how to make the research design

• How it works

How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

Need help with a thesis chapter?

• Hire an expert from ResearchProspect today!
• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

Not sure how to approach a company for your primary research study? Don’t worry. Here we have some tips for you to successfully gather primary study.

Struggling to find relevant and up-to-date topics for your dissertation? Here is all you need to know if unsure about how to choose dissertation topic.

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation 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, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

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

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 12 August 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

• Privacy Policy

Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Tables in Research Paper – Types, Creating Guide...

Research Report – Example, Writing Guide and...

References in Research – Types, Examples and...

What is a Hypothesis – Types, Examples and...

Figures in Research Paper – Examples and Guide

Dissertation – Format, Example and Template

Leave a comment x.

Save my name, email, and website in this browser for the next time I comment.

Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

Need a helping hand?

Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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 ...

11 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

• << Previous: Purpose of Guide
• Next: Design Flaws to Avoid >>
• Last Updated: Aug 13, 2024 12:57 PM
• URL: https://libguides.usc.edu/writingguide

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Starting the research process

A Beginner's Guide to Starting the Research Process

When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.

The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a  problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.

This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.

Table of contents

Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.

First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .

Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.

Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.

>>Read more about narrowing down a research topic

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .

The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.

Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.

To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.

>>Read more about defining a research problem

Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.

A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.

In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .

>>See research question examples

The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.

There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?

You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.

>>Read more about creating a research design

Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.

As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.

You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.

>>Read more about writing a research proposal

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Is this article helpful?

Other students also liked.

• Writing Strong Research Questions | Criteria & Examples

What Is a Research Design | Types, Guide & Examples

• How to Write a Research Proposal | Examples & Templates

More interesting articles

• 10 Research Question Examples to Guide Your Research Project
• How to Choose a Dissertation Topic | 8 Steps to Follow
• How to Define a Research Problem | Ideas & Examples
• How to Write a Problem Statement | Guide & Examples
• Relevance of Your Dissertation Topic | Criteria & Tips
• Research Objectives | Definition & Examples
• What Is a Fishbone Diagram? | Templates & Examples
• What Is Root Cause Analysis? | Definition & Examples

What is your plagiarism score?

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

QuestionPro offers a comprehensive solution for researchers looking to conduct research. With its user-friendly interface, robust data collection and analysis tools, and the ability to integrate results from multiple sources, QuestionPro provides a versatile platform for designing and executing research projects.

Our robust suite of research tools provides you with all you need to derive research results. Our online survey platform includes custom point-and-click logic and advanced question types. Uncover the insights that matter the most.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Jotform vs SurveyMonkey: Which Is Best in 2024

Aug 15, 2024

360 Degree Feedback Spider Chart is Back!

Aug 14, 2024

Jotform vs Wufoo: Comparison of Features and Prices

Aug 13, 2024

Product or Service: Which is More Important? — Tuesday CX Thoughts

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Crit Care Med
• v.23(Suppl 4); 2019 Dec

Understanding Research Study Designs

Priya ranganathan.

Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

In this article, we will look at the important features of various types of research study designs used commonly in biomedical research.

How to cite this article

Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23(Suppl 4):S305–S307.

We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized.

TERMS USED IN RESEARCH DESIGNS

Exposure vs outcome.

Exposure refers to any factor that may be associated with the outcome of interest. It is also called the predictor variable or independent variable or risk factor. Outcome refers to the variable that is studied to assess the impact of the exposure on the population. It is also known as the predicted variable or the dependent variable. For example, in a study looking at nerve damage after organophosphate (OPC) poisoning, the exposure would be OPC and the outcome would be nerve damage.

Longitudinal vs Transversal Studies

In longitudinal studies, participants are followed over time to determine the association between exposure and outcome (or outcome and exposure). On the other hand, in transversal studies, observations about exposure and outcome are made at a single point in time.

Forward vs Backward Directed Studies

In forward-directed studies, the direction of enquiry moves from exposure to outcome. In backward-directed studies, the line of enquiry starts with outcome and then determines exposure.

Prospective vs Retrospective Studies

In prospective studies, the outcome has not occurred at the time of initiation of the study. The researcher determines exposure and follows participants into the future to assess outcomes. In retrospective studies, the outcome of interest has already occurred when the study commences.

CLASSIFICATION OF STUDY DESIGNS

Broadly, study designs can be classified as descriptive or analytical (inferential) studies.

Descriptive Studies

Descriptive studies describe the characteristics of interest in the study population (also referred to as sample, to differentiate it from the entire population in the universe). These studies do not have a comparison group. The simplest type of descriptive study is the case report. In a case report, the researcher describes his/her experience with symptoms, signs, diagnosis, or treatment of a patient. Sometimes, a group of patients having a similar experience may be grouped to form a case series.

Case reports and case series form the lowest level of evidence in biomedical research and, as such, are considered hypothesis-generating studies. However, they are easy to write and may be a good starting point for the budding researcher. The recognition of some important associations in the field of medicine—such as that of thalidomide with phocomelia and Kaposi's sarcoma with HIV infection—resulted from case reports and case series. The reader can look up several published case reports and case series related to complications after OPC poisoning. 1 , 2

Analytical (Inferential) Studies

Analytical or inferential studies try to prove a hypothesis and establish an association between an exposure and an outcome. These studies usually have a comparator group. Analytical studies are further classified as observational or interventional studies.

In observational studies, there is no intervention by the researcher. The researcher merely observes outcomes in different groups of participants who, for natural reasons, have or have not been exposed to a particular risk factor. Examples of observational studies include cross-sectional, case–control, and cohort studies.

Cross-sectional Studies

These are transversal studies where data are collected from the study population at a single point in time. Exposure and outcome are determined simultaneously. Cross-sectional studies are easy to conduct, involve no follow-up, and need limited resources. They offer useful information on prevalence of health conditions and possible associations between risk factors and outcomes. However, there are two major limitations of cross-sectional studies. First, it may not be possible to establish a clear cause–benefit relationship. For example, in a study of association between colon cancer and dietary fiber intake, it may be difficult to establish whether the low fiber intake preceded the symptoms of colon cancer or whether the symptoms of colon cancer resulted in a change in dietary fiber intake. Another important limitation of cross-sectional studies is survival bias. For example, in a study looking at alcohol intake vs mortality due to chronic liver disease, among the participants with the highest alcohol intake, several may have died of liver disease; this will not be picked up by the study and will give biased results. An example of a cross-sectional study is a survey on nurses’ knowledge and practices of initial management of acute poisoning. 3

Case–control Studies

Case–control studies are backward-directed studies. Here, the direction of enquiry begins with the outcome and then proceeds to exposure. Case–control studies are always retrospective, i.e., the outcome of interest has occurred when the study begins. The researcher identifies participants who have developed the outcome of interest (cases) and chooses matching participants who do not have the outcome (controls). Matching is done based on factors that are likely to influence the exposure or outcome (e.g., age, gender, socioeconomic status). The researcher then proceeds to determine exposure in cases and controls. If cases have a higher incidence of exposure than controls, it suggests an association between exposure and outcome. Case–control studies are relatively quick to conduct, need limited resources, and are useful when the outcome is rare. They also allow the researcher to study multiple exposures for a particular outcome. However, they have several limitations. First, matching of cases with controls may not be easy since many unknown confounders may affect exposure and outcome. Second, there may be biased in the way the history of exposure is determined in cases vs controls; one way to overcome this is to have a blinded assessor determining the exposure using a standard technique (e.g., a standardized questionnaire). However, despite this, it has been shown that cases are far more likely than controls to recall history of exposure—the “recall bias.” For example, mothers of babies born with congenital anomalies may provide a more detailed history of drugs ingested during their pregnancy than those with normal babies. Also, since case-control studies do not begin with a population at risk, it is not possible to determine the true risk of outcome. Instead, one can only calculate the odds of association between exposure and outcome.

Kendrick and colleagues designed a case–control study to look at the association between domestic poison prevention practices and medically attended poisoning in children. They identified children presenting with unintentional poisoning at home (cases with the outcome), matched them with community participants (controls without the outcome), and then elicited data from parents and caregivers on home safety practices (exposure). 4

Cohort Studies

Cohort studies resemble clinical trials except that the exposure is naturally determined instead of being decided by the investigator. Here, the direction of enquiry begins with the exposure and then proceeds to outcome. The researcher begins with a group of individuals who are free of outcome at baseline; of these, some have the exposure (study cohort) while others do not (control group). The groups are followed up over a period of time to determine occurrence of outcome. Cohort studies may be prospective (involving a period of follow-up after the start of the study) or retrospective (e.g., using medical records or registry data). Cohort studies are considered the strongest among the observational study designs. They provide proof of temporal relationship (exposure occurred before outcome), allow determination of risk, and permit multiple outcomes to be studied for a single exposure. However, they are expensive to conduct and time-consuming, there may be several losses to follow-up, and they are not suitable for studying rare outcomes. Also, there may be unknown confounders other than the exposure affecting the occurrence of the outcome.

Jayasinghe conducted a cohort study to look at the effect of acute organophosphorus poisoning on nerve function. They recruited 70 patients with OPC poisoning (exposed group) and 70 matched controls without history of pesticide exposure (unexposed controls). Participants were followed up or 6 weeks for neurophysiological assessments to determine nerve damage (outcome). Hung carried out a retrospective cohort study using a nationwide research database to look at the long-term effects of OPC poisoning on cardiovascular disease. From the database, he identified an OPC-exposed cohort and an unexposed control cohort (matched for gender and age) from several years back and then examined later records to look at the development of cardiovascular diseases in both groups. 5

Interventional Studies

In interventional studies (also known as experimental studies or clinical trials), the researcher deliberately allots participants to receive one of several interventions; of these, some may be experimental while others may be controls (either standard of care or placebo). Allotment of participants to a particular treatment arm is carried out through the process of randomization, which ensures that every participant has a similar chance of being in any of the arms, eliminating bias in selection. There are several other aspects crucial to the validity of the results of a clinical trial such as allocation concealment, blinding, choice of control, and statistical analysis plan. These will be discussed in a separate article.

The randomized controlled clinical trial is considered the gold standard for evaluating the efficacy of a treatment. Randomization leads to equal distribution of known and unknown confounders between treatment arms; therefore, we can be reasonably certain that any difference in outcome is a treatment effect and not due to other factors. The temporal sequence of cause and effect is established. It is possible to determine risk of the outcome in each treatment arm accurately. However, randomized controlled trials have their limitations and may not be possible in every situation. For example, it is unethical to randomize participants to an intervention that is likely to cause harm—e.g., smoking. In such cases, well-designed observational studies are the only option. Also, these trials are expensive to conduct and resource-intensive.

In a randomized controlled trial, Li et al. randomly allocated patients of paraquat poisoning to receive either conventional therapy (control group) or continuous veno-venous hemofiltration (intervention). Patients were followed up to look for mortality or other adverse events (outcome). 6

Researchers need to understand the features of different study designs, with their advantages and limitations so that the most appropriate design can be chosen for a particular research question. The Centre for Evidence Based Medicine offers an useful tool to determine the type of research design used in a particular study. 7

Source of support: Nil

Conflict of interest: None

• University Libraries
• Research Guides
• Topic Guides
• Research Methods Guide
• Research Design & Method

Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

• << Previous: Introduction
• Next: Survey Research >>
• Last Updated: Aug 21, 2023 10:42 AM

How to... Design a research study

The design of a piece of research refers to the practical way in which the research was conducted according to a systematic attempt to generate evidence to answer the research question. The term "research methodology" is often used to mean something similar, however different writers use both terms in slightly different ways: some writers, for example, use the term "methodology" to describe the tools used for data collection, which others (more properly) refer to as methods.

On this page

What is research design, sampling techniques, quantitative approaches to research design, qualitative approaches to research design, planning your research design.

The following are some definitions of research design by researchers:

Design is the deliberately planned 'arrangement of conditions for analysis and collection of data in a manner that aims to combine relevance to the research purpose with economy of procedure'.

Selltiz C.S., Wrightsman L.S. and Cook S.W. 1981  Research Methods in Social Relations, Holt, Rinehart & Winston, London, quoted in Jankowicz, A.D.,  Business Research Methods , Thomson Learning, p.190.)

The idea behind a design is that different kinds of issues logically demand different kinds of data-gathering arrangement so that the data will be:

• relevant to your thesis or the argument you wish to present;
• an adequate test of your thesis (i.e. unbiased and reliable);
• accurate in establishing causality, in situations where you wish to go beyond description to provide explanations for whatever is happening around you;
• capable of providing findings that can be generalised to situations other than those of your immediate organisation.

(Jankowicz, A.D.,  Business Research Methods  , Thomson Learning, p. 190)

The design of the research involves consideration of the best method of collecting data to provide a relevant and accurate test of your thesis, one that can establish causality if required (see  What type of study are you undertaking? ), and one that will enable you to generalise your findings.

Design of the research should take account of the following factors, which are briefly discussed below with links to subsequent pages or other parts of the site where there is fuller information.

What is your theoretical and epistemological perspective?

Although management research is much concerned with observation of humans and their behaviour, to a certain extent the epistemological framework derives from that of science. Positivism assumes the independent existence of measurable facts in the social world, and researchers who assume this perspective will want to have a fairly exact system of measurement. On the other hand, interpretivism assumes that humans interpret events and researchers employing this method will adopt a more subjective approach.

What type of study are you undertaking?

Are you conducting an exploratory study, obtaining an initial grasp of a phenomenon, a descriptive study, providing a profile of a topic or institution:

Karin Klenke provides an exploratory study of issues of gender in management decisions in  Gender influences in decision-making processes in top management teams  ( Management Decision , Volume 41 Number 10)

Damien McLoughlin provides a descriptive study of action learning as a case study in  There can be no learning without action and no action without learning  in ( European Journal of Marketing , Volume 38 Number 3/4)

Or it can be explanatory, examining the causal relationship between variables: this can include the testing of hypotheses or examination of causes:

Martin  et al.  examined ad zipping and repetition in  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  ( Marketing Intelligence & Planning , Volume 20 Number 1) with a number of hypotheses e.g. that people are more likely to remember an ad that they have seen repeatedly.

What is your research question?

The most important issue here is that the design you use should be appropriate to your initial question. Implicit within your question will be issues of size, breadth, relationship between variables, how easy is it to measure variables etc.

The two different questions below call for very different types of design:

The example  Dimensions of library anxiety and social interdependence: implications for library services  (Jiao and Onwuegbuzie,  Library Review , Volume 51 Number 2) looks at attitudes and the relationship between variables, and uses very precise measurement instruments in the form of two questionnaires, with 43 and 22 items respectively.

In the example  Equity in Corporate Co-branding  (Judy Motion  et al. ,  European Journal of Marketing , Volume 37 Number 7),  the RQs posit a need to describe rather than to link variables, and the methodology used is one of discourse theory, which involves looking at material within the context of its use by the company.

What sample size will you base your data on?

The sample is the source of your data, and it is important to decide how you are going to select it.

See  Sampling techniques .

What research methods will you use and why?

We referred above to the distinction between methods and methodology. There are two main approaches to methodology – qualitative and quantitative.

For more detail on each of the approaches,  Quantitative approaches to design  and  Qualitative approaches to design  later in this feature.

Note, you do not have to stick to one methodology (although some writers recommend that you do). Combining methodologies is a matter of seeing which part of the design of your research is better suited to which methodology.

How will you triangulate your research?

Triangulation refers to the process of ensuring that any defects in a particular methodology are compensated by use of another at appropriate points in the design. For example, if you carry out a quantitative survey and need more in depth information about particular aspects of the survey you may decide to use in-depth interviews, a qualitative method.

Here are a couple of useful articles to read which cover the issue of triangulation:

• Combining quantitative and qualitative methodologies in logistics research  by John Mangan, Chandra Lalwani and Bernard Gardner ( International Journal of Physical Distribution & Logistics Management , Volume 34 Number 7) looks at ways of combining methodologies in a particular area of research, but much of what they say is generally applicable.
• Quantitative and qualitative research in the built environment: application of "mixed" research approach  by Dilanthi Amaratunga, David Baldry, Marjan Sarshar and Rita Newton ( Work Study , Volume 51 Number 1) looks at the relative merits of the two research approaches, and despite reference to the built environment in the title acts as a very good introduction to quantitative and qualitative methodology and their relative research literatures. The section on triangulation comes under the heading 'The mixed (or balanced) approach'. 

What steps will you take to ensure that your research is ethical?

Ethics in research is a very important issue. You should design the research in such a way that you take account of such ethical issues as:

• informed consent (have the participants had the nature of the research explained to them)?
• checking whether you have permission to transcribe conversations with a tape recorder
• always treating people with respect, consideration and concern.

How will you ensure the reliability of your research?

Reliability

This is about the replicability of your research and the accuracy of the procedures and research techniques. Will the same results be repeated if the research is repeated? Are the measurements of the research methods accurate and consistent? Could they be used in other similar contexts with equivalent results? Would the same results be achieved by another researcher using the same instruments? Is the research free from error or bias on the part of the researcher, or the participants? (E.g. do the participants say what they believe the management, or the researcher, wants? For example, in a survey done on some course material, that on a mathematical module received glowing reports – which led the researcher to wonder whether this was anything to do with the author being the Head of Department!)

How successfully has the research actually achieved what it set out to achieve? Can the results of the study be transferred to other situations? Does x really cause y, in other words is the researcher correct in maintaining a causal link between these two variables? Is the research design sufficiently rigorous, have alternative explanations been considered? Have the findings really be accurately interpreted? Have other events intervened which might impact on the study, e.g. a large scale redundancy programme? (For example, in an evaluation of the use of CDs for self study with a world-wide group of students, it was established that some groups had not had sufficient explanation from the tutors as to how to use the CD. This could have affected their rather negative views.)

Generalisability

Are the findings applicable in other research settings? Can a theory be developed that can apply to other populations? For example, can a particular study about dissatisfaction amongst lecturers in a particular university be applied generally? This is particularly applicable to research which has a relatively wide sample, as in a questionnaire, or which adopts a scientific technique, as with the experiment.

Transferability

Can the research be applied to other situations? Particularly relevant when applied to case studies.

In addition, each of the sections in this feature on quantitative and qualitative approaches to research design contain notes on how to ensure that the research is reliable.

Some basic definitions

In order to answer a particular research question, the researcher needs to investigate a particular area or group, to which the conclusions from the research will apply. The former may comprise a geographical location such as a city, an industry (for example the clothing industry), an organisation/group of organisations such as a particular firm/type of firm, a particular group of people defined by occupation (e.g. student, manager etc.), consumption of a particular product or service (e.g. users of a shopping mall, new library system etc.), gender etc. This group is termed the  research population .

The  unit of analysis  is the level at which the data is aggregated: for example, it could be a study of individuals as in a study of women managers, of dyads, as in a study of mentor/mentee relationships, of groups (as in studies of departments in an organisation), of organisations, or of industries.

Unless the research population is very small, we need to study a subset of it, which needs to be general enough to be applicable to the whole. This is known as a  sample , and the selection of components of the sample that will give a representative view of the whole is known as  sampling technique  . It is from this sample that you will collect your data.

In order to draw up a sample, you need first to identify the total number of people in the research population. This information may be available in a telephone directory, a list of company members, or a list of companies in the area. It is known as a  sampling frame .

In  Networking for female managers' career development  (Margaret Linehan,  Journal of Management Development , Volume 20 Number 10), he sampling technique is described as follows:

"A total of 50 senior female managers were selected for inclusion in this study. Two sources were used for targeting interviewees, the first was a listing of Fortune 500 top companies in England, Belgium, France and Germany, and, second, The Marketing Guide to Ireland. The 50 managers who participated in the study were representative of a broad range of industries and service sectors including: mining, software engineering, pharmaceutical manufacturing, financial services, car manufacturing, tourism, oil refining, medical and state-owned enterprises."

Sampling may be done either a  probability  or a  non-probability  basis. This is an important research design decision, and one which will depend on such factors as whether the theory behind the research is positivist or idealist, whether qualitative or quantitative methods are used etc. Note that the two methods are not mutually exclusive, and may be used for different purposes at different points in the research, say purposive sampling to find out key attitudes, followed by a more general, random approach.

Note that there is a very good section from an online textbook on sampling: see William Trochim's  Research Methods Knowledge Base .

Probability sampling

In  probability  sampling, each member of a given research population has an equal chance of being selected. It involves, literally, the selection of respondents at random from the sampling frame, having decided on the sample size. This type of sampling is more likely if the theoretical orientation of the research is  positivist , and the methodology used is likely to be  quantitative .

Probability sampling can be:

• random  – the selection is completely arbitrary, and a given number of the total population is selected completely at random.
• systematic  – every  nth element  of the population is selected. This can cause a problem if the interval of selection means that the elements share a characteristic: for example, if every fourth seat of a coach is selected it is likely that all the seats will be beside a window.
• stratified   random  – the population is divided into segments, for example, in a University, you could divide the population into academic, administrators, and academic related (related professional staff). A random number of each group is then selected. It has the advantage of allowing you to categorise your population according to particular features. A.D. Jankowicz provides useful advice (Business Research Methods,Thomson Learning, 2000, p.197).

The concept of fit in services flexibility and research: an empirical approach  (Antonio J Verdú-Jover  et al. ,  International Journal of Service Industry Management , Volume 15 Number 5) uses stratified sampling: the study concentrates on three sectors within the EU, chemicals, electronics and vehicles, with the sample being stratified within this sector.

• cluster  – a particular subgroup is chosen at random. The subgroup may be based on a particular geographical area, say you may decide to sample particular areas of the country.

Non probability sampling

Here, the population does not have an equal chance of being selected; instead, selection happens according to some factor such as:

• convenience/accidental  – being present at a particular time e.g. at lunch in the canteen. This is an easy way of getting a sample, but may not be strictly accurate, because the factor you have chosen is based on your convenience rather than on a true understanding of the characteristics of the sample.

In  "Saying is one thing; doing is another": the role of observation in marketing research  ( Qualitative Market Research: An International Journal , Volume 2 Number 1), Matthews and Boote use a two-stage sampling process, with convenience sampling followed by time sampling: see their methodology.

• "key informant technique" – i.e. people with specialist knowledge
• using people at selected points in the organisational hierarchy 
• snowball, with one person being approached and then suggesting others.

In "The benefits of the implementation of the ISO 9000 standard: empirical research in 288 Spanish companies", a sample was selected based on all certified companies in a particular area, because this was where the highest number of certified companies could be found.

• quota  – the assumption is made that there are subgroups in the population, and a quota of respondents is chosen to reflect this diversity. This subgroup should be reasonably representative of the whole, but care should be taken in drawing conclusions for the whole population. For example, a quota sample taken in New York State would not be representative of the whole of the United States.

Monitoring consumer confidence in food safety: an exploratory study , de Jonge  et al . use quota sampling using age, gender, household size and region as selection variables in a food safety survey. Read about the methodology under Materials and methods.

Non probability sampling methods are more likely to be used in qualitative research, with the greater degree of collaboration with the respondents affording the opportunity of greater detail of data gathering. The researcher is more likely to be involved in the process and be adopting an  interpretivist theoretical  stance.

Calculating the sample size

In purposive sampling, this will be determined by judgement; in other more random types of sample it is calculated as a  proportion  of the sampling frame, the key criterion being to ensure that it is representative of the whole. (E.g. 10 per cent is fine for a large population, say over 1000, but for a small population you would want a larger proportion.)

If you are using stratified sampling you may need to adjust your strata and collapse into smaller strata if you find that some of your sample sizes are too small.

The response rate

It is important to keep track of the response rate against your sample frame. If you are depending on postal questionnaires, you will need to plan into your design time to follow up the questionnaires. What is considered to be a good response rate varies according to the type of survey: if you are, say, surveying managers, then a good response would be 50 per cent; for consumer surveys, the response rate is likely to be lower, say 10 to 20 per cent.

The thing that characterises quantitative research is that it is objective. The assumption is that facts exist totally independently and the researcher is a totally  objective  observer of situations, and has no power to influence them. At such, it probably starts from a positivist or empiricist position.

The research design is based on one iteration in collection of the data: the categories are isolated prior to the study, and the design is planned out and generally not changed during the study (as it may be in qualitative research).

What is my research question? What variables am I interested in exploring?

It is usual to start your research by carrying out a  literature review , which should help you formulate a research question.

Part of the task of the above is to help you determine what  variables  you are considering. What are the key variables for your research and what is the relationship between them – are you looking to  explore  issues, to  compare  two variables or to look at  cause and effect ?

The Dutch heart health community intervention "Hartslag Limburg": evaluation design and baseline data  (Gaby Ronda  et al. ,  Health Education , Volume 103 Number 6) describes a trial of a cardiovascular prevention programme which indicated the importance of its further implementation. The key variables are the types of health related behaviours which affect a person's chance of heart disease.

The following studies compare variables:

Service failures away from home: benefits in intercultural service encounters  (Clyde A Warden  et al. ,  International Journal of Service Industry Management , Volume 14 Number 4) compares service encounters (the independent variable) inside and outside Taiwan (the dependent variable) in order to look at certain aspects of 'critical incidents' in intercultural service encounters.

The concept of fit in services flexibility and research: an empirical approach  (Antonio J Verdú-Jover  et al. ,  International Journal of Service Industry Management , Volume 15 Number 5) looks at managerial flexibility in relation to different types of business, service and manufacturing.

They can also look at cause and effect:

In  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  (Brett A.S. Martin  et al. ,  Marketing Intelligence & Planning , Volume 20 Number 1), the authors look at two variables associated with advertising, notably zipping and fast forwarding, and in their effect on a third variable, consumer behaviour - i.e. ability to remember ads. Furthermore, it looks at the interaction between the first two variables - i.e. whether they interact on one another to help increase recall.

What is the hypothesis?

It is usual with quantitative research to proceed from a particular hypothesis. The object of research would then be to test the hypothesis.

In the example quoted above,  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers , the researchers decided to explore a neglected area of the literature: the interaction between ad zipping and repetition, and came up with three hypotheses:

The influence of zipping H1 . Individuals viewing advertisements played at normal speed will exhibit higher ad recall and recognition than those who view zipped advertisements.

Ad repetition effects H2 . Individuals viewing a repeated advertisement will exhibit higher ad recall and recognition than those who see an advertisement once.

Zipping and ad repetition H3 . Individuals viewing zipped, repeated advertisements will exhibit higher ad recall and recognition than those who see a normal speed advertisement that is played once.

What are the appropriate measures to use

It is very important, when designing your research, to understand  what  you are measuring. This will call for a close examination of the issues involved: is your measure suitable to the hypothesis and research question under consideration? The type of scale you will use will dictate the statistical procedure which you can use to analyse your data, and it is important to have an understanding of the latter at the outset in order to obtain the correct level of analysis, and one that will throw the best light on your research question, and help test your hypothesis.

It is also important to understand what type of data you are trying to collect. Are you wanting to collect data that relates simply to different types of categories, for example, men and women (as in, say, differences in decision-making between men and women managers), or do you want to rank the data in some way? Choices as far as the nature of data are concerned again dictate the type of statistical analysis.

Data can be categorised as follows:

• Nominal – Representing particular categories, e.g. men or women.
• Ordinal – Ranked in some way such as order of passing a particular point in a shopping centre.
• Interval – Ranked according to the interval between the data, which remains the same. Most typical of this type of data is temperature.
• Ratio – Where it is possible to measure the difference between different types of data - for example applying a measurement.
• Scalar – This type of data has intervals between it, which are not quantifiable.

Note that some of the above categories, especially 'interval' and 'ratio' are drawn from a scientific model which assumes exact measurement of data (temperature, length etc.). In management research, you are unlikely to want to or be able to apply such a high degree of exactitude, and are more likely to be measuring less exact criteria which do not have an exact interval between them.

Here are some examples of use of data in management research. This one illustrates the use of different categories:

The concept of fit in services flexibility and research: an empirical approach  (see above) uses an approach which itemises the different aspects which the researchers wished to measure flexibility mix, performance and the form's general data. 

This one looks at categories and also at ranked data (ordinal):

In  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  (also see above), the measure involved 2 (speed of ad presentation: normal, fast-forwarded) ×\ 2 (repetition: none, one repetition) between-subjects factorial design.

The following examples look at measures on a scale, which may relate to tangible factors such as frequency, or more intangible ones which relate to attitude or opinion:

How many holidays do you take in a year?

One __  Between 2 and 5 __  Between 5 and 10 __  More than 10 __

Tick the option which most agrees with your views.

Navigating my way around the CD was:

Very easy __  Easy __  Neither easy nor hard __  Hard __  Very hard __

The later type of data are very common in management research, and are known as scalar data. A very common measure for such data is known as the Likert scale:

Strongly agree __________ Agree __________ Neither agree nor disagree __________ Disagree __________ Strongly disagree __________

How will I analyse the data?

Quantitative data are invariably analysed by some sort of statistical means, such as a t-test, a chi test, cluster analysis etc. It is very important to decide at the planning stage what your method of analysis will be: this will in turn affect your choice of measure. Both your analysis and measure should be suitable to test your hypothesis.

You need also to consider what type of package will you need to analyse your data. It may be sufficient to enter it into an Excel spreadsheet, or you may wish to use a statistical package such as SPSS or Mintab.

What are the instruments used in quantitative research?

Or, put more simply, what methods will you use to collect your data?

In scientific research, it is possible to be reasonably precise by generating experiments in laboratory conditions. Whilst the  field experiment  has a place in management research, as does  observation , the most usual instrument for producing quantitative data is the  survey , most often carried out by means of a  questionnaire .

You will find numerous examples of questionnaires and surveys in research published by Emerald, as you will in any database of management research. Questionnaires will be discussed at a later stage but here are some key issues:

• It is important to know exactly what questions you want answers to. A common failing is to realise, once you have got the questionnaire back, that you really need answers to a question which you never asked. Thus the questionnaire should be rigorously researched and the questions phrased as precisely as possible.
• You are more likely to get a response if you give people a reason to respond - commercial companies sometimes offer a prize, which may not be possible or appropriate if you are a researcher in a university, but it is usual in that case to give the reason behind your research, which gives your respondent a context. Even more motivational is the ease with which the questionnaire can be filled in.
• How many responses will I need? This concerns the eventual size of your dataset and depends upon the degree of complexity of your planned analysis, how you are treating your variables (for example, if you are wanting to show the effect of a variable, you will need a larger response size, likewise if you are showing changes in variables).

Other instruments that are used in quantitative research to generate data are experiments, historical records and documents, and observation.

Note that some authors claim that for a design to be a  true experiment , items must be randomly assigned to groups; if there is some sort of control group or multiple measures, then it may be  quasi experimental . If your survey fits neither of these descriptions, it may according to these authors be sufficient for descriptive purposes, but not if you seek to establish a causal relationship.

For more information on types of design, see William Trochim's Research Methods Knowledge Base section on  types of design .

What are the advantages and drawbacks of quantitative research?

The main advantage of quantitative research is that it is easy to determine its rigour: because of the objectivity of quantitative studies, it is easy to replicate them in another situation. For example, a well-constructed questionnaire can be used to analyse job satisfaction in two different companies; likewise, an observation studying consumer behaviour in a shopping centre can take place in two different such centres.

Quantitative methods are also good at obtaining a good deal of reliable data from a large number of sources. Their drawback is that they are heavily dependent on the reliability of the instrument: that is, in the case of the questionnaire, it is vital to ask the right questions in the right way. This in turn is dependent upon having sufficient information about a situation, which is not always possible. In addition, quantitative studies may generate a large amount of data, but the data may lack depth and fail to explain complex human processes such as attitudes to organisational change, or how how learning takes place.

For example, a quantitative study on a piece of educational software may show that on the whole people felt that they had learnt something, but may not necessarily show how they learnt, which an observation could.

For this reason, quantitative methods are often used in conjunction with qualitative methods: for example, qualitative methods of interviewing may be used as a way of finding out more about a situation in order to draw up an informed quantitative instrument; or to explore certain issues which have appeared in the quantitative study in greater depth.

Qualitative research operates from a different epistemological perspective than quantitative, which is essentially objective. It is a perspective that acknowledges the essential difference between the social world and the scientific one, recognising that people do not always observe the laws of nature, but rather comprise a whole range of feelings, observations, attitudes which are essentially subjective in nature. The theoretical framework is thus likely to be interpretivist or realist. Indeed, the researcher and the research instrument are often combined, with the former being the interviewer, or observer – as opposed to quantitative studies where the research instrument may be a survey and the subjects may never see the researcher.

In an  interview for Emerald ,  Professor Slawomir Magala , Editor of the  Journal of Organizational Change Management , has this to say about qualitative methods:

"We follow the view that the social construction of reality is personal, experienced by individuals and between individuals – in fact, the interactions which connect us are the building blocks of reality, and there is much meaning in the space between individuals."

As opposed to the statistical reliance of quantitative research, data from qualitative research is based on observation and words, and analysis is based on interpretation and pattern recognition rather than statistical analysis.

Miles and Huberman list the following as typical criteria of qualitative research:

• Intense and prolonged contact in the field.
• Designed to achieve a holistic or systemic picture.
• Perception is gained from the inside based on actors' understanding.
• Little standardised instrumentation is used.
• Most analysis is done with words.
• There are multiple interpretations available in the data.

Miles, M. and Huberman, A.M. (1994) Qualitative Data Analysis: An Expanded Sourcebook , Sage, London

To what types of research questions is qualitative research relevant?

Qualitative research is best suited to the types of questions which require exploration of data  in depth  over a not particularly large sample. For example, it would be too time consuming to ask questions such as "Please describe in detail your reaction to colour x" to a large number of people, it would be more appropriate to simply ask "Do you like colour x" and give people a "yes/no" option. By asking the former question to a smaller number of people, you would get a more detailed result.

Qualitative research is also best suited to  exploratory  and  comparative  studies; to a more limited extent, it can also be used for  "cause-effect"  type questions, providing these are fairly limited in scope.

One of the strengths of qualitative research is that it allows the researcher to gain an in-depth perspective, and to grapple with complexity and ambiguity. This is what makes it suitable to analysis of  particular  groups or situations, or unusual events.

What is the relationship of qualitative research to hypotheses?

Qualitative research is usually inductive: that is, researchers gather data, and then formulate a hypothesis which can be applied to other situations.

In fact, one of the strengths of qualitative research is that it can proceed from a relatively small understanding of a particular situation, and generate new questions during the course of data collection as opposed to needing to have all the questions set out beforehand. Indeed, it is good practice in quantitative research to go into a situation as free from preconceptions as possible.

How will you analyse the data?

There is not the same need with qualitative research to determine the measure and the method of analysis at an early stage of the research process, mainly because there are no standard ways of analysing data as there are for quantitative research: it is usual to go with whatever is appropriate for the research question. However, because qualitative data usually involves a large amount of transcription (e.g. of taped interviews, videos of focus groups etc.) it is a good idea to have a plan of how this should be done, and to allow time for the transcription process.

There are a couple of attested methods of qualitative data analysis:  content analysis , which involves looking at emerging patterns, and  grounded analysis , which involves going through a number of guided stages and which is closely linked to  grounded theory .

What are the main instruments of qualitative research?

Or put another way, what are the main methods used to collect data? These can be organised according to their methodology (note, the following is not an exhaustive list, for which you should consult a good book on qualitative research):

Ethnographic methods

As the name suggests, this methodology derives from anthropology and involves observing people as a participant within their social and cultural system. Most common methods of data collection are:

• Interviewing, which means discussions with people either on the phone, by email or in person when the purpose is to collect data which is by its nature unquantifiable and more difficult to analyse by statistical means, but which provides in-depth information. The interview can be either:  Structured , which means that the interviewer has a set number of questions.  Semi-structured , which means that the interviewer has a number of questions or a purpose, but the interview can still go off in unanticipated directions.
• Focus groups, which is where a group of people are assembled at one time to give their reaction to a product, or to discuss an issue. There is usually some sort of facilitation which involves either guided discussion or some sort of product demonstration.
• Participant observation – the researcher observes behaviour of people in the organisation, their language, actions, behaviour etc.

For some examples of participant observation, see Methods of empirical research ,  and for examples of interview technique, see  Techniques of data collection and analysis .

Historical analysis

This is literally, the analysis of historical documents of a particular company, industry etc. It is important to understand exactly what your focus is, and also which historical school or theoretical perspective you are drawing on.

Grounded theory

This is an essentially inductive approach, and is applied when the understanding of a particular phenomenen is sought. A feature is that the design of the research has several iterations: there is initial exploration followed by a theory which is then tested.

In  Grounded theory methodology and practitioner reflexivity in TQM research  ( International Journal of Quality & Reliability Management  , Volume 18 Number 2), Leonard and McAdam use grounded theory to explore TQM, on the grounds that quantitative methods "fail to give deep insights and rich data into TQM in practice within organizations", and that it is much more appropriate to listen to the individual experiences of participants. 

Action research

This is a highly participative form of research where the research is carried out in collaboration with those involved in a particular process, which is often concerned with some sort of change.

Narrative methods

This is when the researcher listens to the stories of people in the organisation and triangulates them against official documents.

Discourse theory

This methodology draws on a theory which allows language to have a meaning that is not set but is negotiated through social context.

Helen Francis in  The power of "talk" in HRM-based change  ( Personnel Review , Volume 31 Number 4) describes her use of discourse theory as follows:

"The approach to discourse analysis drew upon Fairclough's seminal work in which discourse is treated as a form of social practice and meaning is something that is essentially fluid and negotiated rather than being authored individually (Fairclough, 1992, 1995).

"For Fairclough (1992, 1995) the analysis of discursive events is three dimensional and includes simultaneously a piece of text, an instance of discursive practice, and an instance of social practice. Text refers to written and spoken language in use, while "discursive practices" allude to the processes by which texts are produced and interpreted. The social practice dimension refers to the institutional and organisational factors surrounding the discursive event and how they might shape the nature of the discursive practice.

"For the purposes of this research, the method of analysis included a description of the language text and how it was produced or interpreted amongst managers and their subordinates. Particular emphasis was placed on investigating the import of metaphors that are characteristic of HRM, and the introduction of HRM-based techniques adopted by change leaders in their attempt to privilege certain themes and issues over others."

Fairclough, N., 1992,  Discourse and Social Change , Polity Press, Cambridge.

Fairclough, N., 1995,  Critical Discourse Analysis: Papers in the Critical Study of Language , Longman, London.

Discourse theory can be applied to the written as well as the spoken word and can be used to analyse marketing literature as in the following example:

Equity in corporate co-branding: the case of Adidas and the all-blacks  by Judy Motion  et al.  ( European Journal of Marketing , Volume 37 Number 7), where discourse theory is used to analyse branding messages.

How rigorous is qualitative research?

It is often considered harder to demonstrate the rigour of qualitative research, simply because it may be harder to replicate the conditions of the study, and apply the data in other similar circumstances. The rigour may partly lie in the ability to generate a theory which can be applied in other situations, and which takes our understanding of a particular area further.

Rigour in qualitative research is greatly aided by:

• confirmability - which does not necessarily mean that someone else would adopt the same conclusion, but rather there is a clear audit trail between your data and your interpretation; and that interpretations are based on a wide range of data (for example, from several interviews rather than just one). (This is related to  triangulation , see below.)
• authenticity - are you drawing on a sufficiently wide range of rich data, do the interpretations ring true, have you considered rival interpretations, do your informants agree with your interpretation?

In  Cultural assumptions in career management: practice implications from Germany;  (Hansen and Willcox,  Career Development International , Volume 2 Number 4), the main method used is ethnographic interviews, and findings are verified by comparing data from the two samples.

Reliability is also enhanced if you can triangulate your data from a number of different sources or methods of data collection, at different times and from different participants.

Dennis Cahill, in  When to use qualitative methods: a new approach  ( Marketing Intelligence & Planning , Volume 14 Number 6), has this to say about the reliability of qualitative research:

"While there are times when qualitative techniques are inappropriate to the research goal, or appropriate only in certain portions of a research project, quantitative techniques do not have universal applicability, either. Although these techniques may be used to measure "reality" rather precisely, they often suffer from a lack of good descriptive material of the type which brings the information to life. This lack is particularly felt in corporate applications where implementation of the results is sought. Therefore, whether one has any interest in the specific research described above, if one is involved in implementation of research results – something we all should be involved in – the use of qualitative research at midpoint is a technique with which we should become familiar.

"It is at this point that some qualitative follow up – interviews or focus groups for example – can serve to flesh out the results, making it possible for people at the firm to understand and internalize those results."

Can qualitative research be used in with quantitative research?

Whereas some researchers only use either qualitative or quantitative methodologies, the two are frequently combined, as when for example qualitative methods are used exploratatively in order to obtain further information prior to developing a quantitative research instrument. In other cases, qualitative methods are used to complement quantitative methods and obtain a greater degree of descriptive richness:

In  When to use qualitative methods: a new approach , Dennis Cahill describes how qualitative methods were used after an extensive questionnaire used to carry out research for a new publication dedicated to the needs of the real estate market. The analysis for the questionnaire produced a five-segment typology (winners, authentics, heartlanders, wannabes and maintainers), which was tested by means of an EYE-TRAC test, when a selected sample was videotaped looking at a magazine of houses for sale.

Once you have established the key features of your design, you need to create an outline project plan which will include a budget and a timetable. In order to do this you need to think first about the activities of your data collection: how much data are you collecting, where etc. (See the section on  Sampling techniques .) You also need to consider your time period for data collection.

Over what time period will you collect your data?

This refers to two types of issues:

Type of study

Should the research be a 'snapshot', examining a particular phenomenon at a particular time, or should it be  longitutinal , examining an issue over a time period? If the latter, the object will be to explore changes over the period.

A longitudinal study of corporate social reporting in Singapore  (Eric W K Tsang,  Accounting, Auditing & Accountability Journal , Volume 11 Number 5) examines social reporting in that country from 1986 to 1995.

Methodology

Sometimes, you may have 'one shot' at the collection of your data - in other words, you plan your sample, your method of data collection, and then analyse the result. This is more likely to be the case if your research approach is more quantitative.

However, other types of research approach involve stages in the collection of data. For example, in  grounded theory  research, data is collected and analysed and then the process is repeated as more is discovered about the subject. Likewise in  action research , there is a cyclical process of data collection, reflection and more collection and analysis.

If you adopt an approach where you  combine quantitative and qualitative methods , then this methodology will dictate that you do a series of studies, whether qualitative followed by quantitative, or vice versa, or qualitative/quantitative/qualitative.

Grounded theory methodology and practitioner reflexivity in TQM research  (Leonard and McAdam,  International Journal of Quality & Reliability Management , Volume 18 Number 2) adopts a three-stage approach to the collection of data.

Doing the plan

The following are some of the costs which need to be considered:

• Travel to interview people.
• Postal surveys, including follow-up.
• The design and printing of the questionnaire, especially if there is use of Optical Mark Reader (OMR) and Optical Character Recognition (OCR) technology.
• Programming to "read" the above.
• Programming the data into meaningful results.
• Transcription of any tape recorded interviews.
• Cost of design of any internet survey.
• Employment of a research assistant.

Timetabling

Make a list of the key stages of your research. Does it have several phases, for example, a questionnaire, then interviews?

How long will each phase take? Take account of factors such as:

• Sourcing your sampling frame
• Determining the sample
• Approaching interview subjects
• Preparations for interviews
• Writing questionnaires
• Response time for questionnaires (include a follow-up stage)
• Analysing the responses
• Writing the report

When doing a schedule, it's tempting to make it as short as possible in the belief that you actually can achieve more in the time than you think. However, it's very important to be as accurate as possible in your scheduling.

Planning is particularly important if you are working to a specific budget and timetable as for example if you are doing a PhD, or if you are working on a funded research project, which has a specific amount of money available and probably also specific deadlines.

What is Research Design? Elements, Types, Examples

Appinio Research · 06.09.2023 · 24min read

Ever wondered what lays the foundation for successful research studies? It all starts with a well-crafted research design. In the world of inquiry, research design is the guiding compass that shapes the entire process, helping you navigate complexities and unlock the doors to meaningful insights. Whether you're embarking on your first research journey or seeking to refine your skills, understanding the art and science of research design is the key to unlocking the true potential of your investigations.

What is Research Design?

Research design is like the roadmap for your research journey. Imagine planning a cross-country trip: you wouldn't hit the road without a clear route, right? Similarly, research design provides the structure and strategy you need to navigate your way through the complexities of a study.

It's the blueprint that outlines the steps you'll take, the methods you'll use, and the goals you aim to achieve.

At its core, research design is all about making smart decisions. It's about choosing the best tools to answer your questions and gather information. Whether you're exploring the effects of a new drug, understanding the habits of a specific demographic, or investigating the behaviors of animals, a well-designed research plan sets the stage for success.

In a nutshell, research design is your guide, helping you collect data, draw conclusions, and make meaningful contributions to your field.

Why is Research Design Important in the Research Process?

Research design plays a crucial role in ensuring the success of your research study. A well-designed research plan:

• Provides structure and direction to your study.
• Helps in clearly defining research objectives and questions.
• Guides the choice of appropriate methodologies and data collection methods .
• Ensures that ethical considerations are addressed.
• Enhances the validity and reliability of your findings.

How Research Design Affects Study Outcomes

Your research design has a direct impact on the outcomes of your study. A well-crafted research plan:

• Increases the likelihood of obtaining accurate and reliable results.
• Enables you to draw valid conclusions and make meaningful interpretations.
• Enhances the credibility and generalizability of your findings.
• Guides the implementation of research procedures in a consistent and organized manner.

Key Elements of a Research Study

A well-designed research study is like a puzzle where every piece fits perfectly to reveal a clear picture. These fundamental elements ensure that your research is structured, meaningful, and capable of generating credible insights.

Clear Research Objectives

Think of research objectives as your guiding stars. They define what you aim to achieve with your study. Clear goals keep you on track, guiding your research questions, methods, and analysis.

Precise Research Questions and Hypotheses

Research questions and hypotheses are the compass that points you in the right direction. They provide focus by outlining what you want to explore and predict. Well-crafted questions and hypotheses make your study purposeful and relevant.

Appropriate Methodology Selection

Choosing a suitable methodology is like selecting the best tool for the job. Quantitative methods are your go-to for measurable data, while qualitative methods help you dive deep into complex human experiences. Mixed methods offer the best of both worlds.

Thoughtful Participant Selection

Selecting the right participants is like assembling a diverse team for a project. Your sample should represent the population you're studying. Choose appropriate sampling techniques and determine the sample size that strikes the right balance between accuracy and feasibility.

Effective Data Collection Strategies

Data collection is like gathering puzzle pieces. Choose methods that align with your research goals. Surveys, interviews, observations, and experiments are just a few of the tools at your disposal.

Reliable Research Instrument Development

Research instruments are your tools for collecting data. Whether it's a questionnaire or an interview guide, they need to be well-constructed, unbiased, and capable of capturing the information you need.

Thoughtful Research Procedure Design

Your research procedure is the timeline that ensures everything happens in the proper order. From recruiting participants to data analysis, a well-structured procedure keeps your study organized and efficient.

Rigorous Data Analysis and Interpretation

Data analysis is where you piece the puzzle together. Applying the right techniques to your data—whether quantitative or qualitative —reveals patterns, relationships, and insights that answer your research questions.

Validity and Reliability Considerations

Validity and reliability are the quality checks of your study. Validity ensures that your measurements are accurate, while reliability guarantees consistency. Addressing these ensures your findings hold true and can be trusted.

Ethical Considerations

Ethical considerations are the foundation of responsible research. Protect participants' rights, ensure their consent, and follow ethical guidelines to conduct your study with integrity.

A well-designed research study brings all these elements together harmoniously, resulting in a comprehensive, credible, and impactful exploration of your chosen research topic.

Types of Research Design

Research design comes in various flavors, each tailored to answer different types of questions and explore diverse aspects of your research topic. Let's dive into the main types of research designs to help you choose the one that aligns with your objectives.

Quantitative Research Designs

Quantitative research is all about numbers and measurements. If you're interested in uncovering patterns, relationships, and trends through numerical data, these designs are your go-to options:

• Experimental Design: This design allows you to manipulate variables to establish cause-and-effect relationships. Think of it as a controlled experiment where you change one thing to see how it impacts another.
• Survey Research: Surveys are your ticket to collecting a lot of data from a wide range of people. Structured questionnaires help gather standardized responses, making it easy to analyze patterns.
• Longitudinal Studies : Imagine tracking a group of people over years to see how they change. Longitudinal studies dive deep into understanding development, behaviors, or changes within a specific group.

Qualitative Research Designs

Qualitative research focuses on understanding the complexities of human experiences, behaviors, and contexts. If you're intrigued by narratives and in-depth insights, consider these designs:

• Case Study: Dive deep into a single subject, exploring it from every angle. It's like zooming in on a single puzzle piece to understand its intricate details.
• Ethnographic Study : If you want to immerse yourself in a culture or community, ethnography is your tool. Live among the people you're studying to grasp their worldviews and practices.
• Grounded Theory: This design is all about building theories from scratch based on the data you collect. It's like letting the information guide you toward new insights and concepts.

Mixed Methods Research

Sometimes, one approach just isn't enough. Mixed methods research combines both quantitative and qualitative methods to give you a comprehensive view of your research topic. It's like using wide-angle and macro lenses together to capture the big picture and the tiny details.

Each research design has its strengths and shines in different situations. The type you choose will depend on your research questions, goals, and the kind of insights you aim to uncover.

How to Define Research Objectives and Questions?

At the heart of every research study are clear and focused objectives, along with well-crafted research questions or hypotheses. We'll dive into the process of formulating these crucial components, ensuring that your study remains on track and purposeful.

1. Formulate Clear Research Objectives

Research objectives outline the specific goals you aim to achieve through your study. Clear and concise (SMART) objectives provide direction and purpose to your research. Here's how to formulate well-crafted research objectives:

• Be Specific: Clearly state what you intend to accomplish.
• Be Measurable: Define outcomes that can be quantified or observed.
• Be Achievable: Set realistic goals within the scope of your study.
• Be Relevant: Ensure that your objectives align with the research problem.
• Be Time-Bound: Specify a timeframe for achieving your objectives.

2. Develop Research Questions and Hypotheses

Research questions and hypotheses guide your study and direct your research efforts. They should be focused, relevant, and provide a clear framework for investigation.

• Research Questions: These are open-ended queries that help you explore a particular topic. They often start with words like "what," "how," or "why." For example: "What are the factors that influence consumer purchasing decisions?"
• Hypotheses: Hypotheses are statements that propose a specific relationship between variables. They are testable predictions about the outcomes of your study. For example: "Increasing the price of a product will result in decreased sales."

3. Ensure Alignment Between Objectives and Questions

It's essential to ensure that your research objectives and questions are well-aligned. Your research questions should directly address your objectives, helping you fulfill the purpose of your study.

By formulating clear research objectives and crafting well-structured questions or hypotheses, you'll establish a strong foundation for your research study.

How to Select Research Participants?

The participants in your research study form the foundation upon which your findings rest. Proper participant selection is crucial for obtaining relevant and reliable data.

Sampling Techniques

Sampling involves selecting a subset of individuals from a larger pool to represent the whole. The choice of sampling technique depends on the research goals and the nature of the population.

• Probability Sampling: Probability sampling ensures that each member of the population has an equal chance of being selected. Common methods include simple random sampling, stratified sampling, and cluster sampling .
• Non-Probability Sampling: Non-probability sampling methods do not guarantee equal representation. These methods include convenience sampling, purposive sampling, and snowball sampling.

Sample Size Determination

Determining the appropriate sample size  is essential to ensure the reliability of your findings. An inadequate sample size might lead to biased results, while an excessively large sample might be wasteful.

Ethical Considerations in Participant Selection

Respecting the rights and well-being of your participants is paramount. Ethical considerations include obtaining informed consent, ensuring participant confidentiality, and minimizing potential harm.

By selecting the right participants and adhering to ethical guidelines, you'll lay the groundwork for collecting meaningful and trustworthy data.

Research Data Collection Strategies

Collecting data is a fundamental step in the research process. The strategies you choose for data collection directly influence the quality and validity of your findings.

Quantitative Data Collection

Quantitative data collection involves gathering numerical information that can be analyzed statistically. Here are some common strategies:

• Surveys and Questionnaires: Surveys and questionnaires allow you to collect standardized responses from a large number of participants. They are useful for obtaining quantitative data on attitudes, preferences, and behaviors.
• Experiments: Experimental design involves manipulating variables to observe their effects. Controlled experiments provide insights into causal relationships, and random assignment helps minimize bias.
• Observations and Secondary Data Analysis: Direct observations of subjects or behaviors can provide valuable data. Additionally, analyzing existing datasets (secondary data) can save time and resources.

Qualitative Data Collection

Qualitative data collection focuses on capturing rich, context-specific information. Here are some effective methods:

• Interviews: Interviews involve direct interaction with participants to gather in-depth insights. Types include structured, semi-structured, and unstructured interviews, each offering a different level of flexibility.
• Focus Groups : Focus groups bring together a small group of participants to discuss a specific topic. This method encourages open discussions and the exploration of diverse perspectives.
• Participant Observation: Participant observation involves immersing yourself in the research setting to understand behaviors, interactions, and dynamics. It's particularly beneficial in ethnographic studies.

Data Validity and Reliability Across Methods

Ensuring the validity and reliability of collected data is crucial for drawing accurate conclusions. Validity refers to the accuracy of measurements, while reliability is the consistency of results. Across quantitative and qualitative methods, these principles apply:

• Quantitative: Ensure survey questions are straightforward, and measures are accurate and consistent.
• Qualitative: Maintain consistency in data collection procedures, and use techniques like member checking and triangulation to enhance validity.

Enhance your data collection strategies with the power of modern research technology. Appinio offers a comprehensive platform designed to streamline both quantitative and qualitative data collection. With user-friendly survey and questionnaire tools and in-depth interview capabilities, Appinio empowers researchers to gather high-quality data effortlessly.

Elevate the validity and reliability of your research with our cutting-edge platform. Book a demo today to explore how Appinio can transform your data collection process and help you achieve more accurate research outcomes!

Book a Demo

How to Develop Research Instruments?

Research instruments, such as surveys, interview protocols, and observation guides, are tools that help you collect data from participants. Developing effective instruments requires careful planning and attention to detail.

How to Construct Survey Instruments?

Surveys are a standard method for collecting data from many participants. To construct an effective survey instrument:

• Define Your Variables: Clearly define the variables you're measuring and ensure they align with your research questions.
• Use Clear Language: Write clear and concise questions using simple language to avoid confusion.
• Avoid Bias: Avoid leading or biased questions that could influence participant responses.
• Include Validity Checks: Incorporate validation questions to ensure respondents are providing accurate information.

How to Create Interview Protocols?

Interviews offer an opportunity to gather in-depth insights directly from participants. To create effective interview protocols:

• Structure Questions: Organize questions logically and flow from general to specific topics.
• Open-Ended Questions: Include open-ended questions encouraging participants to share their thoughts and experiences.
• Probing Questions: Develop probing questions to dig deeper into participant responses and gain deeper insights.

Pre-testing and Piloting Research Instruments

Before launching your research, pre-test or pilot your instruments with a small group of participants. This helps identify issues with clarity, wording, or question order and allows you to refine the instruments for maximum effectiveness.

By investing time in constructing well-designed research instruments, you'll collect accurate and relevant data that contribute to the success of your study.

How to Design the Research Procedure?

The research procedure outlines the step-by-step plan for conducting your study. A well-designed procedure ensures consistency, reliability, and efficiency in data collection.

To design an effective research procedure:

1. Sequence Research Activities

Sequencing research activities involves arranging the order in which different tasks will be carried out. Consider the following when creating your sequence:

• Logical Flow: Ensure that activities are organized in a logical order, from participant recruitment to data analysis.
• Dependencies: Identify tasks that depend on the completion of others and plan accordingly.
• Flexibility: Allow for some flexibility to accommodate unexpected challenges or opportunities.

2. Establish a Data Collection Timeline

Creating a timeline for your research helps you stay on track and manage your resources efficiently. Consider the following when establishing your timeline:

• Breakdown of Tasks: Divide the research process into manageable tasks and allocate time for each.
• Realistic Deadlines: Set realistic deadlines that consider the complexity of each task and potential delays.
• Buffer Periods: Include buffer periods to account for unforeseen delays or revisions.

3. Ensure Consistency in Data Collection Procedures

Consistency is crucial in obtaining reliable and valid data. Establish standardized procedures for data collection:

• Training: Train researchers involved in data collection to follow consistent procedures and protocols.
• Detailed Instructions: Provide clear and detailed instructions for each data collection method.
• Monitoring: Regularly monitor data collection to ensure adherence to procedures and address any issues.

By designing a well-structured research procedure, you'll ensure that your study progresses smoothly, data is collected consistently, and timelines are met. The next step is moving on to the crucial phase of data analysis and interpretation.

Research Data Analysis and Interpretation

Data analysis is the process of transforming raw data into meaningful insights. It's where you draw conclusions and make sense of the information you collected.

Quantitative Data Analysis Techniques

Quantitative data analysis involves processing numerical data to identify patterns and relationships. Here are some common techniques:

• Descriptive Statistics : Descriptive statistics, such as mean, median, and standard deviation, summarize and describe the main features of a dataset.
• Inferential Statistics : Inferential statistics help you draw conclusions about a population based on a sample. Techniques include t-tests , ANOVA , and regression analysis.
• Regression Analysis : Regression analysis helps you understand the relationships between variables and predict outcomes. Linear and logistic regressions are widely used.

Chi-Square Calculator :

t-Test Calculator :

One-way ANOVA Calculator :

Qualitative Data Analysis Approaches

Qualitative data analysis involves interpreting non-numerical data to uncover themes and patterns. Here are some common approaches:

• Thematic Analysis : Thematic analysis involves identifying recurring themes or patterns in qualitative data. It helps you discover meaningful insights and concepts.
• Content Analysis: Content analysis is used to systematically analyze textual or visual content to identify specific patterns, themes, or trends.
• Constant Comparative Method: The constant comparative method involves comparing data points throughout the analysis to uncover patterns and relationships.

Validity and Reliability in Data Analysis

Ensuring the validity and reliability of your data analysis is essential for producing accurate findings:

• Triangulation: Use multiple data sources, methods, or analysts to validate your findings.
• Member Checking: Share your findings with participants to confirm that your interpretations align with their experiences.

By carefully analyzing and interpreting your data, you'll uncover insights that address your research questions and contribute to the overall understanding of your topic.

Validity and Reliability in Research Design

Validity and reliability are essential concepts in research design that ensure the credibility and trustworthiness of your study. In this section, we'll delve into these concepts and explore how they impact the quality of your research.

Internal Validity: Controlling for Confounding Variables

Internal validity refers to the degree to which your study accurately measures the cause-and-effect relationship you intend to study without interference from extraneous variables. To enhance internal validity:

• Control Groups : Use control groups in experimental designs to compare the effects of variables.
• Randomization: Randomly assign participants to groups to ensure unbiased distribution of characteristics.
• Eliminate Confounding Variables: Identify and control for factors that could influence your results but are not part of your research question.

External Validity: Generalizability of Findings

External validity refers to the extent to which your findings can be generalized to a broader population or real-world settings. To enhance external validity:

• Random Sampling: Use random sampling to ensure that your sample is representative of the larger population.
• Ecological Validity: Design your study to mirror real-world situations as closely as possible.
• Replication: Replicate your study with different populations or settings to validate your findings.

How to Ensure Research Reliability and Reproducibility?

Reliability refers to the consistency and stability of your measurements over time and across different conditions. To ensure research reliability:

• Consistent Procedures: Use standardized procedures for data collection and analysis.
• Inter-Rater Reliability: Have multiple researchers analyze data independently to assess agreement.
• Test-Retest Reliability: Repeat measurements on the same subjects to evaluate consistency.

Ethical Considerations in Research Design

Ethical guidelines are a fundamental aspect of research design. Respecting the rights and well-being of participants is paramount. These include:

• Informed Consent: Obtain informed consent from participants, ensuring they understand the study's purpose, procedures, and risks.
• Confidentiality: Protect participant privacy by safeguarding their personal information.
• Institutional Review Board (IRB): Obtain ethical approval from an IRB before conducting research involving human participants.
• Minimizing Harm: Ensure participants are not subjected to unnecessary physical, emotional, or psychological harm.

By addressing these validity, reliability, and ethical considerations, you'll ensure that your research study is rigorous, credible, and contributes meaningfully to the field.

As you progress, it's crucial to communicate your findings effectively. Let's explore how to do that next.

How to Report and Present Research Findings?

Effectively reporting and presenting your research findings is essential for sharing your insights with the academic community and beyond.

1. Structure the Research Report

A well-structured research report communicates your study clearly and concisely. The typical structure includes:

• Title: A clear and informative title that captures the essence of your study.
• Abstract: A brief summary of your research question, methods, findings, and conclusions.
• Introduction: Introduce the research problem, objectives, and significance of the study.
• Literature Review: Review existing research and theories relevant to your topic.
• Methodology: Describe your research design, participants, data collection, and analysis methods.
• Results: Present your findings using tables, charts, and statistical analysis .
• Discussion: Interpret your results, relate them to existing literature, and address implications.
• Conclusion: Summarize your study, restate findings, and suggest future research directions.
• References: Cite sources you've referenced throughout the report.

2. Create Visual Representations of Data

Visual representations, such as graphs, charts, and tables, help convey complex information more easily. Use appropriate visuals to illustrate trends, patterns, and relationships in your data.

3. Write Clear and Compelling Research Summaries

In addition to your full research report, consider creating concise and engaging summaries that capture the essence of your study. These summaries help share findings with a broader audience, such as policymakers or the general public.

By effectively reporting and presenting your research findings, you contribute to disseminating knowledge and ensuring that your study's insights are accessible and impactful.

In conclusion, research design is like the blueprint of your investigation. It's the plan that makes sure everything fits together just right. By choosing the proper methods, asking the right questions, and following ethical guidelines, you're setting yourself up for success. Remember, research design isn't just for the experts—it's a powerful tool anyone can use to uncover knowledge and make informed decisions. So, whether you're analyzing economic trends or trying to understand your customers' preferences, a solid research design will guide you on your path to discovery.

How to Design Your Research in Minutes?

Introducing Appinio , your go-to real-time market research platform! In the world of research design, Appinio stands out as an exciting and intuitive solution that empowers companies to gain instant consumer insights, revolutionizing the way you make data-driven decisions.

Why Appinio?

• Speedy Insights: Say goodbye to waiting days or weeks for results. With Appinio, you can transform your questions into actionable insights in just minutes, allowing you to make swift and informed decisions for your business.
• Streamlined Process: Appinio takes care of all the heavy lifting in research and technology, allowing you to focus on what truly matters—your business goals. We've seamlessly integrated real-time consumer insights into everyday decision-making, making it an essential tool for your research arsenal.
• Market Research Reinvented: Gone are the days of market research being seen as boring, intimidating, and overpriced. Appinio breaks down those barriers, presenting a platform that's exciting, intuitive, and accessible to all. We're on a mission to change the perception of research and show that it can be a dynamic asset for your business growth.

Elevate your research design journey with Appinio and experience the thrill of unlocking insights that drive your business forward—right when you need them.

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

15.08.2024 | 31min read

360-Degree Feedback: Survey, Process, Software, Examples

13.08.2024 | 30min read

What is ANOVA Test? Definition, Types, Examples

08.08.2024 | 30min read

Environmental Analysis: Definition, Steps, Tools, Examples

Educational resources and simple solutions for your research journey

What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

What are the Best Research Funding Sources

Inductive vs. Deductive Research Approach

• How It Works
• PhD thesis writing
• Master thesis writing
• Bachelor thesis writing
• Dissertation writing service
• Dissertation abstract writing
• Thesis proposal writing
• Thesis editing service
• Thesis proofreading service
• Thesis formatting service
• Coursework writing service
• Research paper writing service
• Architecture thesis writing
• Computer science thesis writing
• Engineering thesis writing
• History thesis writing
• MBA thesis writing
• Nursing dissertation writing
• Psychology dissertation writing
• Sociology thesis writing
• Statistics dissertation writing
• Buy dissertation online
• Write my dissertation
• Cheap thesis
• Cheap dissertation
• Custom dissertation
• Dissertation help
• Pay for thesis
• Pay for dissertation
• Senior thesis
• Write my thesis

How To Write A Research Design Like A Pro

The overall strategy that a researcher chooses to address all the different parts of their study in a logical and clear manner is known as a research design.

So, what is research design in research paper? A research design is a general plan explaining what one looks to do so as to answer the research question. Generally, it is a detailed outline of how research or an investigation will take place including; how data will be collected, which tools will be employed and how they will be used, and the ways through with the data will be analyzed.

It lays out the method you use to collect, measure, and analyze information. It states that you do this logically and coherently to ensure that you thoroughly address the research problem with which you are dealing. There are numerous types of research design including:

Action Study Research Design Case Study Research Design Casual Research Design Cohort Research Design Cross-Sectional Research Design Correlational Research Design Descriptive Research Design Experimental Research Design Exploratory Research Design Historical Research Design Longitudinal Research Design Observational Research Design Philosophical Research Design Qualitative Research Design Quantitative Research Design Sequential Research Design

The research paper design you choose depends on the research problem. You should analyze the problem carefully and consider it from numerous perspectives. You may consider using a mixed methods research design which is a combination of any two designs listed above. But you must choose a type of research design that is strong and will make your project progress smoothly.

Example Of A Nursing Research Design

To assess the links between professional satisfaction, job satisfaction, and contributing factors using a quantitative approach, an appropriate method is to gain use questionnaires or surveys that provide numerical data from the sample. To achieve an appropriate sample, a sampling plan should be developed. In this case, the population of concern will be identified. This will be nursing staff members, possibly across a wide range of departments to gain a better insight into the links overall. A stratified sampling method would be appropriate here to ensure that the sample is made up of sub-populations that are in line with the sub-populations of the total: the strata should include gender, number of years in nursing, department, and any other factors that could be confounding variables. This will ensure that the sample is representative of the population of interest. In a population of 1000 nurses, a confidence interval of five, and a confidence level of 99%, the sample size needed is 400. Inclusion criteria will include: nursing staff working at the hospital, ability to speak the language that the survey is administered in, and those that have given informed consent. Exclusion criteria will be visiting nursing staff, staff who are not nurses, and those that do not hold relevant nursing criteria.

Sampling Plan: Qualitative

A more appropriate methodology for qualitative approaches is to use interviews or focus groups. This means that the sample size can be much smaller, often as low as ten. In this case, the sampling plan will have the same population of concern, but a different approach to sampling can be used. It may be more appropriate to use quota, self-selection sampling here, as nursing staff need to be willing to give up some time to respond. This has drawbacks, including self-selection bias, but it would be unethical to force nursing staff to participate in the project, especially considering interviews can take one hour or more. The inclusion and exclusion criteria are as above.

How to Write a Research Design Proposal

For most research problems, you will have to make some tradeoffs. One design can be strong in some areas and weak in other areas. This is the reason many students choose to select more than one design to gather all the information accurately and effectively they need to address the problem. This is one of the first things you should know about how to write research design and methods section.

• Consider Your Practicalities and Priorities

What do we mean when we say you need to think about practicalities and priorities? Another thing to know about how to write design and methodology of the research is asking several questions before settling on one or two methodologies. You will not have the time or resources to conduct tests using several research designs, so you need to write down and answer precisely what your priorities are and the practical nature of your study.

A good place to start is at the library where you have access to other academic studies in your field. You can find similar studies and look at published samples that have been approved by experts in the field. You can also get a sense of the number of resources you have available. Pre-planning is a great way of making sure your project stays on track.

• Determine the Kind of Information You Need

The next to know about how to write a qualitative research design is figuring out the kind of information or data you need to answer the research problem. There are two places where you get this: through primary and secondary data. In your research study, you get original data through experiments, interviews, and surveys. This is information you analyze and incorporate into your research finding.

Your study will also incorporate information gathered by someone else in previous studies. This type of data is available in libraries and online databases where you can look at national statistics, official records, and publications from academic and government sources.

• Identify How You Are Going to Collect Information

Once you know the kind of information you need to gather (qualitative and quantitative) you need to decide where, when, and how you will gather it. How to write a research design requires you to describe your research methods. This means putting in detail the materials, procedures, tools, and techniques you will use and apply. You also need to point out the criteria you will use to choose your participants and sources. (For example, how many participants will you need to fill out services to get a good method to sample).

• Decide How You Are Going to Analyze the Information

Another thing you need to know about how to write a research design relates to the way you are going to analyze the information you collect. The process of analysis is the last step you need to develop your research design. Numerous computer applications will sort through information and retrieve what you need to answer the research problem (For example, Access and Excel). Identify the ones you will use and state this in the research design.

• Draft Your Research Design as You Would Other Sections

Now you can start writing the first draft. You should approach this like you would other academic assignments. Use a draft that lists all the sub-sections you need to address in the research design. Be clear and concise. The research design should not include your opinions. It must show the reader an exact description of the way you conducted your study.

• Revise Your Research Design After Some Time Away

Hindsight is one of the best things that can come from separating yourself from your assignment for a few days. We recommend students remove themselves completely from their work to get a mental break. The distance will help them rethink their writing and make changes that improve the overall quality of an assignment.

The trick is to do stay away a few days instead of just a few hours. The time away from any piece of writing will allow for more self-evaluation that is objective. Many students will find ways to remove, add, or rearrange words, phrases, sentences, and paragraphs that make their assignments stronger.

• Edit and Proofread Your Research Design for Perfection

These two activities are not interchangeable. Editing focuses on deep issues like correcting sentence constructions and word choices. A thorough editing session will improve things like clarity, readability, and tone. Proofreading focuses on details like grammar, punctuation, and misspellings. It will also look at page numbering, formatting, alignment, and visual elements.

Both activities are important stages of the writing process. A great editor will begin his or her work during the revising process. A great proofread will also begin his or her work during the editing process. While they may overlap you should always treat them as two separate tasks and designate enough time to do each without distraction.

• Have a Colleague Review Your Work for Feedback

Having a colleague or peer review your work is an important step to the academic writing process. A person or a group of people that understands your field and the high standard of researching and writing that comes with putting together a great research paper can valuable toward your success at the collegiate and graduate levels. Even if you can only show your work to one person for a few hours, his or her feedback can help you make changes to improve the overall quality of your research design.

Here are some questions you should consider before asking someone to review your work:

Do they understand the research subject and/or topic? Do they know the professor or panel that will grade your work? Have they submitted research studies in the past? Do they have great to excellent grades when it comes to research? Are they committed to providing you constructive criticism and feedback?

What to Do If You Can’t Do the Research Design

You may not have enough time to create this section, especially when you have a short deadline. On these occasions, it is a good idea to find a template for a research design paper. You can find templates online or can refer to published research papers in academic journals. The formats are standard so as long as you apply your words to a template that matches your design approach.

If you need more information or assistance learning about how to write a research design section, our customer support team can point you to more resources or put you in contact with one of our academic writing and editing experts. Each expert has earned either a bachelor’s or master’s degree and specialize in specific disciplines. You can rest assured that you will be assigned someone that knows your field inside and outside and can give you the writing research design and methodology help that you need to excel academically.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Comment * Error message

Name * Error message

Email * Error message

Save my name, email, and website in this browser for the next time I comment.

As Putin continues killing civilians, bombing kindergartens, and threatening WWIII, Ukraine fights for the world's peaceful future.

Ukraine Live Updates

• Basics of Research Process
• Methodology
• Research Design: Definition, Types, Characteristics & Study Examples
• Speech Topics
• Basics of Essay Writing
• Essay Topics
• Other Essays
• Main Academic Essays
• Research Paper Topics
• Basics of Research Paper Writing
• Miscellaneous
• Chicago/ Turabian
• Data & Statistics
• Admission Writing Tips
• Admission Advice
• Other Guides
• Student Life
• Studying Tips
• Understanding Plagiarism
• Academic Writing Tips
• Basics of Dissertation & Thesis Writing

• Essay Guides
• Research Paper Guides
• Formatting Guides
• Admission Guides
• Dissertation & Thesis Guides

Research Design: Definition, Types, Characteristics & Study Examples

Table of contents

Use our free Readability checker

A research design is the blueprint for any study. It's the plan that outlines how the research will be carried out. A study design usually includes the methods of data collection, the type of data to be gathered, and how it will be analyzed. Research designs help ensure the study is reliable, valid, and can answer the research question.

Behind every groundbreaking discovery and innovation lies a well-designed research. Whether you're investigating a new technology or exploring a social phenomenon, a solid research design is key to achieving reliable results. But what exactly does it means, and how do you create an effective one? Stay with our paper writers and find out:

• Detailed definition
• Types of research study designs
• How to write a research design
• Useful examples.

Whether you're a seasoned researcher or just getting started, understanding the core principles will help you conduct better studies and make more meaningful contributions.

What Is a Research Design: Definition

Research design is an overall study plan outlining a specific approach to investigating a research question . It covers particular methods and strategies for collecting, measuring and analyzing data. Students  are required to build a study design either as an individual task or as a separate chapter in a research paper , thesis or dissertation .

Before designing a research project, you need to consider a series aspects of your future study:

• Research aims What research objectives do you want to accomplish with your study? What approach will you take to get there? Will you use a quantitative, qualitative, or mixed methods approach?
• Type of data Will you gather new data (primary research), or rely on existing data (secondary research) to answer your research question?
• Sampling methods How will you pick participants? What criteria will you use to ensure your sample is representative of the population?
• Data collection methods What tools or instruments will you use to gather data (e.g., conducting a survey , interview, or observation)?
• Measurement  What metrics will you use to capture and quantify data?
• Data analysis  What statistical or qualitative techniques will you use to make sense of your findings?

By using a well-designed research plan, you can make sure your findings are solid and can be generalized to a larger group.

Research design example

You are going to investigate the effectiveness of a mindfulness-based intervention for reducing stress and anxiety among college students. You decide to organize an experiment to explore the impact. Participants should be randomly assigned to either an intervention group or a control group. You need to conduct pre- and post-intervention using self-report measures of stress and anxiety.

What Makes a Good Study Design? 

To design a research study that works, you need to carefully think things through. Make sure your strategy is tailored to your research topic and watch out for potential biases. Your procedures should be flexible enough to accommodate changes that may arise during the course of research. 

A good research design should be:

• Clear and methodologically sound
• Feasible and realistic
• Knowledge-driven.

By following these guidelines, you'll set yourself up for success and be able to produce reliable results.

Research Study Design Structure

A structured research design provides a clear and organized plan for carrying out a study. It helps researchers to stay on track and ensure that the study stays within the bounds of acceptable time, resources, and funding.

A typical design includes 5 main components:

• Research question(s): Central research topic(s) or issue(s).
• Sampling strategy: Method for selecting participants or subjects.
• Data collection techniques: Tools or instruments for retrieving data.
• Data analysis approaches: Techniques for interpreting and scrutinizing assembled data.
• Ethical considerations: Principles for protecting human subjects (e.g., obtaining a written consent, ensuring confidentiality guarantees).

Research Design Essential Characteristics

Creating a research design warrants a firm foundation for your exploration. The cost of making a mistake is too high. This is not something scholars can afford, especially if financial resources or a considerable amount of time is invested. Choose the wrong strategy, and you risk undermining your whole study and wasting resources. 

To avoid any unpleasant surprises, make sure your study conforms to the key characteristics. Here are some core features of research designs:

• Reliability   Reliability is stability of your measures or instruments over time. A reliable research design is one that can be reproduced in the same way and deliver consistent outcomes. It should also nurture accurate representations of actual conditions and guarantee data quality.
• Validity For a study to be valid , it must measure what it claims to measure. This means that methodological approaches should be carefully considered and aligned to the main research question(s).
• Generalizability Generalizability means that your insights can be practiced outside of the scope of a study. When making inferences, researchers must take into account determinants such as sample size, sampling technique, and context.
• Neutrality A study model should be free from personal or cognitive biases to ensure an impartial investigation of a research topic. Steer clear of highlighting any particular group or achievement.

Key Concepts in Research Design

Now let’s discuss the fundamental principles that underpin study designs in research. This will help you develop a strong framework and make sure all the puzzles fit together.

Primary concepts

Types of Approaches to Research Design

Study frameworks can fall into 2 major categories depending on the approach to compiling data you opt for. The 2 main types of study designs in research are qualitative and quantitative research. Both approaches have their unique strengths and weaknesses, and can be utilized based on the nature of information you are dealing with. 

Quantitative Research  

Quantitative study is focused on establishing empirical relationships between variables and collecting numerical data. It involves using statistics, surveys, and experiments to measure the effects of certain phenomena. This research design type looks at hard evidence and provides measurements that can be analyzed using statistical techniques. 

Qualitative Research 

Qualitative approach is used to examine the behavior, attitudes, and perceptions of individuals in a given environment. This type of study design relies on unstructured data retrieved through interviews, open-ended questions and observational methods. 

If you need your study done yesterday, leave StudyCrumb a “ write my research paper for me ” notice and have your project completed by experts.

Types of Research Designs & Examples

Choosing a research design may be tough especially for the first-timers. One of the great ways to get started is to pick the right design that will best fit your objectives. There are 4 different types of research designs you can opt for to carry out your investigation:

• Experimental
• Correlational
• Descriptive
• Diagnostic/explanatory.

For more advanced studies, you can even combine several types. Mixed-methods research may come in handy when exploring complex phenomena that cannot be adequately captured by one method alone.

Below we will go through each type and offer you examples of study designs to assist you with selection.

1. Experimental

In experimental research design , scientists manipulate one or more independent variables and control other factors in order to observe their effect on a dependent variable. This type of research design is used for experiments where the goal is to determine a causal relationship. 

Its core characteristics include:

• Randomization
• Manipulation
• Replication.

A pharmaceutical company wants to test a new drug to investigate its effectiveness in treating a specific medical condition. Researchers would randomly assign participants to either a control group (receiving a placebo) or an experimental group (receiving the new drug). They would rigorously control all variables (e.g, age, medical history) and manipulate them to get reliable results.

2. Correlational

Correlational study is used to examine the existing relationships between variables. In this type of design, you don’t need to manipulate other variables. Here, researchers just focus on observing and measuring the naturally occurring relationship.

Correlational studies encompass such features: 

• Data collection from natural settings
• No intervention by the researcher
• Observation over time.

A research team wants to examine the relationship between academic performance and extracurricular activities. They would observe students' performance in courses and measure how much time they spend engaging in extracurricular activities.

3. Descriptive 

Descriptive research design is all about describing a particular population or phenomenon without any interruption. This study design is especially helpful when we're not sure about something and want to understand it better.

Descriptive studies are characterized by such features:

• Random and convenience sampling
• Observation
• No intervention.

A psychologist wants to understand how parents' behavior affects their child's self-concept. They would observe the interaction between children and their parents in a natural setting. Gathered information will help her get an overview of this situation and recognize some patterns.

4. Diagnostic

Diagnostic or explanatory research is used to determine the cause of an existing problem or a chronic symptom. Unlike other types of design, here scientists try to understand why something is happening. 

Among essential hallmarks of explanatory studies are: 

• Testing hypotheses and theories
• Examining existing data
• Comparative analysis.

A public health specialist wants to identify the cause of an outbreak of water-borne disease in a certain area. They would inspect water samples and records to compare them with similar outbreaks in other areas. This will help to uncover reasons behind this accident.

How to Design a Research Study: Step-by-Step Process

When designing your research don't just jump into it. It's important to take the time and do things right in order to attain accurate findings. Follow these simple steps on how to design a study to get the most out of your project.

1. Determine Your Aims 

The first step in the research design process is figuring out what you want to achieve. This involves identifying your research question, goals and specific objectives you want to accomplish. Think whether you want to explore a specific issue or develop a new theory? Setting your aims from the get-go will help you stay focused and ensure that your study is driven by purpose. 

Once  you are clear with your goals, you need to decide on the main approach. Will you use qualitative or quantitative methods? Or perhaps a mixture of both?

2. Select a Type of Research Design

Choosing a suitable design requires considering multiple factors, such as your research question, data collection methods, and resources. There are various research design types, each with its own advantages and limitations. Think about the kind of data that would be most useful to address your questions. Ultimately, a well-devised strategy should help you gather accurate data to achieve your objectives.

3. Define Your Population and Sampling Methods

To design a research project, it is essential to establish your target population and parameters for selecting participants. First, identify a cohort of individuals who share common characteristics and possess relevant experiences. 

For instance, if you are researching the impact of social media on mental health, your population could be young adults aged 18-25 who use social media frequently.

With your population in mind, you can now choose an optimal sampling method. Sampling is basically the process of narrowing down your target group to only those individuals who will participate in your study. At this point, you need to decide on whether you want to randomly choose the participants (probability sampling) or set out any selection criteria (non-probability sampling). 

To examine the influence of social media on mental well-being, we will divide a whole population into smaller subgroups using stratified random sampling . Then, we will randomly pick participants from each subcategory to make sure that findings are also true for a broader group of young adults.

4. Decide on Your Data Collection Methods

When devising your study, it is also important to consider how you will retrieve data.  Depending on the type of design you are using, you may deploy diverse methods. Below you can see various data collection techniques suited for different research designs. 

Data collection methods in various studies

Additionally, if you plan on integrating existing data sources like medical records or publicly available datasets, you want to mention this as well. 

5. Arrange Your Data Collection Process

Your data collection process should also be meticulously thought out. This stage involves scheduling interviews, arranging questionnaires and preparing all the necessary tools for collecting information from participants. Detail how long your study will take and what procedures will be followed for recording and analyzing the data. 

State which variables will be studied and what measures or scales will be used when assessing each variable.

Measures and scales 

Measures and scales are tools used to quantify variables in research. A measure is any method used to collect data on a variable, while a scale is a set of items or questions used to measure a particular construct or concept. Different types of scales include nominal, ordinal, interval, or ratio , each of which has distinct properties

Operationalization 

When working with abstract information that needs to be quantified, researchers often operationalize the variable by defining it in concrete terms that can be measured or observed. This allows the abstract concept to be studied systematically and rigorously. 

Operationalization in study design example

If studying the concept of happiness, researchers might operationalize it by using a scale that measures positive affect or life satisfaction. This allows us to quantify happiness and inspect its relationship with other variables, such as income or social support.

Remember that research design should be flexible enough to adjust for any unforeseen developments. Even with rigorous preparation, you may still face unexpected challenges during your project. That’s why you need to work out contingency plans when designing research.

6. Choose Data Analysis Techniques

It’s impossible to design research without mentioning how you are going to scrutinize data. To select a proper method, take into account the type of data you are dealing with and how many variables you need to analyze. 

Qualitative data may require thematic analysis or content analysis.

Quantitative data, on the other hand, could be processed with more sophisticated statistical analysis approaches such as regression analysis, factor analysis or descriptive statistics.

Finally, don’t forget about ethical considerations. Opt for those methods that minimize harm to participants and protect their rights.

Research Design Checklist

Having a checklist in front of you will help you design your research flawlessly.

• checkbox I clearly defined my research question and its significance.
• checkbox I considered crucial factors such as the nature of my study, type of required data and available resources to choose a suitable design.
• checkbox A sample size is sufficient to provide statistically significant results.
• checkbox My data collection methods are reliable and valid.
• checkbox Analysis methods are appropriate for the type of data I will be gathering.
• checkbox My research design protects the rights and privacy of my participants.
• checkbox I created a realistic timeline for research, including deadlines for data collection, analysis, and write-up.
• checkbox I considered funding sources and potential limitations.

Bottom Line on Research Design & Study Types

Designing a research project involves making countless decisions that can affect the quality of your work. By planning out each step and selecting the best methods for data collection and analysis, you can ensure that your project is conducted professionally.

We hope this article has helped you to better understand the research design process. If you have any questions or comments, ping us in the comments section below.

Entrust us your study and we will find the best research paper writer to complete your project. Count on a unique work and fast turnaround.

FAQ About Research Study Designs

1. what is a study design.

Study design, or else called research design, is the overall plan for a project, including its purpose, methodology, data collection and analysis techniques. A good design ensures that your project is conducted in an organized and ethical manner. It also provides clear guidelines for replicating or extending a study in the future.

2. What is the purpose of a research design?

The purpose of a research design is to provide a structure and framework for your project. By outlining your methodology, data collection techniques, and analysis methods in advance, you can ensure that your project will be conducted effectively.

3. What is the importance of research designs?

Research designs are critical to the success of any research project for several reasons. Specifically, study designs grant:

• Clear direction for all stages of a study
• Validity and reliability of findings
• Roadmap for replication or further extension
• Accurate results by controlling for potential bias
• Comparison between studies by providing consistent guidelines.

By following an established plan, researchers can be sure that their projects are organized, ethical, and reliable.

4. What are the 4 types of study designs?

There are generally 4 types of study designs commonly used in research:

• Experimental studies: investigate cause-and-effect relationships by manipulating the independent variable.
• Correlational studies: examine relationships between 2 or more variables without intruding them.
• Descriptive studies: describe the characteristics of a population or phenomenon without making any inferences about cause and effect.
• Explanatory studies: intended to explain causal relationships.

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

You may also like

How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

How to write a phd research proposal.

• What are the Benefits of Generative AI for Academic Writing?
• How to Avoid Plagiarism When Using Generative AI Tools
• What is Hedging in Academic Writing?  

How to Write Your Research Paper in APA Format

The future of academia: how ai tools are changing the way we do research, you may also like, dissertation printing and binding | types & comparison , what is a dissertation preface definition and examples , how to write your research paper in apa..., how to choose a dissertation topic, how to write an academic paragraph (step-by-step guide), maintaining academic integrity with paperpal’s generative ai writing..., research funding basics: what should a grant proposal..., how to write an abstract in research papers..., how to write dissertation acknowledgements.

Title Page Setup

A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.

Student title page

The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.

Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6

Related handouts

• Student Title Page Guide (PDF, 263KB)
• Student Paper Setup Guide (PDF, 3MB)

Student papers do not include a running head unless requested by the instructor or institution.

Follow the guidelines described next to format each element of the student title page.

Professional title page

The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.

Follow the guidelines described next to format each element of the professional title page.

What is Creative Research?

What is "creative" or "artistic" research how is it defined and evaluated how is it different from other kinds of research who participates and in what ways - and how are its impacts understood across various fields of inquiry.

After more than two decades of investigation, there is no singular definition of “creative research,” no prescribed or prevailing methodology for yielding practice-based research outcomes, and no universally applied or accepted methodology for assessing such outcomes. Nor do we think there should be.

We can all agree that any type of serious, thoughtful creative production is vital

But institutions need rubrics against which to assess outcomes. So, with the help of the Faculty Research Working Group, we have developed a working definition of creative research which centers inquiry while remaining as broad as possible:

Creative research is creative production that produces new knowledge through an interrogation/disruption of form vs. creative production that refines existing knowledge through an adaptation of convention. It is often characterized by innovation, sustained collaboration and inter/trans-disciplinary or hybrid praxis, challenging conventional rubrics of evaluation and assessment within traditional academic environments.

This is where Tisch can lead

Artists are natural adapters and translators in the work of interpretation and meaning-making, so we are uniquely qualified to create NEW research paradigms along with appropriate and rigorous methods of assessment. At the same time, because of Tisch's unique position as a professional arts-training school within an R1 university, any consideration of "artistic" or "creative research" always references the rigorous standards of the traditional scholarship also produced here.

The long-term challenge is two-fold

Over the long-term, Tisch will continue to refine its evaluative processes that reward innovation, collaboration, inter/trans-disciplinary and hybrid praxis. At the same time, we must continue to incentivize faculty and student work that is visionary and transcends the obstacles of convention.

As the research nexus for Tisch, our responsibility is to support the Tisch community as it embraces these challenges and continues to educate the next generation of global arts citizens.

More From Forbes

Research universities: the new home of design education.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

Global design expert and consultant at the  Stanford Institute for Innovation in Developing Economies.

While in the past it was common for design to be taught in stand-alone art schools, art academies or technical colleges, today it is increasingly taught in research universities. Why is this the case, and how is this changing the field of design?

At the turn of the century, my own design education journey took me from classical art schools and academies to research universities. For me, at the time, these two worlds could not have been more different.

Why research universities?

Essentially, research universities are the pinnacles of higher education. As such, they aspire to create and advance new knowledge. By embracing design education, their goal is not to preserve the field of design as it is, but to advance it.

Any institution, to lead, needs to embrace the “new.” Unlike art academies, research universities are not conservatories of art and design. They are incubators for innovation. In these environments, design doesn’t sit still. Designers continuously try to branch out into new forms, integrate new technologies, embrace new knowledge and experiment with new media.

How should the focus of the field change?

Contemporary designers working in research universities should leverage their access to advanced technologies, and they should fuse them into their practice. Designers should experiment with artificial intelligence, virtual and augmented reality, biology, science and ecology. But at the same time, they should also engage with social sciences and the humanities to better express the idea of what it means to be a human in an age defined by technology.

In a research university environment that is primarily defined by science and driven by critical thinking, we need to apply creative thinking to drive breakthrough innovation. Creative thinking provides inspiration, an opportunity for radical experimentation, informal innovation and open-ended inquiry. This is not science, but this approach is complementary to science.

Will design change completely?

Both critical and creative thinking today should drive design. Design should also be human-centric, empirical and evidence-based. This, however, doesn’t mean that traditional design, which has been forged in the field of art, should no longer exist. That is not at all the point I am trying to make. This kind of design is not going anywhere. We will still need it. But we will need to continue expanding the field to stay relevant.

What has research got to do with design?

As the field of design continues to receive greater public attention, many new opportunities will continue to emerge. Contemporary designers are now expected to be socially, culturally and environmentally responsible, and they need to be held accountable for the work they do. But for designers to make well-informed design choices, their practice must be empirical and defined by research.

The main difference between “design by art” and “design by science” is that the first is an inner-directed activity, and the other is an outer-directed activity. One is driven by the desire for self-expression, and the other by the interest in helping others. These two approaches draw their knowledge from two different areas. Research, for example — especially one grounded in social sciences — helps designers to better understand the needs of the people they are trying to help. This way of working doesn’t diminish the ability of the designers to express themselves but elevates the importance of the work that they do.

What are the challenges for the field of design?

If I need to sum up in one sentence the purpose of the field of design today, then I would say that designers must challenge existing social conventions, and at the same time, aspire to improve them. Designers should fully embrace the zeitgeist of their time, but they always must look beyond what is only relevant right now.

There is, however, one problem that we must address. The field of design, as it stands, often promotes a monoculture of theory and history, and the field needs to be more diverse and inclusive. The dominant canons of design, which are typically “Western” in principle, tend to exclude worldviews that do not fit in this strict framework. In the world in which we live today, design reaches everyone. Yet, as my friend from New Zealand, the Māori designer Dr. Johnson Witehira,  points out , “Growing up the only Māori thing in my house was the people.” This is why it is important for designers to adequately respond to the needs of what are often underrepresented minority communities.

Why do we need ‘better’ design?

One of the many purposes of design today is to enhance the well-being of our society. The field of design has a strong track record of helping communities by providing better healthcare solutions, raising their standards of living and even reducing crime. Being inclusive and providing access to design for everyone is essential for creating a healthy and thriving society.

Why are research universities the right environment for design education?

Inclusive design programs can have a major impact on the local community, their sustainability and their economy. These kinds of far-reaching design programs are best suited to be developed at research universities. The reason for this is that designers need to be able to collaborate with a wide range of disciplinary experts in order to adequately address the complex challenges of our times.

This, however, is not a new idea. Design has always been an integrative discipline and designers are very capable of transcending disciplinary boundaries. The only difference is that today, designers need environments that provide them with much greater access to knowledge, technology and resources than ever before. In return, designers can bring divergent disciplines together and deliver new ways of looking at existing problems. This is necessary because there is only so much innovation that any single discipline can deliver on its own. And even more importantly, any new innovation today — technological or otherwise — must be human-centric in order to be widely embraced. This is why comprehensive research universities are increasingly becoming a new playground for design.

Forbes Agency Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?

• Editorial Standards
• Reprints & Permissions

New Clues Have Emerged About the Sudden Disappearance of an Ancient American City

Researchers are still trying to figure out why 50,000 residents suddenly abandoned this sprawling, cosmopolitan settlement.

Gear-obsessed editors choose every product we review. We may earn commission if you buy from a link. Why Trust Us?

The cultural, religious, and economic center of Mississippian culture, Cahokia was once home to as many as 20,000 people, a population that rivaled many European cities of the time. The population of “Greater Cahokia,” a collection of outlying farms and villages, seems to have peaked at around 50,000 sometime around 1100 A.D. Religious festivities, days-long festivals, and raucous sporting events filled the townsfolk’s lives.

But just 250 years later, Cahokia was abandoned, the grass on its 120 earthen mounds growing tall and untended. Researchers are still trying to figure out why. Some have proposed that Cahokia’s inhabitants used up the area’s natural resources, overhunting and deforesting the land surrounding the major city. Others have hypothesized that frequent droughts and floods doomed the city, while some theories center around the arrival of political unrest, outsiders, or disease.

Recent scholarship has refuted a few of these theories without isolating what exactly spelled out Cahokia’s demise. If anything, the mystery of the lost city has only deepened with time.

CAHOKIA WAS STRATEGICALLY LOCATED at the confluence of the Missouri, Mississippi, and Illinois Rivers in a fertile region known as the American Bottom.

For a while, Cahokia was an ordinary-sized settlement, but around 1000 A.D., agriculture—especially corn—became a food staple for Native tribes, transforming nomadic societies into more permanent settlements. It was during this time that Cahokia, with rich soil and easy access to water, underwent its “big bang,” drawing new residents from many Mississippian cultures: the Natchez, the Pensacola, the Choctaw, and others. In fact, when archeologists analyzed teeth found at the site, they discovered that immigrants from other places made up about one-third of the population.

With its population booming, Cahokia quickly became more than just a large, agrarian community, but soon transformed into a government, commercial, and religious capital as well. Topping out at about nine square miles, Cahokia soon had 120 earthen mounds inside its borders.

The city received the name Cahokia hundreds of years later after the French arrived to the area in the 17th century and were greeted by the Cahokia Nation, a tribe in the Illinois Confederation. However, scholars believe that the tribe likely did not inhabit the city during its heyday.

BECAUSE THE PEOPLE OF CAHOKIA left behind no written records—and because its inhabitants dispersed long before Europeans arrived—knowledge about what life was like in the city has come primarily from archaeological investigation .

The presence of certain interior fortifications suggests that there was a social hierarchy in Cahokia. The city’s largest mound, Monk’s Mound (named by French Trappists in the 1800s), held a large building where Cahokia’s political and spiritual leaders convened. At its base, a town center—surrounded by a perimeter wall two miles in circumference—would have been the site of religious celebrations and ceremonies.

There is evidence that those ceremonies and celebrations could get pretty rowdy. Cahokians regularly imbibed “black drink,” a concoction made from the leaves of a holly tree with a caffeine content six times that of strong coffee, and archeologists have excavated waste pits that contained as many as 2,000 deer carcasses, seemingly from one massive rager.

Everyday life was busy for most Cahokia residents. Men hunted, farmed, and cleared land for wood; women tended the home, made pottery, and wove textiles. Most homes were on the other side of the perimeter wall, single-room abodes that were connected via intricate and well-planned courtyards and pathways. In fact, Cahokians plotted out an east-west road that still serves as the current-day route to St. Louis.

The many mounds that dotted Cahokia were used as burial sites, some for mass graves . Archeologists have stumbled upon a few that contain evidence of ritualized human sacrifice. Others—with skeletons featuring bashed skulls, decapitations, and projectile wounds—indicate political violence. It’s just one of many tantalizing clues that point to the mighty city’s eventual downfall.

CAHOKIA’S POPULATION PROBABLY PEAKED around 1100 AD, according to AJ White, a molecular archeologist at California State University, Long Beach, who has studied the area by analyzing its ancient poop . White and his colleagues analyzed sediment cores taken from nearby Horseshoe Lake for fecal molecules, finding that “the eleventh century showed the highest populations, and the lowest in the fourteenth,” he says. “In fact, almost 1400 on the dot, the population bottoms out.”

So, how did a thriving ancient metropolis of some 50,000 wither away so suddenly? There are a few prevailing theories.

Pointing to evidence of Cahokia’s cultural diversity—as well as human remains that suggest political violence—some scholars argue that internal dissension could have led to the city’s demise. Alternatively, trouble could have come from outside. In any case, between 1175 and 1275 A.D., the perimeter wall was rebuilt four times, indicating some conflict with neighboring tribes.

Another theory is that Cahokia simply ran out of natural resources ; the perimeter wall alone would have required 20,000 logs. Beyond the wood needed for large, communal projects, individual residents would have required a consistent supply of wood for building, heating, and cooking. Not only would widespread deforestation have a deleterious impact on life in Cahokia, but it would also have changed the immediate ecosystem, as the denuded ground would have lost the ability to hold water, meaning more runoff and flooding in the region. While this theory has been popular for more than three decades, recent excavations have found no evidence of erosion or unstable soil during the reign of Cahokia.

Perhaps the most popular explanation for Cahokia’s abandonment is the occurrence of natural disasters like floods and droughts. Some scholars have demonstrated that Cahokia emerged during a time of reduced flooding and that its decline followed the return of large floods. Events like these, they argue, would have likely triggered crop failures, devastating the large city.

White’s scholarship supports this theory. Those sediment cores he analyzed for fecal molecules also showed evidence of periods of floods and drought, which coincided with “a big drop off” in population. “Climatic events such as droughts and floods likely played a role in the depopulation of Cahokia,” the archeologist says.

IN JULY, RESEARCHERS WHO ANALYZED carbon isotopes left behind by plants growing during the occupation of Cahokia published a paper challenging the theory that drought would have had a calamitous impact on the city of Cahokia.

“Our study combined with decades of research about the diversity of Cahokia crops and other food sources, makes us skeptical that food shortages played a decisive role in the abandonment of the city,” Bureau of Land Management archeologist Caitlin Rankin says in an email. Rankin argues that even if a drought did impact agriculture in the region, the farmers of Cahokia grew at least eight different crops, and residents “had access to an enormous freshwater fishery, one of the great flyways of the world, perennial wetland plants, and ‘food forests’ full of nuts and fruits that had been shaped by local communities for millennia,” meaning it would have been difficult for them to go hungry.

“This doesn’t mean that it might not have affected other parts of the landscape, including some of the places where they grew food. It also may have destabilized the region even if it didn’t cause crop failure at Cahokia,” Rankin explains.

White echoes this conclusion. Repeated floods and droughts likely “shocked the system,” he says. “It would have affected some people more than others, but anytime you have a disruption of the status quo—you’re likely to see change.”

While the mystery is far from solved, it’s possible that the inhabitants of Cahokia merely drifted away over time as the area became less hospitable, much like people today in parts of the world especially affected by climate change. This could mean that Cahokia died not by the bullet of one smoking gun, but rather by 1,000 cuts.

“This was a really complicated place, and there’s likely a really complicated explanation,” White says. “We need to figure out how we can unite all these different data—agricultural, economic, cultural, climatic. Societies deal with all these things at once, and a change in one can rock all the others.”

Ashley Stimpson is a freelance journalist who writes most often about science, conservation, and the outdoors. Her work has appeared in the Guardian, WIRED, Nat Geo, Atlas Obscura, and elsewhere. She lives in Columbia, Maryland, with her partner, their greyhound, and a very bad cat. 

.css-cuqpxl:before{padding-right:0.3125rem;content:'//';display:inline;} Pop Mech Pro .css-xtujxj:before{padding-left:0.3125rem;content:'//';display:inline;}

The Army is Building the Next Tiltrotor Aircraft

Are Underwater UFOs an Imminent Threat?

U.S. Not Ready for a Major War: Report

First F-16s Arrive in Ukraine

A WWII Vet’s Wild Ride on Top of an Airplane Tail

This Laser Could Unlock Interstellar Travel

How This 400-Year-Old Error Solved a Solar Mystery

UFO Sightings are Spiking. This Man Knows Why.

‘Bomber Raptor’ Concept Looks Like the Future

Roman Wall From the Fight Against Spartacus Found

Israel’s New $1 Billion Laser Will Be Unstoppable

Skip to Content

• News + Events

Other ways to search:

• Events Calendar

Devendorf bridges engineering and craft communities with new initiative

Weaving and engineering might seem unrelated on the surface, but they in fact have a great deal in common. Laura Devendorf, assistant professor at the ATLAS Institute and Information Science, is determined to build bridges among practitioners across these disciplines to unlock the potential for new lines of scientific and creative innovation.

To support this work, Devendorf, who directs the Unstable Design Lab, was recently awarded a U.S. National Science Foundation grant of $297,630 for phase one of a larger project entitled, “Cultivating an Ecosystem for Interdisciplinary Smart Textiles Research.”

The aim of this new research, as Devendorf describes it, is to, “take this software that we built for doing complex weave drafting and transition it to a project where it is sustained and grown by a larger community of weavers and [those] who we call ‘textile-adjacent engineers’.” That way, AdaCAD can develop and adapt to the needs of a wider user-base over time as all good open source software does.

This work exemplifies the radically interdisciplinary work that the ATLAS Institute champions. By bringing together disparate experts and communities—in this case, artists, artisans, engineers and researchers—we create new approaches to discovery.

From adjacent to integrated Textiles are pervasive, yet often misunderstood in engineering spaces. The surprising mathematical complexity, materials knowledge and innovation that have arisen over centuries of textile practice are often overlooked. At the same time, weavers who come from a tradition of craftsmanship, art and design may be unfamiliar with meaningful advances in the engineering space.

By bringing together these two worlds, Devendorf hopes to open up opportunities for breakthroughs in technology and craft, whether that is in advancing electronic-textile science or pushing the boundaries of artistic expression.

For example, an engineering team may seek ways to monitor health without the use of adhesives often required for electrodes, while weavers already have options for materials and techniques that could replace such adhesives. Yet both groups are often unaware of each other’s needs and skills. But overcoming this knowledge gap, Devendorf believes engineers and weavers could together achieve greater impact. 

There are signs this is beginning to take place, and she aims to speed the process. Research labs at CalTech and MIT as well as industrial design studios around the globe have acquired digital looms to experiment with weaving advanced materials and experimental forms.

Devendorf and her team have begun interviewing people from a range of overlapping disciplines. They include a weaver with a fine arts background who now works on woven implantables for a medical devices company and a textiles expert researching stronger, lighter woven materials for the aviation industry. Still others in the cohort are studying zero-waste, “whole garment” clothing manufacturing and human-computer interaction surfaces on the body. The range of applications for textiles is growing at an impressive pace.

Devendorf explains this recent uptick in interest: “Ten years ago, it was all about the maker movement and digital fabrication, and it took a while before people realized that textile machines are also fabrication machines that can do things that we're still trying to get 3D printers to do. Textiles are inherently multi-material. They are flexible, they can be made stiff, they can be soft. It's a totally different mindset to control a textile machine [compared] to a printer that is making stacks. There’s a big learning gap there, but you see a lot of fabrication people jumping in.” 

The TC2 Loom by Digital Weaving Norway has also expanded access to advanced weaving techniques as a first-of-its-kind prototype-scale digital jacquard loom that is programmable with a bitmap image. Now you no longer need a factory-scale setup to experiment with textiles fabrication.

What’s next For Phase II, the team will focus on cultivating the ecosystem through on-the-ground work with communities and creating opportunities for practitioners to share what they are making. Devendorf also aims to expand opportunities for craftspeople in scientific research and product design spaces.

She explains, “we have huge problems to tackle as a society. I believe that engineering can address some of those, but I don't think we can do it if we don't have access to every possible technique… We're overlooking a huge set of practices and people in communities that have knowledge we need to solve some of these bigger challenges. My hunch is that craftspeople understand materials, process and machinery where so much of engineering is happening at a simulation level [while] trying to engineer materials that behave like the simulations.”

By fostering interconnectivity between engineering and weaving communities, Devendorf and her team in the Unstable Design Lab will position textiles as a leading source for innovative solutions to global challenges.

• featurenews

Related Articles

Wear it, then recycle: ATLAS Designers make dissolvable textiles from gelatin

Public-private partnership drives attention for ATLAS research in augmented and mixed reality

ATLAS Institute Faculty Nationally Recognized for Radical Creativity and Invention