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Research Design – Types, Methods and Examples

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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

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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).

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• Last Updated: Aug 21, 2023 10:42 AM

What Is Research Methodology? Types, Process, Examples In Research Design

Research methodology is the backbone of any successful study, providing a structured approach to collecting and analysing data. It encompasses a broad spectrum of methods, each with specific processes and applications, tailored to answer distinct research questions.

This article will explore various types of research methodologies, delve into their processes, and illustrate with examples how they are applied in real-world research.

Understanding these methodologies is essential for any researcher aiming to conduct thorough and impactful studies.

Types Of Research Methodology

Research methodology contains various strategies and approaches to conduct scientific research, each tailored to specific types of questions and data.

Think of research methodology as the master plan for your study. It guides you on why and how to gather and analyse data, ensuring your approach aligns perfectly with your research question.

This methodology includes deciding between qualitative research, which explores topics in depth through interviews or focus groups, or quantitative research, which quantifies data through surveys and statistical analysis.

There is even an option to mix both, and approach called the mixed method.

If you’re analysing the lived experiences of individuals in a specific setting, qualitative methodologies allow you to capture the nuances of human emotions and behaviours through detailed narratives.

Quantitative methodologies would enable you to measure and compare these experiences in a more structured, numerical format.

Choosing a robust methodology not only provides the rationale for the methods you choose but also highlights the research limitations and ethical considerations, keeping your study transparent and grounded.

It’s a thoughtful composition that gives research its direction and purpose, much like how an architect’s plan is essential before the actual construction begins.

Qualitative Research Methodology

Qualitative research dives deep into the social context of a topic. It collects words and textual data rather than numerical data.

Within the family, qualitative research methodologies can be broken down into several approaches: 

Ethnography: Deeply rooted in the traditions of anthropology, you immerse yourself in the community or social setting you’re studying when conducting an ethnography study.

Case Study Research:  Here, you explore the complexity of a single case in detail. This could be an institution, a group, or an individual. You might look into interviews, documents, and reports, to build a comprehensive picture of the subject.

Grounded Theory:  Here, you try to generate theories from the data itself rather than testing existing hypotheses. You might start with a research question but allow your theories to develop as you gather more data.

Narrative Research:  You explore the stories people tell about their lives and personal experiences in their own words. Through techniques like in-depth interviews or life story collections, you analyse the narrative to understand the individual’s experiences.

Discourse Analysis: You analyse written or spoken words to understand the social norms and power structures that underlie the language used. This method can reveal a lot about the social context and the dynamics of power in communication. 

These methods help to uncover patterns in how people think and interact. For example, in exploring consumer attitudes toward a new product, you would likely conduct focus groups or participant observations to gather qualitative data.

This method helps you understand the motivations and feelings behind consumer choices.

Quantitative Research Methodology

Quantitative research relies on numerical data to find patterns and test hypotheses. This methodology uses statistical analysis to quantify data and uncover relationships between variables.

There are several approaches in quantitative research:

Experimental Research:  This is the gold standard when you aim to determine causality. By manipulating one variable and controlling others, you observe changes in the dependent variables.

Survey Research: A popular approach, because of its efficiency in collecting data from a large sample of participants. By using standardised questions, you can gather data that are easy to analyse statistically. 

Correlational Research: This approach tries to identify relationships between two or more variables without establishing a causal link. The strength and direction of these relationships are quantified, albeit without confirming one variable causes another.

Longitudinal Studies: You track variables over time, providing a dynamic view of how situations evolve. This approach requires commitment and can be resource-intensive, but the depth of data they provide is unparalleled.

Cross-sectional Studies: Offers a snapshot of a population at a single point in time. They are quicker and cheaper than longitudinal studies. 

Mixed Research Methodology

Mixed methods research combines both approaches to benefit from the depth of qualitative data and the breadth of quantitative analysis.

You might start with qualitative interviews to develop hypotheses about health behaviours in a community. Then, you could conduct a large-scale survey to test these hypotheses quantitatively.

This approach is particularly useful when you want to explore a new area where previous data may not exist, giving you a comprehensive insight into both the empirical and social dimensions of a research problem.

Factors To Consider When Deciding On Research Methodology

When you dive into a research project, choosing the right methodology is akin to selecting the best tools for building a house.

It shapes how you approach the research question, gather data, and interpret the results. Here are a couple of crucial factors to keep in mind.

Research Question Compatibility

The type of research question you pose can heavily influence the methodology you choose. Qualitative methodologies are superb for exploratory research where you aim to understand concepts, perceptions, and experiences.

If you’re exploring how patients feel about a new healthcare policy, interviews and focus groups would be instrumental.

Quantitative methods are your go-to for questions that require measurable and statistical data, like assessing the prevalence of a medical condition across different regions.

Data Requirements

Consider what data is necessary to address your research question effectively. Qualitative data can provide depth and detail through:

• images, and

This makes qualitative method ideal for understanding complex social interactions or historical contexts. 

Quantitative data, however, offers the breadth and is often numerical, allowing for a broad analysis of patterns and correlations.

If your study aims to investigate both the breadth and depth, a mixed methods approach might be necessary, enabling you to draw on the strengths of both qualitative and quantitative data.

Resources and Constraints

While deciding on research methodology, you must evaluate the resources available, including:

• funding, and

Quantitative research often requires larger samples and hence, might be more costly and time-consuming.

Qualitative research, while generally less resource-intensive, demands substantial time for data collection and analysis, especially if you conduct lengthy interviews or detailed content analysis.

If resources are limited, adapting your methodology to fit these constraints without compromising the integrity of your research is crucial.

Skill Set and Expertise

Your familiarity and comfort level with various research methodologies will significantly affect your choice.

Conducting sophisticated statistical analyses requires a different skill set than carrying out in-depth qualitative interviews.

If your background is in social science, you might find qualitative methods more within your wheelhouse; whereas, a postgraduate student in epidemiology might be more adept at quantitative methods.

It’s also worth considering the availability of workshops, courses, or collaborators who could complement your skills.

Ethical and Practical Considerations

Different methodologies raise different ethical concerns.

In qualitative research, maintaining anonymity and dealing with sensitive information can be challenging, especially when using direct quotes or detailed descriptions from participants.

Quantitative research might involve considerations around participant consent for large surveys or experiments.

Practically, you need to think about the sampling design to ensure it is representative of the population studied. Non-probability sampling might be quicker and cheaper but can introduce bias, limiting the generalisability of your findings.

By meticulously considering these factors, you tailor your research design to not just answer the research questions effectively but also to reflect the realities of your operational environment.

This thoughtful approach helps ensure that your research is not only robust but also practical and ethical, standing up to both academic scrutiny and real-world application.

What Is Research Methodology? Answered

Research methodology is a crucial framework that guides the entire research process. It involves choosing between various qualitative and quantitative approaches, each tailored to specific research questions and objectives.

Your chosen methodology shapes how data is gathered, analysed, and interpreted, ultimately influencing the reliability and validity of your research findings.

Understanding these methodologies ensures that researchers can effectively write research proposal, address their study’s aims and contribute valuable insights to their field.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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What Is Research Methodology?

I f you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Learn More About Methodology

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I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

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I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

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Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

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how do i reference this?

MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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hello sir/ma’am, i didn’t find yet that what type of research methodology i am using. because i am writing my report on CSR and collect all my data from websites and articles so which type of methodology i should write in dissertation report. please help me. i am from India.

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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.

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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.

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What Is a Research Design? | Definition, Types & Guide

Introduction

Parts of a research design, types of research methodology in qualitative research, narrative research designs, phenomenological research designs, grounded theory research designs.

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

A research design in qualitative research is a critical framework that guides the methodological approach to studying complex social phenomena. Qualitative research designs determine how data is collected, analyzed, and interpreted, ensuring that the research captures participants' nuanced and subjective perspectives. Research designs also recognize ethical considerations and involve informed consent, ensuring confidentiality, and handling sensitive topics with the utmost respect and care. These considerations are crucial in qualitative research and other contexts where participants may share personal or sensitive information. A research design should convey coherence as it is essential for producing high-quality qualitative research, often following a recursive and evolving process.

Theoretical concepts and research question

The first step in creating a research design is identifying the main theoretical concepts. To identify these concepts, a researcher should ask which theoretical keywords are implicit in the investigation. The next step is to develop a research question using these theoretical concepts. This can be done by identifying the relationship of interest among the concepts that catch the focus of the investigation. The question should address aspects of the topic that need more knowledge, shed light on new information, and specify which aspects should be prioritized before others. This step is essential in identifying which participants to include or which data collection methods to use. Research questions also put into practice the conceptual framework and make the initial theoretical concepts more explicit. Once the research question has been established, the main objectives of the research can be specified. For example, these objectives may involve identifying shared experiences around a phenomenon or evaluating perceptions of a new treatment.

Methodology

After identifying the theoretical concepts, research question, and objectives, the next step is to determine the methodology that will be implemented. This is the lifeline of a research design and should be coherent with the objectives and questions of the study. The methodology will determine how data is collected, analyzed, and presented. Popular qualitative research methodologies include case studies, ethnography , grounded theory , phenomenology, and narrative research . Each methodology is tailored to specific research questions and facilitates the collection of rich, detailed data. For example, a narrative approach may focus on only one individual and their story, while phenomenology seeks to understand participants' lived common experiences. Qualitative research designs differ significantly from quantitative research, which often involves experimental research, correlational designs, or variance analysis to test hypotheses about relationships between two variables, a dependent variable and an independent variable while controlling for confounding variables.

Literature review

After the methodology is identified, conducting a thorough literature review is integral to the research design. This review identifies gaps in knowledge, positioning the new study within the larger academic dialogue and underlining its contribution and relevance. Meta-analysis, a form of secondary research, can be particularly useful in synthesizing findings from multiple studies to provide a clear picture of the research landscape.

Data collection

The sampling method in qualitative research is designed to delve deeply into specific phenomena rather than to generalize findings across a broader population. The data collection methods—whether interviews, focus groups, observations, or document analysis—should align with the chosen methodology, ethical considerations, and other factors such as sample size. In some cases, repeated measures may be collected to observe changes over time.

Data analysis

Analysis in qualitative research typically involves methods such as coding and thematic analysis to distill patterns from the collected data. This process delineates how the research results will be systematically derived from the data. It is recommended that the researcher ensures that the final interpretations are coherent with the observations and analyses, making clear connections between the data and the conclusions drawn. Reporting should be narrative-rich, offering a comprehensive view of the context and findings.

Overall, a coherent qualitative research design that incorporates these elements facilitates a study that not only adds theoretical and practical value to the field but also adheres to high quality. This methodological thoroughness is essential for achieving significant, insightful findings. Examples of well-executed research designs can be valuable references for other researchers conducting qualitative or quantitative investigations. An effective research design is critical for producing robust and impactful research outcomes.

Each qualitative research design is unique, diverse, and meticulously tailored to answer specific research questions, meet distinct objectives, and explore the unique nature of the phenomenon under investigation. The methodology is the wider framework that a research design follows. Each methodology in a research design consists of methods, tools, or techniques that compile data and analyze it following a specific approach.

The methods enable researchers to collect data effectively across individuals, different groups, or observations, ensuring they are aligned with the research design. The following list includes the most commonly used methodologies employed in qualitative research designs, highlighting how they serve different purposes and utilize distinct methods to gather and analyze data.

The narrative approach in research focuses on the collection and detailed examination of life stories, personal experiences, or narratives to gain insights into individuals' lives as told from their perspectives. It involves constructing a cohesive story out of the diverse experiences shared by participants, often using chronological accounts. It seeks to understand human experience and social phenomena through the form and content of the stories. These can include spontaneous narrations such as memoirs or diaries from participants or diaries solicited by the researcher. Narration helps construct the identity of an individual or a group and can rationalize, persuade, argue, entertain, confront, or make sense of an event or tragedy. To conduct a narrative investigation, it is recommended that researchers follow these steps:

Identify if the research question fits the narrative approach. Its methods are best employed when a researcher wants to learn about the lifestyle and life experience of a single participant or a small number of individuals.

Select the best-suited participants for the research design and spend time compiling their stories using different methods such as observations, diaries, interviewing their family members, or compiling related secondary sources.

Compile the information related to the stories. Narrative researchers collect data based on participants' stories concerning their personal experiences, for example about their workplace or homes, their racial or ethnic culture, and the historical context in which the stories occur.

Analyze the participant stories and "restore" them within a coherent framework. This involves collecting the stories, analyzing them based on key elements such as time, place, plot, and scene, and then rewriting them in a chronological sequence (Ollerenshaw & Creswell, 2000). The framework may also include elements such as a predicament, conflict, or struggle; a protagonist; and a sequence with implicit causality, where the predicament is somehow resolved (Carter, 1993).

Collaborate with participants by actively involving them in the research. Both the researcher and the participant negotiate the meaning of their stories, adding a credibility check to the analysis (Creswell & Miller, 2000).

A narrative investigation includes collecting a large amount of data from the participants and the researcher needs to understand the context of the individual's life. A keen eye is needed to collect particular stories that capture the individual experiences. Active collaboration with the participant is necessary, and researchers need to discuss and reflect on their own beliefs and backgrounds. Multiple questions could arise in the collection, analysis, and storytelling of individual stories that need to be addressed, such as: Whose story is it? Who can tell it? Who can change it? Which version is compelling? What happens when narratives compete? In a community, what do the stories do among them? (Pinnegar & Daynes, 2006).

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A research design based on phenomenology aims to understand the essence of the lived experiences of a group of people regarding a particular concept or phenomenon. Researchers gather deep insights from individuals who have experienced the phenomenon, striving to describe "what" they experienced and "how" they experienced it. This approach to a research design typically involves detailed interviews and aims to reach a deep existential understanding. The purpose is to reduce individual experiences to a description of the universal essence or understanding the phenomenon's nature (van Manen, 1990). In phenomenology, the following steps are usually followed:

Identify a phenomenon of interest . For example, the phenomenon might be anger, professionalism in the workplace, or what it means to be a fighter.

Recognize and specify the philosophical assumptions of phenomenology , for example, one could reflect on the nature of objective reality and individual experiences.

Collect data from individuals who have experienced the phenomenon . This typically involves conducting in-depth interviews, including multiple sessions with each participant. Additionally, other forms of data may be collected using several methods, such as observations, diaries, art, poetry, music, recorded conversations, written responses, or other secondary sources.

Ask participants two general questions that encompass the phenomenon and how the participant experienced it (Moustakas, 1994). For example, what have you experienced in this phenomenon? And what contexts or situations have typically influenced your experiences within the phenomenon? Other open-ended questions may also be asked, but these two questions particularly focus on collecting research data that will lead to a textural description and a structural description of the experiences, and ultimately provide an understanding of the common experiences of the participants.

Review data from the questions posed to participants . It is recommended that researchers review the answers and highlight "significant statements," phrases, or quotes that explain how participants experienced the phenomenon. The researcher can then develop meaningful clusters from these significant statements into patterns or key elements shared across participants.

Write a textual description of what the participants experienced based on the answers and themes of the two main questions. The answers are also used to write about the characteristics and describe the context that influenced the way the participants experienced the phenomenon, called imaginative variation or structural description. Researchers should also write about their own experiences and context or situations that influenced them.

Write a composite description from the structural and textural description that presents the "essence" of the phenomenon, called the essential and invariant structure.

A phenomenological approach to a research design includes the strict and careful selection of participants in the study where bracketing personal experiences can be difficult to implement. The researcher decides how and in which way their knowledge will be introduced. It also involves some understanding and identification of the broader philosophical assumptions.

Grounded theory is used in a research design when the goal is to inductively develop a theory "grounded" in data that has been systematically gathered and analyzed. Starting from the data collection, researchers identify characteristics, patterns, themes, and relationships, gradually forming a theoretical framework that explains relevant processes, actions, or interactions grounded in the observed reality. A grounded theory study goes beyond descriptions and its objective is to generate a theory, an abstract analytical scheme of a process. Developing a theory doesn't come "out of nothing" but it is constructed and based on clear data collection. We suggest the following steps to follow a grounded theory approach in a research design:

Determine if grounded theory is the best for your research problem . Grounded theory is a good design when a theory is not already available to explain a process.

Develop questions that aim to understand how individuals experienced or enacted the process (e.g., What was the process? How did it unfold?). Data collection and analysis occur in tandem, so that researchers can ask more detailed questions that shape further analysis, such as: What was the focal point of the process (central phenomenon)? What influenced or caused this phenomenon to occur (causal conditions)? What strategies were employed during the process? What effect did it have (consequences)?

Gather relevant data about the topic in question . Data gathering involves questions that are usually asked in interviews, although other forms of data can also be collected, such as observations, documents, and audio-visual materials from different groups.

Carry out the analysis in stages . Grounded theory analysis begins with open coding, where the researcher forms codes that inductively emerge from the data (rather than preconceived categories). Researchers can thus identify specific properties and dimensions relevant to their research question.

Assemble the data in new ways and proceed to axial coding . Axial coding involves using a coding paradigm or logic diagram, such as a visual model, to systematically analyze the data. Begin by identifying a central phenomenon, which is the main category or focus of the research problem. Next, explore the causal conditions, which are the categories of factors that influence the phenomenon. Specify the strategies, which are the actions or interactions associated with the phenomenon. Then, identify the context and intervening conditions—both narrow and broad factors that affect the strategies. Finally, delineate the consequences, which are the outcomes or results of employing the strategies.

Use selective coding to construct a "storyline" that links the categories together. Alternatively, the researcher may formulate propositions or theory-driven questions that specify predicted relationships among these categories.

Develop and visually present a matrix that clarifies the social, historical, and economic conditions influencing the central phenomenon. This optional step encourages viewing the model from the narrowest to the broadest perspective.

Write a substantive-level theory that is closely related to a specific problem or population. This step is optional but provides a focused theoretical framework that can later be tested with quantitative data to explore its generalizability to a broader sample.

Allow theory to emerge through the memo-writing process, where ideas about the theory evolve continuously throughout the stages of open, axial, and selective coding.

The researcher should initially set aside any preconceived theoretical ideas to allow for the emergence of analytical and substantive theories. This is a systematic research approach, particularly when following the methodological steps outlined by Strauss and Corbin (1990). For those seeking more flexibility in their research process, the approach suggested by Charmaz (2006) might be preferable.

One of the challenges when using this method in a research design is determining when categories are sufficiently saturated and when the theory is detailed enough. To achieve saturation, discriminant sampling may be employed, where additional information is gathered from individuals similar to those initially interviewed to verify the applicability of the theory to these new participants. Ultimately, its goal is to develop a theory that comprehensively describes the central phenomenon, causal conditions, strategies, context, and consequences.

Ethnographic research design

An ethnographic approach in research design involves the extended observation and data collection of a group or community. The researcher immerses themselves in the setting, often living within the community for long periods. During this time, they collect data by observing and recording behaviours, conversations, and rituals to understand the group's social dynamics and cultural norms. We suggest following these steps for ethnographic methods in a research design:

Assess whether ethnography is the best approach for the research design and questions. It's suitable if the goal is to describe how a cultural group functions and to delve into their beliefs, language, behaviours, and issues like power, resistance, and domination, particularly if there is limited literature due to the group’s marginal status or unfamiliarity to mainstream society.

Identify and select a cultural group for your research design. Choose one that has a long history together, forming distinct languages, behaviours, and attitudes. This group often might be marginalized within society.

Choose cultural themes or issues to examine within the group. Analyze interactions in everyday settings to identify pervasive patterns such as life cycles, events, and overarching cultural themes. Culture is inferred from the group members' words, actions, and the tension between their actual and expected behaviours, as well as the artifacts they use.

Conduct fieldwork to gather detailed information about the group’s living and working environments. Visit the site, respect the daily lives of the members, and collect a diverse range of materials, considering ethical aspects such as respect and reciprocity.

Compile and analyze cultural data to develop a set of descriptive and thematic insights. Begin with a detailed description of the group based on observations of specific events or activities over time. Then, conduct a thematic analysis to identify patterns or themes that illustrate how the group functions and lives. The final output should be a comprehensive cultural portrait that integrates both the participants (emic) and the researcher’s (etic) perspectives, potentially advocating for the group’s needs or suggesting societal changes to better accommodate them.

Researchers engaging in ethnography need a solid understanding of cultural anthropology and the dynamics of sociocultural systems, which are commonly explored in ethnographic research. The data collection phase is notably extensive, requiring prolonged periods in the field. Ethnographers often employ a literary, quasi-narrative style in their narratives, which can pose challenges for those accustomed to more conventional social science writing methods.

Another potential issue is the risk of researchers "going native," where they become overly assimilated into the community under study, potentially jeopardizing the objectivity and completion of their research. It's crucial for researchers to be aware of their impact on the communities and environments they are studying.

The case study approach in a research design focuses on a detailed examination of a single case or a small number of cases. Cases can be individuals, groups, organizations, or events. Case studies are particularly useful for research designs that aim to understand complex issues in real-life contexts. The aim is to provide a thorough description and contextual analysis of the cases under investigation. We suggest following these steps in a case study design:

Assess if a case study approach suits your research questions . This approach works well when you have distinct cases with defined boundaries and aim to deeply understand these cases or compare multiple cases.

Choose your case or cases. These could involve individuals, groups, programs, events, or activities. Decide whether an individual or collective, multi-site or single-site case study is most appropriate, focusing on specific cases or themes (Stake, 1995; Yin, 2003).

Gather data extensively from diverse sources . Collect information through archival records, interviews, direct and participant observations, and physical artifacts (Yin, 2003).

Analyze the data holistically or in focused segments . Provide a comprehensive overview of the entire case or concentrate on specific aspects. Start with a detailed description including the history of the case and its chronological events then narrow down to key themes. The aim is to delve into the case's complexity rather than generalize findings.

Interpret and report the significance of the case in the final phase . Explain what insights were gained, whether about the subject of the case in an instrumental study or an unusual situation in an intrinsic study (Lincoln & Guba, 1985).

The investigator must carefully select the case or cases to study, recognizing that multiple potential cases could illustrate a chosen topic or issue. This selection process involves deciding whether to focus on a single case for deeper analysis or multiple cases, which may provide broader insights but less depth per case. Each choice requires a well-justified rationale for the selected cases. Researchers face the challenge of defining the boundaries of a case, such as its temporal scope and the events and processes involved. This decision in a research design is crucial as it affects the depth and value of the information presented in the study, and therefore should be planned to ensure a comprehensive portrayal of the case.

Qualitative and quantitative research designs are distinct in their approach to data collection and data analysis. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research prioritizes understanding the depth and richness of human experiences, behaviours, and interactions.

Qualitative methods in a research design have to have internal coherence, meaning that all elements of the research project—research question, data collection, data analysis, findings, and theory—are well-aligned and consistent with each other. This coherence in the research study is especially crucial in inductive qualitative research, where the research process often follows a recursive and evolving path. Ensuring that each component of the research design fits seamlessly with the others enhances the clarity and impact of the study, making the research findings more robust and compelling. Whether it is a descriptive research design, explanatory research design, diagnostic research design, or correlational research design coherence is an important element in both qualitative and quantitative research.

Finally, a good research design ensures that the research is conducted ethically and considers the well-being and rights of participants when managing collected data. The research design guides researchers in providing a clear rationale for their methodologies, which is crucial for justifying the research objectives to the scientific community. A thorough research design also contributes to the body of knowledge, enabling researchers to build upon past research studies and explore new dimensions within their fields. At the core of the design, there is a clear articulation of the research objectives. These objectives should be aligned with the underlying concepts being investigated, offering a concise method to answer the research questions and guiding the direction of the study with proper qualitative methods.

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Charmaz, K. (2006). Constructing grounded theory. London: Sage.

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Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Ollerenshaw, J. A., & Creswell, J. W. (2000, April). Data analysis in narrative research: A comparison of two “restoring” approaches. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

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Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. Ontario, Canada: University of Western Ontario.

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage

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Research Design and Methodology

Submitted: 23 January 2019 Reviewed: 08 March 2019 Published: 07 August 2019

DOI: 10.5772/intechopen.85731

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There are a number of approaches used in this research method design. The purpose of this chapter is to design the methodology of the research approach through mixed types of research techniques. The research approach also supports the researcher on how to come across the research result findings. In this chapter, the general design of the research and the methods used for data collection are explained in detail. It includes three main parts. The first part gives a highlight about the dissertation design. The second part discusses about qualitative and quantitative data collection methods. The last part illustrates the general research framework. The purpose of this section is to indicate how the research was conducted throughout the study periods.

• research design
• methodology
• data sources

Author Information

Kassu jilcha sileyew *.

• School of Mechanical and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia

*Address all correspondence to: [email protected]

1. Introduction

Research methodology is the path through which researchers need to conduct their research. It shows the path through which these researchers formulate their problem and objective and present their result from the data obtained during the study period. This research design and methodology chapter also shows how the research outcome at the end will be obtained in line with meeting the objective of the study. This chapter hence discusses the research methods that were used during the research process. It includes the research methodology of the study from the research strategy to the result dissemination. For emphasis, in this chapter, the author outlines the research strategy, research design, research methodology, the study area, data sources such as primary data sources and secondary data, population consideration and sample size determination such as questionnaires sample size determination and workplace site exposure measurement sample determination, data collection methods like primary data collection methods including workplace site observation data collection and data collection through desk review, data collection through questionnaires, data obtained from experts opinion, workplace site exposure measurement, data collection tools pretest, secondary data collection methods, methods of data analysis used such as quantitative data analysis and qualitative data analysis, data analysis software, the reliability and validity analysis of the quantitative data, reliability of data, reliability analysis, validity, data quality management, inclusion criteria, ethical consideration and dissemination of result and its utilization approaches. In order to satisfy the objectives of the study, a qualitative and quantitative research method is apprehended in general. The study used these mixed strategies because the data were obtained from all aspects of the data source during the study time. Therefore, the purpose of this methodology is to satisfy the research plan and target devised by the researcher.

2. Research design

The research design is intended to provide an appropriate framework for a study. A very significant decision in research design process is the choice to be made regarding research approach since it determines how relevant information for a study will be obtained; however, the research design process involves many interrelated decisions [ 1 ].

This study employed a mixed type of methods. The first part of the study consisted of a series of well-structured questionnaires (for management, employee’s representatives, and technician of industries) and semi-structured interviews with key stakeholders (government bodies, ministries, and industries) in participating organizations. The other design used is an interview of employees to know how they feel about safety and health of their workplace, and field observation at the selected industrial sites was undertaken.

Hence, this study employs a descriptive research design to agree on the effects of occupational safety and health management system on employee health, safety, and property damage for selected manufacturing industries. Saunders et al. [ 2 ] and Miller [ 3 ] say that descriptive research portrays an accurate profile of persons, events, or situations. This design offers to the researchers a profile of described relevant aspects of the phenomena of interest from an individual, organizational, and industry-oriented perspective. Therefore, this research design enabled the researchers to gather data from a wide range of respondents on the impact of safety and health on manufacturing industries in Ethiopia. And this helped in analyzing the response obtained on how it affects the manufacturing industries’ workplace safety and health. The research overall design and flow process are depicted in Figure 1 .

Research methods and processes (author design).

3. Research methodology

To address the key research objectives, this research used both qualitative and quantitative methods and combination of primary and secondary sources. The qualitative data supports the quantitative data analysis and results. The result obtained is triangulated since the researcher utilized the qualitative and quantitative data types in the data analysis. The study area, data sources, and sampling techniques were discussed under this section.

3.1 The study area

According to Fraenkel and Warren [ 4 ] studies, population refers to the complete set of individuals (subjects or events) having common characteristics in which the researcher is interested. The population of the study was determined based on random sampling system. This data collection was conducted from March 07, 2015 to December 10, 2016, from selected manufacturing industries found in Addis Ababa city and around. The manufacturing companies were selected based on their employee number, established year, and the potential accidents prevailing and the manufacturing industry type even though all criterions were difficult to satisfy.

3.2 Data sources

3.2.1 primary data sources.

It was obtained from the original source of information. The primary data were more reliable and have more confidence level of decision-making with the trusted analysis having direct intact with occurrence of the events. The primary data sources are industries’ working environment (through observation, pictures, and photograph) and industry employees (management and bottom workers) (interview, questionnaires and discussions).

3.2.2 Secondary data

Desk review has been conducted to collect data from various secondary sources. This includes reports and project documents at each manufacturing sectors (more on medium and large level). Secondary data sources have been obtained from literatures regarding OSH, and the remaining data were from the companies’ manuals, reports, and some management documents which were included under the desk review. Reputable journals, books, different articles, periodicals, proceedings, magazines, newsletters, newspapers, websites, and other sources were considered on the manufacturing industrial sectors. The data also obtained from the existing working documents, manuals, procedures, reports, statistical data, policies, regulations, and standards were taken into account for the review.

In general, for this research study, the desk review has been completed to this end, and it had been polished and modified upon manuals and documents obtained from the selected companies.

4. Population and sample size

4.1 population.

The study population consisted of manufacturing industries’ employees in Addis Ababa city and around as there are more representative manufacturing industrial clusters found. To select representative manufacturing industrial sector population, the types of the industries expected were more potential to accidents based on random and purposive sampling considered. The population of data was from textile, leather, metal, chemicals, and food manufacturing industries. A total of 189 sample sizes of industries responded to the questionnaire survey from the priority areas of the government. Random sample sizes and disproportionate methods were used, and 80 from wood, metal, and iron works; 30 from food, beverage, and tobacco products; 50 from leather, textile, and garments; 20 from chemical and chemical products; and 9 from other remaining 9 clusters of manufacturing industries responded.

4.2 Questionnaire sample size determination

A simple random sampling and purposive sampling methods were used to select the representative manufacturing industries and respondents for the study. The simple random sampling ensures that each member of the population has an equal chance for the selection or the chance of getting a response which can be more than equal to the chance depending on the data analysis justification. Sample size determination procedure was used to get optimum and reasonable information. In this study, both probability (simple random sampling) and nonprobability (convenience, quota, purposive, and judgmental) sampling methods were used as the nature of the industries are varied. This is because of the characteristics of data sources which permitted the researchers to follow the multi-methods. This helps the analysis to triangulate the data obtained and increase the reliability of the research outcome and its decision. The companies’ establishment time and its engagement in operation, the number of employees and the proportion it has, the owner types (government and private), type of manufacturing industry/production, types of resource used at work, and the location it is found in the city and around were some of the criteria for the selections.

The determination of the sample size was adopted from Daniel [ 5 ] and Cochran [ 6 ] formula. The formula used was for unknown population size Eq. (1) and is given as

where n  = sample size, Z  = statistic for a level of confidence, P  = expected prevalence or proportion (in proportion of one; if 50%, P  = 0.5), and d  = precision (in proportion of one; if 6%, d  = 0.06). Z statistic ( Z ): for the level of confidence of 95%, which is conventional, Z value is 1.96. In this study, investigators present their results with 95% confidence intervals (CI).

The expected sample number was 267 at the marginal error of 6% for 95% confidence interval of manufacturing industries. However, the collected data indicated that only 189 populations were used for the analysis after rejecting some data having more missing values in the responses from the industries. Hence, the actual data collection resulted in 71% response rate. The 267 population were assumed to be satisfactory and representative for the data analysis.

4.3 Workplace site exposure measurement sample determination

The sample size for the experimental exposure measurements of physical work environment has been considered based on the physical data prepared for questionnaires and respondents. The response of positive were considered for exposure measurement factors to be considered for the physical environment health and disease causing such as noise intensity, light intensity, pressure/stress, vibration, temperature/coldness, or hotness and dust particles on 20 workplace sites. The selection method was using random sampling in line with purposive method. The measurement of the exposure factors was done in collaboration with Addis Ababa city Administration and Oromia Bureau of Labour and Social Affair (AACBOLSA). Some measuring instruments were obtained from the Addis Ababa city and Oromia Bureau of Labour and Social Affair.

5. Data collection methods

Data collection methods were focused on the followings basic techniques. These included secondary and primary data collections focusing on both qualitative and quantitative data as defined in the previous section. The data collection mechanisms are devised and prepared with their proper procedures.

5.1 Primary data collection methods

Primary data sources are qualitative and quantitative. The qualitative sources are field observation, interview, and informal discussions, while that of quantitative data sources are survey questionnaires and interview questions. The next sections elaborate how the data were obtained from the primary sources.

5.1.1 Workplace site observation data collection

Observation is an important aspect of science. Observation is tightly connected to data collection, and there are different sources for this: documentation, archival records, interviews, direct observations, and participant observations. Observational research findings are considered strong in validity because the researcher is able to collect a depth of information about a particular behavior. In this dissertation, the researchers used observation method as one tool for collecting information and data before questionnaire design and after the start of research too. The researcher made more than 20 specific observations of manufacturing industries in the study areas. During the observations, it found a deeper understanding of the working environment and the different sections in the production system and OSH practices.

5.1.2 Data collection through interview

Interview is a loosely structured qualitative in-depth interview with people who are considered to be particularly knowledgeable about the topic of interest. The semi-structured interview is usually conducted in a face-to-face setting which permits the researcher to seek new insights, ask questions, and assess phenomena in different perspectives. It let the researcher to know the in-depth of the present working environment influential factors and consequences. It has provided opportunities for refining data collection efforts and examining specialized systems or processes. It was used when the researcher faces written records or published document limitation or wanted to triangulate the data obtained from other primary and secondary data sources.

This dissertation is also conducted with a qualitative approach and conducting interviews. The advantage of using interviews as a method is that it allows respondents to raise issues that the interviewer may not have expected. All interviews with employees, management, and technicians were conducted by the corresponding researcher, on a face-to-face basis at workplace. All interviews were recorded and transcribed.

5.1.3 Data collection through questionnaires

The main tool for gaining primary information in practical research is questionnaires, due to the fact that the researcher can decide on the sample and the types of questions to be asked [ 2 ].

In this dissertation, each respondent is requested to reply to an identical list of questions mixed so that biasness was prevented. Initially the questionnaire design was coded and mixed up from specific topic based on uniform structures. Consequently, the questionnaire produced valuable data which was required to achieve the dissertation objectives.

The questionnaires developed were based on a five-item Likert scale. Responses were given to each statement using a five-point Likert-type scale, for which 1 = “strongly disagree” to 5 = “strongly agree.” The responses were summed up to produce a score for the measures.

5.1.4 Data obtained from experts’ opinion

The data was also obtained from the expert’s opinion related to the comparison of the knowledge, management, collaboration, and technology utilization including their sub-factors. The data obtained in this way was used for prioritization and decision-making of OSH, improving factor priority. The prioritization of the factors was using Saaty scales (1–9) and then converting to Fuzzy set values obtained from previous researches using triangular fuzzy set [ 7 ].

5.1.5 Workplace site exposure measurement

The researcher has measured the workplace environment for dust, vibration, heat, pressure, light, and noise to know how much is the level of each variable. The primary data sources planned and an actual coverage has been compared as shown in Table 1 .

Planned versus actual coverage of the survey.

The response rate for the proposed data source was good, and the pilot test also proved the reliability of questionnaires. Interview/discussion resulted in 87% of responses among the respondents; the survey questionnaire response rate obtained was 71%, and the field observation response rate was 90% for the whole data analysis process. Hence, the data organization quality level has not been compromised.

This response rate is considered to be representative of studies of organizations. As the study agrees on the response rate to be 30%, it is considered acceptable [ 8 ]. Saunders et al. [ 2 ] argued that the questionnaire with a scale response of 20% response rate is acceptable. Low response rate should not discourage the researchers, because a great deal of published research work also achieves low response rate. Hence, the response rate of this study is acceptable and very good for the purpose of meeting the study objectives.

5.1.6 Data collection tool pretest

The pretest for questionnaires, interviews, and tools were conducted to validate that the tool content is valid or not in the sense of the respondents’ understanding. Hence, content validity (in which the questions are answered to the target without excluding important points), internal validity (in which the questions raised answer the outcomes of researchers’ target), and external validity (in which the result can generalize to all the population from the survey sample population) were reflected. It has been proved with this pilot test prior to the start of the basic data collections. Following feedback process, a few minor changes were made to the originally designed data collect tools. The pilot test made for the questionnaire test was on 10 sample sizes selected randomly from the target sectors and experts.

5.2 Secondary data collection methods

The secondary data refers to data that was collected by someone other than the user. This data source gives insights of the research area of the current state-of-the-art method. It also makes some sort of research gap that needs to be filled by the researcher. This secondary data sources could be internal and external data sources of information that may cover a wide range of areas.

Literature/desk review and industry documents and reports: To achieve the dissertation’s objectives, the researcher has conducted excessive document review and reports of the companies in both online and offline modes. From a methodological point of view, literature reviews can be comprehended as content analysis, where quantitative and qualitative aspects are mixed to assess structural (descriptive) as well as content criteria.

A literature search was conducted using the database sources like MEDLINE; Emerald; Taylor and Francis publications; EMBASE (medical literature); PsycINFO (psychological literature); Sociological Abstracts (sociological literature); accident prevention journals; US Statistics of Labor, European Safety and Health database; ABI Inform; Business Source Premier (business/management literature); EconLit (economic literature); Social Service Abstracts (social work and social service literature); and other related materials. The search strategy was focused on articles or reports that measure one or more of the dimensions within the research OSH model framework. This search strategy was based on a framework and measurement filter strategy developed by the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) group. Based on screening, unrelated articles to the research model and objectives were excluded. Prior to screening, researcher (principal investigator) reviewed a sample of more than 2000 articles, websites, reports, and guidelines to determine whether they should be included for further review or reject. Discrepancies were thoroughly identified and resolved before the review of the main group of more than 300 articles commenced. After excluding the articles based on the title, keywords, and abstract, the remaining articles were reviewed in detail, and the information was extracted on the instrument that was used to assess the dimension of research interest. A complete list of items was then collated within each research targets or objectives and reviewed to identify any missing elements.

6. Methods of data analysis

Data analysis method follows the procedures listed under the following sections. The data analysis part answered the basic questions raised in the problem statement. The detailed analysis of the developed and developing countries’ experiences on OSH regarding manufacturing industries was analyzed, discussed, compared and contrasted, and synthesized.

6.1 Quantitative data analysis

Quantitative data were obtained from primary and secondary data discussed above in this chapter. This data analysis was based on their data type using Excel, SPSS 20.0, Office Word format, and other tools. This data analysis focuses on numerical/quantitative data analysis.

Before analysis, data coding of responses and analysis were made. In order to analyze the data obtained easily, the data were coded to SPSS 20.0 software as the data obtained from questionnaires. This task involved identifying, classifying, and assigning a numeric or character symbol to data, which was done in only one way pre-coded [ 9 , 10 ]. In this study, all of the responses were pre-coded. They were taken from the list of responses, a number of corresponding to a particular selection was given. This process was applied to every earlier question that needed this treatment. Upon completion, the data were then entered to a statistical analysis software package, SPSS version 20.0 on Windows 10 for the next steps.

Under the data analysis, exploration of data has been made with descriptive statistics and graphical analysis. The analysis included exploring the relationship between variables and comparing groups how they affect each other. This has been done using cross tabulation/chi square, correlation, and factor analysis and using nonparametric statistic.

6.2 Qualitative data analysis

Qualitative data analysis used for triangulation of the quantitative data analysis. The interview, observation, and report records were used to support the findings. The analysis has been incorporated with the quantitative discussion results in the data analysis parts.

6.3 Data analysis software

The data were entered using SPSS 20.0 on Windows 10 and analyzed. The analysis supported with SPSS software much contributed to the finding. It had contributed to the data validation and correctness of the SPSS results. The software analyzed and compared the results of different variables used in the research questionnaires. Excel is also used to draw the pictures and calculate some analytical solutions.

7. The reliability and validity analysis of the quantitative data

7.1 reliability of data.

The reliability of measurements specifies the amount to which it is without bias (error free) and hence ensures consistent measurement across time and across the various items in the instrument [ 8 ]. In reliability analysis, it has been checked for the stability and consistency of the data. In the case of reliability analysis, the researcher checked the accuracy and precision of the procedure of measurement. Reliability has numerous definitions and approaches, but in several environments, the concept comes to be consistent [ 8 ]. The measurement fulfills the requirements of reliability when it produces consistent results during data analysis procedure. The reliability is determined through Cranach’s alpha as shown in Table 2 .

Internal consistency and reliability test of questionnaires items.

K stands for knowledge; M, management; T, technology; C, collaboration; P, policy, standards, and regulation; H, hazards and accident conditions; PPE, personal protective equipment.

7.2 Reliability analysis

Cronbach’s alpha is a measure of internal consistency, i.e., how closely related a set of items are as a group [ 11 ]. It is considered to be a measure of scale reliability. The reliability of internal consistency most of the time is measured based on the Cronbach’s alpha value. Reliability coefficient of 0.70 and above is considered “acceptable” in most research situations [ 12 ]. In this study, reliability analysis for internal consistency of Likert-scale measurement after deleting 13 items was found similar; the reliability coefficients were found for 76 items were 0.964 and for the individual groupings made shown in Table 2 . It was also found internally consistent using the Cronbach’s alpha test. Table 2 shows the internal consistency of the seven major instruments in which their reliability falls in the acceptable range for this research.

7.3 Validity

Face validity used as defined by Babbie [ 13 ] is an indicator that makes it seem a reasonable measure of some variables, and it is the subjective judgment that the instrument measures what it intends to measure in terms of relevance [ 14 ]. Thus, the researcher ensured, in this study, when developing the instruments that uncertainties were eliminated by using appropriate words and concepts in order to enhance clarity and general suitability [ 14 ]. Furthermore, the researcher submitted the instruments to the research supervisor and the joint supervisor who are both occupational health experts, to ensure validity of the measuring instruments and determine whether the instruments could be considered valid on face value.

In this study, the researcher was guided by reviewed literature related to compliance with the occupational health and safety conditions and data collection methods before he could develop the measuring instruments. In addition, the pretest study that was conducted prior to the main study assisted the researcher to avoid uncertainties of the contents in the data collection measuring instruments. A thorough inspection of the measuring instruments by the statistician and the researcher’s supervisor and joint experts, to ensure that all concepts pertaining to the study were included, ensured that the instruments were enriched.

8. Data quality management

Insight has been given to the data collectors on how to approach companies, and many of the questionnaires were distributed through MSc students at Addis Ababa Institute of Technology (AAiT) and manufacturing industries’ experience experts. This made the data quality reliable as it has been continually discussed with them. Pretesting for questionnaire was done on 10 workers to assure the quality of the data and for improvement of data collection tools. Supervision during data collection was done to understand how the data collectors are handling the questionnaire, and each filled questionnaires was checked for its completeness, accuracy, clarity, and consistency on a daily basis either face-to-face or by phone/email. The data expected in poor quality were rejected out of the acting during the screening time. Among planned 267 questionnaires, 189 were responded back. Finally, it was analyzed by the principal investigator.

9. Inclusion criteria

The data were collected from the company representative with the knowledge of OSH. Articles written in English and Amharic were included in this study. Database information obtained in relation to articles and those who have OSH area such as interventions method, method of accident identification, impact of occupational accidents, types of occupational injuries/disease, and impact of occupational accidents, and disease on productivity and costs of company and have used at least one form of feedback mechanism. No specific time period was chosen in order to access all available published papers. The questionnaire statements which are similar in the questionnaire have been rejected from the data analysis.

10. Ethical consideration

Ethical clearance was obtained from the School of Mechanical and Industrial Engineering, Institute of Technology, Addis Ababa University. Official letters were written from the School of Mechanical and Industrial Engineering to the respective manufacturing industries. The purpose of the study was explained to the study subjects. The study subjects were told that the information they provided was kept confidential and that their identities would not be revealed in association with the information they provided. Informed consent was secured from each participant. For bad working environment assessment findings, feedback will be given to all manufacturing industries involved in the study. There is a plan to give a copy of the result to the respective study manufacturing industries’ and ministries’ offices. The respondents’ privacy and their responses were not individually analyzed and included in the report.

11. Dissemination and utilization of the result

The result of this study will be presented to the Addis Ababa University, AAiT, School of Mechanical and Industrial Engineering. It will also be communicated to the Ethiopian manufacturing industries, Ministry of Labor and Social Affair, Ministry of Industry, and Ministry of Health from where the data was collected. The result will also be availed by publication and online presentation in Google Scholars. To this end, about five articles were published and disseminated to the whole world.

12. Conclusion

The research methodology and design indicated overall process of the flow of the research for the given study. The data sources and data collection methods were used. The overall research strategies and framework are indicated in this research process from problem formulation to problem validation including all the parameters. It has laid some foundation and how research methodology is devised and framed for researchers. This means, it helps researchers to consider it as one of the samples and models for the research data collection and process from the beginning of the problem statement to the research finding. Especially, this research flow helps new researchers to the research environment and methodology in particular.

Conflict of interest

There is no “conflict of interest.”

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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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.

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Research Methodologies

Research design, external validity, internal validity, threats to validity.

• What are research methodologies?
• What are research methods?
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According to Jenkins-Smith, et al. (2017), a research design is the set of steps you take to collect and analyze your research data.  In other words, it is the general plan to answer your research topic or question.  You can also think of it as a combination of your research methodology and your research method.  Your research design should include the following: 

• A clear research question
• Theoretical frameworks you will use to analyze your data
• Key concepts
• Your hypothesis/hypotheses
• Independent and dependent variables (if applicable)
• Strengths and weaknesses of your chosen design

There are two types of research designs:

• Experimental design: This design is like a standard science lab experiment because the researcher controls as many variables as they can and assigns research subjects to groups.  The researcher manipulates the experimental treatment and gives it to one group.  The other group receives the unmanipulated treatment (or not treatment) and the researcher examines affect of the treatment in each group (dependent variable).  This design can have more than two groups depending on your study requirements.
• Observational design: This is when the researcher has no control over the independent variable and which research participants get exposed to it.  Depending on your research topic, this is the only design you can use.  This is a more natural approach to a study because you are not controlling the experimental treatment.  You are allowing the variable to occur on its own without your interference.  Weather experiments are a great example of observational design because the researcher has no control over the weather and how it changes.

When considering your research design, you will also need to consider your study's validity and any potential threats to its validity.  There are two types of validity: external and internal validity.  Each type demonstrates a degree of accuracy and thoughtfulness in a study and they contribute to a study's reliability.  Information about external and internal validity is included below.

External validity is the degree to which you can generalize the findings of your research study.  It is determining whether or not the findings are applicable to other settings (Jenkins-Smith, 2017).  In many cases, the external validity of a study is strongly linked to the sample population.  For example, if you studied a group of twenty-five year old male Americans, you could potentially generalize your findings to all twenty-five year old American males.  External validity is also the ability for someone else to replicate your study and achieve the same results (Jenkins-Smith, 2017).  If someone replicates your exact study and gets different results, then your study may have weak external validity.

Questions to ask when assessing external validity:

• Do my conclusions apply to other studies?
• If someone were to replicate my study, would they get the same results?
• Are my findings generalizable to a certain population?

Internal validity is when a researcher can conclude a causal relationship between their independent variable and their dependent variable.  It is a way to verify the study's findings because it draws a relationship between the variables (Jenkins-Smith, 2017).  In other words, it is the actual factors that result in the study's outcome (Singh, 2007).  According to Singh (2007), internal validity can be placed into 4 subcategories:

• Face validity: This confirms the fact that the measure accurately reflects the research question.
• Content validity: This assesses the measurement technique's compatibility with other literature on the topic.  It determines how well the tool used to gather data measures the item or concept that the researcher is interested in.
• Criterion validity: This demonstrates the accuracy of a study by comparing it to a similar study.
• Construct validity: This measures the appropriateness of the conclusions drawn from a study.

According to Jenkins-Smith (2017), there are several threats that may impact the internal and external validity of a study:

Threats to External Validity

• Interaction with testing: Any testing done before the actual experiment may decrease participants' sensitivity to the actual treatment.
• Sample misrepresentation: A population sample that is unrepresentative of the entire population.
• Selection bias: Researchers may have bias towards selecting certain subjects to participate in the study who may be more or less sensitive to the experimental treatment.
• Environment: If the study was conducted in a lab setting, the findings may not be able to transfer to a more natural setting.

Threats to Internal Validity

• Unplanned events that occur during the experiment that effect the results.
• Changes to the participants during the experiment, such as fatigue, aging, etc.
• Selection bias: When research subjects are not selected randomly.
• If participants drop out of the study without completing it.
• Changing the way the data is collected or measured during the study.
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What is: Research Design

What is research design.

Research design refers to the overall strategy that you choose to integrate the different components of your study in a coherent and logical way, thereby ensuring you will effectively address the research problem. It constitutes the blueprint for the collection, measurement, and analysis of data. A well-structured research design helps to ensure that the results of a study are valid, reliable, and applicable to real-world scenarios.

Types of Research Design

There are several types of research designs, each suited for different research objectives. The most common types include descriptive, correlational, experimental, and quasi-experimental designs. Descriptive research design focuses on providing a detailed account of a phenomenon, while correlational design examines the relationship between two or more variables. Experimental design, on the other hand, involves manipulating one variable to determine its effect on another, and quasi-experimental design is similar but lacks random assignment.

Importance of Research Design

The importance of research design cannot be overstated. A well-thought-out research design helps researchers to avoid biases, ensures the reliability of data, and enhances the validity of the results. It also provides a clear framework for the research process, making it easier to communicate findings to stakeholders. Furthermore, a robust research design can significantly contribute to the credibility of the research and its findings.

Components of Research Design

Key components of research design include the research question, hypothesis, variables, population, sampling methods, and data collection techniques. The research question guides the entire study, while the hypothesis provides a testable prediction. Variables are the elements that are manipulated or measured, and the population refers to the larger group from which a sample is drawn. Sampling methods determine how participants are selected, and data collection techniques outline how information will be gathered.

Qualitative vs. Quantitative Research Design

Research designs can be broadly categorized into qualitative and quantitative approaches. Qualitative research design focuses on exploring phenomena through in-depth understanding and subjective interpretation, often using methods such as interviews and focus groups. In contrast, quantitative research design emphasizes numerical data and statistical analysis, employing surveys and experiments to gather measurable information. Each approach has its strengths and weaknesses, and the choice between them depends on the research objectives.

Steps in Developing a Research Design

Developing a research design involves several critical steps. First, researchers must clearly define the research problem and formulate specific research questions. Next, they should conduct a literature review to understand existing knowledge and identify gaps. Following this, researchers need to select an appropriate research design type, determine the sample size, and choose data collection methods. Finally, they must outline the data analysis plan to interpret the results effectively.

Challenges in Research Design

Researchers often face various challenges in designing their studies. These challenges may include limited resources, time constraints, ethical considerations, and difficulties in accessing the target population. Additionally, researchers must be vigilant about potential biases that could affect the validity of their findings. Addressing these challenges requires careful planning and a thorough understanding of the research context.

Ethical Considerations in Research Design

Ethical considerations are paramount in research design. Researchers must ensure that their studies adhere to ethical guidelines, which include obtaining informed consent from participants, ensuring confidentiality, and minimizing harm. Ethical research design not only protects participants but also enhances the credibility of the research. Researchers should also consider the implications of their findings and how they may affect the broader community.

Evaluating Research Design

Evaluating the effectiveness of a research design involves assessing its reliability, validity, and applicability. Reliability refers to the consistency of the results, while validity pertains to the accuracy of the measurements and the extent to which the study addresses the research question. Applicability relates to how well the findings can be generalized to other contexts. Researchers should critically analyze these aspects to ensure the robustness of their study.

Conclusion of Research Design

In summary, research design is a fundamental aspect of the research process that influences the quality and credibility of the findings. By carefully considering the various components, types, and ethical implications of research design, researchers can enhance the effectiveness of their studies and contribute valuable insights to their fields.

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Observation of Interactions in Adolescent Group Therapy: A Mixed Methods Study

Eulàlia arias-pujol, m teresa anguera.

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Edited by: Pietro Cipresso, IRCCS Istituto Auxologico Italiano, Italy

Reviewed by: Antonio Calcagnì, University of Trento, Italy; Eleonora Riva, Università degli Studi di Milano, Italy

*Correspondence: Eulàlia Arias-Pujol [email protected]

This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology

Received 2017 Jan 13; Accepted 2017 Jun 29; Collection date 2017.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Group psychotherapy is a useful clinical practice for adolescents with mental health issues. Groups typically consist of young people of similar ages but with different personalities, and this results in a complex communication network. The goal of group psychoanalytic psychotherapy is to improve participants' mentalization abilities, facilitating interactions between peers and their therapist in a safe, containing environment. The main aim of this study was to analyze conversation turn-taking between a lead therapist, a co-therapist, and six adolescents over the course of 24 treatment sessions divided into four blocks over 8 months. We employed a mixed-methods design based on systematic observation, which we consider to be a mixed method itself, as the qualitative data collected in the initial observation phase is transformed into quantitative data and subsequently interpreted qualitatively with the aid of clinical vignettes. The observational methodology design was nomothetic, follow-up, and multidimensional. The choice of methodology is justified as we used an ad - hoc observation instrument combining a field format and a category system. Interobserver agreement was analyzed quantitatively by Cohen's kappa using the free QSEQ5 software program. Once we had confirmed the reliability of the data, these were analyzed by polar coordinate analysis, which is a powerful data reduction technique that provides a vector representation of relationships between categories. The results show significant relationships between the therapist and (1) the activation of turn-taking by the participants and the co-therapist and silence and (2) conversation-facilitating interventions and interventions designed to improve mentalization abilities. Detailed analysis of questions demonstrating interest in others showed how the communication changed from radial interactions stemming from the therapist at the beginning of therapy to circular interactions half way through. Repetition was found to be a powerful conversation facilitator. The results also illustrate the role of the therapist, who (1) did not facilitate interventions by all participants equally, (2) encouraged turn-taking from more inhibited members of the group, (3) stimulated conversation from the early stages of therapy, and (4) favored mentalization toward the end. Despite its complexity, polar coordinate analysis produces easy-to-interpret results in the form of vector maps.

Keywords: group therapy, adolescent interactions, mixed-method, polar coordinates analysis, mentalization

Introduction

Peer groups are a natural setting for young people (Erikson, 1968 ). In the social context, Malekoff ( 2014 ) and Tellerman ( 2001 ) consider group work to be a protective factor for teenagers, pre-teenagers, and their families. In the field of public health, group psychotherapy is a useful clinical practice for adolescents with varying mental health issues (Reid and Kolvin, 1993 ; Cramer-Azima, 2002 ). Adolescent mental health disorders have increased over the last three decades (Nuffield Foundation, 2013 ) and today's teenagers have higher rates of anxiety, behavioral problems, and mood disorders (Merikangas et al., 2010 ).

Little has been published on group therapy in children or adolescents. Most of the studies conducted to date have reported on brief cognitive-behavioral interventions with specified diagnostic populations (Pollock and Kymissis, 2001 ). There has also been research into group counseling and psychotherapy with children and adolescents that indicates that the peer feedback that occurs in such settings is a key part of the process of change (Shechtman, 2007 ). The theoretical orientation behind this study was a combination of interpersonal and psychodynamic theories. Pingitore ( 2016 ) validated the benefits of interpersonal group therapy, an approach originally proposed by Yalom ( 2005 ), by quantitatively analyzing audio recordings of interventions by eight adolescents who took part in a process-oriented psychotherapy group for 3 months. Within a Kleinian psychoanalytic framework and following the contributions of Devi and Fenn ( 2012 ) published a systematic thematic analysis of a latency-aged children's group. Through clinical extracts, they showed how the children shifted from paranoid-schizoid functioning to depressive functioning over the course of therapy. They concluded that psychotherapy was beneficial in latency-aged children, as it provided them with the opportunity to observe and try to attach meaning to the interactions of other people, to respond to these interactions, to initiate contact and to help and be helped in a safe environment. Such experiences improve individuals' ability to recognize and observe mental states in both themselves and others and to develop empathy.

More research has been conducted in adults. A recent meta-analysis of group psychotherapy for social anxiety disorders concluded that group interventions were as effective as individual psychotherapy or pharmacotherapy (Barkowski et al., 2016 ). Group therapy is also beneficial for adults with moderate or severe depression (Pylvänäinen et al., 2015 ) or eating disorders (Simpson et al., 2010 ) and it has been shown to reduce symptoms of anxiety, depression, and avoidance in adults with personality disorders (Skewes et al., 2015 ). Schwartze et al. ( 2016 ) recently published a meta-analysis that showed that cognitive behavioral therapy was effective for patients with obsessive-compulsive disorder. Another randomized controlled study that compared the outcomes of short- and long-term psychodynamic psychotherapy (90-min weekly sessions for 20 or 80 weeks) in 167 adult outpatients with mood, anxiety, and personality disorders found that patients in both groups made significant gains, and concluded that short- and long-term therapy seemed equally effective for typical outpatients seeking group psychotherapy, with the exception of symptomatic distress, for which a more favorable treatment effect was found for the long-term therapy (Lorentzen et al., 2013 ). A recent open prospective controlled study showed the efficacy of short-term dynamic group psychotherapy (37–39 sessions lasting 75 min over 9 months) in primary care patients with depressive symptoms (Bros et al., 2016 ).

In pyschotherapy research, there is growing concern for integrating qualitative methods, which provide a more holistic view of the person, and quantitative methods, which seek to provide a more objective view (Lutz and Hill, 2009 ). Despite the dearth of publications in the last decade, there are encouraging signs of a growing interest in the use of mixed-methods research in psychology (Roberts and Povee, 2014 ). By integrating complementary perspectives derived from quantitative and qualitative methods and analyses, mixed-methods research offers both rigor and flexibility and is likely to see an increase in future years (Anguera and Hernández-Mendo, 2016 ).

In this article, we describe the results of a study based on systematic observation, which we consider to be a mixed method in itself (Anguera and Hernández-Mendo, 2016 ). The study consisted of systematically observing video-recordings of adolescent group therapy sessions over a period of several months. The observation produced a large set of qualitative conversational data, subsequently analyzed quantitatively via polar coordinate analysis to detect changes in behaviors over the course of therapy.

The aim of the group therapy analyzed was to promote autonomy and maturity through interactions between peers and their therapist in a safe, containing environment (Torras de Beà, 2013 ). Group sessions of this type produce complex communication networks. Participants are typically young people of similar ages with different personalities who have difficulty relating to others and often perform poorly at school.

Psychodynamic interventions have been described as “conversation therapies,” as the relationship between the person seeking treatment and the therapist forms the basis of the therapy (Malmberg and Fenton, 2008 ). We studied group communication as a conversation in which we analyzed turn-taking (who) and content (what).

Foulkes ( 1986 ) described two roles for group analysis leaders, or conductors: a role as dynamic-administrator and a role as analyst-interpreter. The function of the first is to set up the group, establish norms and boundaries, and create a safe, supportive, and containing environment designed to increase participation, expressiveness, and interaction and communication. The function of the second, by contrast, is related to mental activity, and consists of observation, listening, and understanding, and the ability to put into words everything they are understanding.

In the group studied, interventions by a therapist largely seek to (a) facilitate conversation and (b) promote mentalization, i.e., stimulate thought, reflection, and understanding about oneself and one's relationships with others.

In the ad - hoc observation instrument used in the study, we labeled this first group of interventions DYN, as they have a dynamic, stimulating function. They are interventions in the form os a request or question in which the emitter (the therapist or participants) show interest in the life of the receiver. Demonstrating interest in others by asking questions, allowing them to intervene, and showing curiosity in their answers is considered to be a specific benefit of group therapy as opposed to individual therapy (Yalom, 2005 ; Torras de Beà, 2013 ). In previous studies, we saw that DYN interventions were very common in all sessions and that over the course of therapy, their use increased among participants and decreased among therapists, forming significant behavioral patterns (Arias and Anguera, 2004 , 2005 ; Arias, 2011 ).

The second group of interventions in the observation instrument was called MNT to reflect the concept of mentalization described by Fonagy et al. (Fonagy, 1991 ; Fonagy et al., 1995 ), which is understood as the ability to explain and give meaning to one's own behaviors and those of others within a process of mental representation, thoughts, desires, and expectations. This ability is not innate: it needs to be developed within a safe, affective environment, which in psychoanalytic group psychotherapy is achieved by maintaining a stable internal and external setting while containing anxieties. MNT interventions are part of the therapist's role (Bateman and Fonagy, 2012 ), while DYN interventions correspond to either the therapist or the participants over the course of the sessions.

At the beginning of these group sessions, communication is generally radial, i.e., it diverges outwards toward the participants from the formal leader of the group, the therapist. With time, it becomes circular, with participants spontaneously intervening and demonstrating interest in each other. This shift in the direction of communication is an indicator of the group process, and our aim was to objectively analyze this process by studying the therapist's interventions.

The main aim of this study was to apply polar coordinate analysis to analyze conversation turn-taking and DYN and MNT interventions in a group therapy program involving a lead therapist, a co-therapist, and six adolescents. The program consisted of 24 group sessions, divided into four blocks, held over a period of 8 months.

Materials and methods

In this mixed-methods study, we applied systematic observation, which meets the rigorous standards of scientific inquiry while at the same time offers the flexibility needed in real-life settings. Observational methodology permits the capture of spontaneous behaviors as they occur in a natural environment (Sackett, 1978 ; Anguera, 1979 , 2003 ; Bakeman and Quera, 1995b , 2011 ; Portell et al., 2015a , b ). It is thus an ideal methodology for studying communication in group therapy, and has proven to be suitable for studying the changes that occur over the course of therapy (Pascual-Leone et al., 2009 ).

There are eight possible study designs in observational methodology (Blanco-Villaseñor et al., 2003 ; Sánchez-Algarra and Anguera, 2013 ). The design used in this study was N/F/M (nomothetic/follow-up/multidimensional). It was nomothetic because we conducted a parallel analysis of the therapist, the co-therapist, and six adolecents, follow-up because we performed both intersessional analyses (24 successive sessions) and intrasessional analyses (sequential recording of all behaviors from the start to finish of each session), and multidimensional because the ad - hoc observation instrument contained various dimensions selected on the basis of the theoretical framework and our experience.

The systematic observation was non-participative and the behaviors were highly perceivable.

Participants

There were eight participants: the therapist (T), the co-therapist (coT), and six adolescents (G, D, JM, F, L, M). The adolescents (four boys and two girls) had requested support at the Center for Child and Adolescent Mental Health of the Eulàlia Torras de Beà Foundation in Barcelona, Spain. They all had difficulties relating to others and difficulties learning at school; they had normal or normal-low intelligence according to the Weschler Intelligence Scale for Children–Fourth Edition (WISC-IV, Weschler, 2006 ). Two had a mild behavioral disorder, three had anxiety problems, and one tended to disconnect (Table 1 , codes ICD-9-CM, Ministerio de Sanidad, Servicios Sociales e Igualdad, 2014 ).

Patient characteristics.

Pseudonyms have been used to protect confidentiality .

The inclusion criteria were (a) an age of 12–15 years and (b) recommendation for group therapy following diagnostic evaluation at the Mental Health Center. The exclusion criteria were (a) anticipated difficulty attending all the therapy sessions and (b) contraindication for group therapy.

The group was led by an expert therapist, assisted by a co-therapist who participated as an observer. Both were clinical psychologists trained in group psychoanalytic psychotherapy.

In accordance with the principles of the Declaration of Helsinki and the Ethical Code of the General Council of the Official College of Psychologists of Spain, the participants were informed that they were being filmed. They were shown the location of the video cameras, which were positioned discretely to minimize reactivity bias. Informed consent was also obtained from the parents of the minors.

Instruments

In systematic observation (Anguera, 2003 ; Sánchez-Algarra and Anguera, 2013 ), a distinction is made between recording instruments (i.e., those used to record or code data) and observation instruments (purposed-designed instruments to analyze a given subject).

Recording instrument

The group sessions were recorded using two video cameras, two microphones, two video units, and two screens comprising a closed-circuit television system. The dataset was built in the software program GSEQ5, v.5.1 (Bakeman and Quera, 2011 ) using an initial transcription of the video content. In accordance with the principles of the Declaration of Helsinki and the Ethical Code of the General Council of the Official College of Psychologists of Spain, the participants were informed that they were being filmed. They were shown the location of the video cameras, which were positioned discretely to minimize reactivity bias.

According to the terminology proposed by Bakeman ( 1978 ), the data recorded were type II data, i.e., they were concurrent (as we considered various dimensions and each behavior needs to be coded using a specific code) and event-based (as the behaviors were coded as they occurred, thereby providing information on order and sequence, two essential factors for our study). It is also possible to record duration, but this was not relevant to the purpose of our study. Once annotated, each behavior generates a co-occurrence of codes (corresponding to the different dimensions) and is methodologcally considered to be a multievent (Bakeman, 1978 ). A total of 30,436 multievents were coded in our study.

Observation instrument

The ad - hoc observation instrument used in the study combined a field format and category systems. It is a flexible instrument in which the different dimensions considered can be broken down into different categories according to the theoretical framework and experience. Considering the specific goals of the study and based on previous experiences (Arias and Anguera, 2004 , 2005 ), the observation instrument was redesigned to include 15 forms of communication. These forms, or dimensions of communication, were derived from the work of Torras de Beà ( 2013 ) on group psychotherapy and of Tusón ( 1995 ) and Calsamiglia and Tusón ( 1999 ) on conversation analysis.

The 15 dimensions included in the observation instrument are Facilitating of conversation, Reflective function, Expressivity, Defensive expressions, Dislike, Ordering, Humor, Confrontation, Exclamation, Degradation of vocal behavior, Whispering, Touching, Noise, Surrounding noise, and Silence (Table 2 ). Each of these dimensions was broken down to build a category system that fulfilled the requirements of exhaustivity and mutual exclusivity (Anguera, 2003 ).

Dimensions and category systems in the observation instrument for therapists and patients.

It should be noted that some dimensions gave rise to a single category, but given their conceptual relevance, we considered it important to include them as dimensions in the instrument. The dimensions and categories are shown in Table 2 .

The parents of the six adolescents were notified that their children had been proposed for group therapy after a diagnostic evaluation period. In addition, they all agreed to participate in a parallel group led by another therapist.

All the sessions were video-recorded and transcribed in full. Thirty sessions were held but due to technical difficulties with the recording, six were discarded because of poor audio. Therefore, 24 sessions were included in the final analysis. Each of the sessions lasted an hour. The sessions were grouped into four periods spanning an 8-month period.

Data quality control analysis: inter-observer agreement

For the data quality control analysis, two observers analyzed and coded four of the therapy sessions. They had been previously trained using the approach described by Anguera ( 2003 ). Agreement was assessed quantitatively using Cohen's kappa statistic (Cohen, 1960 , 1968 ) in GSEQ5 (version 5.1) following the recommendations of Bakeman and Quera ( 1995a , b , 2001 , 2011 ). According to the criteria of Landis and Koch ( 1977 ), the level of agreement was “almost perfect”, with kappa values ranging between 0.86 and 0.93 for all the sessions.

Data analysis

Polar coordinate analysis was used to analyze DYN and MNT interventions in accordance with the study objective. Polar coordinate analysis is a commonly used quantitative analytical method in observational methodology that identifies the statistical relationship between a behavior of interest (referred to in polar coordinate analysis as the focal behavior ) and other behaviors (referred to as conditional behaviors ). Associations between pairs of behaviors are represented graphically by vectors. Polar coordinate analysis requires a prior stage consisting of lag sequential analysis (Bakeman, 1978 , 1991 ), a technique used to reveal behavioral patterns based on occurrence of behaviors after (prospective) or before (retrospective) a given behavior (as the focal behavior is known in lag sequential analysis). The technique is based on calculating conditional and unconditional probabilities (based, respectively, on matched frequencies and simple frequencies) for each of the time lags considered, which may be positive or negative.

Lag sequential analysis produces large volumes of data, which are subsequently reduced through a powerful data reduction algorithm based on the Z sum = Σ z n parameter proposed by Cochran ( 1954 ), where z is the standard value corresponding to each lag for each of the conditional behaviors (known as target behaviors) and n is the number of lags considered. The Z sum is calculated for each target behavior for both positive lags (prospective Z sum ) and negative lags (retrospective Z sum ). The technique thus yields a statistical relationship between the given behavior and each of the target behaviors, which is reflected by a prospective and a retrospective Z sum value, as proposed by Sackett ( 1980 , 1987 ). To optimize the procedure, Anguera ( 1997 ) proposed a modification to the original technique (1980, 1987) based on the concept of genuine retrospectivity. This modified technique has been used on multiple occasions in the past two decades and was employed in the current study.

Polar coordinate analysis integrates the prospective and retrospective perspectives with the help of a vectorial map that contains four quadrants in which the prospective and retrospective Z sum values are plotted along the X and Y axis, respectively. Each target behavior analysis thus can be located in one of the four quadrants depending on the combination of negative/positive signs (Table 3 ).

Polar coordinate analysis results corresponding to interventions by the therapist (T) as the focal behavior and interventions by the participants (G D JM F L M), interventions by the co-therapist (CT), and silence as conditional behaviors.

Significant relationships (p < 0.05) between the focal behavior and conditional behaviors .

Polar coordinate analysis uses the prospective and retrospective Z sum values for each conditional behavior to calculate the length and angle of the corresponding vector, thus allowing these to be graphically represented. The length of the vector is √ ( Z s u m 2 P r o s p e c t i v e + Z s u m 2 R e t r o s p e c t i v e ) , and is considered to be statistically significant ( p < 0.05) when it exceeds 1.96 The angle of the vector is calculated as follows: φ = arc sen Z s u m R e t r o s p e c t i v e L e n g t h and it is then adjusted according to the quadrant in which it is located: quadrant I (0 < φ <90) = φ; quadrant II (90 < φ <180) = 180 − φ; quadrant III (180 < φ < 270) = 180 + φ; quadrant IV (270° < φ < 360°) = 360° − φ.

The meanings of the different quadrants are shown in Figure 1 .

Characteristics of the quadrants in which the vectors are located according to the activation (+) or inhibition (–) sign carried by the Prospective and Retrospective Zsum values.

Quadrants I and III are symmetrical in terms of the relationship they depict between the focal behavior and the different conditional behaviors they contain. Quadrant I (++) indicates mutual activation while quadrant III (−) indicates mutual inhibition. Quadrants II and IV, in turn, depict asymmetrical relationships. Quadrant II (−+) indicates that the focal behavior inhibits but at the same time is activated by the conditional behaviors, while quadrant IV (+−) indicates the opposite (i.e., the focal behavior activates and is inhibited by the corresponding conditional behaviors).

The polar coordinate analysis for this study was performed in HOISAN v. 1.6.3.2 (Hernández-Mendo et al., 2012 ), which contains all the necessary modules and also produces partial results for adjusted residuals and z values in addition to analytical parameters and polar coordinate maps. The analysis was conducted by exporting the data file from GSEQ5 to HOISAN.

Polar coordinate analysis has been used in certain areas of clinical psychology, such as groups of children with autistic siblings (Venturella, 2016 ). It has also been widely applied in sports (Perea et al., 2012 ; Robles-Prieto et al., 2014 ; Echeazarra et al., 2015 ; López-López et al., 2015 ; Morillo-Baro et al., 2015 ; Sousa et al., 2015 ; Castañer et al., 2016 , 2017 ; Aragón et al., 2017 ) and school settings (Herrero Nivela, 2000 ; Anguera et al., 2003 ; López et al., 2016 ; Santoyo et al., 2017 ). As a final note of interest, when Sackett ( 1980 ) first presented polar coordinate analysis, he used it to study turn-taking in conversation.

Results and discussion

In the sections below, we describe the relationships detected between interventions by the therapist and the group participants using polar coordinate analysis.

Relationships between turn-taking by the therapist, turn-taking by the participants and the co-therapist, and silence

The focal behavior was intervention by the therapist (T) and the conditional behaviors were interventions by the participants (G, D, JM, F L, and M), interventions by the co-therapist (coT), and silence (Q) in the four blocks of sessions spanning 8 weeks.

As shown in Table 3 , the majority of results were significant.

The graphs in Figure 2 show the vectors representing turn-taking by the participants and the co-therapist and silence. In the case of the adolescents, some of the vectors are located in the mutual inhibition quadrant (quadrant III) while others are located in the mutual activation quadrant (quadrant I). On analyzing the four blocks of sessions grouped by time, it can clearly be seen that the turn-taking behavior by D, L, and M changed over the course of therapy, that of the co-therapist and silence remained stable.

Vectors corresponding to interventions by the therapist (T) as the focal behavior and interventions by the participants (G, D, JM, F L, and M), interventions by the co-therapist (coT), and silence (Q) as conditional behaviors. Session blocks 1-2-3-4 (from left to right).

Relationship between the therapist and DYN and MNT interventions

Again, the focal behavior was intervention by the therapist (T) and the conditional behaviors were the DYN categories FF, FO, RP, RT, QA, QC, and QV and the MNT category.

The majority of results in this case were also significant (Table 4 ).

Polar coordinate analysis results with interventions by the therapist (T) as the focal behavior and DYN categories (broken down) and MNT as conditional behaviors.

The graphs in Figure 3 show the vectors for the different relationships distributed among the four quadrants. On examining the figures by blocks of time, it can be seen that the vectors tend to form clusters, with the majority located in the mutual activation quadrant (quadrant I) by the end of the therapy. Note that the length of the radius for repetition (RP) and the quadrant in which it was located (quadrant I) remained stable over the four periods.

Vectors corresponding to interventions by the therapist (T) as the focal behavior and conversationfacilitating DYN categories (FF, FO, RP, RT, QA, QC, QV) and the mentalizing or reflective function MNT category as conditional behaviors. Session blocks 1-2-3-4 (from left to right).

Below we discuss the significance of the relationships detected by polar coordinate analysis in five sections. We also illustrate our findings with clinical vignettes containing coded transcripts of the interventions.

Turn-taking by the therapist and the adolescents

All the significant results are located in two opposing quadrants, indicating two clearly differentiated types of relationship: mutual activation and mutual inhibition. The therapist always facilitates intervention by Fred, the participant with the greatest difficulty relating to others, and in the early phases of therapy, she also encourages interaction from Danny, John M, and Meg. Her interventions never activate those of the two impulsive participants, Gabriel and Lucy. This does not mean that she excludes these participants, simply that they intervene on their own initiative. The changes detected in Danny, John M, and Meg are an indication of the progress they make over the therapy. Block 1 is characterized by radial communication between the therapist and all the participants. Vignette 1 shows an example of an interaction between the therapist and Danny (Table 5 ).

Clinical vignette 1.

However, not all interactions are the same. Gabriel and Lucy, for example, spontaneously take turns in these early sessions (Table 6 ).

Clinical vignette 2.

Lucy raises conflicts about herself that interest everyone (Table 7 ).

Clinical vignette 3.

John M is a reserved person with anxiety problems. He has difficulty intervening and when he does, he often mumbles, says very little, and adheres to what has just been said (Table 8 ).

Clinical vignette 4.

Haen and Weil ( 2010 ) have highlighted the difficulties that adolescents have engaging during this initial stage of therapy. In our study, as the therapy progresses, the adolescents start to communicate much more naturally and spontaneously and bring up issues that concern them, such as going out, the end of the school year, and their expectations for the coming year. Vignette 5, which contains an excerpt from this last block, shows how Danny, John M, Lucy, and Meg chat freely amongst themselves, without encouragement from the therapist. Amidst jokes, exclamations, gestures, and laughter, they talk about meeting outside the group and about their fears of traveling alone on the train or underground for the first time (Table 9 ).

Clinical vignette 5.

Turn-taking by the therapist and the co-therapist

The co-therapist and the therapist was mutually activated (quadrant 1). The co-therapist's interventions reflect her role of interfering as little as possible in the group dynamics. They complement those of the main therapist. Together, they form a team and create and maintain a safe environment (Shechtman 2007; Torras de Beà, 2013 ; Malekoff, 2014 ).

The therapist and silence

The therapist generates silence but also breaks it (quadrant 1).

The examples below show how the adolescents fall silent when faced with difficult issues, such as verbalizing why they are in the group or talking about their relationship with their parents or their concerns about sexuality (Tables 10 – 12 ).

Clinical vignette 6.

Clinical vignette 8.

Clinical vignette 7.

The therapist and DYN interventions

The different strategies for facilitating conversation (FF, FO, RP, RT, QA, QC, and QV) showed varying patterns of change over the course of therapy but converged at the end.

Repetition (RP) was the most powerful strategy, as it activated conversation from the start of the therapy program. The next most powerful strategies were phatic function (FF) and greetings (FO). The transcripts of the sessions show that in the early sessions, it was the therapist who verbally greeted the adolescents (by saying hello and goodbye). However, few of them responded and the others returned the greeting or made a non-verbal gesture. This behavior changes after the first block, indicating an increase in reciprocity between the therapist and the participants.

The appearance of QA (questions directed at others) in the second half of the therapy is, in our opinion, a highly significant indicator of the group process. It tells us that the communication is no longer radial and that the adolescents have achieved one of the most important benefits of group therapy, which is showing interest in others (Yalom, 2005 ) in the presence of the therapist (Torras de Beà, 2013 ).

It is also interesting to see how QV (repetition of a previous utterance in the form of a question) changes from being mutually inhibitory to being mutually activating. We think that this strategy initially surprised the adolescents but was then gradually adopted by them. The same was not observed for QC (clarifying questions), which were used only by the therapist when the adolescents were “doing their own thing” and she was “excluded” from the group. Examples of what she said were: “I'm not quite following you now…maybe I'm being a bit dense, can you help me understand what's going on?” This strategy is similar to the attitude of respectful curiosity shown by therapists in the Adolescent Mentalization-Based Integrative Treatment (AMBIT) approach (mentalizing stance), which is designed to help put a halt to non-mentalization mental states (Benvington et al., 2012 ; Dangerfield, 2016 ).

Bringing back a central topic of conversation (RT) and suggesting looking at this in greater depth was only done by the therapist.

At the end of therapy, all the categories in the DYN dimension except RT are located in the mutually activating quadrant. This supports the idea that the communication strategies used by the therapist were adopted by the participants, enabling them to talk more autonomously and facilitating their personal growth (Yalom, 2005 ; Torras de Beà, 2013 ).

The therapist and MNT interventions

The changes observed in the MNT category, which corresponds to interventions aimed at improving the adolescents' mentalization abilities, also reflect interesting aspects of the group process. The MNT category changed from inhibitory (quadrant III) to partially inhibitory (quadrant II) and finally to mutually activating (quadrant I). The changes also show that the therapist's role changed over time, as mentalization strategies were only used by her. We can deduce that the participants gradually overcame their early inhibitions and dependence and acquired more sophisticated mentalizing abilities, helping them to become more aware of themselves and of others. This result is consistent with the concept known as the interpretative function of the therapist within the theories of Foulkes ( 1986 ) and Torras de Beà ( 2013 ).

Conclusions

Polar coordinate analysis provides a new approach for gaining insights into dialogue in group pyschotherapy. The results show that the technique provides a novel means of analyzing the role of the therapist and describing her conversational style. The therapist proved to be an expert in creating a communicative environment that allowed the adolescents to grow. She employed four core strategies: (1) she did not facilitate communication equally for all participants, (2) she encouraged turn-taking by the more inhibited members of the group, (3) she stimulated conversation from the early stages of therapy, and (4) she promoted mentalization toward the end of therapy.

We were particularly pleased to see that the use of repetition (RP) facilitated communication flows from the beginning. The positive results indicate that rather than simply acting as an echo or a loudspeaker, this strategy produces a mirroring effect similar to that described in the social biofeedback theory of parental affect-mirring (Gergely and Watson, 1996 ), in which the person talking, apart from being listened to, is brought into a mirror-like interaction. This regulatory effect is a prerequisite for the mentalization process that facilitates the development of the self (Fonagy et al., 2002 ).

Observational methodology and polar coordinate analysis could prove to be of great value for detecting changes in psychotherapy models based on spoken conversation.

Author contributions

EA developed the project. MA performed the method section and polar coordinate analysis. Both authors have participated in the writing of the article.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank all those at the Center for Child and Adolescent Mental Health of the Eulàlia Torras de Beà Foundation in Barcelona, Spain, who so willingly helped to make this study possible, as well as all the adolescents and families who participated.

Funding. This study was supported by the Catalan government under grant number 2014 SGR 1088 for the project Grup de recerca comunicació i salut (COMSAL) and Grup de recerca i innovació en dissenys (GRID) and under Grant number 2014 SGR 971 for the project Tecnologia i aplicació multimedia i digital als dissenys observacionals . We also gratefully acknowledge the support of the Spanish government (Ministerio de Economía y Competitvidad) within the Projects Avances metodológicos y tecnológicos en el estudio observacional del comportamiento deportivo [Grant PSI2015-71947-REDT; MINECO/FEDER, UE] (2015-2017), and La actividad f í sica y el deporte como potenciadores del estilo de vida saludable: evaluación del comportamiento deportivo desde metodolog í as no intrusivas [Grant DEP2015-66069-P; MINECO/FEDER, UE] (2016-2018). Lastly, we also acknowledge the support of Ramon Llull University (PGRiD of FPCEE Blanquerna) and University of Barcelona (Vice-Chancellorship of Doctorate and Research Promotion).

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Market Research: A How-To Guide and Template

Discover the different types of market research, how to conduct your own market research, and use a free template to help you along the way.

MARKET RESEARCH KIT

5 Research and Planning Templates + a Free Guide on How to Use Them in Your Market Research

Updated: 02/21/24

Published: 03/30/16

Today's consumers have a lot of power. As a business, you must have a deep understanding of who your buyers are and what influences their purchase decisions.

Enter: Market Research.

Whether you're new to market research or not, I created this guide to help you conduct a thorough study of your market, target audience, competition, and more. Let’s dive in.

Table of Contents

What is market research?

Primary vs. secondary research, types of market research, how to do market research, market research report template, market research examples.

Market research is the process of gathering information about your target market and customers to verify the success of a new product, help your team iterate on an existing product, or understand brand perception to ensure your team is effectively communicating your company's value effectively.

Market research can answer various questions about the state of an industry. But if you ask me, it's hardly a crystal ball that marketers can rely on for insights on their customers.

Market researchers investigate several areas of the market, and it can take weeks or even months to paint an accurate picture of the business landscape.

However, researching just one of those areas can make you more intuitive to who your buyers are and how to deliver value that no other business is offering them right now.

How? Consider these two things:

• Your competitors also have experienced individuals in the industry and a customer base. It‘s very possible that your immediate resources are, in many ways, equal to those of your competition’s immediate resources. Seeking a larger sample size for answers can provide a better edge.
• Your customers don't represent the attitudes of an entire market. They represent the attitudes of the part of the market that is already drawn to your brand.

The market research services market is growing rapidly, which signifies a strong interest in market research as we enter 2024. The market is expected to grow from roughly $75 billion in 2021 to $90.79 billion in 2025 .

Free Market Research Kit

• SWOT Analysis Template
• Survey Template
• Focus Group Template

Download Free

All fields are required.

You're all set!

Click this link to access this resource at any time.

Why do market research?

Market research allows you to meet your buyer where they are.

As our world becomes louder and demands more of our attention, this proves invaluable.

By understanding your buyer's problems, pain points, and desired solutions, you can aptly craft your product or service to naturally appeal to them.

Market research also provides insight into the following:

• Where your target audience and current customers conduct their product or service research
• Which of your competitors your target audience looks to for information, options, or purchases
• What's trending in your industry and in the eyes of your buyer
• Who makes up your market and what their challenges are
• What influences purchases and conversions among your target audience
• Consumer attitudes about a particular topic, pain, product, or brand
• Whether there‘s demand for the business initiatives you’re investing in
• Unaddressed or underserved customer needs that can be flipped into selling opportunity
• Attitudes about pricing for a particular product or service

Ultimately, market research allows you to get information from a larger sample size of your target audience, eliminating bias and assumptions so that you can get to the heart of consumer attitudes.

As a result, you can make better business decisions.

To give you an idea of how extensive market research can get , consider that it can either be qualitative or quantitative in nature — depending on the studies you conduct and what you're trying to learn about your industry.

Qualitative research is concerned with public opinion, and explores how the market feels about the products currently available in that market.

Quantitative research is concerned with data, and looks for relevant trends in the information that's gathered from public records.

That said, there are two main types of market research that your business can conduct to collect actionable information on your products: primary research and secondary research.

Primary Research

Primary research is the pursuit of first-hand information about your market and the customers within your market.

It's useful when segmenting your market and establishing your buyer personas.

Primary market research tends to fall into one of two buckets:

• Exploratory Primary Research: This kind of primary market research normally takes place as a first step — before any specific research has been performed — and may involve open-ended interviews or surveys with small numbers of people.
• Specific Primary Research: This type of research often follows exploratory research. In specific research, you take a smaller or more precise segment of your audience and ask questions aimed at solving a suspected problem.

Secondary Research

Secondary research is all the data and public records you have at your disposal to draw conclusions from (e.g. trend reports, market statistics, industry content, and sales data you already have on your business).

Secondary research is particularly useful for analyzing your competitors . The main buckets your secondary market research will fall into include:

• Public Sources: These sources are your first and most-accessible layer of material when conducting secondary market research. They're often free to find and review — like government statistics (e.g., from the U.S. Census Bureau ).
• Commercial Sources: These sources often come in the form of pay-to-access market reports, consisting of industry insight compiled by a research agency like Pew , Gartner , or Forrester .
• Internal Sources: This is the market data your organization already has like average revenue per sale, customer retention rates, and other historical data that can help you draw conclusions on buyer needs.
• Focus Groups
• Product/ Service Use Research
• Observation-Based Research
• Buyer Persona Research
• Market Segmentation Research
• Pricing Research
• Competitive Analysis Research
• Customer Satisfaction and Loyalty Research
• Brand Awareness Research
• Campaign Research

1. Interviews

Interviews allow for face-to-face discussions so you can allow for a natural flow of conversation. Your interviewees can answer questions about themselves to help you design your buyer personas and shape your entire marketing strategy.

2. Focus Groups

Focus groups provide you with a handful of carefully-selected people that can test out your product and provide feedback. This type of market research can give you ideas for product differentiation.

3. Product/Service Use Research

Product or service use research offers insight into how and why your audience uses your product or service. This type of market research also gives you an idea of the product or service's usability for your target audience.

4. Observation-Based Research

Observation-based research allows you to sit back and watch the ways in which your target audience members go about using your product or service, what works well in terms of UX , and which aspects of it could be improved.

5. Buyer Persona Research

Buyer persona research gives you a realistic look at who makes up your target audience, what their challenges are, why they want your product or service, and what they need from your business or brand.

6. Market Segmentation Research

Market segmentation research allows you to categorize your target audience into different groups (or segments) based on specific and defining characteristics. This way, you can determine effective ways to meet their needs.

7. Pricing Research

Pricing research helps you define your pricing strategy . It gives you an idea of what similar products or services in your market sell for and what your target audience is willing to pay.

8. Competitive Analysis

Competitive analyses give you a deep understanding of the competition in your market and industry. You can learn about what's doing well in your industry and how you can separate yourself from the competition .

9. Customer Satisfaction and Loyalty Research

Customer satisfaction and loyalty research gives you a look into how you can get current customers to return for more business and what will motivate them to do so (e.g., loyalty programs , rewards, remarkable customer service).

10. Brand Awareness Research

Brand awareness research tells you what your target audience knows about and recognizes from your brand. It tells you about the associations people make when they think about your business.

11. Campaign Research

Campaign research entails looking into your past campaigns and analyzing their success among your target audience and current customers. The goal is to use these learnings to inform future campaigns.

• Define your buyer persona.
• Identify a persona group to engage.
• Prepare research questions for your market research participants.
• List your primary competitors.
• Summarize your findings.

1. Define your buyer persona.

You have to understand who your customers are and how customers in your industry make buying decisions.

This is where your buyer personas come in handy. Buyer personas — sometimes referred to as marketing personas — are fictional, generalized representations of your ideal customers.

Use a free tool to create a buyer persona that your entire company can use to market, sell, and serve better.

10 Free Competitive Analysis Templates

Track and analyze your competitors with these ten free planning templates.

• SWOT Analysis
• Battle Cards
• Feature Comparison
• Strategic Overview

Identifying Content Competitors

Search engines are your best friends in this area of secondary market research.

To find the online publications with which you compete, take the overarching industry term you identified in the section above, and come up with a handful of more specific industry terms your company identifies with.

A catering business, for example, might generally be a “food service” company, but also consider itself a vendor in “event catering,” “cake catering,” or “baked goods.” Once you have this list, do the following:

• Google it. Don't underestimate the value in seeing which websites come up when you run a search on Google for the industry terms that describe your company. You might find a mix of product developers, blogs, magazines, and more.
• Compare your search results against your buyer persona. If the content the website publishes seems like the stuff your buyer persona would want to see, it's a potential competitor, and should be added to your list of competitors.

5. Summarize your findings.

Feeling overwhelmed by the notes you took? We suggest looking for common themes that will help you tell a story and create a list of action items.

To make the process easier, try using your favorite presentation software to make a report, as it will make it easy to add in quotes, diagrams, or call clips.

Feel free to add your own flair, but the following outline should help you craft a clear summary:

• Background: Your goals and why you conducted this study.
• Participants: Who you talked to. A table works well so you can break groups down by persona and customer/prospect.
• Executive Summary : What were the most interesting things you learned? What do you plan to do about it?
• Awareness: Describe the common triggers that lead someone to enter into an evaluation. (Quotes can be very powerful.)
• Consideration: Provide the main themes you uncovered, as well as the detailed sources buyers use when conducting their evaluation.
• Decision: Paint the picture of how a decision is really made by including the people at the center of influence and any product features or information that can make or break a deal.
• Action Plan: Your analysis probably uncovered a few campaigns you can run to get your brand in front of buyers earlier and/or more effectively. Provide your list of priorities, a timeline, and the impact it will have on your business.

Within a market research kit, there are a number of critical pieces of information for your business‘s success. Let’s take a look at these elements.

Pro Tip: Upon downloading HubSpot's free Market Research Kit , you'll receive editable templates for each of the given parts of the kit, instructions on how to use the kit, and a mock presentation that you can edit and customize.

What Is a Competitive Analysis — and How Do You Conduct One?

The Beginner's Guide to the Competitive Matrix [+ Templates]

What is a Competitive Analysis — and How Do You Conduct One?

9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

SWOT Analysis: How To Do One [With Template & Examples]

28 Tools & Resources for Conducting Market Research

TAM, SAM & SOM: What Do They Mean & How Do You Calculate Them?

How to Run a Competitor Analysis [Free Guide]

5 Challenges Marketers Face in Understanding Audiences [New Data + Market Researcher Tips]

Causal Research: The Complete Guide

Free Guide & Templates to Help Your Market Research

Marketing software that helps you drive revenue, save time and resources, and measure and optimize your investments — all on one easy-to-use platform