritise marriage over pursuing their careers or because marriage is culturally valued in the communities where the hospitals are located. However, there is a connection between married status and the risk levels that impact healthcare workers. For instance, married healthcare workers have additional duties in addition to their jobs, which increases their exposure to risk. Healthcare workers’ responsibilities towards their families have a significant impact on how well they function and perform at work.

The majority of healthcare personnel who participated in the study at Muvonde and Driefontein Sanatorium hospitals indicated that their highest level of education was a diploma, however, some of them held certificates. This is mainly because the majority of medical training institutions in Zimbabwe provide hospital workers with diplomas and certificates rather than degrees. The inquiry concerning the level of education for healthcare workers was raised on the grounds that it influences how they might interpret potential risks at their work environment and how to alleviate them. Higher educated medical practitioners are more aware of the risks related to their work and they can prepare more effectively for occupational risks before they occur [ 42 ]. This suggests that there is an association between the level of education of healthcare practitioners and safety attitudes and safety practices. The results obtained at Muvonde hospital and Driefontein Sanatorium hospital demonstrated that most of the healthcare workers indicated 5–10 years range as their work experience. However, very few indicated 16 years or above as their work experience range. This proposes that turnover rates at Driefontein Sanatorium hospital and Muvonde hospital are high and this leads to the availability of less experienced and youthful workforce. Asking healthcare workers their work experience was vital because experienced hospital workers can be better equipped with better methods for managing risks when performing their tasks. According to [ 12 ] an association exists between work experience and effectiveness of safety measures used to manage work-related risks.

According to the results obtained at Driefontein Sanatorium hospital and Muvonde hospital ergonomic, chemical, psychosocial, biological and physical risks were identified. Regarding risks identified by healthcare workers at Muvonde hospital, the majority indicated ergonomic risks. Duties performed by healthcare workers involve manual tasks, pushing, transferring and lifting patients as well as repetitive tasks which expose them to ergonomic risks. Most (30.9%) of the healthcare workers at Driefontein Sanatorium hospital reported biological risks because the hospital deals mainly with contagious diseases such as tuberculosis. Some of the healthcare workers at both Muvonde and Driefontein Sanatorium hospital reported chemical risks. In hospitals healthcare employees are exposed to various types of chemicals such as disinfection chemicals used for cleaning and disinfecting equipment and facilities. According to [ 16 ] workers who perform their tasks in healthcare facilities are affected with various types of work-related risks such as psychosocial, physical, biological, ergonomic and chemical risks.

Based on the distribution of ergonomic risks at Driefontein Sanatorium hospital and Muvonde hospital, standing for a long time was reported by most of the questionnaire respondents. Duties performed by healthcare professionals require them to stand for long periods of time. Healthcare workers stand for a long time when providing services to patients. In hospitals workers usually stand in queues and move around the hospital on their feet helping patients on wheelchairs or stretchers to get their services. Results showing the distribution of physical risks at Muvonde hospital and Driefontein Sanatorium hospital indicated that sharp objects were specified by most of the healthcare workers who participated during the study. In healthcare facilities there is high use of sharp objects such as needles, razorblades, scalps and scissors however, if safety precautions are not followed when using them they expose workers to injuries, cuts and pricks. The findings of the study coincide with the findings of [ 4 ] which indicates that healthcare workers are exposed to sharp injuries as a result of continuous use of sharp objects in hospitals. Regarding distribution of biological risks at Driefontein Sanatorium hospital and Muvonde hospital, healthcare workers stated contact with wounds, viral infections, vomitus, urine of patients, breathing contaminated air and blood spillage. Blood spillage was reported as the major biological risk by the majority of healthcare employees. Healthcare workers are mainly exposed to blood spillages when carrying out surgeries and procedures. However, blood carries various types of contagious agents for example, hepatitis B, hepatitis C, HIV/AIDS and some blood-borne pathogens which can be transferred from one person to the other through contact with blood which is contaminated.

Regarding the distribution of chemical risks mercury, latex gloves, anaesthetic gases, sanitizers and cleaning detergents were reported by questionnaire respondents at Muvonde hospital and Driefontein Sanatorium rural hospitals. Majority of the healthcare workers who participated during the study stated sanitizers. This occurs because sanitizers are mostly used by healthcare workers after procedures to sanitize their hands in order to prevent cross infection. Sanitizer contains alcohol that kills bacteria; however, it exposes healthcare employees to skin irritation [ 17 , 31 ]. Findings of the study on distribution of psychosocial risks at Driefontein Sanatorium hospital and Muvonde hospital indicated fatigue, over work, abuse and dealing with severely ill patients. Nevertheless, most of the healthcare employees specified dealing with very ill patients. Caring severely ill patients is highly demanding which increases the rate of stress affecting workers in hospitals.

Results indicated that risks identified at Muvonde hospital and Driefontein Sanatorium hospital are caused by various aspects notably, use of personal protective equipment/cloth for a long time, department the worker is allocated at the hospital, poor management, negative safety behaviour, poor training, gender, age, inadequate equipment, shortage of labour and pressure due to high workload. Regarding the mentioned causes of risks at Muvonde and Driefontein Sanatorium hospitals, the majority of the healthcare workers specified pressure due to high workload. The increasing number of patients can prompt long working hours and increase workload to healthcare workers. As a result of this, healthcare professionals may experience physical and psychosocial side effects, including exhaustion, burnout and illnesses linked to stress caused by overwork. This implies that safety in hospitals is compromised by strain brought by heavy workload, putting healthcare personnel at risk.

Based on the results obtained at Muvonde and Driefontein Sanatorium hospital pertaining to ergonomic risks, respondents indicated muscular strain, shoulder discomfort, neck pain and back injuries. However, most of the healthcare workers reported back injuries. These findings imply that back injuries are a serious problem in both hospitals as a result medical practitioners need to pay attention to them. Because of the nature of their jobs, healthcare personnel frequently suffer from back injuries. Regular lifting, moving and transferring of patients puts a lot of strain on the backs of healthcare professionals. Furthermore, working in awkward positions is a common requirement for healthcare professionals, which can potentially lead to back injuries. Back injuries can also result from pushing large items like wheelchairs and hospital beds. Results of the study also indicate that Covid-19, HIV/AIDS and tuberculosis were indicated as effects of biological risks. At Muvonde hospital and Driefontein Sanatorium hospital influenza, cuts/pricks and needle stick injuries were specified as effects of physical risks. However, with the two rural hospitals the majority of the study participants stated needle stick injuries. The explanations behind such a high frequency of needle stick injuries at Driefontein Sanatorium hospital and Muvonde hospital could be due to poor training of healthcare workers on how to handle needle sticks with care. Additionally, in hospitals there is high use of needle sticks due to their piercing nature.

Healthcare professionals at Muvonde and Driefontein Sanatorium hospital have reported birth defects, skin irritation, allergies, asthma and pulmonary irritation as effects of chemical risks. Similar to Driefontein Sanatorium hospital, where the majority of healthcare workers (48.5%) reported skin irritation, the majority of hospital employees at Muvonde hospital (39.4%) expressed experiencing skin irritation. Healthcare personnel are susceptible to skin irritation due to their exposure to a variety of chemicals used in hospitals, including cleaning agents/products, disinfectants and sterilising solutions. When chemicals come into contact with the skin or when they are inhaled they result in allergic reactions [ 33 ]. Stress, exhaustion, anxiety, sleeplessness, chronic fatigue and blood pressure are among the psychosocial dangers that healthcare staff at Muvonde hospital and Driefontein Sanatorium hospital encounter. The study’s results support those of [ 3 , 21 ] that psychosocial hazards such as anxiety, stress, blood pressure and exhaustion can have an impact on healthcare professionals in Zimbabwe. The majority of healthcare professionals within the two rural hospitals specified that psychosocial risks cause them to experience stress. This could be caused by a number of factors, including stress inducing variables including workload, interpersonal issues and job uncertainty. Psychosocial risk has been linked to fatigue, which may be brought on by extended workdays and unfavourable working conditions.

Findings of the study indicated that work safety measures applied at Muvonde hospital and Driefontein Sanatorium hospital include safety inspection, safety training, monitoring worker’s safety behaviour, proper disposal of waste, provision of personal protective equipment and other measures such as screening workers for hepatitis B/C and tuberculosis. The study found that most healthcare employees at Muvonde and Driefontein Sanatorium hospitals stated use of PPE/C. This suggests that the two hospitals prioritise the use of PPE/C as a way for controlling occupational risks. In sub-Saharan Africa, hospitals prioritise the use of PPE/C and during the outbreak of Covid-19 pandemic they imported PPE/C to cover scarcity in healthcare facilities [ 32 ]. Healthcare professionals at Muvonde hospital and Driefontein Sanatorium hospital identified safety inspection as a risk management strategy. This is due to the fact that safety inspections are a useful tool for spotting possible risks and addressing them before they endanger patients, employees as well as visitors. Safety inspections assist in ensuring that appropriate infection control procedures are implemented.

Safety workshops and training have been recognised by healthcare professionals at Muvonde and Driefontein Sanatorium hospitals as crucial steps in reducing workplace risks. This is because healthcare professionals are subjected to a wide range of risks therefore, safety workshops and training give them the knowledge and abilities they need to recognise possible hazards, evaluate risks and put control measures in place. Healthcare personnel at Muvonde hospital and Driefontein Sanatorium hospital indicated that one institutional risk management strategy was the appropriate disposal of waste. Healthcare professionals may bring up waste disposal as a safety precaution because hospitals have stricter policies and procedures related to waste in place. Healthcare professionals stated appropriate waste management as a risk-reduction strategy because inappropriate hospital waste disposal can spread infectious diseases and seriously jeopardise public health. Proper waste storage of medical waste protect healthcare workers from risks associated with improper waste disposal [ 43 ].

Results of the study shows that most of the healthcare workers at Driefontein Sanatorium hospital and Muvonde hospital agree that there are safety policies within the hospitals. This indicates a high level of confidence in the hospital’s safety protocols and procedures. So the hospital’s commitment to ensuring the safety of everyone within its premises is commendable and should serve as an example to other healthcare facilities. The positive response from the questionnaire respondents could be due to the hospital’s strict adherence to regulatory requirements. This indicates that healthcare workers recognize the significance of having well-defined policies in place to ensure their safety while performing their duties. As a result the policies can create a culture of safety within the workplace, where employees are aware of the risks associated with their job and take proactive measures to mitigate them. According to [ 25 ] proper safety policies improves safety communication within the hospital departments and encourages hospital workers to protect patients and safeguard their own well-being.

The outcomes at Muvonde hospital and Driefontein Sanatorium hospital are consistent with each other since, when it came to work safety measures, most healthcare workers at both facilities rated them as good, very good, excellent with a minority indicating poor. Many factors may have contributed to the majority of respondents’ ratings of the institutional safety measures as good, very good and excellent. For example, hospitals may have spent money on staff training programmes to make sure they are aware of and adhere to appropriate safety procedures. Furthermore, hospitals might have conducted routine audits to find possible risks and hazards at work and take appropriate measures to address them. The small percentage of respondents who gave work safety analysis a low percentage rating might be the result of other staff members’ ignorance. Lastly, there might not be enough accountability or enforcement systems in place to guarantee that every employee follows the right safety procedures. This suggests that in order to guarantee that all factors are sufficiently addressed, there is space for improvement in hospital risk management procedures.

The results of the Chi-square test show a correlation between rating the effectiveness of work safety measures and work experience. This means that a health worker’s work experience affects how effective the measures used to manage risks are rated. This might be a result of a number of issues, including improved procedure knowledge, increased confidence in hospital employees’ abilities to recognise and reduce risks and increased awareness of the significance of workplace safety. Furthermore, workers with greater work experience might have witnessed more accidents or incidents at work, which might have improved their awareness of safety precautions that reduce risks. The results are in line with [ 16 ] who show that worker’s experience has a significant impact on how effective safety measures are rated for managing losses.

Conclusion and recommendations

The study indicated healthcare workers in hospitals of Chirumanzu district are affected with work-related risks as a result of inadequate equipment, poor training, negative safety behaviour, poor management and pressure due to high workload. As a result of this, the study has pinpointed areas where these hospitals’ work safety analysis performance needs to improve, highlighting the necessity of through risk assessments, the use of safety procedures and employee training to reduce possible dangers. Furthermore, the study has emphasised how important it is to promote a safety and accountability oriented culture in these healthcare environments. The research’s conclusions allow the formulation of the following recommendations to improve the effectiveness of work safety analysis in Chirumanzu District’s rural hospitals: To identify possible risks and weaknesses in the workplace, hospital management must perform comprehensive risk assessments. Assessment should include the tools and physical infrastructure as well as human factors. Additionally, healthcare facilities should develop and implement strict safety policies and procedures to manage hazards that have been identified. This covers procedures for managing hazardous items, preventing infections and responding to emergencies. Moreover, staff training and education should be improved. Regular training on work safety methods and procedures should be provided to healthcare staff. This will enable them to identify any hazards and take appropriate action. Hospital administration should place a high priority on fostering a culture of safety among employees by fostering open dialogue about safety issues and encouraging responsibility at all levels. In hospitals work safety efforts should receive sufficient funding, which should go towards purchasing safety gear, improving infrastructure and continuing training courses.

Data availability

The data generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Shabani, T., Jerie, S. & Shabani, T. Assessment of work safety analysis performance among rural hospitals of Chirumanzu district of midlands province, Zimbabwe. BMC Health Serv Res 24 , 938 (2024). https://doi.org/10.1186/s12913-024-11425-x

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Received : 26 February 2024

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DOI : https://doi.org/10.1186/s12913-024-11425-x

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