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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• 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 objectives 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, other interesting articles, frequently asked questions about research design.

• 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.

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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
• 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 analyzing 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, organizations, 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 generalize 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 generalize 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, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors 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 kinds of 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.

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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 high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization 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 research bias and ensure a representative sample?

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

Using descriptive statistics , you can summarize 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 analyzing 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.

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

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

 Statistics

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

Research bias

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

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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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.

Why is research methodology important?

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 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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

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

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

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

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

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

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

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

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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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A Comprehensive Guide to Methodology in Research

Table of Contents

Research methodology plays a crucial role in any study or investigation. It provides the framework for collecting, analyzing, and interpreting data, ensuring that the research is reliable, valid, and credible. Understanding the importance of research methodology is essential for conducting rigorous and meaningful research.

In this article, we'll explore the various aspects of research methodology, from its types to best practices, ensuring you have the knowledge needed to conduct impactful research.

What is Research Methodology?

Research methodology refers to the system of procedures, techniques, and tools used to carry out a research study. It encompasses the overall approach, including the research design, data collection methods, data analysis techniques, and the interpretation of findings.

Research methodology plays a crucial role in the field of research, as it sets the foundation for any study. It provides researchers with a structured framework to ensure that their investigations are conducted in a systematic and organized manner. By following a well-defined methodology, researchers can ensure that their findings are reliable, valid, and meaningful.

When defining research methodology, one of the first steps is to identify the research problem. This involves clearly understanding the issue or topic that the study aims to address. By defining the research problem, researchers can narrow down their focus and determine the specific objectives they want to achieve through their study.

How to Define Research Methodology

Once the research problem is identified, researchers move on to defining the research questions. These questions serve as a guide for the study, helping researchers to gather relevant information and analyze it effectively. The research questions should be clear, concise, and aligned with the overall goals of the study.

After defining the research questions, researchers need to determine how data will be collected and analyzed. This involves selecting appropriate data collection methods, such as surveys, interviews, observations, or experiments. The choice of data collection methods depends on various factors, including the nature of the research problem, the target population, and the available resources.

Once the data is collected, researchers need to analyze it using appropriate data analysis techniques. This may involve statistical analysis, qualitative analysis, or a combination of both, depending on the nature of the data and the research questions. The analysis of data helps researchers to draw meaningful conclusions and make informed decisions based on their findings.

Role of Methodology in Research

Methodology plays a crucial role in research, as it ensures that the study is conducted in a systematic and organized manner. It provides a clear roadmap for researchers to follow, ensuring that the research objectives are met effectively. By following a well-defined methodology, researchers can minimize bias, errors, and inconsistencies in their study, thus enhancing the reliability and validity of their findings.

In addition to providing a structured approach, research methodology also helps in establishing the reliability and validity of the study. Reliability refers to the consistency and stability of the research findings, while validity refers to the accuracy and truthfulness of the findings. By using appropriate research methods and techniques, researchers can ensure that their study produces reliable and valid results, which can be used to make informed decisions and contribute to the existing body of knowledge.

Steps in Choosing the Right Research Methodology

Choosing the appropriate research methodology for your study is a critical step in ensuring the success of your research. Let's explore some steps to help you select the right research methodology:

Identifying the Research Problem

The first step in choosing the right research methodology is to clearly identify and define the research problem. Understanding the research problem will help you determine which methodology will best address your research questions and objectives.

Identifying the research problem involves a thorough examination of the existing literature in your field of study. This step allows you to gain a comprehensive understanding of the current state of knowledge and identify any gaps that your research can fill. By identifying the research problem, you can ensure that your study contributes to the existing body of knowledge and addresses a significant research gap.

Once you have identified the research problem, you need to consider the scope of your study. Are you focusing on a specific population, geographic area, or time frame? Understanding the scope of your research will help you determine the appropriate research methodology to use.

Reviewing Previous Research

Before finalizing the research methodology, it is essential to review previous research conducted in the field. This will allow you to identify gaps, determine the most effective methodologies used in similar studies, and build upon existing knowledge.

Reviewing previous research involves conducting a systematic review of relevant literature. This process includes searching for and analyzing published studies, articles, and reports that are related to your research topic. By reviewing previous research, you can gain insights into the strengths and limitations of different methodologies and make informed decisions about which approach to adopt.

During the review process, it is important to critically evaluate the quality and reliability of the existing research. Consider factors such as the sample size, research design, data collection methods, and statistical analysis techniques used in previous studies. This evaluation will help you determine the most appropriate research methodology for your own study.

Formulating Research Questions

Once the research problem is identified, formulate specific and relevant research questions. These questions will guide your methodology selection process by helping you determine what type of data you need to collect and how to analyze it.

Formulating research questions involves breaking down the research problem into smaller, more manageable components. These questions should be clear, concise, and measurable. They should also align with the objectives of your study and provide a framework for data collection and analysis.

When formulating research questions, consider the different types of data that can be collected, such as qualitative or quantitative data. Depending on the nature of your research questions, you may need to employ different data collection methods, such as interviews, surveys, observations, or experiments. By carefully formulating research questions, you can ensure that your chosen methodology will enable you to collect the necessary data to answer your research questions effectively.

Implementing the Research Methodology

After choosing the appropriate research methodology, it is time to implement it. This stage involves collecting data using various techniques and analyzing the gathered information. Let's explore two crucial aspects of implementing the research methodology:

Data Collection Techniques

Data collection techniques depend on the chosen research methodology. They can include surveys, interviews, observations, experiments, or document analysis. Selecting the most suitable data collection techniques will ensure accurate and relevant data for your study.

Data Analysis Methods

Data analysis is a critical part of the research process. It involves interpreting and making sense of the collected data to draw meaningful conclusions. Depending on the research methodology, data analysis methods can include statistical analysis, content analysis, thematic analysis, or grounded theory.

Ensuring the Validity and Reliability of Your Research

In order to ensure the validity and reliability of your research findings, it is important to address these two key aspects:

Understanding Validity in Research

Validity refers to the accuracy and soundness of a research study. It is crucial to ensure that the research methods used effectively measure what they intend to measure. Researchers can enhance validity by using proper sampling techniques, carefully designing research instruments, and ensuring accurate data collection.

Ensuring Reliability in Your Study

Reliability refers to the consistency and stability of the research results. It is important to ensure that the research methods and instruments used yield consistent and reproducible results. Researchers can enhance reliability by using standardized procedures, ensuring inter-rater reliability, and conducting pilot studies.

A comprehensive understanding of research methodology is essential for conducting high-quality research. By selecting the right research methodology, researchers can ensure that their studies are rigorous, reliable, and valid. It is crucial to follow the steps in choosing the appropriate methodology, implement the chosen methodology effectively, and address validity and reliability concerns throughout the research process. By doing so, researchers can contribute valuable insights and advances in their respective fields.

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Research Methods Guide: Research Design & Method

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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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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 methods, such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

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

Research Design Elements

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

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

Characteristics of Research Design

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

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

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

Research Design Types

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

Qualitative research

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

Quantitative research

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

Qualitative Research vs Quantitative Research

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

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

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

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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

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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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• 6. Cochran WG. Sampling Techniques. 3rd ed. New York: John Wiley & Sons; 1977
• 7. Saaty TL. The Analytical Hierarchy Process. Pittsburg: PWS Publications; 1990
• 8. Sekaran U, Bougie R. Research Methods for Business: A Skill Building Approach. 5th ed. New Delhi: John Wiley & Sons, Ltd; 2010. pp. 1-468
• 9. Luck DJ, Rubin RS. Marketing Research. 7th ed. New Jersey: Prentice-Hall International; 1987
• 10. Wong TC. Marketing Research. Oxford, UK: Butterworth-Heinemann; 1999
• 11. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951; 16 :297-334
• 12. Tavakol M, Dennick R. Making sense of Cronbach’s alpha. International Journal of Medical Education. 2011; 2 :53-55. DOI: 10.5116/ijme.4dfb.8dfd
• 13. Babbie E. The Practice of Social Research. 12th ed. Belmont, CA: Wadsworth; 2010
• 14. Polit DF, Beck CT. Generating and Assessing Evidence for Nursing Practice. 8th ed. Williams and Wilkins: Lippincott; 2008

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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Research Design

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This chapter introduces methods to design the research. Research design is the blueprint of how to conduct research from conception to completion. It requires careful crafts to ensure success. The initial step of research design is to theorize key concepts of the research questions, operationalize the variables used to measure the key concepts, and carefully identify the levels of measurements for all the key variables. After theorization of the key concepts, a thorough literature search and synthetization is imperative to explore extant studies related to the research questions. The purpose of literature review is to retrieve ideas, replicate studies, or fill the gap for issues and theories that extant research has (or has not) investigated.

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Borrego, M., Douglas, E. P., & Amelink, C. T. (2009). Quantitative, qualitative, and mixed research methods in engineering education. Journal of Engineering Education, 98 (1), 53–66.

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Research Design

Same as research approach, different textbooks place different meanings on research design. Some authors consider research design as the choice between qualitative and quantitative research methods. Others argue that research design refers to the choice of specific methods of data collection and analysis. Research design is also placed as a master plan for conducting a research project and this appears to be the most authentic explanation of the term.

 In your dissertation you can define research design as a general plan about what you will do to answer the research question. [1] It is a framework for choosing specific methods of data collection and data analysis.

Research design can be divided into two groups:  exploratory  and  conclusive . Exploratory research, according to its name merely aims to explore specific aspects of the research area. Exploratory research does not aim to provide final and conclusive answers to research questions. The researcher may even change the direction of the study to a certain extent, however not fundamentally, according to new evidences gained during the research process.

Conclusive research, on the contrary, generate findings that can be practically useful for decision-making. The following Table 1 illustrates the main differences between exploratory and conclusive research in relation to important components of a dissertation.

Table 1 Major differences between exploratory and conclusive research design [2]

The following can be mentioned as examples with exploratory design:

• A critical analysis of argument of mandatory CSR for UK private sector organisations
• A study into contradictions between CSR program and initiatives and business practices: a case study of Philip Morris USA
• An investigation into the ways of customer relationship management in mobile marketing environment

Studies listed above do not aim to generate final and conclusive evidences to research questions. These studies merely aim to explore their respective research areas.

Conclusive research  can be divided into two categories:  descriptive  and  causal . Descriptive research design, as the name suggests, describes specific elements, causes, or phenomena in the research area.

Table 2 Examples for descriptive research design

Causal research design , on the other hand, is conducted to study cause-and-effect relationships.  Table 3 below illustrates some examples for studies with causal research design.

Table 3 Examples for studies with causal design

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research designs. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  methods of data collection ,  data analysis  and  sampling  are explained in this e-book in simple words.

John Dudovskiy

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

[2] Source: Pride and Ferrell (2007)

Home » Education » Difference Between Research Methods and Research Design

Difference Between Research Methods and Research Design

Main difference – research methods vs research design.

Research methods and research design are terms you must know before starting a research project. Both these elements are essential to the success of a research project. However, many new researchers assume research methods and research design to be the same. Research design is the overall structure of a research project. For example, if you are building a house, you need to have a good idea about what kind of house you are going to build; you cannot do anything without knowing this. A research design is the same – you cannot proceed with the research study without having a proper research design. Research methods are the procedures that are used to collect and analyze data. Thus, the main difference between research methods and research design is that research design is the overall structure of the research study whereas research methods are the various processes, procedures, and tools used to collect and analyze data.

1. What are Research Methods?      – Definition, Features, Characteristics

2. What is Research Design?      – Definition, Features, Characteristics

What are Research Methods

Research methods are concerned with the various research processes, procedures, and tools – techniques of gathering information, various ways of analyzing them. Research problems can be categorized into two basic sections: qualitative research and quantitative research . Researchers may use one or both of these methods (mixed method) in their research studies. The type of research method you choose would depend on your research questions or problem and research design.

The main aim of a research study is to produce new knowledge or deepen the existing understanding of a field. This can be done by three forms.

Exploratory research – identifies and outlines a problem or question

Constructive research – tests theories and suggests solutions to a problem or question

Empirical research – tests the viability of a solution using empirical evidence

What is a Research Design

Research design is the overall plan or structure of the research project. It indicates what type of study is planned and what kind of results are expected from this project. It specifically focuses on the final results of the research. It is almost impossible to proceed with a research project without a proper research design. The main function of a research design is to make sure that the information gathered throughout the research answers the initial question unambiguously. In other words, the final outcomes and conclusions of the research must correspond with the research problems chosen at the beginning of the research.

A research design can be,

Descriptive (case study, survey, naturalistic observation, etc.)

Correlational (case-control study, observational study, etc.)

Experimental (experiments)

Semi-experimental (field experiment, quasi-experiment, etc.)

Meta-analytic (meta-analysis)

Review ( literature review , systematic review)

Research Methods : Research methods are the procedures that will be used to collect and analyze data.

Research Design: Research design is the overall structure of the research.

Research Methods: Research methods focus on what type of methods are more suitable to collect and analyze the evidence we need.

Research Design: Research design focuses on what type of study is planned and what kind of results are expected from the research.

Research Methods: Research methods depend on the research design.

Research Design: Research design is based on the research question or problem.

De Vaus, D. A. 2001. Research design in social research. London: SAGE.

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Learners develop a proposed research project, from conception of an idea to preparation of a study protocol. Learners discover how to formulate a research question, reviewing the literature, critically appraise the literature, develop a research justification statement, and define the parameters of a research project (including the aims, objectives, design, outcomes, data collection methods and data analysis procedures). Learners also realise how to prepare a study protocol that may be suitable for publication in a peer-reviewed journal.

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Flipped online teaching of histology and embryology with design thinking: design, practice and reflection

• Yan Guo 1 ,
• Xiaomei Wang 1 ,
• Yang Gao 1 ,
• Haiyan Yin 1 ,
• Qun Ma 1 &
• Ting Chen 2  

BMC Medical Education volume  24 , Article number:  388 ( 2024 ) Cite this article

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Flexible hybrid teaching has become the new normal of basic medical education in the postepidemic era. Identifying ways to improve the quality of curriculum teaching and achieve high-level talent training is a complex problem that urgently needs to be solved. Over the course of the past several semesters, the research team has integrated design thinking (DT) into undergraduate teaching to identify, redesign and solve complex problems in achieving curriculum teaching and professional talent training objectives.

This study is an observational research. A total of 156 undergraduate stomatology students from Jining Medical University in 2021 were selected to participate in two rounds of online flipped teaching using the design thinking EDIPT (empathy, definition, idea, prototype, and test) method. This approach was applied specifically to the chapters on the respiratory system and female reproductive system. Data collection included student questionnaires, teacher-student interviews, and exam scores. GraphPad Prism software was used for data analysis, and the statistical method was conducted by multiple or unpaired t test.

According to the questionnaire results, the flipped classroom teaching design developed using design thinking methods received strong support from the majority of students, with nearly 80% of students providing feedback that they developed multiple abilities during the study process. The interview results indicated that teachers generally believed that using design thinking methods to understand students' real needs, define teaching problems, and devise instructional design solutions, along with testing and promptly adjusting the effectiveness through teaching practices, played a highly positive role in improving teaching and student learning outcomes. A comparison of exam scores showed a significant improvement in the exam scores of the class of 2021 stomatology students in the flipped teaching chapters compared to the class of 2020 stomatology students, and this difference was statistically significant. However, due to the limitation of the experimental chapter scope, there was no significant difference in the overall course grades.

The study explores the application of design thinking in histology and embryology teaching, revealing its positive impact on innovative teaching strategies and students' learning experience in medical education. Online flipped teaching, developed through design thinking, proves to be an effective and flexible method that enhances student engagement and fosters autonomous learning abilities.

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Research background and motivation

Histology is the study of the microstructure and related functions of the human body [ 1 ], while embryology studies the laws and mechanisms of ontogenesis and development; these two sciences are interrelated and self-contained [ 2 ]. As one of the important professional core programs of most medical specialties, Histology and Embryology (HE) has been an indispensable curriculum bridge between normal microstructure and pathological changes in tissue and organs.

The teaching targets of HE are mainly first-year undergraduate students in clinical medicine, psychiatry, stomatology, nursing, etc. The importance of fostering the development of empathy in undergraduate students is continuously emphasized in international recommendations for medical education [ 3 ]. Freshmen have a certain ability to think logically and analyse problems, but this ability is limited, and they have a yet to develop familiarity with scientific research hotspots. Moreover, they are often unaware of their creative potential, and this phenomenon often causes them to passively accept knowledge, and their autonomous learning ability and student participation in class are less than that of upperclassmen. These first year students face the need to develop scientific literacy and the ability to integrate theory with practice [ 4 ]. However, traditional teaching methods may have failed to fully meet students' need for a profound understanding of these abstract concepts, leading to challenges such as low interest in learning and inadequate knowledge absorption. Consequently, educators urgently need to seek innovative teaching strategies to enhance students' learning experience and academic performance.

In the information age, teacher teaching is no longer a simple superposition of knowledge and teaching methods but a fusion innovation of technology and teaching oriented to a more complex learning environment. The Teacher Standards issued by the American Educational Technology International Association note that the important role of future teachers is that of a "designer" [ 5 ]. DT combines a creative and innovative approach to dealing with complex problems, which serves as a valuable tool for those seeking to improve the challenging issues in medical education [ 6 ]. DT is a process of analysis that relies on the deconstruction of ideas and a creative process that relies on the construction of ideas. There are no judgements in DT. This eliminates the fear of failure and encourages maximum input and participation. Wild ideas are welcome since they often lead to the most creative solutions. Everyone is a designer, and DT is a way to apply design methodologies to any situation [ 5 ].

In the field of education, DT has been advocated as a means to promote the cultivation of innovative talent through innovative teaching methods. With the help of DT, and adhering learning as the concept in teaching, the transformation of teaching allows learners to explore real needs in real life scenes, to propose innovative solutions to meet those needs through teamwork, and to test the effectiveness of those solutions through prototype production. This process facilitates the further application of constructivism [ 7 ].

In the process of both conventional teaching and teaching innovation, the research team utilizes the “EDIPT” (Empathy, Define, Ideate, Prototype and TEST) DT theory [ 8 ] which originating in the Stanford University Design School to design teacher activities and student activities and select technical tools [ 9 ]. The basic process is shown in Fig.  1 . The team is very accustomed to consciously applying DT methodology when facing difficulties and challenges to consistently obtain the desired results [ 10 ]. This study sets the teaching objectives and plans of a large cycle (one semester) to guide the teaching implementation of a small cycle (one section or one chapter); Then, small-cycle teaching feedback and achievement accumulation promote the progress of large-cycle teaching to ensure the coherence, effectiveness and improvement of teaching reform. For example, the difficult problem in the process of cardiovascular system embryogenesis is atrial separation; the team uses cardboard and plastic film to construct room partition "products" [ 11 ] to provide vivid explanations and body movements for clearer explication. In another example, they integrate scattered knowledge points including cleavage, blastocyst formation and implantation into a unified narrative called "the initial journey." It solves the pain point that the dynamic abstraction of embryology knowledge is difficult to intuitively understand. The above are two examples of using EDIPT steps of design thinking to solve teaching pain points.

Problem solving steps incorporating DT

Research objectives and significance

In the 2021 Horizon Report: Teaching and Learning Edition, blended learning was once again selected as the key technology affecting the future development and practice of higher education [ 12 ], demonstrating great application potential. In this format, the teaching team adheres to the following practical principles to promote more blended learning courses to ensure high-quality outcomes [ 13 ]. In the recent period of epidemic prevention and control, effective online teaching combines asynchronous and synchronous delivery modes, addresses knowledge learning and ability development, and highlights interaction in teaching activities to improve the online teaching experience for both teachers and students and enhance the overall quality of online teaching. Online teaching is not simply an emergency measure taken during the epidemic but rather represents the future trend of education.

The aim of this study is to explore the application of design thinking in the teaching of histology and embryology courses. By investigating the impact of design thinking in the teaching process, we aim to gain a deeper understanding of the effects of this innovative teaching strategy on students' learning experience and academic performance, as well as its potential applications in medical education.

The significance of this research lies in its contribution to medical education with novel teaching methods and strategies. By incorporating design thinking, educators can better cater to students' learning needs and enhance their comprehension and mastery of the subject matter. Furthermore, this study contributes to the expansion of teaching research in the field of medical education, providing valuable insights for educational reform and improvements in teaching quality.

The analysis of the correlation between design thinking and this study

Design thinking plays a crucial role in formulating the educational reform. During the empathize phase, an in-depth understanding of teachers' and students' needs and challenges is achieved. This includes considering teachers' expectations and pedagogical beliefs, as well as students' learning styles and feedback, leading to a clear definition of the problem and setting specific objectives for the educational reform. In the define phase, the importance of improving teachers' pedagogical approaches and methods, and cultivating students' creative learning and competencies is underscored. This serves as the foundation for selecting appropriate teaching strategies and establishes the specific direction for incorporating design thinking in the flipped classroom model. During the ideate phase, innovative thinking is employed to explore diverse teaching strategies. For enhancing teachers' pedagogical approaches, approaches such as case-based teaching and collaborative learning are recommended to stimulate students' intrinsic motivation for active learning. For promoting students' creative learning and overall competencies, methods like project-based learning and critical thinking cultivation are considered to facilitate holistic student development. In the prototype phase, the devised teaching strategies are implemented in the flipped classroom setting. Continuous prototyping and rapid experimentation facilitate the collection of valuable feedback and data from students and teachers, enabling further optimization of the teaching strategies to align with the original intent of design thinking. Finally, in the test phase, a comprehensive evaluation of the teaching implementation is conducted. By collecting and analyzing data, the study delves deep into the impact of the educational reform on teachers' pedagogical beliefs and students' creative learning and overall competencies. This process provides crucial feedback and evidence for the ongoing improvement of the educational reform.

In conclusion, the selection of flipped classroom as a pedagogical strategy is closely guided by design thinking principles. Through the application of design thinking, this observational study aims to enhance teachers' pedagogical approaches and methods while fostering students' creative learning and overall competencies, thus promoting the successful implementation of the educational reform.”

Flipped classroom sessions can also allow learners to gain competence through their educational endeavours [ 14 ]. As Bransford writes, “To develop competence in an area, students must: a) have a deep foundation of factual knowledge, b) understand facts and ideas in the context of a conceptual framework, and c) organize knowledge in ways that facilitate retrieval and application” [ 15 ]. Flipped classrooms can lead to competence in factual knowledge by fostering mastery of content through content understanding and application, as in traditional classrooms [ 16 ].

“O-PIRAS” Flipped classroom

The flipped classroom teaching model used in this study was formed and adjusted on the basis of Professor Jianpeng Guo's “O-PIRTAS” model. The flipped teaching mode can enable both teachers and students to acquire further abilities through teaching activities [ 17 ].

The first step(O: Objective) in flipped classroom teaching design is to formulate two types of teaching objectives: low level and high level. The lower level teaching objectives include two cognitive objectives from Bloom's classification of teaching objectives: the memory and understanding of knowledge, while the higher level teaching objectives include four cognitive objectives from Bloom's classification: application, analysis, evaluation and creation, as well as objectives pertaining to movement skills and emotion [ 18 ]. The second step is to design a preparation activity (P: Preparation) for students to complete before class, which helps students form necessary prior knowledge and stimulates their learning motivation by exploring relevant issues prior to the class [ 19 ]. The third step is for teachers to send teaching materials (I: Instructional video) to their students for pre-class learning to facilitate their early acquisition of knowledge [ 19 ]. Fourth, teaching is transferred from online classes to offline classes. The teacher briefly reviews (R: Review) the video content before class to help students quickly focus on and prepare for the next stage of learning both cognitively and psychologically. Fifth, teachers should design classroom activities (A: Activity) appropriate to high-level teaching objectives to promote in-depth learning and successfully achieve high-level objectives. Sixth, teachers should conduct classroom summaries (S: Summary), reflection and improvement to help students form integrated structured knowledge. The six steps of flipping the classroom link form a closed loop, which can be summarized as in Fig.  2 .

Process of “O-PIRAS” flipped teaching

Research method and data collection

Conveniently selecting 156 undergraduate students majoring in Dentistry from the 2021 cohort of Jining Medical University, we designated classes 1 to 3 as the class of 2021 stomatology students. As the class of 2020 stomatology students, we chose 155 undergraduate students majoring in Dentistry from the 2020 cohort, also from classes 1 to 3. Prior to the start of the study, we conducted communication sessions with both teachers and students, ensuring that all students were well-informed about the study and provided their consent. The two groups of students had the same course hours, faculty resources, learning materials, and learning spaces. The only difference was the application of design thinking methods in course and teaching design, including the implementation of flipped classroom teaching, specifically tailored for the 2021 cohort of students.

Data collection was conducted through various methods, including distributing questionnaires, conducting pre-, mid-, and post-research interviews, and recording course and corresponding chapter test scores. The implementation chapter selected the respiratory system, which plays a bridging role within histology, and the female reproductive system, which plays a transitional role between histology and embryology.

Before studying "Respiratory System", students have already mastered the basic methods of using design thinking to learn histology, and have a deep understanding of the four basic tissues and two types of organs (hollow and substantial organs). The main organs of the respiratory system—the trachea and lungs—belong to two types, respectively. The female reproductive system, as the concluding chapter of histology, is separated from the flipped classroom of the respiratory system by two weeks, leaving appropriate time for teachers to iteratively design and students to adapt to new methods. Four surveys were administered during the research process: Pre-flipped classroom survey for Chapter 16 "Respiratory System", Post-flipped classroom survey for Chapter 16 "Respiratory System", Pre-flipped classroom survey for Chapter 19 "Female Reproductive System", and Post-flipped classroom survey for Chapter 19 "Female Reproductive System", to gather student feedback and opinions on the teaching methods. The questionnaires were designed based on the research objectives and questions, and were refined through pre-testing to ensure clarity, accuracy, and appropriateness of the questions and options. The questionnaire mainly includes the following dimensions: ⑴Basic information of students, Q 1–3; ⑵ Learning and satisfaction: Q 4, What is the division of labor in your group in this cooperation? Q 7, About flipping class, how long will you spend studying before class? Q 6 Compared with the last flip class, are you satisfied with the teacher's teaching time in this flip class? Q 12, What are you most satisfied with this flip class? (3) Learning experience and ability improvement: Q 5, What kind of class learning form do you like best in flip class? Q 8, What are your learning pain points or difficulties after this flip class? Q 9, What abilities have you improved in this flip class? ⑷ Classroom Improvement and Feedback: Q 11, What are the advantages of this flip class compared with the last flip class? Q 10, In the course of embryo formation, do you like to use flip class for multiple course contents? Q13, What suggestions do you have for improving the embryo flipping class? Interviews were conducted at various stages, including before the study to understand teaching pain points, during the research process to gauge teachers' and students' attitudes and perspectives on the teaching activities, and after the study to obtain overall feedback. Additionally, we conducted both stage-specific and overall tests, and promptly collected relevant data for comparative analysis with the class of 2020 stomatology students. These data provided comprehensive insights into the performance and experiences of students in both the experimental and class of 2020 stomatology studentss.

Application of design thinking in course design

In course design, we employed design thinking methods to redesign the histology and embryology curriculum. Firstly, we gained a deep understanding of students' learning needs and interests to define course objectives and content. Secondly, we innovatively designed online materials and videos to enhance the appeal and practicality of the learning experience. We encouraged students to actively participate in discussions and problem-solving during class to unleash their creative potential. Additionally, we continuously optimized the teaching content and methods through iteration and feedback to ensure a sustained improvement in teaching effectiveness. Through the application of design thinking in course design, we expected to optimize the teaching process, enhance students' learning experiences, and improve their academic performance.

Design and implementation of flipped teaching

The HE course covers 22 chapters, totaling 60 h, including 44 h of theoretical classes and 16 h of practical classes. The theoretical teaching is roughly divided into three stages: the first stage consists of 12 h, focusing on introducing the four basic human tissues; the second stage comprises 18 h, covering the structure of human organs and systems; and the third stage spans 14 h, elucidating the process of human embryonic development. To facilitate a deep understanding and mastery of human tissue structures, four practical classes, each lasting 4 h, are incorporated to complement the theoretical content.

The entire course relies on a blended teaching approach, combining online and offline instruction, leveraging the resources of Shandong's top undergraduate course in HE, and utilizing the "Zhidao" flipped classroom tool. At the beginning of the course, the teachers introduce the purpose, teaching process, weekly plan, grading components, and assessment methods of incorporating design thinking into the blended HE teaching. The flipped classroom teaching for the class of 2021 stomatology students is set between two stage tests to investigate whether this innovative teaching method has an impact on students' test scores.

The teaching team consists of 4 associate professors and 3 lecturers, with an average teaching experience of 11.4 years in teaching nursing major foundation courses and possessing rich teaching experience. In addition, the project leader and team teachers have undergone multiple training sessions in design thinking innovation and systematic training in domestic and on-campus blended teaching theories.

At the beginning of the semester, the curriculum teaching plan should be formulated, and chapters suitable for flipped teaching should be selected according to the teaching plan” and content characteristics [ 20 ]. Teaching and research team members should jointly analyse the teaching content and formulate the flipped classroom syllabus [ 21 ], clarify teaching objectives (knowledge objectives, ability objectives and emotional objectives, i.e., low-order objectives and high-order objectives), develop chapter teaching plans and teaching courseware (traditional classrooms are obviously different from flipped classrooms) [ 22 ], record pre-class video (design the course content in a fragmented way and systematically present it in accordance with the teaching plan) [ 23 ], divide students into groups and engage with all students through “zhidao” teaching software and the QQ class committee. The specific design and implementation plan for the preparation of the above teaching materials for a flipped classroom course on the respiratory system. The teaching team seminar is held three weeks before the class.

While completing the preparation of teaching materials in accordance with the teaching plan, the team clarified what methods and tasks teachers and students should complete before and during the implementation of the flipped classroom so that everyone can understand the design intent of these teaching activities to facilitate more satisfactory teaching results.

Practice processes and instructional evaluation

The teaching design was discussed and approved by all members of the research team and used in the classroom teaching of respiratory system conversion with slightly modified specific content. One week before class, it was distributed through the zhizhuishu teaching platform to all the students [ 24 ] participating in flipped classroom teaching. The resources provided to students include preview materials, textbook chapters, courseware, videos, etc.; Preview questions, some questions related to preview materials, guide students to think and explore, stimulate learning interest and initiative; Learning objectives, clarify the knowledge objectives, ability objectives, and literacy objectives for pre class learning. In addition, there are also learning platforms (Wisdom Tree Online Course- https://coursehome.zhihuishu.com/courseHome/1000007885/199185/20#onlineCourse ),WeChat class group chat, learning community. In flipping the method of respiratory system class delivery, the team first tried to perform a complete flip of the class. At the beginning of the class, the teacher clarified six themes, and then the group spokespersons demonstrated their understanding of all the knowledge points, including key points and difficulties, in class by drawing lots. The teams provided feedback for each other. The teacher only played a guiding role in the activities involving the entirety of the class. After summarizing the classroom content, the teacher assigned homework, such as creating mind maps and engaging in thematic discussions on the learning platform, and distributed the questionnaire regarding the group pre-class preparations, classroom activities and learning experiences for the respiratory system flipped classroom. The questionnaire mainly consists of the following questions. How was the work divided among your team for this activity? What kind of in-class learning style do you like best in the flipped classroom? Compared with the last flipped classroom, are you satisfied with the length of teaching in this flipped classroom? How long do you spend on pre-class learning for a flipped class? What are your learning pain points or difficulties after this flipped lesson? What abilities have you improved in this flipped classroom? Are you satisfied with the length of lectures in this class compared with that in the last flipped class? What percentage of the course content do you prefer to be delivered by the flipped classroom model? Compared with the last flipped classroom, what are the advantages of this flipped classroom? What you are most satisfied with in this flipped lesson? Please offer suggestions for the improvement of your flipped class on embryos.

According to the steps and links involved in DT, when the “product” (teaching plan) is tested and problems are found, the design team should complete the iteration as soon as possible to better meet the needs of “customers” (students)[ 25 , 26 ]. Three days after the questionnaires, the teaching team adjusted the flipped classroom teaching design scheme for the Female Reproductive System course according to the questionnaire results, and arranged the pre-class tasks one week prior to the class, which differed from the previous class. Explanations of key points and difficult points were appropriately added to the teaching design, which did not depend on students as thoroughly as it had the last time, reducing the difficulty of the flipped classroom to a certain extent, improving students' level in participation, and improving the learning effect and teaching quality of the class.

A total of four questionnaires were distributed before and after the two flipped classes, and video recordings were made of the flipped classroom teaching process for a nursing and a stomatology class. Tencent conference recording instructions were issued by teachers. HE course scores consisted of three parts, including the usual score (30%), experimental score (10%) and final score (60%). The course scores of the 2021 nursing class and stomatology class were derived from the education management system of Jining Medical College, and the course scores of the nursing and stomatology majors who did not classes that had implemented online flipped classroom teaching in 2020 were derived as a control. Comparing the proportion of students in each of two grades, the total correct response rate of test questions, and the correct response rate of respiratory system and female reproductive system course test questions delivered through flipped classroom teaching were analysed using GraphPad Prism software through the statistical method of multiple or unpaired t tests.

Teaching strategies developed using design thinking methods improves multiple student abilities

According to the results of the questionnaire distributed before the beginning of the first flipped class, 51.2% of the students reported not understanding the new learning method and that they could not check the data, 21.6% of the students were not interested in flipped lessons and preferred traditional passive learning methods, 25.6% of the students said that they did not have strong self-control and were unwilling to take the initiative to learn, 56.8% of the students said that they had a great fear of speaking in front of their classmates and that their public speaking skills were not strong, and 46.4% of the students did not know how make suitable PowerPoint Presentation (PPT). After two sessions of flipped classroom learning, the majority of students felt that their pain points had been effectively solved and various abilities had been developed. The results of the question after the flipped classroom teaching of the female reproductive system are shown in Table  1 .

Positive feedback and growth experience of students in teaching strategies developed using design thinking methods

The informal discussion following the flipped lesson on the female reproductive system shows that compared with the "Teacher almost let go" response in the previous respiratory system flipping class, the students are more inclined to respond with "The teacher will solve the problems left in our preview," "Feedback is provided between groups, and the groups are complementary," "The teacher emphasizes the key points, explains the process in detail, and plays videos to consolidate knowledge," and " the teacher commented on the performance of the group speaker". The students thought that after two sessions of participation in a flipped classroom, "We are more active in learning and the classroom design is more live," and "The students are more involved and confident." "By applying design thinking to study the course of organizational design, I have found new learning methods and approaches, and successfully applied these learning methods to other courses, which has benefited me greatly."

The comparison results of grades

Under the premise that there is no significant difference in the difficulty of test questions and other criteria between the flipped and traditional classrooms, the class of 2021 stomatology students' course scores showed a slight improvement. However, there was no significant difference in the distribution of the number of students in each score segment compared to the class of 2020 stomatology students. In contrast, for the chapters that implemented flipped classroom teaching, specifically the respiratory system and female reproductive system chapters, the class of 2021 stomatology students' test scores showed a significant increase. The difference between the two groups was statistically significant. The details are depicted in Fig.  3 .

Distribution of final exam scores for the two graduating classes. A The proportion of students in different grades, no significant difference Statistical method: Multiple t tests. B Total accuracy, no significant difference. Statistical method: Unpaired test. C The accuracy of flipped classroom chapters, unpaired test, P  < 0.05. Mean ± SEM of column A 0.7075 ± 0.009587 N  = 3, Mean ± SEM of column B 0.7913 ± 0.02872 N  = 3

Firstly, significant achievements have been made in enhancing students' overall abilities through the application of design thinking methods in formulating flipped classroom teaching strategies. Preliminary surveys revealed various challenges faced by students before the commencement of the flipped classes, including difficulties in understanding new learning methods, lack of interest in flipped classes, low self-discipline, and fear of public speaking. However, after two sessions of flipped classroom learning, the majority of students believe that their pain points have been effectively addressed, and various skills have been developed. This aligns with the findings of previous research by Awan OA [ 15 ], indicating that the application of design thinking methods in teaching strategies can significantly enhance students' subject engagement and skill development.

Secondly, regarding the positive feedback and students' growth experiences in applying design thinking methods to formulate teaching strategies, there is a positive trend observed in informal discussions following the flipped classroom on the female reproductive system. Students tend to perceive a more proactive role played by teachers in the flipped classroom, addressing the issues they encountered during previewing. Students also highlighted the complementary feedback provided among groups, emphasizing the importance of teamwork. Additionally, students positively acknowledged the efforts of teachers in emphasizing key points, providing detailed explanations of processes, and reinforcing knowledge through video presentations. They believe that this teaching approach stimulates their interest in learning and enhances their motivation. This aligns with the findings of research by Scheer A [ 7 ] and Deitte LA [ 11 ], supporting the positive impact of design thinking methods in education.

Finally, the results of the performance comparison indicate that there is no significant difference between flipped classroom and traditional classroom based on criteria such as question difficulty. However, the overall grades of the 2021 cohort of dental medicine students have shown a slight improvement. Specifically, in the chapters on the respiratory and female reproductive systems within the flipped courses, the exam scores of the 2021 cohort students have significantly increased, and this difference is statistically significant. This suggests that the flipped classroom teaching formulated through design thinking methods has a significant positive impact on the development of subject-specific skills in specific chapters. This aligns with the relevant findings of Cheng X [ 1 ], further emphasizing the instructional advantages of design thinking methods in specific topics.

Main finding

The team used DT to reveal the pain point that flexible mixed teaching can not guarantee students' participation and the realization of teaching objectives, and the application of online flip classroom teaching solved this problem well Students play a leading role in this kind of teaching, so they need to devote more time and energy to preview textbooks and consult relevant materials before class to improve their autonomous learning ability It is helpful to cultivate team spirit in flip teaching in the form of group, which is helpful to cultivate team leadership and management ability. The main requirements of mixed teaching are to integrate pre-recorded videos into the course as a whole and provide online learning resources to supplement face-to-face teaching in an organized and selective way [ 27 ] As assessment expert Mag says, if you are teaching something that cannot be assessed, you are already in an awkward position-that is, you can't explain the teaching content clearly [ 28 ] Therefore, reasonable teaching objectives in mixed teaching can make teachers and students reach a common understanding and consensus on learning results, enhance emotional communication and resonance between teachers and students, and jointly promote the implementation of curriculum teaching. The successful implementation of online flip class needs certain network and students' enthusiasm and cooperation At the same time, teachers need to be particularly familiar with the curriculum to design lectures and targeted comments [ 29 ].

Limitations and future research

In this study, the respiratory system and female reproductive system in HE were selected as subjects for conducting flipped classroom teaching. The examination results shoe that although the overall course performance has not significantly improved, the accuracy of the chapter test questions in flipped classrooms significantly improved, which demonstrates that this teaching method can improve students' learning performance while cultivating their various abilities. It is worth expanding the scope of implementation to more chapters. However, not all chapters are suitable for flipped classroom teaching. Because the two chapters involved in this paper belong to the "organs and systems" module, it does not fully reflect the applicability of this research in this course. Some chapters of the basic tissue module and embryogenesis module are also the scope of our future teaching research In addition, what is the highest proportion of total course hours converted to flip teaching? All these problems need further study in the future. What is the most appropriate ratio of total course hours to convert into flipped teaching? These issues need to be further studied in the future.

When DT is introduced into education, evaluating students' learning and development becomes more important than evaluating students' design products or knowledge and ability. Changes in consciousness and attitude include whether they can fully participate in current cognitive activities, learn independently, communicate and cooperate, and continuously monitor and adjust themselves. By clarifying this guidance, we can formulate or select appropriate evaluation criteria through a literature review during the implementation of the project and adjust the subsequent research conditions in a timely manner according to the evaluation results.

Online flipped teaching is an effective way to integrate DT into the flexible and mixed teaching of HE, which can effectively enhance students' learning input and cultivate students' autonomous learning ability. This research aims to reshape the method of classroom teaching through the deep integration of modern information technology into pedagogical design. Future work should appropriately expand the scope of flipped teaching content and explore the appropriate proportion of course content. In the course design, various forms of cross-professional cooperation with clinical doctors should be increased as much as possible, and the contents of flipped classroom should be expanded from basic knowledge to clinical skills.

Through the application of design thinking in the teaching of histology and embryology courses, we have gained a deeper understanding of its positive impact on innovative teaching strategies, improvement of students' learning experience and academic performance, and the potential value it holds in medical education. We have discovered that the "product" developed through design thinking, namely online flipped teaching, serves as an effective and flexible blended teaching method. It not only enhances students' engagement in learning and fosters their autonomous learning abilities but also encourages both teachers and students to cultivate their innovative capabilities and reshape classroom teaching approaches. Moving forward, further exploration should be undertaken to determine the optimal balance for expanding the content of flipped teaching, to continually uncover its potential in medical education.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

Abbreviations

Histology and Embryology

Design Thinking

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Acknowledgements

We thank Jining Medical College and Shandong Provincial Education Department for support.

Undergraduate Teaching Reform Research Project of Shandong Provincial Education Department No. M2021364, M2022159. Research on classroom teaching reform key program in Jining Medical College [2022] No. 2022KT001.

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College of Basic Medicine, Jining Medical University, 133 Hehua Road, Jining, 272067, China

Yan Guo, Xiaomei Wang, Yang Gao, Haiyan Yin & Qun Ma

Academic Affair Office, Jining Medical University, 133 Hehua Road, Jining, 272067, China

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Contributions

Yan G, Ting C: Conceptualization, Methodology, Writing-original draft preparation. Xiaomei W, Yang G: Interview, Data curation, Formal Analysis. Haiyan Y, Qun M: Formal analysis, Writing-reviewing and editing. The author(s) read and approved the final manuscript.

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Correspondence to Ting Chen .

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Research involving human participants, human material, or human data was performed in accordance with the Declaration of Helsinki. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the Ethics Review Committee of Jining Medical University (No. JNMC-2020-JC-013). Informed consent was obtained from all subjects and/or their legal guardians. The consent was obtained in written form.

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Guo, Y., Wang, X., Gao, Y. et al. Flipped online teaching of histology and embryology with design thinking: design, practice and reflection. BMC Med Educ 24 , 388 (2024). https://doi.org/10.1186/s12909-024-05373-7

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Received : 23 February 2023

Accepted : 29 March 2024

Published : 09 April 2024

DOI : https://doi.org/10.1186/s12909-024-05373-7

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• Flexible hybrid teaching
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