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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.
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.
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 .
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.
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.
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.
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?
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
- Null hypothesis
- Statistical power
- Probability distribution
- Effect size
- Poisson distribution
- 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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How to Write a Research Design – Guide with Examples
Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023
A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the research questions .
It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.
Below are the key aspects of the decision-making process:
- Data type required for research
- Research resources
- Participants required for research
- Hypothesis based upon research question(s)
- Data analysis methodologies
- Variables (Independent, dependent, and confounding)
- The location and timescale for conducting the data
- The time period required for research
The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.
Your project’s validity depends on the data collection and interpretation techniques. A strong research design reflects a strong dissertation , scientific paper, or research proposal .
Step 1: Establish Priorities for Research Design
Before conducting any research study, you must address an important question: “how to create a research design.”
The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.
Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.
If one research design is weak in one area, then another research design can cover that weakness. For instance, a dissertation analyzing different situations or cases will have more than one research design.
- Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
- Quantitative research is good for the statistical part of the project, but it may not provide an in-depth understanding of the topic .
- Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.
While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;
- Do you have enough time to gather data and complete the write-up?
- Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
- Do you have in-depth knowledge about the different statistical analysis and data collection techniques to address the research questions or test the hypothesis ?
If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.
Step 2: Data Type you Need for Research
Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:
Primary Data Vs. Secondary Data
Qualitative vs. quantitative data.
Also, see; Research methods, design, and analysis .
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Step 3: Data Collection Techniques
Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.
It is time to determine your research method to address the research problem . Research methods involve procedures, techniques, materials, and tools used for the study.
For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your dissertation’s structure .
The following table shows the characteristics of the most popularly employed research methods.
Step 4: Procedure of Data Analysis
Use of the correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;
Quantitative Data Analysis
The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.
This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.
Qualitative Data Analysis
Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.
You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.
Step 5: Write your Research Proposal
The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results and conclusion .
Read our guidelines to write a research proposal if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.
The research methodology or research design, on the other hand, is generally written in the past tense.
How to Write a Research Design – Conclusion
A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.
Above mentioned five steps are the answer to how to write a research design. So, follow these steps to formulate the perfect research design for your dissertation .
Research Prospect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.
Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, Research Prospect can help. We also offer PhD level research paper writing services.
Frequently Asked Questions
What is research design.
Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.
How to write a research design?
To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.
How to write the design section of a research paper?
In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.
How to write a research design in methodology?
To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.
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How to write a strong research design.
- Research Designs Definition
- Research Design Format
- 1. Introduction
- 2. Discussion of theories and hypotheses
- 3. Methodology
- 4. Conclusion
- 5. References
- How to Write a Research Design
- Step 1. Create an overview of the approach
- Step 2. Consider the limitations
- Step 3. Assess the best type of research design
- Step 4. Choose the sample demographics
- Step 5. Choose a data collection technique
- Step 6. Discuss all the aspects of the methodology
- Step 7. Write the introduction, theoretical discussion and conclusion
- Types of Quantitative Research Design
- Research Design Example
- 2. Discussion of theories
- Get Help with Research Design from CustomEsayMeister
A research design is a framework of a research’s overall methodology. Researchers create a research design paper to map out their plans and have a guideline that they will follow throughout their study. The paper is a document that contains information regarding the data collection and analysis process. The document should help researchers answer research questions and ensure a smooth flow of the process. Researchers may write the document as a standalone paper or a section of a dissertation proposal .
A research design format includes an introduction, a discussion of theories and hypotheses, methodology, conclusion, and references. Individuals can follow this format whether they are writing a standalone paper or a part of a dissertation proposal.
The introduction of a research design paper is similar to most academic essay introductions . The section should introduce the topic of the paper. This means providing a definition and background information about the topic that can act as a hook for the paper. The introduction should also state the significance of the research.
This section of the research design paper contains an extensive discussion on the theories and other factors related to the research questions. The researchers should also state their hypotheses in this section. They should identify the main ideas of their paper and how it affects the study. The researchers should also indicate the factors that they will utilize in the data collection process. For example, research regarding distance learning will involve factors like teaching methods and computer literacy. The researchers should then discuss these factors and state that they will take them into account during the data collection.
The methodology section will contain the most important content of the paper. Here, the researchers will discuss their overall methodology. This will involve a discussion about the type of research design, the scope of their research, measurement tools, analysis process, and data collection technique. The researchers can add more subsections depending on the nature of their study and their chosen methodology.
The conclusion will simply summarize the document and state additional information that the researchers did not discuss in the previous sections. If an individual is writing the paper for a project proposal, they may not need to include a conclusion. However, some instructors may still require the conclusion. Students should verify this with their instructors to avoid issues with their format.
The reference section is where the writer will list all the sources they used for the document. This should include the sources that individuals used in the theoretical discussion section and other discussions. Similar to the conclusion, a research design for a proposal will not require a reference list. This is because proposals and other papers will have a designated reference list at the end of the document. Only a standalone research design paper should include a reference list.
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Researchers can use different types of approaches which can be qualitative or quantitative. The approach will depend on the researchers’ preference and topic. To create an overview of the approach, researchers should assess if their study will allow them to use qualitative or quantitative methods. They may also take into account the purpose of the study. This will help them decide how the data collection process will proceed. The researchers can read previous studies and find out the common method that other experts in the fields have used.
Once the researchers have an overview of their approach, they should then consider the limitations of their study. This means identifying their timeline, data accessibility, research skills, instructor approval, and other similar factors. Assessing the limitation is an important process to avoid making major revisions in the paper. The researchers should ensure that their initial approach is feasible and that they have the necessary skills to perform it.
After considering the limitations and creating an overview of their approach, the researchers should be able to choose the best type of research design for their study. There are several types under the qualitative and quantitative approaches. The different types will suit certain studies and provide varying data. Aside from these approaches, the researchers should also consider if they require primary or secondary data.
Once the researchers chose a type of research design, they can then begin to identify their demographics. The demographics will be the group of subjects that the researchers will use to retrieve data. A demographic can be a group of people, animals, plants, places, objects, and other feasible subjects. The researchers will choose their demographics based on their approach and data collection method.
After deciding on a demographic, the researchers can then choose a data collection method. There are various data collection methods such as surveys, interviews, questionnaires, experiments, observations, reading books, and others. A study’s data collection technique will depend on the research design type and the demographics.
For quantitative approaches, the researchers may use questionnaires, surveys, controlled observations, polls, and interviews to collect data. These methods allow the researchers to gather quantifiable information. For a qualitative approach, researchers can utilize interviews, observations, gathering secondary data, and focus groups. These methods focus more on gathering unquantifiable information.
Once the researchers have identified their preferred approach, demographics, and techniques, they can then proceed with discussing all these aspects in the paper. They should discuss all these aspects in the methodology section of the paper. They can designate a specific subsection for each aspect to have a better structure for the document.
The methodology section is the middle section of a research design paper, however, it is best to write it first since it contains most of the important parts of the document. The researchers can create a draft for the initial writing of the paper and revise it once they complete the whole document.
After the researcher has written the most important parts of the paper, they can then begin constructing the rest of the document. The paper will begin with an introduction that will provide background information and state the significance of the study. The theoretical discussion will contain extensive arguments and explanations about the topic as well as the hypotheses. The conclusion will be a summarization of the whole paper.
Researchers use quantitative research designs when they aim to collect measurable data. The approach focuses on sampling, measuring the number of responses, and experimenting. Quantitative research design types include descriptive, correlative, quasi-experimental, and experimental types.
- Descriptive. Researchers can use the descriptive design to describe characteristics, trends, means, and other measurable quantities. Studies that aim to quantify a similar factor among a specific demographic will benefit from the descriptive type.
- Correlative. A correlative design aims to assess a quantifiable relationship between different variables. The correlative type is perfect for studies that focus on comparing two or more samples.
- Quasi-experimental. Researchers use a quasi-experimental design to quantify a cause-and-effect relationship between different samples. Studies that focus on a single group will benefit from this type.
- Experimental. The experimental design also aims to quantify a cause-and-effect relationship. However, it involves having an experimental group and a controlled group. This design type is good for any study that aims to assess a causal relationship.
Researchers studying behaviors commonly use a qualitative approach in their studies. These types of studies often collect subjective data which are unquantifiable and require qualitative analysis. Qualitative research design types include ethnographic, narrative, grounded theory, case study, phenomenology, and hermeneutics.
- Ethnographic. In ethnography, the researcher directly interacts with the respondents in their natural environment or community. The researcher will take note of their observations and first-hand experiences.
- Narrative. In a narrative design, the researcher writes a narrative about the respondent’s life experiences. Researchers use this type if they are studying the life and behavior of a specific individual.
- Grounded Theory. The grounded theory design aims to establish or modify a theory. The method requires the researcher to analyze qualitative data and develop a new perspective regarding the topic.
- Phenomenology. In phenomenology, the researcher aims to understand an event or phenomenon through the experiences of an individual. Researchers often use this method when studying unusual behavior or events.
- Hermeneutics. The hermeneutics design focuses on interpreting the meaning behind words, art, culture, events, and ideas. Researchers studying subjective topics should use the hermeneutic approach in their study.
Below are some example sections for a research design paper. The example sections should help individuals better understand the structure of the document. It is important to note that some professors may provide specific instructions that can change the document’s format.
“The paper will focus on the issue of financially unstable students failing to meet distance learning requirements. Distance learning requires students to use devices such as laptops, tablets, and smartphones. Most of these devices are expensive and unaffordable for financially unstable students. This study is necessary to help address this significant issue that can affect the lives of students and future professionals…”
“Students from families with financial constraints are often less tech-savvy and would find it difficult to navigate in an online learning environment (Dhawan, 2020). Some students from these types of families may also fail to afford the necessary device and services for e-learning…”
“ Main Hypothesis: Students with financial constraints are more likely to drop out of e-learning environments due to their limitations.”
“ Type of research design: The proponents will conduct a quantitative study to assess the dropout rate of students with financial constraints. The proponents will utilize a quasi-experimental design and use online surveys…”
“ Scope of the research: The proponents will focus on students that recently dropped out of e-learning environments. The sample students will be from four different e-learning programs…”
“ Measurement tools and data collection technique: The proponents will distribute online surveys to the respondents which will contain questions that will assess the reason behind the dropouts. The study will retrieve primary data from the respondents…”
“The proponents will use quantitative research to retrieve primary data from students who recently dropped out of e-learning programs. The study will assess the reason behind the dropouts which may include financial constraints, computer illiteracy, motivation, and parental influence…”
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7 simple steps to efficient research design with example
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Wondering where to start your research project? Here are 7 steps to research design that you can apply today!
As we explained last week, Motivated Academics are curious to brainstorm and test new research ideas . But once you come up with your research idea and develop your hypothesis, how do you know what type of research design to use so that you collect reliable data? That’s where research design comes into the academic picture!
I want to emphasise one thing – the generation of new research ideas is one of the most crucial aspects that you will need to learn as an academic. Each research project and scholarly paper depends on your ideas. But great ideas without efficient research design process and research execution are just that – great ideas without meaningful outputs.
That’s why Motivated Academics are eager to TEST their innovative concepts and ideas to make sure these bring tangible benefits to the economy, society and environment we all live in.
How do you test your ideas then? Well, that’s what research design is for!
What is research design?
Let me first define what’s research design. According to Oxford Reference, a research design is “a detailed proposal relating to a defined piece of [research] endeavour, which includes a definition of a problem, subject, or hypothesis for investigation; the background and context to the investigation; the proposed means and methods of the investigation; the work plan and timetable; details of the proposed investigators, management arrangements, and quality control procedures; and a table of costs.”
This definition was originally presented for archaeological endeavours and activities, but I believe it can be extended to any research area. At the end of the day, it is all about defining the design of a scientifically sound method that will allow you to produce a sufficient amount of data to test your hypothesis.
How to design your research in 6 simple steps?
Now that you know what research design is, let me share a simple 7-step framework that will help you develop a well-planned research design. I’ll try to support this research design with example where possible.
Step 1: Reflect on your hypothesis
The hypothesis you’ve developed for your research includes a piece of crucial information about your research question and variables that you want to evaluate.
Therefore, your research design should start by reflecting on your research hypothesis. You should be able to answer the following questions:
- what variables or parameters you should investigate?
- what information do you need to collect in your research to test your hypothesis?
- how much data do you need to test your hypothesis?
This information is crucial in the next step.
Step 2: Decide on research approach
Now there are two main approaches to your research approach: qualitative approach or quantitative approach.
The qualitative approach aims to characterise the relationships between the dependent and independent variables using mathematical tools to correlate the observations from your data. This usually generates quantitative data that are numerical and help you evaluate different case studies.
The quantitative approach aims to characterise the relationship between the dependent and independent variables using observations and non-mathematical data (pictures, recordings, text, transcripts, interviews, focus groups) to draw insights and conclusions.
You can also use mixed methods that combine both qualitative and quantitative approaches. With this approach to research, you can get more comprehensive answers to your research problem and you can deduct more informed conclusions.
Step 3: Select the type of research design
Wondering what are 4 types of research design? Here is the answer.
For quantitative research design, you can use experimental design, quasi-experimental design, correlational design, and descriptive design for your quantitative research. Here’s a brief overview of these research design types:
An experimental design seeks to determine a cause and effect correlation of variables that you study. Your experimental research design usually involves independent and dependent variables, and you are trying to understand how the change in the independent variable influences the dependent variables. The samples you use are often randomised. Your experiments are usually performed in a controlled environment.
A quasi-experimental design is similar to the experimental design, but it relies on pre-existing groups (non-randomised samples). Your experiments are usually performed in a natural environment.
A correlational design focuses on determining whether a correlation exists between the variables under question and how strongly these are correlated. It heavily relies on a statistical analysis of your data.
A descriptive design relies on understanding the characteristics of a single sample, such as averages and standard deviations. This type of design may not need a hypothesis as usually, you are focusing on understanding the existing data.
For qualitative research design, the most commonly used types of research design include case study, ethnography, grounded theory, and phenomenology.
Case study design aims to understand and explain the experience of a defined sample via direct observation and interaction with that sample. The focus is placed on the description of the experience.
Ethnography design aims to describe the characteristics of a selected culture. This is achieved via a selection of variables and characteristics to consider in the study and data collection via literature review, culture immersion, direct observation and interaction with subjects. The outcome is the description of culture.
Grounded theory design aims to discover the problems and challenges in society and how members of society deal with these. It involves an iterative process of “formulation, testing and re-development of propositions until a theory is developed” . The data is collected via interviews or observations. The outcome is a new theory that is supported with data.
Phenomenology design aims to describe the experiences from the individuals perspective. It aims to understand the feelings and experiences with regard to a specific phenomenon. Usually, there are no defined steps in terms of the data collections to allow space for creativity. The outcome is the understanding of the subject’s point of view and structural explanation of the specific phenomenon.
Step 4: Define your population and sampling method
Once you decided on the research design process that will provide the best set of data to test your hypothesis, it’s crucial to define the amount of data, aka. population, and sampling method.
I often find that researchers tend to generate way too much data, which makes it difficult to decide what data to include in your chapter or research article.
Therefore, it’s crucial to decide the characteristics of the population you want to study at the outset of your research to maintain focus. For example, your population can be characterised by a specific demographic or comprise a given set of technologies.
Once you understand what data you need to collect to test your hypothesis, you need to decide how this data will be sampled. This is in particular important when you’re dealing with large populations, e.g., people in a specific country, and it is impossible to get data on all of them. Instead, you will collect data based on a representative sample of that population. But let’s leave a discussion like this for our further articles.
To sample data from your population, you can use two main sampling methods.
Probabilistic sampling relies on using random methods to generate data, usually involving some sort of mathematical representation of the population you evaluate. It allows you to truly understand the correlations and characteristics of the population via thorough statistical analysis.
Deterministic sampling, aka, non-probabilistic sampling, relies on non-random selection of the sample and is usually easier to perform. However, it is subject to bias depending on how your sample is selected.
Step 5: Select data collection method
Now that you know what data you need to collect, it is important to decide how exactly you are going to collect your data. This is one of the most crucial aspects of the research design process, as you will actually decide what kind of tools you will use in your research.
At this stage, it is crucial to distinguish between the primary data and secondary data.
The former focuses on collecting new data via surveys, questionnaires, interviews, observations, mathematical modelling and so on.
The latter uses the data that has already been published in the literature. Therefore, the collection of the secondary data largely relies on literature review, database search, raw data mining and so on.
Step 6: Design your data collection process
We’re almost there! So you know what methods you’re going to use, what parameters to measure, and how to sample them. Now it’s time to determine the minimum amount of data necessary for your research.
Remember, you only need to generate a sufficient amount of data that is necessary to prove or disprove your hypothesis. Oversampling will neither give you more additional information nor improve the accuracy of your results – it will just consume more of your time during the data collection period.
To determine the minimum amount of data necessary for your research, you may use rules of thumb in your area (i.e. 3 repetitions of the same experiment), Taguchi design or fractional factorial design.
I feel that it is important to mention that you also need to develop a data management plan as early as possible in your research. This is especially true during questionnaire design or qualitative design, as you need to make sure you properly deal with the sensitive data and adhere to your funder’s data management requirements.
Importantly, when you decide on the amount of data to collect, your research design must have the following characteristics:
- Objectivity: the research methods you use in your work should allow you to objectively test your hypothesis. This means that your results should be free from bias and should not include subjective opinions. This is difficult to achieve, as we tend to promote our interests. We also make assumptions in our work. Therefore, for others to understand what you did and why you did it, it is essential to explicitly state the assumptions and approach of your work. It is important to ensure that your work could be replicated by other researchers so that they could verify your results and conclusions, and built on your research
- Repeatability: the research method you use in your work should be reliable and accurate. This means that whenever you conduct similar research under similar conditions using the same method, your results should be similar and lead to the same conclusions. This is especially true for statistical and experimental work, so your research design must include a sufficient number of observations to allow for uncertainty analysis. This is to ensure that your results are representative and not influenced by the research design. This is also important for any sort of computational or analytical research, where you develop models or correlations to represent the system or concept that you analyse. Why? Well, you need to make sure that the methods you used are valid, which means these represent the actual system accurately.
- Validity: the research method that you select needs to support you in testing and verifying your hypothesis, and subsequently answering your research questions. Your methods should help you to produce and analyse your data objectively to draw conclusions in the context of your hypothesis and research question. When selecting methods ask yourself what kind of data you’ll need to test your hypothesis.
- Generalisation: finally, it is important that you select the appropriate size of your test sample so that the analysis you do is representative and can be generalised to the entire population, not just the sample that you’re considering. For example, if you’re analysing whether sharing research via social media increases the visibility of your profile, you should include at least 100, or in ideal case 1000, researchers in your study so that you could generalise the findings.
Step 7: Develop approach to data analysis
Now that you’ve designed the data collection process, it’s time to decide how you are going to analyse your data. You can do it by using the qualitative and quantitative data analysis tools, following the research design approach you selected in Step 2.
For the qualitative data analysis, you’ll use your statistical analysis skills including descriptive statistics, inferential statistics, and hypothesis testing.
Descriptive statistics will help you to understand the characteristics of your data, especially in terms of statistical distribution, the central tendency of data and variability of data. The inferential statistics will help you understand the relationships between your data, and generalise the characteristics of the population based on your sample. Finally, using hypothesis testing you can understand whether the correlations you derived are statistically significant.
For qualitative data analysis, you may consider using thematic analysis or discourse analysis. The former focuses on the understanding the data content and its wider implications to determine key themes. The latter focuses on contextualising the data and is commonly used to understand the communication patterns.
Of course, other qualitative and quantitative tools do exist. You would usually find these via a comprehensive literature review of your research field .
Now you understand why research design is a crucial aspect of each research project, especially at the early stage of your PhD degree.
Having a clear path to the delivery of your research will reduce the anxiety and uncertainty associated with your work. Follow the 7-step process outlined in this article to develop your research design!
But there is one thing that I want to share with you here that helped me to successfully complete my PhD and publish my work in top journals. When you follow your initial research design process, you’ll generate new ideas and avenues – don’t be worried to pivot from your initial plan as you go through your project.
Always follow the most exciting ideas, as long as they are consistent with the main objective of your project!
What kind of research do you pursue? Qualitative or quantitative?
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- GETTING STARTED
Getting to the main article
Choosing your route
Setting research questions/ hypotheses
Building the theoretical case
Setting your research strategy
The quantitative research design that you set in your dissertation should reflect the type of research questions/hypotheses that you have set. When we talk about quantitative research designs, we are typically referring to research following either a descriptive , experimental , quasi-experimental and relationship-based research design, which we will return to shortly. However, there are also specific goals that you may want to achieve within these research designs. You may want to: (Goal A) explore whether there is a relationship between different variables; (Goal B) predict a score or a membership of a group; or (Goal C) find out the differences between groups you are interested in or treatment conditions that you want to investigate:
GOAL A Exploring the relationship between variables
Are you trying to determine if there is a relationship between two or more variables, and what this relationship is? This kind of design is used to answer questions such as: Is there a relationship between height and basketball performance? Are males more likely to be smokers than females? Does you level of anxiety reduce your exam ability?
GOAL B Predicting a score or a membership of a group
Are you trying to examine whether one variable's value (i.e., the dependent or outcome variable) can be predicted based on another's (i.e., the independent variable). These designs answer questions such as: Can I predict 10km run time based on an individual's aerobic capacity? Can I predict exam anxiety based on knowing the number of hours spent revising? Can I predict whether someone is classified as computer literate based on their performance in different computer tasks? Can I predict an individual's preferred transport (car/motorcycle) based on their response to a risk questionnaire?
GOAL C Testing for differences between groups or treatment conditions
Are you trying to test for differences between groups (e.g., exam performance of males and females) or treatment conditions (e.g., employee turnover among employees (a) given a bonus and (b) not given a bonus)? This type of design aims to answer questions such as: What is the difference in jump height between males and females? Can an exercise-training programme lead to a reduction in blood sugar levels? Do stressed males and females respond differently to different stress-reduction therapies? In each of these cases, we have different groups that we are comparing (e.g., males versus females), and we may also have different treatments (e.g., the example of multiple stress-reduction therapies).
Goals A and B reflect the use of relationship-based research questions/hypotheses, whilst goal C reflects the use of comparative research questions/hypotheses. Just remember that in addition to relating and comparing (i.e., relationship-based and comparative research questions/hypotheses), quantitative research can also be used to describe the phenomena we are interested in (i.e., descriptive research questions). These three basic approaches (i.e., describing , relating and comparing ) can be seen in the following example:
Let's imagine we are interested in examining Facebook usage amongst university students in the United States .
We could describe factors relating to the make-up of these Facebook users, quantifying how many (or what proportion) of these university students were male or female, or what their average age was. We could describe factors relating to their behaviour, such as how frequently they used Facebook each week or the reasons why they joined Facebook in the first place (e.g., to connect with friends, to store all their photos in one place, etc.).
We could compare some of these factors (i.e., those factors that we had just described). For example, we could compare how frequently the students used Facebook each week, looking for differences between male and female students.
We could relate one or more of these factors (e.g., age) to other factors we had examined (e.g., how frequently students used Facebook each week) to find out if there were any associations or relationships between them. For example, we could relate age to how frequently the students used Facebook each week. This could help us discover if there was an association or relationship between these variables (i.e., age and weekly Facebook usage), and if so, tell us something about this association or relationship (e.g., its strength, direction, and/or statistical significance).
These three approaches to examining the constructs you are interested in (i.e., describing , comparing and relating ) are addressed by setting descriptive research questions, and/or comparative or relationship-based research questions/hypotheses. By this stage, you should be very clear about the type of research questions/hypotheses you are addressing, but if you are unsure, refer back to the Research Questions & Hypotheses section of the Fundamentals part of Lærd Dissertation now.
If you are exploring the relationship between variables (i.e., Goal A ), you are likely to be following a relationship-based research design (i.e., a type of non-experimental research design). However, if you are predicting the score or a membership of a group (i.e., Goal B ) or testing for differences between groups or treatment conditions (i.e., Goal C ), you are likely to be following either an experimental or quasi-experimental research design. Unless you already understand the differences between experimental, quasi-experimental and relationship-based research designs, you should read about these different research designs in the Research Designs section of the Fundamentals part of Lærd Dissertation now. You need to do this for two main reasons:
You will have to state which type of research design you are using in your dissertation when writing up the Research Design section of your Chapter Three: Research Strategy .
The research design that you use has a significant influence on your choice of research methods , the research quality of your findings, and even aspects of research ethics that you will have to think about.
Once you are familiar with the four types of research design (i.e., descriptive, experimental, quasi-experimental and relationship-based), you need to think about the route that you are adopting, and the approach within that route in order to set the research design in your dissertation:
- ROUTE A: Duplication
- ROUTE B: Generalisation
- ROUTE C: Extension
Route A: Duplication
If you are taking on Route A: Duplication , you would typically not be expected to make any changes to the research design used in the main journal article when setting the research design for your dissertation. After all, the purpose of the dissertation is duplication , where you are, in effect, re-testing the study in the main journal article to see if the same (or similar) findings are found. An important aspect of such re-testing is typically the use of the same research strategy applied in the main journal article. As such, if an experimental research design was used in the main journal article, with 3 groups (e.g., two treatment groups and one control group), your dissertation would also use an experimental design with the same group characteristics (i.e., 3 groups, with two treatment groups and one control group). The research design you used would also have the same goals as those in the main journal article (e.g., the goal of relating two constructs, perhaps study time and exam performance, in order to answer a relationship-based research question/hypothesis).
However, there are some instances where, from a practical standpoint, you may find that it is not possible to use the same research design, perhaps because an experimental research design was used, but you are unable to randomly selected people from the population you can get access to, forcing you to use a quasi-experimenta l research design. But the goal will be to use the same research design in your dissertation as the one applied in the main journal article. Again, you can learn about the differences between experimental and quasi-experimental designs in the Research Designs section of the Fundamentals part of Lærd Dissertation.
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.  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 
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.
 Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th edition, Pearson Education Limited
 Source: Pride and Ferrell (2007)
Research Design 101
Everything You Need To Get Started (With Examples)
By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023
Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.
Overview: Research Design 101
What is research design.
- Research design types for quantitative studies
- Video explainer : quantitative research design
- Research design types for qualitative studies
- Video explainer : qualitative research design
- How to choose a research design
- Key takeaways
Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.
Understanding different types of research designs is essential as helps ensure that your approach is suitable given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.
The problem with defining research design…
One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.
Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!
In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.
Research Design: Quantitative Studies
Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental .
Descriptive Research Design
As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.
For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.
The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.
Correlational Research Design
Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.
For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).
As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).
That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…
Need a helping hand?
Experimental Research Design
Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.
For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.
Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.
Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.
Quasi-Experimental Research Design
Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.
For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.
Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.
All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .
Research Design: Qualitative Studies
There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.
Phenomenological Research Design
Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.
For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.
Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.
Grounded Theory Research Design
Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.
As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).
Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .
As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.
Ethnographic Research Design
Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .
All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.
As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.
As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.
Case Study Design
With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .
As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.
While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.
As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.
How To Choose A Research Design
Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.
Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!
Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.
Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.
Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.
Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.
Recap: Key Takeaways
We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:
- Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
- Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
- Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
- When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.
If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .
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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.
This was really helpful. thanks
Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.
Please is there any template for a case study research design whose data type is a secondary data on your repository?
This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.
This post is helpful, easy to understand, and deconstructs what a research design is. Thanks
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- Guide on research designs
- How to create a research design
Consider your priorities
- Determine the type of data needed
How to collect the data
How to analyze the data, quantitative data analysis, qualitative data analysis, writing your research proposal.
What comes first, the research design or research problem selection? Read on this guide from our dissertation writing service if you are struggling to answer this question. Any research paper is based on the hypothesis, datum, and methodology. These things though are not written down in the instructions from the professor. As an independent executor of the research, a student is expected to figure out what type of research the topic requires. No worries if you find it hard to answer this question. Here is an overview of the research designs and their purposes.
No doubt the research writing and creating a research design is a dynamic process full of changes and adjustments. Although it seems that your research questions should be one of the constant elements, it is far from reality. When you start assessing the resources, timing, and relevance of every research question, you might want to shift the focus of your research. In any case, this is normal, adjustments are acceptable on the stage of conducting the study. It is even possible that the final list of research questions of your paper will be approved after all the work is done. Make sure you know how to form a research question properly.
So, start your process by prioritizing your objectives. Put the instructor’s requirements on top, as well as all factors that can influence your grade. It will help you see what your limits are and what are the goals you have to achieve. In the end, you do not have all the time and resources in the world to polish the study for ages. You want to get it done, and done successfully.
Determine the type of data needed
At this stage, you will get closer to identifying the type of research you deal with, whether it is a qualitative or quantitative one. Analyze the research questions and objectives you drafted and identify how you want to collect the selection. If you plan to analyze the subjective experiences of participants, arrange the study where you can be observing the behaviors than the research is qualitative. If your data collection is performed by assessments and taking measurements then your research is a quantitative one. Read our guide to find out more about what is qualitative research and what sets it apart from a quantitative one.
The whole paper is based on the data you collect. You are supposed to interpret the findings after analyzing data. So, make sure you think about the ways and tools you are going to use. Note that copying the findings of some research on the internet is plagiarism, so avoid this type of borrowings, they will be noticed. Here are some tools that can be used for your qualitative and quantitative researches:
- Method of conducting a case study research (note that the results will be very specific and narrow since a case study is limited to a small group, individual or event);
- Interviews, questionnaires, surveys;
Is there a difference in the analysis of behavioral patterns and measurements? For sure, depending on the data you deal with, your approach to analysis will vary.
Opting for the quantitative analysis means you are trying to identify some descriptive statistics (range, frequency, etc.). This type of data is collected via polls, interviews, surveys that include mathematical figures (rating, multiple-choice questions, etc.).
With the qualitative analysis, the principle of interpreting applies. Based on the data received from observations or experiments the researcher is drawing explanations, conclusions, and interpretation of the findings.
Reasons are abundant why the research proposal is an inevitable part of the thesis or dissertation. A research proposal starts with a research problem. Based on the research problem is the formulation of the problem statement that can be thought of as consisting of a research question and a research purpose. A clear research question and purpose directs the entire research project, including the data analysis. Asking relevant research questions is essential for gathering the right data and consequently to provide the analysis with the necessary input to ultimately answer the research questions. The answer to a research question is knowledge. The research goal indicates what the knowledge obtained will be used for. In other words, why is it worthwhile to answer the research question? What is the use of the whole endeavour? It is important that the research question and research purpose match. To start, it provides the validity of your paper. It answers the questions:
- What is it that you are researching?
- What are the reasons and need for this research?
- Your plan on achieving all the objectives.
Write out step-by-step instructions for completing the method. Remember that this paper is written for an academic audience familiar with the processes of your discipline. Include all processes and crucial information, but don't worry about detailed descriptions of common procedures unless you need them for your own notes.
Check your work. It is a wise idea to have a peer review your research design before you execute it or turn in the paper. Your peer can offer feedback on your method as a whole and note any places within the plan where you have offered too much or too little detail, or are missing steps.
Mind that the research proposal is also a sample of your writing style and a showcase of your academic writing skills. Note that the formatting and structure of your paper a well as the proposal will be assessed and significantly influence your overall grade. Visit our blog post to learn more about how to write a research proposal .
If we read the case study definition, this is a published report about an event, situation, person or group that has been studied. The main idea of this document is to deeply investigate the chosen subject. Depending on the topic, all the case studies could be separated into next 4 groups:illustrati...
Before you start any form of study, get a clear understanding of what a research problem is and learn to formulate it properly. After defining it, you can start writing your paper. It means that research problems or questions are the fuel driving the entire scientific process and they serve as the f...
Qualitative research is useful for those people who are trying to learn more about the market their business is trying to succeed in. In this article our experts will explain you how to write qualitative research and make everything properly. Continue reading and find out what is the difference betw...
Organizing Academic Research Papers: Types of Research Designs
- Purpose of Guide
- Design Flaws to Avoid
- Glossary of Research Terms
- Narrowing a Topic Idea
- Broadening a Topic Idea
- Extending the Timeliness of a Topic Idea
- Academic Writing Style
- Choosing a Title
- Making an Outline
- Paragraph Development
- Executive Summary
- Background Information
- The Research Problem/Question
- Theoretical Framework
- Citation Tracking
- Content Alert Services
- Evaluating Sources
- Primary Sources
- Secondary Sources
- Tertiary Sources
- What Is Scholarly vs. Popular?
- Qualitative Methods
- Quantitative Methods
- Using Non-Textual Elements
- Limitations of the Study
- Common Grammar Mistakes
- Avoiding Plagiarism
- Footnotes or Endnotes?
- Further Readings
- Annotated Bibliography
- Dealing with Nervousness
- Using Visual Aids
- Grading Someone Else's Paper
- How to Manage Group Projects
- Multiple Book Review Essay
- Reviewing Collected Essays
- About Informed Consent
- Writing Field Notes
- Writing a Policy Memo
- Writing a Research Proposal
Before beginning your paper, you need to decide how you plan to design the study .
The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you can use, not the other way around!
General Structure and Writing Style
Action research design, case study design, causal design, cohort design, cross-sectional design, descriptive design, experimental design, exploratory design, historical design, longitudinal design, observational design, philosophical design, sequential design.
Kirshenblatt-Gimblett, Barbara. Part 1, What Is Research Design? The Context of Design. Performance Studies Methods Course syllabus . New York University, Spring 2006; Trochim, William M.K. Research Methods Knowledge Base . 2006.
The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem as unambiguously as possible. In social sciences research, obtaining evidence relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe a phenomenon. However, researchers can often begin their investigations far too early, before they have thought critically about about what information is required to answer the study's research questions. Without attending to these design issues beforehand, the conclusions drawn risk being weak and unconvincing and, consequently, will fail to adequate address the overall research problem.
Given this, the length and complexity of research designs can vary considerably, but any sound design will do the following things:
- Identify the research problem clearly and justify its selection,
- Review previously published literature associated with the problem area,
- Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem selected,
- Effectively describe the data which will be necessary for an adequate test of the hypotheses and explain how such data will be obtained, and
- Describe the methods of analysis which will be applied to the data in determining whether or not the hypotheses are true or false.
Kirshenblatt-Gimblett, Barbara. Part 1, What Is Research Design? The Context of Design. Performance Studies Methods Course syllabus . New Yortk University, Spring 2006.
Definition and Purpose
The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out (the action in Action Research) during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and the cyclic process repeats, continuing until a sufficient understanding of (or implement able solution for) the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.
What do these studies tell you?
- A collaborative and adaptive research design that lends itself to use in work or community situations.
- Design focuses on pragmatic and solution-driven research rather than testing theories.
- When practitioners use action research it has the potential to increase the amount they learn consciously from their experience. The action research cycle can also be regarded as a learning cycle.
- Action search studies often have direct and obvious relevance to practice.
- There are no hidden controls or preemption of direction by the researcher.
What these studies don't tell you?
- It is harder to do than conducting conventional studies because the researcher takes on responsibilities for encouraging change as well as for research.
- Action research is much harder to write up because you probably can’t use a standard format to report your findings effectively.
- Personal over-involvement of the researcher may bias research results.
- The cyclic nature of action research to achieve its twin outcomes of action (e.g. change) and research (e.g. understanding) is time-consuming and complex to conduct.
Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Locoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605.; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.
A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about a phenomenon.
- Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
- A researcher using a case study design can apply a vaiety of methodologies and rely on a variety of sources to investigate a research problem.
- Design can extend experience or add strength to what is already known through previous research.
- Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and extension of methods.
- The design can provide detailed descriptions of specific and rare cases.
- A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
- The intense exposure to study of the case may bias a researcher's interpretation of the findings.
- Design does not facilitate assessment of cause and effect relationships.
- Vital information may be missing, making the case hard to interpret.
- The case may not be representative or typical of the larger problem being investigated.
- If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your intepretation of the findings can only apply to that particular case.
Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.
Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.
Conditions necessary for determining causality:
- Empirical association--a valid conclusion is based on finding an association between the independent variable and the dependent variable.
- Appropriate time order--to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
- Nonspuriousness--a relationship between two variables that is not due to variation in a third variable.
- Causality research designs helps researchers understand why the world works the way it does through the process of proving a causal link between variables and eliminating other possibilities.
- Replication is possible.
- There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
- Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
- Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
- If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and therefore to establish which variable is the actual cause and which is the actual effect.
Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.
Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."
- Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
- Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
- The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies on cohort designs.
- Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
- Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
- Either original data or secondary data can be used in this design.
- In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
- Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
- Because of the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.
Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.
Cross-sectional research designs have three distinctive features: no time dimension, a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure diffrerences between or from among a variety of people, subjects, or phenomena rather than change. As such, researchers using this design can only employ a relative passive approach to making causal inferences based on findings.
- Cross-sectional studies provide a 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
- Unlike the experimental design where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
- Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
- Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
- Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
- Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
- Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
- Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
- Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical contexts.
- Studies cannot be utilized to establish cause and effect relationships.
- Provide only a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
- There is no follow up to the findings.
Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods. Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.
Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.
- The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject.
- Descriptive research is often used as a pre-cursor to more quantitatively research designs, the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
- If the limitations are understood, they can be a useful tool in developing a more focused study.
- Descriptive studies can yield rich data that lead to important recommendations.
- Appoach collects a large amount of data for detailed analysis.
- The results from a descriptive research can not be used to discover a definitive answer or to disprove a hypothesis.
- Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
- The descriptive function of research is heavily dependent on instrumentation for measurement and observation.
Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.
A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental Research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.
- Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “what causes something to occur?”
- Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
- Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
- Approach provides the highest level of evidence for single studies.
- The design is artificial, and results may not generalize well to the real world.
- The artificial settings of experiments may alter subject behaviors or responses.
- Experimental designs can be costly if special equipment or facilities are needed.
- Some research problems cannot be studied using an experiment because of ethical or technical reasons.
- Difficult to apply ethnographic and other qualitative methods to experimental designed research studies.
Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.
An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to. The focus is on gaining insights and familiarity for later investigation or undertaken when problems are in a preliminary stage of investigation.
The goals of exploratory research are intended to produce the following possible insights:
- Familiarity with basic details, settings and concerns.
- Well grounded picture of the situation being developed.
- Generation of new ideas and assumption, development of tentative theories or hypotheses.
- Determination about whether a study is feasible in the future.
- Issues get refined for more systematic investigation and formulation of new research questions.
- Direction for future research and techniques get developed.
- Design is a useful approach for gaining background information on a particular topic.
- Exploratory research is flexible and can address research questions of all types (what, why, how).
- Provides an opportunity to define new terms and clarify existing concepts.
- Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
- Exploratory studies help establish research priorities.
- Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
- The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings.
- The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value in decision-making.
- Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.
Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.
The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute your hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, logs, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.
- The historical research design is unobtrusive; the act of research does not affect the results of the study.
- The historical approach is well suited for trend analysis.
- Historical records can add important contextual background required to more fully understand and interpret a research problem.
- There is no possibility of researcher-subject interaction that could affect the findings.
- Historical sources can be used over and over to study different research problems or to replicate a previous study.
- The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
- Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
- Interpreting historical sources can be very time consuming.
- The sources of historical materials must be archived consistentally to ensure access.
- Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
- Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
- It rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.
Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58; Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.
A longitudinal study follows the same sample over time and makes repeated observations. With longitudinal surveys, for example, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study and is sometimes referred to as a panel study.
- Longitudinal data allow the analysis of duration of a particular phenomenon.
- Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
- The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
- Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
- The data collection method may change over time.
- Maintaining the integrity of the original sample can be difficult over an extended period of time.
- It can be difficult to show more than one variable at a time.
- This design often needs qualitative research to explain fluctuations in the data.
- A longitudinal research design assumes present trends will continue unchanged.
- It can take a long period of time to gather results.
- There is a need to have a large sample size and accurate sampling to reach representativness.
Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.
This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.
- Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe (data is emergent rather than pre-existing).
- The researcher is able to collect a depth of information about a particular behavior.
- Can reveal interrelationships among multifaceted dimensions of group interactions.
- You can generalize your results to real life situations.
- Observational research is useful for discovering what variables may be important before applying other methods like experiments.
- Observation researchd esigns account for the complexity of group behaviors.
- Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and difficult to replicate.
- In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
- There can be problems with bias as the researcher may only "see what they want to see."
- There is no possiblility to determine "cause and effect" relationships since nothing is manipulated.
- Sources or subjects may not all be equally credible.
- Any group that is studied is altered to some degree by the very presence of the researcher, therefore, skewing to some degree any data collected (the Heisenburg Uncertainty Principle).
Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010.
Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:
- Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
- Epistemology -- the study that explores the nature of knowledge; for example, on what does knowledge and understanding depend upon and how can we be certain of what we know?
- Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
- Can provide a basis for applying ethical decision-making to practice.
- Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
- Brings clarity to general guiding practices and principles of an individual or group.
- Philosophy informs methodology.
- Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
- Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
- Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
- Limited application to specific research problems [answering the "So What?" question in social science research].
- Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
- While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
- There are limitations in the use of metaphor as a vehicle of philosophical analysis.
- There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.
Chapter 4, Research Methodology and Design . Unisa Institutional Repository (UnisaIR), University of South Africa; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, D.C.: Falmer Press, 1994; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.
- The researcher has a limitless option when it comes to sample size and the sampling schedule.
- Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method. Useful design for exploratory studies.
- There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce extensive.
- Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed.
- The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more sample can be difficult.
- Because the sampling technique is not randomized, the design cannot be used to create conclusions and interpretations that pertain to an entire population. Generalizability from findings is limited.
- Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.
Rebecca Betensky, Harvard University, Course Lecture Note slides ; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis . Wikipedia.
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