in designing a research project what are the bases you consider

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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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, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Designing research projects

How to design better research projects, and how to develop your skill as someone who generates research projects.

Eleanor C Sayre

Designing good research studies is an important part of becoming a researcher, no matter what your field is. The exercises on this page are aimed at junior researchers who are designing their first studies in education research. If you’ve already done one or two projects, these exercises will help you get better at seeking funding and developing more projects. If you’ve never done research before, these exercises will help your first project be more successful.

If you are looking to design an education research project, the exercises on this page will help you. If you’re looking for advice on how to plan research projects is a good choice. You might also look at research process models to help you think about how research projects progress, or Iterative Design to think about to structure them for maximum likelihood of success.

If you’re doing video-based observational research, here’s a good companion piece to consider. If you’re thinking about Design-based research, check out this article .

More broadly, check out all articles tagged with “ doing research ”.

What does my project need?

Every project in education research needs to address four areas. While the details of these areas can be (should be) emergent, well-formed and successful research projects identify as much as possible ahead of time.

Every project needs:

Additionally, when you present your work for publication or funding, you will need to consider two more areas:

We’re going to leave these two aside for now because they reference a broader sense of where the research community is, what societal needs are, and how your project fits into a much larger narrative. Those considerations are outside the scope of this guide, though you might consider reading ahead to other guides on writing. Let’s work on the four primary areas.

In a good research project, the four areas are all tightly related and supportive of each other. You should develop them in concert with each other. The exercises on this page will help you design a research study, and they will also help you develop your design skills in general.

Details of the four areas

Research questions.

Your research question(s) tie together your theoretical frameworks, methods, and access. They give purpose to your data collection and analysis. Answering them generates new knowledge about human behavior. In the ordinary process of doing research or thinking about the world, you will ask lots of questions. As you pursue some of them, you’ll develop follow-up questions and related lines of inquiry.

Research Question templates

If you’re in the very beginning stages of thinking about your project, you might need help brainstorming some possible research questions. Here are some templates to get you started. It’s not an exhaustive list.

• Theory X says A, but theory Y says B. How can they be made commensurate?
• This paper used population A, but I have population B. How can I apply their findings to my population?
• Surveys shows that students can do X. What is the actual process of learning to do X?
• What are the moderating factors which control success at task X?
• Our previous work shows X happens sometimes. Why does X occur?
• What’s better at teaching X, curriculum A or curriculum B?
• How do teachers make sense of X in light of Y?

Making your research question better

When you have an idea about what you’d like to investigate, you need to refine your ideas into a research question that suggests how you will answer it and how you will know when it is answered.

A good research question has the following properties:

• It is phrased a question, not a statement of problem
• Specific enough to be answerable
• Open to complicated, robust answers
• Interesting to investigate

You will want to have two versions of your research question: one that uses regular language, and a longer one (possibly with subquestions!) that uses specific, technical language.

This exercise helps you refine your ideas into a research question.

Write your question in the form of a question. Use regular language.

Make it specific. Your research question needs to be answerable in principle, and your research design needs to have a high likelihood of answering it.

• If your research question uses comparison language, what are you comparing? For example, if your research question is about whether a new curriculum is “better” or if students are learning “more”, what will you be comparing it to? Do you need to collect baseline data? Will you be able to run a treatment group and a control group at the same time?
• If your research question uses development language (e.g. “learning”), over what time are your subjects changing? An hour? Four years? Their lives? How will you know if change is durable? how will you know if it occurs at all?

Open it up to complicated, robust answers.

• If your research question has a binary answer (“does X happen?”), revise it to permit a more subtle answer (“to what extent does X happen?”; “how much does Y mediate X happening?”; “under what conditions can X be optimized to happen?”)
• If your research question is too specific (“what is the correlation coefficient of X with Y?”), you are too specific. Revise your question to have more robust answers (“how do X and Y relate?”; “what factors affect X and by how much?”)

Check: does answering this question sound like fun to you?

• If you refined your question so much that finding the answer sounds boring, trivial, or insurmountably hard, try new ways to refine it so that it really captures your interest in this topic.

In the process of refining your research question, you might realize that there are a bunch of interesting sub-questions to pursue. Go ahead and list them out, and follow this same process to refine them. Your refinements probably also include technical language and reserved words that mean something specific to the research project. Define each reserved word and link it to specific theoretical frameworks, methods, or data streams.

A good research question is a living question. As you interact with theories and data , it will necessarily change. The more specific you can make it in the beginning, the better you will be able to see it change and adjust your future work in an intentional way. You may find it useful to read Engle et al’s “ Progressive Refinement of Hypotheses in Video-Supported Research ” to understand how research questions can change and in response to repeated engagement with data, and Iterative Design to think about how to design for this feature.

The Access area is about practical constraints on your project: what populations do you have access to, and in which modalities? how much time do you have, and which analysis resources can you marshal? Of all the areas, Access is the one which is usually fixed earliest in the project, because the kinds and amounts of data you have access to are usually determined before you can collect any data at all, and the scope of your project is usually outside your control.

Questions that detail your access to data:

• What kinds of people will you measure? Some examples: introductory students, pre-service teachers, graduate teaching assistants, third graders in a specific elementary school.
• In what modalities can you collect data from them? Some examples: I can talk to them individually in interviews once per person, I can video them in class every day, I can put a problem on their final exam, I have three years of archival data but cannot collect new data, etc.
• How many people / how much data? One or two significant figures are ok here: about 10 students, about 300 students, about 20 hours of video, about 100 matched pre-post tests, etc

You probably can’t answer all of these questions alone. Get specific guidance from your collaborators, advisor, and people who control your access to research subjects (their instructors, their principals, the registrar, the data librarian, etc) – the members of your Advisory Board . At early design stages, you don’t need to seek IRB approval yet, and you don’t need written permission from every stakeholder. When your study is more fully designed , you will talk to these people again to firm up the details of your access and adjust your research questions and methods.

Questions that detail your access to resources:

• How long can you spend collecting data? How long analyzing it?
• How many researchers will be involved in data collection and analysis? What are their skill levels?
• How much data (and what kinds) can you reasonably expect to collect / analyze in the amount of time and effort that are available to you?

It is entirely possible that you have access to more data and analysis resources than you will need or use in your project. That’s great! You don’t need to collect (or use) everything. Alternately, you might not have enough access (or the right kind of access) to do the study you really want to do. That’s disappointing. You will need to adjust your research questions and methods in light of how much (and what kinds) of data you can collect or analyze with your resources.

On rare occasions, you can use your research questions to argue for access to more resources or different modalities. For example, suppose your research question is about student epistemology and persistence, and you already have access to students’ attitude survey scores. You might be able to ask the registrar for students’ demographics and final grades to enhance your analysis.

Theoretical frameworks

The role of your theoretical frameworks is to tell you why your observations are meaningful and in what ways your analyses generate new knowledge. Without a theoretical framework, your observations are meaningless and your work is unpublishable.

The primer on theory covers what you need theory for, an organizing framework for deciding which theory or theories to use, some theory options in education research, and some other common considerations.

The best theoretical frameworks are a) explicit; b) well-matched to your research question and methods; and c) intentionally chosen. There isn’t a “best framework” for everyone, or even every research question, and there are a lot of options available.

I’m using “Theoretical framework” in a loose sense to include things like knowledge-in-pieces , communities of practice, speech genres, models of institutional change, error-based learning, etc. (I’ve used all of these, and there are a lot more out there.) Some people use the phrase “theoretical-methodological framework” to acknowledge that good frameworks must tie theory, methods, and data together.

In this article, I’m not going to explore those subtleties.

Methodology

The role of your methodology is to tell you how to generate observations to answer your research question, how to convert those observations into data , and how to analyze that data. While theoretical frameworks are mostly concerned with why those observations and analyses are meaningful or interesting, methodologies are mostly concerned with the practicality of converting observations into analyses and the reasons for those analyses.

It is becoming a lot more common in discipline-based education research to be explicit about the methods that you choose and why. While it used to be sufficient in papers to outline what you did, now you also need to discuss why you did it and how it fits into a broader research tradition.

Many projects – especially large projects – coordinate multiple kinds of data and multiple kinds of analyses in order to make robust conclusions. This is (broadly) called “mixed-methods” or “multi-methods” design. There are lots of ways to mix methods well (and some ways to do it badly). If your research questions demand multi- or mixed-methods, you will need to write sub-research questions and choose theoretical frameworks for each method, and you will need to think about how the analyses from each method will interact to generate new knowledge. Before you jump into a mixed-methods design, ask yourself carefully if your research questions really warrant it, and if your access really allows it.

Sources for theoretical frameworks and methodologies:

There are books and papers written on this subject. Some of them are textbook-style for students; others are monograph style for researchers. To find them, you will have to step outside your particular discipline and look at the broader educational research literature, the learning sciences, or psychology (depending on your research questions).

• The Journal of the Learning Sciences has an excellent series on methodology and many beautiful papers on theory.
• Reviews in PER has a few papers with brief overviews of some kinds of methods and theories.
• Probably the most highly-cited book on methods is Creswell’s book on research design. It is not comprehensive, but it is extensive.
• There’s a quick overview of coding qualitative data (aimed at UX researchers) on Delve
• Shayan Doroudi wrote an excellent primer on learning theories.
• When you read papers , make note of their frameworks and methods (and their citations!).

You can also talk to other humans!

• Talk to your advisor or collaborators about what they would use (or require you to use).
• Write a one-page prospectus that outlines what you want to do and why you think it’s interesting or important, and send it to someone who does similar work. Ask them (nicely) for suggestions.

Develop the four areas in concert for a specific research project

In this exercise, you’re going to iteratively refine each of the four areas so that they are tightly integrated with each other.

On a whiteboard, write down a preliminary research question. If you don’t have a preliminary research question, start with one of the research question templates or do the exercise on making better research questions.

Write down what kind of access you have. Be specific about what populations, what kind of resources you have to undertake this research and how long it will take, and what kind of data modalities are available to you.

If you’re structurally constrained (by your funder, or your advisor, or your equipment) to use particular methods or theories, write them down as well.

Return to your research question, and update it so that it is constrained to the populations you have access to (as well as other structural constraints). It will get longer and more detailed. That’s great.

Which theories support your research question? Write them down. Amend your research question to explicitly reference at least one theoretical framework. If your question is about how individuals develop, you might look at the Resource Framework . If it’s about how communities form, try Communities of Practice. If you don’t know any theories, what have you read that makes you think this would be an interesting research question? You might need to use two or three frameworks in concert with each other to fully answer your research question.

What kind of data will you collect? Here’s a quick overview of the common kinds of data .

• Make sure that your access permits this kind of data, that your theoretical framework will be able to use the data from it, and that it will be able to answer your research question.
• Amend your research question and theoretical framework(s) to reflect the kind of observations you will collect. You might triangulate across several different data streams: preliminary surveys will identify participants for in depth interviews , and you ask them for their homework, for example.

How much data will you need to collect or analyze to show the effects you are looking for? Part of the answer to this question is about where you plan to publish your results at the end of your study: if you want to exhaustively prove your solution, you need a lot of evidence, but if you are only looking to prove its existence, you don’t need as much. Even a thoroughly theory-driven, theory-generating project needs something data-like (reinterpretation of old data, for example).

• If your project is based on finding patterns of human behavior, there are formalized methods for estimating effect sizes (generally known as a “power analysis” or “power estimate”). A quick-n-dirty estimate is that your error bars will go like 1/sqrt(N). If you can estimate differences in your treatments based on the literature, you can guess about how many subjects you will need. If your estimates suggest you will need many more subjects than you have access to, you need to revise your research question.
• If your project is based on finding cases of human behavior, you will need to think carefully about episode selection. How many episodes will you need to prove your point substantially? A good estimate is 3-5, most of which should be similar and one of which should be contrasting. More or fewer are possible.

Adjust your research question and methods in light of how much data you will be able to generate.

Write down a preliminary data collection and analysis plan.

• You may find that drawing a logic model or conjecture map is helpful. You may find that a narrative of what you’re planning to do and how is helpful.
• Compare your plan with your chosen theories and research question. Does your plan make use of your theories? Is it likely to answer your research question? Is it possible with the time and resources you have allotted?

Imagine that everything in data collection goes swimmingly and all of your data are fantastic. What does the answer to your research question look like? To what extent can you answer it with your methods and access? If course, you won’t know exactly what the answer will be – if you already knew, it wouldn’t be research – but you should be able to guess at an approximate shape to the answer.

• If you think you’ll need additional kinds of data to better triangulate an answer your question, amend your access and methods.
• If you think you’ll need a lot more data than you can get, amend your research question.

Another process which can help with intentional research design is conjecture mapping ; you might also consider the emergent processes outlined in “ Progressive Refinement… ”. If your research project is larger than you can complete in one semester, you are strongly encouraged to think about an iterative design using the principles in Planning Research Projects . Alternately, if your research project has a substantial curriculum development aspect, you should consider Design-Based Research (DBR). Lastly, you might consider whether your project is research at all: maybe you’re doing evaluation, not research .

Develop your skill in designing research projects

These exercises will develop your skill in designing research projects. If you do them a lot, then designing research studies will become a habit for you.

When you read papers , imagine using their theory and methods with a different population, or using their access with different theory and methods, or their research question with different access and methods. Make notes about your choices, so that later you can cite these papers in your own work. This exercise also makes you a better reader of papers.

Read through the abstracts of NSF’s recent awards for either IUSE or EDU:CORE . For every project, imagine that you have been given a supplement to do some research related to that project. What would be interesting? What would be possible, but not personally interesting? What would be exciting, but you don’t know very much about? You should be able to find something personally interesting or exciting in almost all of the projects. Design a study for each. This exercise also makes you a better citizen of the broader education research community, because you will know a lot more about the shape of current work in the community.

Read through the NSF’s upcoming deadlines for programs sponsored by Directorate for STEM Education (EDU), particularly the DUE and DRL divisions. For each one, sketch out a research study: what would you investigate? who might you partner with? This exercise also makes you a better researcher, because you will become more knowledgeable about how to frame your work to get funding.

Generative writing is the biggest tool in your researcher toolbox. Go back to your old notes about research designs, and enrich them with your new thoughts as you learn more.

Check out all articles in this Handbook tagged with “ doing research ”.

Read this delightful piece by the former editor of Sociology of Education.

Read this paper on quality in qualitative research design: Tracy, S.J., 2010. Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative inquiry, 16(10), pp.837-851.

Read this paper on elements of research project designs.

Read this overview on designing projects for the scholarship of teaching and learning.

Additional topics to consider

Planning research projects.

How to develop a timeline for an education research project that makes space for emergence.

Evaluation and Research

What is the difference between evaluation and research?

Data and Access

What are the common kinds of data in education research, and what are their affordances and constraints?

This article was first written on January 1, 2015, and last modified on May 30, 2024.

Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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14 Comments

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

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

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

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

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

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

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how to cite this page

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

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Wow! This post has an awful explanation. Appreciated.

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2 Considerations in Designing Your Research Approach

Once you’ve identified your area of interest, sorted through and analyzed the literature to identify the problem you’d like to address, and developed both a purpose and a question; the next step is to design your study. This chapter will provide a basic overview of the considerations any researcher must think about as they design a research study.

Chapter 2: Learning Objectives

As you work to identify the best approach to identify an answer to your research question, you will be able to:

• Compare the conceptualization and operational activities of the process
• Discuss the difference between an independent and dependent variable
• Discuss the importance of sampling
• Contrast research approaches
• Demonstrate a systematic approach to selecting a research design

Understanding the Language of Research

As you work to determine which approach you will consider in order to best answer your question, you’ll need to consider how to address both the conceptual and operational components of your inquiry. As we discussed in Chapter 1; theory often informs practice (deductive approaches). Theory is often discussed in terms of abstract, or immeasurable, constructs. Because of the ambiguous nature of theory, it is important to conceptualize the parameters of your investigation. Conceptualizing is the process of defining what is or is not included in your description of a specific construct.

Understanding Theoretical and Contextual Framework

You may consider the theoretical or contextual framework for your study as the ‘lens’ through which you want your reader to view the work from. That is, this is your opportunity frame their experience with this information through your educated perspective on the material.

How Will You Determine the Subjective Aspects of Your Work?

Consider exploring one’s motivation to advance their education:

• That is if you’re determining whether clinicians who have advanced credentials are more motivated at work; you’ll need to create a clear delineation between motivation and effort and work out how to measure each of these independently

Operationalization is the process of defining concepts or constructs in a measurable way. As you dive into the ‘HOW’ you will go about your research, you will need to understand the terminology related to study design

As we discussed in Chapter 1, there are several kinds of Variables. As a reminder, a variable is an objective and measurable representation of a theoretical construct. An independent variable is a variable which causes an effect on the dependent, or outcome variable. Note that there may be more than one independent variable in your study. Therefore, the dependent variable is the variable which you are measuring as an effect of an intervention or influence; you can think of this as the outcome variable. Identifying at least these two variables is an essential first step in designing your study. This is because how you explore the relationship between your effect (independent variable) and outcome (dependent variable) with help guide your methodology. Other variables to consider include mediating variables , which are variables that are explained by both the independent and dependent variables. Moderating variables influence the relationship between the independent and dependent variables and control variables which may have an impact on the dependent variable but does not help to explain the dependent variable.

Assigning Dependent and Independent Variables

You would like to determine the relationship between weight and tidal volume:

• Dependent Variable : Which variable DEPENDS on the other? Or, which variable will define the OUTCOME? ( Tidal volume)
• Independent Variable : Does the variable INFLUENCE, HELP EXPLAIN, or have an IMPACT on the dependent variable? (Weight)

You would like to determine whether the number of hours spent in clinical training influences post training test scores :

• Dependent Variable : Score on post training test
• Independent Variable : Number of hours in clinical training

Identifying and assigning the dependent and independent variable(s) is one of the most important research activities as this will help guide you toward the type of information you’ll be collecting and what you will do with that information. However, as you consider both the outcome (dependent) variable and the impact (independent) variable, it is also important to consider what other variables may influence the relationship between these two primary variables.

There are very few instances wherein you can control EVERY variable. However, it is your job as a researcher to plan for, acknowledge, and attempt to address anything that may influence the results you present.

levels of measurement can be thought of as values within each variable. For example, traditionally, the variable ‘Gender’ had two values: male or female. The modern variable of ‘Gender’ may have several values which are used to delineate each potential designation within the variable. Each value represents a specific designation of measure.

Values of measures may be considered quantitative (numeric); in our example of traditional gender you may assign a numeric (quantitative) value to male and female as either ‘1’ and ‘2’, respectively. Values may also be assigned non-numerically; meaning they are qualitative. It is important to note that if you want to analyze non-numeric data, it must be coded first.

Understanding and Assigning Value

You may create a question asking respondents to rank their agreement with a statement on a scale ranging from strongly disagree to strongly agree. Although qualitative in nature, we can assign a numeric value to each level of measurement as a ‘code’.

• 1= Strongly Disagree
• 2= Somewhat Disagree
• 3= Neither Disagree nor Agree
• 4= Somewhat Agree
• 5= Strongly Agree

By doing this, we can explore relationships between the attributes and variables using quantitative statistical methods.

Levels of measurement

One of the most important aspects of operationalizing a theoretical construct is to determine the level(s) of measurement. This is done by assessing the types of variables and values:

• Nominal : also called categorical. This level of measurement is used to describe a variable with two or more values BUT there is no intrinsic ordering to the categories

Example of a Nominal Variable

You would like to collect information about the gender (variable) of individuals participating in your study. Your level of measures may be:

You may then assign these measures a numeric value:

• Non-Binary=3
• Ordinal : This level of measurement is used to describe variable values that have a specific rank order. BUT that order does not indicate a specificity between ranks.

Example of an Ordinal Variable

You provide a scale of agreement for respondents to indicate their level of agreement with the use of a current policy within the hospital:

• Strongly Agree
• Strongly Disagree

Note: Those who strongly disagree with the use of this policy disapprove MORE than do those who disagree; however, there is no quantifiable value for how much more.

• Interval : You’ll use this level of measurement for variable values which are rank ordered AND have specified intervals between ranks and can tell you ‘how much more’.

Example of an Interval Variable

You classify the ages of the participants in your study:

• 18-24 years old
• 25-30 years old
• 31-35 years old
• >35 years old

NOTE: 35 is 5 more than 30. The quantifiable ‘how much more’ is what distinguishes age as an interval variable.

• Ratio : Ratio values have all of the qualities of a nominal, ordinal, and/or interval scale BUT ALSO have a ‘true zero’. In this case true zero indicates a lack of the underlying construct (i.e. it does not exist). Additionally, there is a ratio between points on this particular scale. That is, in this case, 10 IS twice that of 5.

Example of a Ratio Variable

You are doing a pre and post bronchodilator treatment trial for a new drug. You must establish baseline heart rate in your treatment group:

• Pulse rate is a ratio variable because the scale has an absolute zero (asystole) and there is a ratio between the number of times the heart beats (i.e. a change in heart rate of 10 beats per minute)

Identification of variable and values is essential to a successful project. Not only will doing this early in the process allow you to predict factors that may affect your research question, but it will also guide you toward the type of data you will collect and determine what kind of statistical analyses you will likely be performing in order to understand and present the results of your work.

Scales are used to glean insight into a situation or phenomenon and can be used to help quantify information that would otherwise be difficult to understand or convey. Although there are several types of scales used by researchers, we’ll focus on the two of the most common:

• Binary scale : Nominal scale that offers two possible outcomes, or values. Questions that force a respondent to answer either ‘yes’ or ‘no’ utilize a binary scale. IF you offer more than two options, your scale is no longer binary, but is still a nominal scaled item

• Likert scales : Likert scales are popular for measuring ordinal data and include indications from respondents. Data can be quantified using codes assigned to responses and an overall summation for each attribute can be associated with each respondent

Sampling is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalized back to the population of interest. Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. An example of probability sampling is simple random sampling wherein you include ALL possible participants in a population and utilize a method to randomly select a sample that is representative of that population. Nonprobability Sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, nonprobability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalized back to the population. An example of nonprobability sampling is utilizing a convenience sample of participants due to your close proximity or access to them.

Why does sampling matter?

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response. In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution, which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a “statistic” is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not “statistic” because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error. Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown below, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical (and equal to the population parameter), and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples, while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample. Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error. In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

The mean value of a sample statistic in a sampling distribution is presumed to be an estimate of the unknown population parameter. Based on the spread of this sampling distribution (i.e., based on standard error), it is also possible to estimate confidence intervals for that prediction population parameter. Confidence interval is the estimated probability that a population parameter lies within a specific interval of sample statistic values. All normal distributions tend to follow a 68-95-99 percent rule (see below), which says that over 68% of the cases in the distribution lie within one standard deviation of the mean value (μ 1σ), over 95% of the cases in the distribution lie within two standard deviations of the mean (μ 2σ), and over 99% of the cases in the distribution lie within three standard deviations of the mean value (μ 3σ). Since a sampling distribution with an infinite number of samples will approach a normal distribution, the same 68-95-99 rule applies, and it can be said that:

• (Sample statistic one standard error) represents a 68% confidence interval for the population parameter.
• (Sample statistic two standard errors) represents a 95% confidence interval for the population parameter.
• (Sample statistic three standard errors) represents a 99% confidence interval for the population parameter.

A sample is “biased” (i.e., not representative of the population) if its sampling distribution cannot be estimated or if the sampling distribution violates the 68-95-99 percent rule. As an aside, note that in most regression analysis where we examine the significance of regression coefficients with p<0.05, we are attempting to see if the sampling statistic (regression coefficient) predicts the corresponding population parameter (true effect size) with a 95% confidence interval. Interestingly, the “six sigma” standard attempts to identify manufacturing defects outside the 99% confidence interval or six standard deviations (standard deviation is represented using the Greek letter sigma), representing significance testing at p<0.01.

Types of Research Designs

There are many different approaches to research. The list provided here is not exhaustive by any means; rather, this is a brief list of the most common approaches you may identify as you review the literature related to your interest.

Experimental

Experimental research is typically performed in a controlled environment so that the researcher can manipulate an independent variable and measure the outcome (dependent variable) between a group of subjects who received the manipulated variable (intervention) and a group of subjects who did not receive the intervention. This type of design typically adheres to the scientific method in order to test a hypothesis. A hypothesis is a proposed explanation for a phenomenon and serves as the starting point for the investigation.  You may see a hypothesis indicated as (H O ), also called the null hypothesis. This is to differentiate it from an alternative hypothesis (H 1 or H A ), which is any hypothesis other than the null.

Development of the Hypothesis

There are two types of hypotheses, the null (HO) and an alternative (H 1 or H A )

• H O = There is no significant difference between length of stay for patients diagnosed with COPD and those diagnosed with CHF.
• H 1 or H A = There is a significant difference between length of stay for patients diagnosed with COPD and those diagnosed with CHF

NOTE: Accepting the null hypothesis would mean that your data confirm that there is no difference. Rejecting the null would mean that your data indicated that there is a significant difference in patient outcomes for these groups; therefore, rejecting the null means accepting an alternative hypothesis.

Randomized Experimental : Participants are randomly assigned to either a treatment (intervention) or a control group. Typically, the treatment group receives an intervention (independent variable) and the outcome of each group is considered dependent variables and compared for effect. Independent variables in this case are considered active in that this variable can be manipulated.

Example of Randomized Experimental Approach

You would like to assess outcomes as they relate to the post delivery resuscitation of  very low birthweight infants in the delivery room. You have decided that one group will receive direct intubation and surfactant (intervention group) in the delivery room and the other will receive the standard care of CPAP (control group). Participants will be randomly assigned to groups and as a bonus, the assignment to groups will be blinded. You will then compare the difference between participants in each group regarding need for oxygen at 36 weeks adjusted gestational age.

• Dependent Variable: Need for oxygen at 36 weeks adjusted gestational age
• Independent Variable (Active) : Administration of surfactant

Quasi Experimental : Similar to the randomized experimental approach aside from the random assignment. In quasi-experimental approaches, participants are NOT randomly assigned; however, one group does receive an intervention while the control group does not and outcomes are still compared. The independent variable is also active.

Example of Quasi Experimental Approach

You would like to assess outcomes as they relate to the post delivery resuscitation of  very low birthweight infants in the delivery room. You have decided that one group will receive direct intubation and surfactant (intervention group) in the delivery room and the other will receive the standard care of CPAP (control group). Participants will be assigned to groups based on administration of maternal steroids. You will then compare the difference between participants in each group regarding need for oxygen at 36 weeks adjusted gestational age.

Non Experimental

Non-experimental approaches include a wide variety of approaches; therefore, it is difficult to list them all in a succinct way here. However, it is safe to say that a study approach is considered non-experimental when there lacks intentional manipulation of the independent variable.

Comparative approach : Groups are compared to reveal differences in outcome (dependent variable). Groups are typically formed based on independent variables that cannot be manipulated but are important to the study. This type of independent variable is known as an attribute independent variable. In this approach there are a few categories (2-4 levels) of attribute independent variables that are then compared.

Example of Comparative Approach

You would like to investigate the perceptions of first and second year student-instructor engagement and student learning and instructor motivation in the clinical environment.

• Dependent Variable : Student perception of experience (2 levels: First and second year)
• Independent Variable : Student-instructor engagement in learning and motivation

Associational or Correlational approach : Two or more variables for the same group of participants are explored for relationships. Independent variables are also attributive in this approach; meaning, they can be manipulated to impact the dependent variable. Variables included in this approach are typically continuous or have at least five ordered categories.

Example of Associational or Correlational Approach

You would like to conduct a study to better understand practitioner attitudes about the future of the profession.

• Dependent Variable: Attitude about the future of the profession
• Independent Variable(s): Age, gender, autonomy

Descriptive research : Projects which only gather data which can be described, not inferred. That is, results and data collected cannot be inferred back to the population nor can comparisons or associations be made. Many qualitative studies are considered descriptive. This is done by considering only one variable at a time and there is no independent variable.

Example of Descriptive Research

You would like to describe the development of a protocol to implement high flow nasal cannula as an intermediate therapy for acute respiratory failure to be used in the Emergency Department at your institution. You plan to compare rates of intubation before and after implementation of the protocol.

• You are DESCRIBING a process
• You may collect and compare data using descriptive statistics

It is important to note that it is possible to have more than one approach in one research project. This is because the approach selected is specific to the question that has been asked. If there is more than one question asked, it is reasonable to assume that more than one approach may be used.

There are a few areas of research that although fit under the category of non-experimental; do not quite fit within the classifications presented here. Two of these areas are quality improvement (QI) projects and protocol development.

Quality improvement (QI) projects: The purpose of a QI project is to evaluate the performance of systems, processes, or practices to determine whether either function or operational improvements are needed. Using tools such as the SQUIRE explanation and elaboration guidelines , is extremely helpful in developing, conducting, and analyzing a thorough and impactful QI project.

The SQUIRE guidelines focus on the following four questions:

• Why did you start?
• What did you do?
• What did you find?
• What does it mean?

These four questions are then expanded upon to help develop the systematic approach to your inquiry and presentation of your findings. An extended investigation of this method is covered in Chapter 6.

Protocol Development

Before we dig too deep into the development of protocols, a clarification needs to be made regarding vocabulary relating to projects of this nature. Although frequently used interchangeably, the terms protocol and guideline are not synonymous. A protocol is described as an official procedure or system of rules governing a process. A guideline is a suggested course of action, policy, or conduct. In healthcare, this is an important distinction; a protocol is a course of action to which treatment must follow without deviation whereas a guideline, although firmly rooted in evidence, allows for deviation based on best judgment of a clinician or presentation of a specific case. Through a research lens, this distinction is important because the process by which these two objectives are realized are very different. The complete process for the development of guidelines which are generalizable beyond a specific situation is best outlined by the World Health Organization Handbook

The development of both guidelines often involves a team of people who are charged with first evaluating the existing evidence and then contributing an interpretation of that evidence toward the consensus of best practice. This is why guidelines are typically issued by federal or state agencies or professional organization. Protocols are generally less generalizable due to contextual constraints. However, even organizational protocols are not developed by a single individual. This does not mean, however, that you cannot begin the process of developing a guideline or protocol for your organization on you own; rather, it is important to frame the work you contribute as the foundation upon which a group can work toward the consensus of best practice. Typically, this initial work is referred to as a narrative review. A narrative review can be described as a broad perspective on a topic which may or may not be impacted by bias. This type of review differs from a systematic review in that it is understood that a narrative review may not encompass all relevant literature on a relevant topic as might a systematic review. Another note; the development of both guidelines and protocols is often an iterative process requiring several cycles of evaluation and revision.  A systematic review is described as exhaustive review of the literature relevant to a specific topic. In addition to being exhaustive, a systematic review includes methodology which is both explicit and reproducible to select, evaluate, and synthesize ALL available evidence. A meta-analysis is a systematic approach to evaluating the data from independent studies of the same subject to evaluate overall trends. Often, a meta-analysis is part of a systematic review.

Selecting your approach

As we’ve discussed, there are several factors which will guide your approach selection. Emphasis should be placed on the development of your purpose and problem statements as well as your research question. Ambiguity in these areas may cause some confusion as you begin to consider what approach you will take to answer your question.  Here we will work to narrow the scope of your approach using a systematic process and answering a few specific questions:

Step 1: Outlining your general purpose

Understanding the overarching goal of your study will help direct the rest of your approach. Here, you will ask yourself “What am I trying to do?”.

Step 2: Identifying your general approach

Earlier we discussed the difference between experimental and non-experimental approaches. As we mentioned, these are two broad categories of approaches. Your general purpose will determine which of these two general approaches you take. The determination here will point you toward a more focused, or specific, approach.

• Experimental: Experimental research is typically performed in a controlled environment so that the researcher can manipulate an independent variable and measure the outcome (dependent variable) between a group of subjects who received the manipulated variable (intervention) and a group of subjects who did not receive the intervention. A true experimental approach means that you have random selection or assignment of participants. All other elements aside, if you do NOT have randomization incorporated into your approach, your approach becomes quasi-experimental.
• Non-experimental: Nonexperimental research is an extremely broad category of approaches. Therefore, the simplest way to explain non-experimental research is to simply state that this approach lacks the manipulation of an independent variable. That is, you are not imposing an intervention on one group and comparing the outcome with a control group. Rather, you may have attribute independent variables which influence, or impact, the dependent variable, but the purpose of the research is not the direct manipulation of that variable. There are several different types of non-experimental research approaches, as we will soon see; however, it is important to understand that descriptive research is always classified as nonexperimental.

Step 3: Narrowing down your specific purpose

Now that you’ve decided what the general purpose and approach, you can begin to really narrow down the ‘how’ of your research. I find that this is best done by again asking yourself what you are really trying to do. Now that you understand the boundaries of your purpose and approach, you can work to understand the fine points about what types of interactions between variables you’re looking to explore and determine.

Step 4 : Selecting your specific approach

As you can see, there are specific words you should pay attention to when you’re describing your purpose. Given these key words, like ‘determine causality’, or ‘compare groups’, you’ll have a bit more direction as to what approach is most appropriate to identify the best answer to your question. Once we know what it is we really want to do with the information we’re planning to gather (variables), we can select an approach. Selecting your specific approach

Key Takeaways

There are several important concepts presented in this chapter:

• The theoretical/conceptual framework is the frame, or lens, that YOU build for your reader. It is the perspective through which you would like them to view your work.
• Constructs represent abstract theory
• Variables are the concrete measures of constructs
• There are several different types of variables; however, understanding the relationship between the independent variable (impact variable) and the dependent variable (outcome variable) is extremely important
• Attributes are levels within variables
• Attributes and variables must be classified in terms of measurement: Nominal, ordinal, interval, and ratio variables each represent different information and must be assessed correctly to have meaning
• Sampling is very important because whether your sample represents the larger population is an important factor in how your research is presented and interpreted
• There are A LOT of different approaches to research. Systematically approaching the selection of your approach by first defining your problem and purpose statements and your research question will be helpful as you narrow your focus on the which approach best captures the interaction between or among variables

Crawford, L.M., Burkholder, G.J., Cox, K.A. (2020). Writing the Research Proposal. In G.J. Burkholder, K.A Cox, L.M. Crawford, and J.H. Hitchcock (Eds.), Research design and methods: An applied guide for the scholar-practitioner (pp. 309-334). Sage Publications

Gliner, J.A., Morgan, G.A., & Leech, N.L. (2017). Research methods in applied settings: An integrated approach to design and analysis. Routledge

• This section can be attributed to Bhattacherjee, A. (2012) published under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License ↵

Defining a construct through your lens as a researcher. How you are choosing to describe the boundaries associated with your work

A measurable representation of an abstract construct

A variable that can explain another variable. A variable which may be manipulated (active) or describes (attribute) to affect an outcome

The variable which is measured as an outcome and is affected by the independent variable(s)

Variables that are explained by both the independent and dependent variables

Influence the relationship between the independent and dependent variable

A variable which has an impact on the dependent variable, but does not explain the outcome (dependent variable)

values within each variable.

The assignment of a number to an attribute to describe a variable

Variable with two or more layers but without a specific order

A variable which has a specific rank order but no specificity between the ranks

Rank ordered variable with specified intervals between ranks

Has a true zero within the scale against which it is measured

A tool, or measure, used to quantify material that may be difficult to do so otherwise

Nominal scale with two potential outcomes

Used to measure ordinal data with a ranking system

Method of selecting a subset of the population to study.

Method of sampling wherein potential for sampling is equally likely for the entire population

Method of sampling where in the likelihood of being selected into a sample is not equal across the population

Visual representation of how a sample falls around a mean

A proposed explanation for the observed phenomenon

A form of experimental study design were participants are randomly assigned to either an intervention or control group

A form of experimental design involving both intervention and control groups but lacks randomization

Groups of participants are compared to identify differences in outcome

Two or more variables for the SAME group of participants are explored for relationships

Research projects wherein data gathered and described, but no relationships are inferred

A subset of nonexperimental research wherein the performance of systems, processes, or practices are evaluated for either efficiency or effectiveness

An official procedure or system

A suggested course of action

A broad perspective on a topic, typically from the perspective of a single author

An exhaustive review of literature relevant to a specific topic; typically performed by a group of people

Systematic approach to evaluating data from independent studies on a topic to evaluate or identify trends

Research performed in a controlled environment in which a researcher can manipulate an independent variable and measure a dependent variable (outcome)

Broad category of research approaches which lack the manipulation of an independent variable

Practical Research: A Basic Guide to Planning, Doing, and Writing Copyright © by megankoster. All Rights Reserved.

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Principles of Sociological Inquiry

Chapter 5: research design, chapter 5 research design, how to design a research project.

Now that you’ve figured out what to study, you need to figure out how to study it. Your library research can help in this regard. Reading published studies is a great way to familiarize yourself with the various components of a research project. It will also bring to your attention some of the major considerations to keep in mind when designing a research project. We’ll say more about reviewing the literature near the end of this chapter, but we’ll begin with a focus on research design. We’ll discuss the decisions you need to make about the goals of your research, the major components of a research project, along with a few additional aspects of designing research.

5.1 Goals of the Research Project

Learning objectives.

• Understand and describe the differences among exploratory, descriptive, and explanatory research.
• Define and provide an example of idiographic research.
• Define and provide an example of nomothetic research.
• Identify circumstances under which research would be defined as applied and compare those to circumstances under which research would be defined as basic.

A recent news story about college students’ addictions to electronic gadgets (Lisk, 2011) Lisk, J. (2011). Addiction to our electronic gadgets. Retrieved from http://www.cnn.com/video/#/video/health/2011/03/01/hm.election.addiction.cnn?iref=allsearch describes findings from some current research by Professor Susan Moeller and colleagues from the University of Maryland ( http://withoutmedia.wordpress.com ). The story raises a number of interesting questions. Just what sorts of gadgets are students addicted to? How do these addictions work? Why do they exist, and who is most likely to experience them?

Sociological research is great for answering just these sorts of questions. But in order to answer our questions well, we must take care in designing our research projects. In this chapter, we’ll consider what aspects of a research project should be considered at the beginning, including specifying the goals of the research, the components that are common across most research projects, and a few other considerations.

One of the first things to think about when designing a research project is what you hope to accomplish, in very general terms, by conducting the research. What do you hope to be able to say about your topic? Do you hope to gain a deep understanding of whatever phenomenon it is that you’re studying, or would you rather have a broad, but perhaps less deep, understanding? Do you want your research to be used by policymakers or others to shape social life, or is this project more about exploring your curiosities? Your answers to each of these questions will shape your research design.

Exploration, Description, Explanation

You’ll need to decide in the beginning phases whether your research will be exploratory, descriptive, or explanatory. Each has a different purpose, so how you design your research project will be determined in part by this decision.

Researchers conducting exploratory research Research that aims to satisfy a researcher’s curiosity about a topic or test the feasibility of a more extensive study. are typically at the early stages of examining their topics. These sorts of projects are usually conducted when a researcher wants to test the feasibility of conducting a more extensive study; he or she wants to figure out the lay of the land, with respect to the particular topic. Perhaps very little prior research has been conducted on this subject. If this is the case, a researcher may wish to do some exploratory work to learn what method to use in collecting data, how best to approach research subjects, or even what sorts of questions are reasonable to ask. A researcher wanting to simply satisfy his or her own curiosity about a topic could also conduct exploratory research. In the case of the study of college students’ addictions to their electronic gadgets, a researcher conducting exploratory research on this topic may simply wish to learn more about students’ use of these gadgets. Because these addictions seem to be a relatively new phenomenon, an exploratory study of the topic might make sense as an initial first step toward understanding it.

In my research on child-free adults, I was unsure what the results might be when first embarking on the study. There was very little empirical research on the topic, so the initial goal of the research was simply to get a better grasp of what child-free people’s lives are like and how their decision to be child free shapes their relationships and everyday experiences. Conducting exploratory research on the topic was a necessary first step, both to satisfy my curiosity about the subject and to better understand the phenomenon and the research participants in order to design a larger, subsequent study.

Sometimes the goal of research is to describe or define a particular phenomenon. In this case, descriptive research Research that aims to describe or define. would be an appropriate strategy. A descriptive study of college students’ addictions to their electronic gadgets, for example, might aim to describe patterns in how use of gadgets varies by gender or college major or which sorts of gadgets students tend to use most regularly.

Researchers at the Princeton Review conduct descriptive research each year when they set out to provide students and their parents with information about colleges and universities around the United States ( http://www.princetonreview.com ). They describe the social life at a school, the cost of admission, and student-to-faculty ratios (to name just a few of the categories reported). Although students and parents may be able to obtain much of this information on their own, having access to the data gathered by a team of researchers is much more convenient and less time consuming.

Market researchers also rely on descriptive research to tell them what consumers think of their products. In fact, descriptive research has many useful applications, and you probably rely on findings from descriptive research without even being aware that that is what you are doing.

Finally, sociological researchers often aim to explain why particular phenomena work in the way that they do. Research that answers “why” questions is referred to as explanatory research Research that aims to identify causes and effects. . In this case, the researcher is trying to identify the causes and effects of whatever phenomenon he or she is studying. An explanatory study of college students’ addictions to their electronic gadgets might aim to understand why students become addicted. Does it have anything to do with their family histories? With their other extracurricular hobbies and activities? With whom they spend their time? An explanatory study could answer these kinds of questions.

There are numerous examples of explanatory social scientific investigations. For example, in a recent study, Dominique Simons and Sandy Wurtele (2010) Simons, D. A., & Wurtele, S. K. (2010). Relationships between parents’ use of corporal punishment and their children’s endorsement of spanking and hitting other children. Child Abuse & Neglect, 34 , 639–646. sought to discover whether receiving corporal punishment from parents led children to turn to violence in solving their interpersonal conflicts with other children. In their study of 102 families with children between the ages of 3 and 7, the researchers found that experiencing frequent spanking did, in fact, result in children being more likely to accept aggressive problem-solving techniques. Another example of explanatory research can be seen in Robert Faris and Diane Felmlee’s research (2011; American Sociological Association, 2011) Faris, R., & Felmlee, D. (2011). Status struggles: Network centrality and gender segregation in same- and cross-gender aggression. American Sociological Review, 76 , 48–73; the American Sociological Association wrote a press release summarizing findings from the study. You can read it at http://asanet.org/press/Press_Release_Popular_Kids_More_Likely_to_Torment_Peers.cfm . The study has also been covered by several media outlets: Pappas, S. (2011). Popularity increases aggression in kids, study finds. Retrieved from http://www.livescience.com/11737-popularity-increases-aggression-kids-study-finds.html on the connections between popularity and bullying. They found, from their study of 8th, 9th, and 10th graders in 19 North Carolina schools, that as adolescents’ popularity increases, so, too, does their aggression. This pattern was found until adolescents reached the top 2% in the popularity ranks. After that, aggression declines.

Idiographic or Nomothetic?

Once you decide whether you will conduct exploratory, descriptive, or explanatory research, you will need to determine whether you want your research to be idiographic or nomothetic. A decision to conduct idiographic research Exhaustive, detailed descriptions or explanations of a singular or very small number of individuals, phenomena, or groups. means that you will attempt to explain or describe your phenomenon exhaustively. While you might have to sacrifice some breadth of understanding if you opt for an idiographic explanation, you will gain a much deeper, richer understanding of whatever phenomenon or group you are studying than you would if you were to pursue nomothetic research. A decision to conduct nomothetic research General, broad descriptions or explanations of many individuals, phenomena, or groups. , on the other hand, means that you will aim to provide a more general, sweeping explanation or description of your topic. In this case, you sacrifice depth of understanding in favor of breadth of understanding.

Let’s look at some specific examples. As a graduate student, I conducted an in-depth study of breast cancer activism (Blackstone, 2003). Blackstone, A. (2003). Racing for the cure and taking back the night: Constructing gender, politics, and public participation in women’s activist/volunteer work . PhD dissertation, Department of Sociology, University of Minnesota, Minneapolis, MN. To do so, I joined an organization of local activists and participated in just about every aspect of the organization over a period of about 18 months. Perhaps it goes without saying, but over the course of a year and a half of participant observation, I learned quite a bit about this organization and its members. In other words, the study revealed the particular idiosyncrasies of the group, but it did not reveal much about the inner workings of other breast cancer activist organizations. Armed with an in-depth understanding about this single group, the study made a contribution to knowledge about how activists operate. For one thing, the organization I observed happened to be one of the largest and most well known of its type at the time, and many other organizations in the movement looked to this organization for ideas about how to operate. Understanding how this model organization worked was important for future activist efforts in a variety of organizations. Further, the study revealed far more intimate details of the inner workings of an activist organization than had it, say, instead been a survey of the top 50 breast cancer organizations in the United States (though that would have been an interesting study as well).

My collaborative research on workplace sexual harassment (Uggen & Blackstone, 2004), Uggen, C., & Blackstone, A. (2004). Sexual harassment as a gendered expression of power. American Sociological Review, 69 , 64–92. on the other hand, aims to provide more sweeping descriptions and explanations. For this nomothetic research project, we mailed surveys to a large sample of young workers who look very much like their peers in terms of their jobs, social class background, gender, and other categories. Because of these similarities, we have been able to speak generally about what young workers’ experiences with sexual harassment are like. In an idiographic study of the same topic, the research team might follow a few workers around every day for a long period of time or conduct a series of very detailed, and lengthy, interviews with 10 or 15 workers.

Applied or Basic?

Finally, you will need to decide what sort of contribution you hope to make with your research. Do you want others to be able to use your research to shape social life? If so, you may wish to conduct a study that policymakers could use to change or create a specific policy. Perhaps, on the other hand, you wish to conduct a study that will contribute to sociological theories or knowledge without having a specific applied use in mind. In the example of the news story on students’ addictions to technological gadgets, an applied study of this topic might aim to understand how to treat such addictions. A basic study of the same topic, on the other hand, might examine existing theories of addiction and consider how this new type of addiction does or does not apply; perhaps your study could suggest ways that such theories may be tweaked to encompass technological addictions.

In Chapter 1 “Introduction” , we learned about both applied and basic research. When designing your research project, think about where you envision your work fitting in on the applied–basic continuum. Recognize, however, that even basic research may ultimately be used for some applied purpose. Similarly, your applied research might not turn out to be applicable to the particular real-world social problem you were trying to solve, but it might better our theoretical understanding of some phenomenon. In other words, deciding now whether your research will be basic or applied doesn’t mean that will be its sole purpose forever. Basic research may ultimately be applied, and applied research can certainly contribute to general knowledge. Nevertheless, it is important to think in advance about what contribution(s) you hope to make with your research.

Key Takeaways

• Exploratory research is usually conducted when a researcher has just begun an investigation and wishes to understand her or his topic generally.
• Descriptive research is research that aims to describe or define the topic at hand.
• Explanatory research is research that aims to explain why particular phenomena work in the way that they do.
• Idiographic investigations are exhaustive; nomothetic investigations are more general.
• While researchers may start out having some idea about whether they aim to conduct applied or basic research, it is also important to keep in mind that applied research may contribute to basic understandings and that basic research may turn out to have some useful application.
• Describe a scenario in which exploratory research might be the best approach. Similarly, describe a scenario in which descriptive and then explanatory research would be the preferred approach.
• Which are you more drawn to personally, applied or basic research? Why?

5.2 Qualitative or Quantitative? Some Specific Considerations

• Describe the role of causality in quantitative research as compared to qualitative research.
• Identify, define, and describe each of the three main criteria for causality.
• Describe the difference between and provide examples of independent and dependent variables.
• Define units of analysis and units of observation, and describe the two common errors people make when they confuse the two.
• Define hypothesis, be able to state a clear hypothesis, and discuss the respective roles of quantitative and qualitative research when it comes to hypotheses.

In Chapter 1 “Introduction” , we discussed the importance of understanding the differences between qualitative and quantitative research methods. Because this distinction is relevant to how researchers design their projects, we’ll revisit it here.

When designing a research project, how issues of causality are attended to will in part be determined by whether the researcher plans to collect qualitative or quantitative data. Causality The idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief. refers to the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief. In other words, it is about cause and effect.

In a qualitative study, it is likely that you will aim to acquire an idiographic understanding of the phenomenon that you are investigating. Using our example of students’ addictions to electronic gadgets, a qualitative researcher might aim to understand the multitude of reasons that two roommates exhibit addictive tendencies when it comes to their various electronic devices. The researcher might spend time in the dorm room with them, watching how they use their devices, follow them to class and watch them there, observe them at the cafeteria, and perhaps even observe them during their free time. At the end of this very intensive, and probably exhausting, set of observations, the researcher should be able to identify some of the specific causes of each student’s addiction. Perhaps one of the two roommates is majoring in media studies, and all her classes require her to have familiarity with and to regularly use a variety of electronic gadgets. Perhaps the other roommate has friends or family who live overseas, and she relies on a variety of electronic devices to communicate with them. Perhaps both students have a special interest in playing and listening to music, and their electronic gadgets help facilitate this hobby. Whatever the case, in a qualitative study that seeks idiographic understanding, a researcher would be looking to understand the plethora of reasons (or causes) that account for the behavior he or she is investigating.

In a quantitative study, on the other hand, a researcher is more likely to aim for a nomothetic understanding of the phenomenon that he or she is investigating. In this case, the researcher may be unable to identify the specific idiosyncrasies of individual people’s particular addictions. However, by analyzing data from a much larger and more representative group of students, the researcher will be able to identify the most likely, and more general, factors that account for students’ addictions to electronic gadgets. The researcher might choose to collect survey data from a wide swath of college students from around the country. He might find that students who report addictive tendencies when it comes to their gadgets also tend to be people who can identity which of Steven Seagal’s movies he directed, are more likely to be men, and tend to engage in rude or disrespectful behaviors more often than nonaddicted students. It is possible, then, that these associations can be said to have some causal relationship to electronic gadget addiction. However, items that seem to be related are not necessarily causal. To be considered causally related in a nomothetic study, such as the survey research in this example, there are a few criteria that must be met.

The main criteria for causality have to do with plausibility, temporality, and spuriousness. Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. For example, if we attend a series of lectures during which a student’s incessant midclass texting or web surfing gets in the way of our ability to focus on the lecture, we might begin to wonder whether people who have a propensity to be rude are more likely to have a propensity to be addicted to their electronic gadgets (and therefore use them during class). However, the fact that there might be a relationship between general rudeness and gadget addiction does not mean that a student’s rudeness could cause him to be addicted to his gadgets. In other words, just because there might be some correlation A relationship between two variables. between two variables does not mean that a causal relationship between the two is really plausible.

The criterion of temporality In social science, this refers to the rule that a cause must precede an effect in time. means that whatever cause you identify must precede its effect in time. As noted earlier, a survey researcher examining the causes of students’ electronic gadget addictions might find that more men than women exhibit addictive tendencies when it comes to their electronic gadgets. Thus the researcher has found a correlation between gender and addiction. So does this mean that a person’s gadget addiction determines his or her gender? Probably not, not only because this doesn’t make any sense but also because a person’s gender identity is most typically formed long before he or she is likely to own any electronic gadgets. Thus gender precedes electronic gadget ownership (and subsequent addiction) in time.

Finally, a spurious relationship A relationship in which two variables appear to be causal but can in fact be explained by some third variable. is one in which an association between two variables appears to be causal but can in fact be explained by some third variable. In the example of a survey assessing students’ addictions to electronic gadgets, the researcher might have found that those who can identify which of Steven Seagal’s films the actor himself directed also exhibit addiction to their electronic gadgets. In case you’re curious, a visit to the Internet Movie Database will tell you that Seagal directed just one of his films, 1994’s On Deadly Ground : http://www.imdb.com/name/nm0000219 . This relationship is exemplified in Figure 5.5 .

So does knowledge about Seagal’s directorial prowess cause gadget addiction? Probably not. A more likely explanation is that being a man makes a person both more likely to know about Seagal’s films and more likely to be addicted to electronic gadgets. In other words, there is a third variable that explains the relationship between Seagal movie knowledge and electronic gadget addiction. This relationship is exemplified in Figure 5.6 .

Let’s consider a few additional, real-world examples of spuriousness. Did you know, for example, that high rates of ice cream sales have been shown to cause drowning? Of course that’s not really true, but there is a positive relationship between the two. In this case, the third variable that causes both high ice cream sales and increased deaths by drowning is time of year, as the summer season sees increases in both (Babbie, 2010). Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. Here’s another good one: it is true that as the salaries of Presbyterian ministers in Massachusetts rise, so, too, does the price of rum in Havana, Cuba. Well, duh, you might be saying to yourself. Everyone knows how much ministers in Massachusetts love their rum, right? Not so fast. Both salaries and rum prices have increased, true, but so has the price of just about everything else (Huff & Geis, 1993). Huff, D., & Geis, I. (1993). How to lie with statistics . New York, NY: Norton. Finally, research shows that the more firefighters present at a fire, the more damage is done at the scene. What this statement leaves out, of course, is that as the size of a fire increases so, too, does the amount of damage caused as does the number of firefighters called on to help (Frankfort-Nachmias & Leon-Guerro, 2011). Frankfort-Nachmias, C., & Leon-Guerro, A. (2011). Social statistics for a diverse society (6th ed.). Thousand Oaks, CA: Pine Forge Press. In each of these examples, it is the presence of a third variable that explains the apparent relationship between the two original variables.

In sum, the following criteria must be met in order for a correlation to be considered causal:

• The relationship must be plausible.
• The cause must precede the effect in time.
• The relationship must be nonspurious.

What we’ve been talking about here is relationships between variables. When one variable causes another, we have what researchers call independent and dependent variables. In the example where gender was found to be causally linked to electronic gadget addiction, gender would be the independent variable and electronic gadget addiction would be the dependent variable. An independent variable A variable that causes another. is one that causes another. A dependent variable A variable that is caused by another. is one that is caused by another. Dependent variables depend on independent variables.

Relationship strength is another important factor to take into consideration when attempting to make causal claims if your research approach is nomothetic. I’m not talking strength of your friendships or marriage (though of course that sort of strength might affect your likelihood to keep your friends or stay married). In this context, relationship strength refers to statistical significance. The more statistically significant a relationship between two variables is shown to be, the greater confidence we can have in the strength of that relationship. We’ll discuss statistical significance in greater detail in Chapter 7 “Sampling” . For now, keep in mind that for a relationship to be considered causal, it cannot exist simply because of the chance selection of participants in a study.

Some research methods, such as those used in qualitative and idiographic research, are not conducive to making predictions about when events or behaviors will occur. In these cases, what we are instead able to do is gain some understanding of the circumstances under which those causal relationships occur: to understand the how of causality. Qualitative research sometimes relies on quantitative work to point toward a relationship that may be interesting to investigate further. For example, if a quantitative researcher learns that men are statistically more likely than women to become addicted to their electronic gadgets, a qualitative researcher may decide to conduct some in-depth interviews and observations of men and women to learn more about how the different contexts and circumstances of men’s and women’s lives might shape their respective chances of becoming addicted. In other words, the qualitative researcher works to understand the contexts in which various causes and effects occur.

Units of Analysis and Units of Observation

Another point to consider when designing a research project, and which might differ slightly in qualitative and quantitative studies, has to do with units of analysis The entity that a researcher wishes to be able to say something about at the end of his or her study; the main focus of the study. and units of observation The item (or items) that a researcher actually observes, measures, or collects in the course of trying to learn something about his or her unit of analysis. . These two items concern what you, the researcher, actually observe in the course of your data collection and what you hope to be able to say about those observations. A unit of analysis is the entity that you wish to be able to say something about at the end of your study, probably what you’d consider to be the main focus of your study. A unit of observation is the item (or items) that you actually observe, measure, or collect in the course of trying to learn something about your unit of analysis. In a given study, the unit of observation might be the same as the unit of analysis, but that is not always the case. Further, units of analysis are not required to be the same as units of observation. What is required, however, is for researchers to be clear about how they define their units of analysis and observation, both to themselves and to their audiences.

More specifically, your unit of analysis will be determined by your research question. Your unit of observation, on the other hand, is determined largely by the method of data collection that you use to answer that research question. We’ll take a closer look at methods of data collection in Chapter 8 “Survey Research: A Quantitative Technique” through Chapter 12 “Other Methods of Data Collection and Analysis” . For now, let’s go back to the example we’ve been discussing over the course of this chapter, students’ addictions to electronic gadgets. We’ll consider first how different kinds of research questions about this topic will yield different units of analysis. Then we’ll think about how those questions might be answered and with what kinds of data. This leads us to a variety of units of observation.

If we were to ask, “Which students are most likely to be addicted to their electronic gadgets?” our unit of analysis would be the individual. We might mail a survey to students on campus, and our aim would be to classify individuals according to their membership in certain social classes in order to see how membership in those classes correlated with gadget addiction. For example, we might find that majors in new media, men, and students with high socioeconomic status are all more likely than other students to become addicted to their electronic gadgets. Another possibility would be to ask, “How do students’ gadget addictions differ, and how are they similar?” In this case, we could conduct observations of addicted students and record when, where, why, and how they use their gadgets. In both cases, one using a survey and the other using observations, data are collected from individual students. Thus the unit of observation in both examples is the individual. But the units of analysis differ in the two studies. In the first one, our aim is to describe the characteristics of individuals. We may then make generalizations about the populations to which these individuals belong, but our unit of analysis is still the individual. In the second study, we will observe individuals in order to describe some social phenomenon, in this case, types of gadget addictions. Thus our unit of analysis would be the social phenomenon.

Another common unit of analysis in sociological inquiry is groups. Groups of course vary in size, and almost no group is too small or too large to be of interest to sociologists. Families, friendship groups, and street gangs make up some of the more common microlevel groups examined by sociologists. Employees in an organization, professionals in a particular domain (e.g., chefs, lawyers, sociologists), and members of clubs (e.g., Girl Scouts, Rotary, Red Hat Society) are all mesolevel groups that sociologists might study. Finally, at the macro level, sociologists sometimes examine citizens of entire nations or residents of different continents or other regions.

A study of student addictions to their electronic gadgets at the group level might consider whether certain types of social clubs have more or fewer gadget-addicted members than other sorts of clubs. Perhaps we would find that clubs that emphasize physical fitness, such as the rugby club and the scuba club, have fewer gadget-addicted members than clubs that emphasize cerebral activity, such as the chess club and the sociology club. Our unit of analysis in this example is groups. If we had instead asked whether people who join cerebral clubs are more likely to be gadget-addicted than those who join social clubs, then our unit of analysis would have been individuals. In either case, however, our unit of observation would be individuals.

Organizations are yet another potential unit of analysis that social scientists might wish to say something about. As you may recall from your introductory sociology class, organizations include entities like corporations, colleges and universities, and even night clubs. At the organization level, a study of students’ electronic gadget addictions might ask, “How do different colleges address the problem of electronic gadget addiction?” In this case, our interest lies not in the experience of individual students but instead in the campus-to-campus differences in confronting gadget addictions. A researcher conducting a study of this type might examine schools’ written policies and procedures, so his unit of observation would be documents. However, because he ultimately wishes to describe differences across campuses, the college would be his unit of analysis.

Of course, it would be silly in a textbook focused on social scientific research to neglect social phenomena as a potential unit of analysis. I mentioned one such example earlier, but let’s look more closely at this sort of unit of analysis. Many sociologists study a variety of social interactions and social problems that fall under this category. Examples include social problems like murder or rape; interactions such as counseling sessions, Facebook chatting, or wrestling; and other social phenomena such as voting and even gadget use or misuse. A researcher interested in students’ electronic gadget addictions could ask, “What are the various types of electronic gadget addictions that exist among students?” Perhaps the researcher will discover that some addictions are primarily centered around social media such as chat rooms, Facebook, or texting while other addictions center on gadgets such as handheld, single-player video games or DVR devices that discourage interaction with others. The resultant typology of gadget addictions would tell us something about the social phenomenon (unit of analysis) being studied. As in several of the preceding examples, however, the unit of observation would likely be individual people.

Finally, a number of social scientists examine policies and principles, the last type of unit of analysis we’ll consider here. Studies that analyze policies and principles typically rely on documents as the unit of observation. Perhaps a researcher has been hired by a college to help it write an effective policy against electronic gadget addiction. In this case, the researcher might gather all previously written policies from campuses all over the country and compare policies at campuses where addiction rates are low to policies at campuses where addiction rates are high.

In sum, there are many potential units of analysis that a sociologist might examine, but some of the most common units include the following:

• Individuals
• Organizations
• Social phenomena
• Policies and principles

Table 5.1 “Units of Analysis and Units of Observation: An Example Using a Hypothetical Study of Students’ Addictions to Electronic Gadgets” includes a summary of the preceding discussion of units of analysis and units of observation.

Table 5.1 Units of Analysis and Units of Observation: An Example Using a Hypothetical Study of Students’ Addictions to Electronic Gadgets

One common error we see people make when it comes to both causality and units of analysis is something called the ecological fallacy Occurs when claims are made about individuals based on group-level data. . This occurs when claims about one lower-level unit of analysis are made based on data from some higher-level unit of analysis. In many cases, this occurs when claims are made about individuals, but only group-level data have been gathered. For example, we might want to understand whether electronic gadget addictions are more common on certain campuses than on others. Perhaps different campuses around the country have provided us with their campus percentage of gadget-addicted students, and we learn from these data that electronic gadget addictions are more common on campuses that have business programs than on campuses without them. We then conclude that business students are more likely than nonbusiness students to become addicted to their electronic gadgets. However, this would be an inappropriate conclusion to draw. Because we only have addiction rates by campus, we can only draw conclusions about campuses, not about the individual students on those campuses. Perhaps the sociology majors on the business campuses are the ones that caused the addiction rates on those campuses to be so high. The point is we simply don’t know because we only have campus-level data. By drawing conclusions about students when our data are about campuses, we run the risk of committing the ecological fallacy.

On the other hand, another mistake to be aware of is reductionism Occurs when claims about groups are made based on individual-level data. . Reductionism occurs when claims about some higher-level unit of analysis are made based on data from some lower-level unit of analysis. In this case, claims about groups or macrolevel phenomena are made based on individual-level data. An example of reductionism can be seen in some descriptions of the civil rights movement. On occasion, people have proclaimed that Rosa Parks started the civil rights movement in the United States by refusing to give up her seat to a white person while on a city bus in Montgomery, Alabama, in December 1955. Although it is true that Parks played an invaluable role in the movement, and that her act of civil disobedience gave others courage to stand up against racist policies, beliefs, and actions, to credit Parks with starting the movement is reductionist. Surely the confluence of many factors, from fights over legalized racial segregation to the Supreme Court’s historic decision to desegregate schools in 1954 to the creation of groups such as the Student Nonviolent Coordinating Committee (to name just a few), contributed to the rise and success of the American civil rights movement. In other words, the movement is attributable to many factors—some social, others political, others economic. Did Parks play a role? Of course she did—and a very important one at that. But did she cause the movement? To say yes would be reductionist.

It would be a mistake to conclude from the preceding discussion that researchers should avoid making any claims whatsoever about data or about relationships between variables. While it is important to be attentive to the possibility for error in causal reasoning about different levels of analysis, this warning should not prevent you from drawing well-reasoned analytic conclusions from your data. The point is to be cautious but not abandon entirely the social scientific quest to understand patterns of behavior.

In some cases, the purpose of research is to test a specific hypothesis or hypotheses. At other times, researchers do not have predictions about what they will find but instead conduct research to answer a question or questions, with an open-minded desire to know about a topic, or to help develop hypotheses for later testing. A hypothesis A statement drawn from theory that posits a researcher’s expectation about the relationship between two or more variables. Hypotheses are often causal though they do not have to be. is a statement, sometimes but not always causal, describing a researcher’s expectation regarding what he or she anticipates finding. Often hypotheses are written to describe the expected relationship between two variables (though this is not a requirement). To develop a hypothesis, one needs to have an understanding of the differences between independent and dependent variables and between units of observation and units of analysis. Hypotheses are typically drawn from theories and usually describe how an independent variable is expected to affect some dependent variable or variables. Researchers following a deductive approach to their research will hypothesize about what they expect to find based on the theory or theories that frame their study. If the theory accurately reflects the phenomenon it is designed to explain, then the researcher’s hypotheses about what he or she will observe in the real world should bear out.

Let’s consider a couple of examples. In my collaborative research on sexual harassment (Uggen & Blackstone, 2004), Uggen, C., & Blackstone, A. (2004). Sexual harassment as a gendered expression of power. American Sociological Review, 69 , 64–92. we once hypothesized, based on feminist theories of sexual harassment, that “more females than males will experience specific sexually harassing behaviors.” What is the causal relationship being predicted here? Which is the independent and which is the dependent variable? In this case, we hypothesized that a person’s sex (independent variable) would predict her or his likelihood to experience sexual harassment (dependent variable).

Sometimes researchers will hypothesize that a relationship will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and legalization of marijuana. Perhaps you’ve done some reading in your crime and deviance class and, based on the theories you’ve read, you hypothesize that “age is negatively related to support for marijuana legalization.” In fact, there are empirical data that support this hypothesis. Gallup has conducted research on this very question since the 1960s. For more on their findings, see Carroll, J. (2005). Who supports marijuana legalization? Retrieved from http://www.gallup.com/poll/19561/who-supports-marijuana-legalization.aspx What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their supporting marijuana legalization decreases. Thus as age (your independent variable) moves in one direction (up), support for marijuana legalization (your dependent variable) moves in another direction (down). If writing hypotheses feels tricky, it is sometimes helpful to draw them out. Figure 5.8 “Hypothesis Describing the Expected Relationship Between Sex and Sexual Harassment” and Figure 5.9 “Hypothesis Describing the Expected Direction of Relationship Between Age and Support for Marijuana Legalization” depict each of the two hypotheses we have just discussed.

Figure 5.8 Hypothesis Describing the Expected Relationship Between Sex and Sexual Harassment

Figure 5.9 Hypothesis Describing the Expected Direction of Relationship Between Age and Support for Marijuana Legalization

Note that you will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a relationship has been shown to exist with absolute certainty and that there is no chance that there are conditions under which the hypothesis would not bear out. Instead, researchers tend to say that their hypotheses have been supported (or not). This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining a relationship will be discovered. Researchers may also discuss a null hypothesis The assumption that no relationship exists between variables in question. , one that predicts no relationship between the variables being studied. If a researcher rejects the null hypothesis, he or she is saying that the variables in question are somehow related to one another.

Quantitative and qualitative researchers tend to take different approaches when it comes to hypotheses. In quantitative research, the goal often is to empirically test hypotheses generated from theory. With a qualitative approach, on the other hand, a researcher may begin with some vague expectations about what he or she will find, but the aim is not to test one’s expectations against some empirical observations. Instead, theory development or construction is the goal. Qualitative researchers may develop theories from which hypotheses can be drawn and quantitative researchers may then test those hypotheses. Both types of research are crucial to understanding our social world, and both play an important role in the matter of hypothesis development and testing.

• In qualitative studies, the goal is generally to understand the multitude of causes that account for the specific instance the researcher is investigating.
• In quantitative studies, the goal may be to understand the more general causes of some phenomenon rather than the idiosyncrasies of one particular instance.
• Quantitative research may point qualitative research toward general causal relationships that are worth investigating in more depth.
• In order for a relationship to be considered causal, it must be plausible and nonspurious, and the cause must precede the effect in time.
• A unit of analysis is the item you wish to be able to say something about at the end of your study while a unit of observation is the item that you actually observe.
• When researchers confuse their units of analysis and observation, they may be prone to committing either the ecological fallacy or reductionism.
• Hypotheses are statements, drawn from theory, which describe a researcher’s expectation about a relationship between two or more variables.
• Qualitative research may point quantitative research toward hypotheses that are worth investigating.
• Do a Google News search for the term ecological fallacy . Chances are good you’ll come across a number of news editorials using this term. Read a few of these editorials or articles, and print one out. Demonstrate your understanding of the term ecological fallacy by writing a short answer discussing whether the author of the article you printed out used the term correctly.
• Pick two variables that are of interest to you (e.g., age and religiosity, gender and college major, geographical location and preferred sports). State a hypothesis that specifies what you expect the relationship between those two variables to be. Now draw your hypothesis, as in Figure 5.5 and Figure 5.6 .

5.3 Triangulation

• Define triangulation.
• Provide an example of triangulation.
• Understand the benefits of triangulation.

Up to this point, we have discussed research design as though it is an either/or proposition. Either you will collect qualitative data or you will collect quantitative data. Either your approach will be idiographic or it will be nomothetic. In truth, you don’t necessarily have to choose one approach over another. In fact, some of the most highly regarded social scientific investigations combine approaches in an effort to gain the most complete understanding of their topic possible. Using a combination of multiple and different research strategies is called triangulation The use of several different research strategies to enhance understanding of a topic. .

Think about the examples we’ve discussed of potential studies of electronic gadget addiction. Now imagine that you could conduct two, or even three, of those studies instead of just one. What if you could conduct a survey of students on campus, a content analysis of campus policies, and observations of students in their natural environments (Brewer & Hunter, 1989; Tashakkori & Teddlie, 1989)? Triangulation isn’t just about using multiple strategies of data collection. Triangulation of measures occurs when researchers use multiple approaches to measure a single variable. Triangulation of theories occurs when researchers rely on multiple theories to help explain a single event or phenomenon. If you’d like to learn more about triangulation, the following sources may be of interest: Brewer, J., & Hunter, A. (1989). Multimethod research: A synthesis of styles . Newbury Park, CA: Sage; Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches . Thousand Oaks, CA: Sage. Aside from being pretty exhausted, and possibly broke, you’d probably end up with a fairly comprehensive understanding of the causes and consequences of, and campus responses to, students’ electronic gadget addictions. And certainly a more comprehensive understanding is better than a less comprehensive one. The drawback, of course, is that you may not have the resources, because of either limited time or limited funding, to conduct such a wide-ranging study.

At this stage, you may be telling yourself (or screaming at me) that it would be nearly impossible to conduct all these studies yourself. You have a life, after all. The good news is that you don’t necessarily have to do everything on your own in order to take advantage of the analytic benefits of triangulation. Perhaps someone already has conducted a large survey of the topic you wish to study. You could find out how those results compare with your one-on-one interviews with people on the same topic. Or perhaps you wish to administer a survey to test the generality of some findings that have been reached through the use of field methods. Whatever the case, don’t forget about all the good research that has come before you that can help strengthen your investigation. Also keep in mind that qualitative and quantitative research methods can be complementary. Triangulation is one way to take advantage of the best in both approaches.

• Triangulation refers to using multiple research strategies in a single research project.
• Triangulation allows researchers to take advantage of the strengths of various methods and at the same time work to overcome some of each method’s weaknesses.
• Select one of the potential research topics you identified while reading Chapter 4 “Beginning a Research Project” . Discuss how you might study the topic if triangulation were your goal.
• Working with the same topic in mind, find two different sociological studies of the same topic. How do the two studies complement each other? Are there ways in which the weaknesses in one study are overcome in the other?

5.4 Components of a Research Project

• Describe useful strategies to employ when searching for literature.
• Describe why sociologists review prior literature and how they organize their literature reviews.
• Identify the main sections contained in scholarly journal articles.
• Identify and describe the major components researchers need to plan for when designing a research project.

In this section, we’ll examine the most typical components that make up a research project, bringing in a few additional components to those we have already discussed. Keep in mind that our purpose at this stage is simply to provide a general overview of research design. The specifics of each of the following components will vary from project to project. Further, the stage of a project at which each of these components comes into play may vary. In later chapters, we will consider more specifically how these components work differently depending on the research method being employed.

Searching for Literature

Familiarizing yourself with research that has already been conducted on your topic is one of the first stages of conducting a research project and is crucial for coming up with a good research design. But where to start? How to start? In Chapter 4 “Beginning a Research Project” , you learned about some of the most common databases that house information about published sociological research. As you search for literature, you may have to be fairly broad in your search for articles.

I’m guessing you may feel you’ve heard enough about electronic gadget addiction in this chapter, so let’s consider a different example here. On my campus, much to the chagrin of a group of student smokers, smoking was recently banned. These students were so upset by the idea that they would no longer be allowed to smoke on university grounds that they staged several smoke-outs during which they gathered in populated areas around campus and enjoyed a puff or two together.

A student in my research methods class wanted to understand what motivated this group of students to engage in activism centered around what she perceived to be, in this age of smoke-free facilities, a relatively deviant act. Were the protesters otherwise politically active? How much effort and coordination had it taken to organize the smoke-outs? The student researcher began her research by attempting to familiarize herself with the literature on her topic. Yet her search in Sociological Abstracts for “college student activist smoke-outs,” yielded no results. Concluding there was no prior research on her topic, she informed me that she would need an alternative assignment to the annotated bibliography A list of sources relevant to a person’s research project. The list is usually presented in alphabetical order, using the citation format of the researcher’s profession. It includes a brief summary of each source’s point of focus, theoretical argument, and major findings underneath each citation. Some annotated bibliographies also contain a brief critique or evaluation of each source. I required since there was no literature for her to review. How do you suppose I responded to this news? What went wrong with this student’s search for literature?

In her first attempt, the student had been too narrow in her search for articles. But did that mean she was off the hook for completing the annotated bibliography assignment? Absolutely not. Instead, she went back to Sociological Abstracts and searched again using different combinations of search terms. Rather than searching for “college student activist smoke-outs” she tried, among other sets of terms, “college student activism.” This time her search yielded a great many articles. Of course, they were not focused on prosmoking activist efforts, but they were focused on her population of interest, college students, and on her broad topic of interest, activism. I suggested that reading articles on college student activism might give her some idea about what other researchers have found in terms of what motivates college students to become involved in activist efforts. I also suggested she could play around with her search terms and look for research on activism centered on other sorts of activities that are perceived by some as deviant, such as marijuana use or veganism. In other words, she needed to be broader in her search for articles.

While this student found success by broadening her search for articles, her reading of those articles needed to be narrower than her search. Once she identified a set of articles to review by searching broadly, it was time to remind herself of her specific research focus: college student activist smoke-outs. Keeping in mind her particular research interest while reviewing the literature gave her the chance to think about how the theories and findings covered in prior studies might or might not apply to her particular point of focus. For example, theories on what motivates activists to get involved might tell her something about the likely reasons the students she planned to study got involved. At the same time, those theories might not cover all the particulars of student participation in smoke-outs. Thinking about the different theories then gave the student the opportunity to focus her research plans and even to develop a few hypotheses about what she thought she was likely to find.

Reviewing the Literature

Developing an annotated bibliography is often one of the early steps that researchers take as they begin to familiarize themselves with prior research on their topic. A second step involves a literature review in which a researcher positions his or her work within the context of prior scholarly work in the area. A literature review addresses the following matters: What sorts of questions have other scholars asked about this topic? What do we already know about this topic? What questions remain? As the researcher answers these questions, he or she synthesizes what is contained in the literature, possibly organizing prior findings around themes that are relevant to his or her particular research focus.

I once advised an undergraduate student who conducted a research project on speciesism, the belief that some species are superior to or have more value and rights than others. Her research question was “Why and how do humans construct divisions between themselves and animals?” This student organized her review of literature around the two parts of her research question: the why and the how. In the “why” section of her literature review, she described prior research that addressed questions of why humans are sometimes speciesist. She organized subsections around the three most common answers that were presented in the scholarly literature. She used the same structure in the “how” section of her literature review, arranging subsections around the answers posed in previous literature about how humans construct divisions between themselves and animals. This organizational scheme helped readers understand what we already know about the topic and what theories we rely on to help make sense of the topic. In addition, by also highlighting what we still don’t know, it helped the student set the stage for her own empirical research on the topic.

The preceding discussion about how to organize a review of scholarly literature assumes that we all know how to read scholarly literature. Yes, yes, I understand that you must know how to read. But reading scholarly articles can be a bit more challenging than reading a textbook. Here are a few pointers about how to do it successfully. First, it is important to understand the various sections that are typically contained in scholarly journals’ reports of empirical research. One of the most important and easiest to spot sections of a journal article is its abstract A short paragraph at the beginning of a journal article that summarizes the author’s research question(s), research method(s), and key findings. , the short paragraph at the beginning of an article that summarizes the author’s research question, methods used to answer the question, and key findings. The abstract may also give you some idea about the theoretical proclivities of the author. As a result, reading the abstract gives you both a framework for understanding the rest of the article and the punch line. It tells you what the author(s) found and whether the article is relevant to your area of inquiry.

After the abstract, most journal articles will contain the following sections (although exact section names are likely to vary): introduction, literature review, methodology, findings, and discussion. Of course, there will also be a list of references cited, Lists of references cited are a useful source for finding additional literature in an area. and there may be a few tables, figures, or appendices at the end of the article as well. While you should get into the habit of familiarizing yourself with articles you wish to cite in their entirety , there are strategic ways to read journal articles that can make them a little easier to digest. Once you have read the abstract and determined that this is an article you’d like to read in full, read through the discussion section at the end of the article next. Because your own review of literature is likely to emphasize findings from previous literature, you should make sure that you have a clear idea about what those findings are. Reading an article’s discussion section helps you understand what the author views as the study’s major findings and how the author perceives those findings to relate to other research.

As you read through the rest of the article, think about the elements of research design that we have covered in this chapter. What approach does the researcher take? Is the research exploratory, descriptive, or explanatory? Is it inductive or deductive? Idiographic or nomothetic? Qualitative or quantitative? What claims does the author make about causality? What are the author’s units of analysis and observation? Use what you have learned in this chapter about the promise and potential pitfalls associated with each of these research elements to help you responsibly read and understand the articles you review. Future chapters of this text will address other elements of journal articles, including choices about measurement, sampling, and research method. As you learn about these additional items, you will increasingly gain more knowledge that you can apply as you read and critique the scholarly literature in your area of inquiry.

Additional Important Components

Thinking about the overarching goals of your research project and finding and reviewing the existing literature on your topic are two of the initial steps you’ll take when designing a research project. Forming a clear research question, as discussed in Chapter 4 “Beginning a Research Project” , is another crucial step. There are a number of other important research design components you’ll need to consider, and we will discuss those here.

At the same time that you work to identify a clear research question, you will probably also think about the overarching goals of your research project. Will it be exploratory, descriptive, or explanatory? Will your approach be idiographic or nomothetic, inductive or deductive? How you design your project might also be determined in part by whether you aim for your research to have some direct application or if your goal is to contribute more generally to sociological knowledge about your topic. Next, think about what your units of analysis and units of observation will be. These will help you identify the key concepts you will study. Once you have identified those concepts, you’ll need to decide how to define them, and how you’ll know that you’re observing them when it comes time to collect your data. Defining your concepts, and knowing them when you see them, has to do with conceptualization and operationalization, the focus of Chapter 6 “Defining and Measuring Concepts” . Of course, you also need to know what approach you will take to collect your data. Thus identifying your research method is another important part of research design. You also need to think about who your research participants will be and what larger group(s) they may represent. These topics will be the focus of Chapter 7 “Sampling” . Last, but certainly not least, you should consider any potential ethical concerns that could arise during the course of your research project. These concerns might come up during your data collection, but they might also arise when you get to the point of analyzing or sharing your research results.

Decisions about the various research components do not necessarily occur in sequential order. In fact, you may have to think about potential ethical concerns even before zeroing in on a specific research question. Similarly, the goal of being able to make generalizations about your population of interest could shape the decisions you make about your method of data collection. Putting it all together, the following list shows some of the major components you’ll need to consider as you design your research project:

• Research question
• Literature review
• Research strategy (idiographic or nomothetic, inductive or deductive)
• Research goals (basic or applied)
• Units of analysis and units of observation
• Key concepts (conceptualization and operationalization)
• Method of data collection
• Research participants (sample and population)
• Ethical concerns
• When identifying and reading relevant literature, be broad in your search for articles, but be narrower in your reading of articles.
• Writing an annotated bibliography can be a helpful first step to familiarize yourself with prior research in your area of interest.
• Literature reviews summarize and synthesize prior research.
• Literature reviews are typically organized around substantive ideas that are relevant to one’s research question rather than around individual studies or article authors.
• When designing a research project, be sure to think about, plan for, and identify a research question, a review of literature, a research strategy, research goals, units of analysis and units of observation, key concepts, method(s) of data collection, population and sample, and potential ethical concerns.
• Find and read a complete journal article that addresses a topic that is of interest to you (perhaps using Sociological Abstracts, which is introduced in Chapter 4 “Beginning a Research Project” ). In four to eight sentences, summarize the author’s research question, theoretical framing, methods used, and major findings. Reread the article, and see how close you were in reporting these key elements. What did you understand and remember best? What did you leave out? What reading strategies may have helped you better recall relevant details from the article?
• Using the example of students’ electronic gadget addictions, design a hypothetical research project by identifying a plan for each of the nine components of research design that are presented in this section.
• Principles of Sociological Inquiry: Qualitative and Quantitative Methods. Provided by : Saylor Academy. Located at : https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Research Methods Guide: Research Design & Method

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

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Research for Organizing

A Toolkit for Participatory Action Research from TakeRoot Justice

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DESIGNING YOUR RESEARCH PROJECT

This section is designed to assist you with the planning phase of your Participatory Action Research (PAR) project. The section includes activities that will enable your group to make informed decisions about starting a research project, developing research goals and questions, choosing a research method, and creating a plan and timeline to guide your research. It also includes tools that will help your team to design and plan your overall research project.

Download Activity 2.1

Activity: 2.1 Developing Research Goals and Questions

Purpose of Activity:

The purpose of this activity is to have participants discuss the goals and purpose of the research project. After you’ve discussed what the research is trying to accomplish and why your organization is doing it, the participants will come up with research questions that will guide the research process.

By the End of Activity Participants Will:

• Discuss the social or policy change you want to bring about through your research and campaign work
• Discuss why research is useful or relevant to your organization or campaign
• Determine the overarching questions you want to answer through your research

Before this Activity Participants Will Need to:

Be introduced to the basics of Participatory Action Research (PAR)

Decide that PAR is right for your organization

Tools Needed:

Copies of Tool 2.1: Guiding Questions for Developing Research Goals and Questions

Materials Needed:

Butcher paper

Research Goals

Research Question

Intended Audience:

Community Members

Time Needed:

Part I:  What and Why of PAR  (20 minutes)

Facilitator Instructions:

1. Provide a brief summary of your campaign to set the context for the discussion 2. Explain that today we will have a discussion about using participatory action research in our campaign. We are going to try to begin to develop goals and questions that can guide our research. 3.  Write “What?” at the top of a piece of butcher paper, and go through the questions below with the participants. Record responses on butcher paper, and keep the paper for later. (If you have 7 or more participants you can break out into small groups).

…is the social or policy change you want to bring about at the end of the day?

…are the overarching questions you want to answer through your research?

…information do you need to better understand and document the issues you are addressing?

…primary question do you want to answer with your research?

4.  Once you’ve answered each “What?” question sufficiently, write “Why?” on a new sheet of butcher paper. Go through each of the questions below with participants. Record responses.

…is research useful or important for your organization? Will it be used…

…internally, to inform and assess needs in the community?

…externally, to mobilize and educate community members or elected officials around an issue?

Part II:  Developing Research Goals (20 minutes)

• Put up a piece of butcher paper that says “Research Goals: What you want to accomplish with your research?”
• Facilitate a discussion based upon your group’s answer to the “What” and “Why” questions that leads the group to establish the goals of the research and the research questions.
• Ask the question: based on the answers to the “What” and “Why” questions, what are our goals for this research? What do we want to accomplish through doing this research?
• Ask people to popcorn responses and record their responses on butcher paper.
• Explain that now that we have some research goals, we need to frame those goals as questions in order to conduct research.

Part III: Developing Research Questions (20 minutes)

• Frame the activity: explain that part of being a researcher is to ask questions and find answers.  To design a research project you need to first figure out what big questions you want to answer.  We will use our list of goals to figure out what questions we want to ask
• Put up a piece of butcher paper that says: “Research Questions: What big questions do you want to answer with your research?”  Also write an example of a research question on the butcher paper.  For example, if one of our goals is to document rapid development of luxury housing in our neighborhood, our question would be, “What is the current state of housing development in our neighborhood?”
• Ask the question: based on the answers to the “What” and “Why” questions and the goals we just created, what big questions do we want to answer through our research?
• Explain to the groups that these goals and questions will be the foundation for your research design and implementation.

Download Activity 2.2

Activity: 2.2 Choosing Your Research Method

This activity is designed to help organizers and members understand the various options for how they can conduct research and choose the research method(s) they will use.

• Finalize research goals and questions
• Understand relevant research methods
• Discuss the strengths and weaknesses of different research methods
• Decide the research method appropriate for your group

Develop research goals and research questions

Copies of Tool 2.2: Guiding Questions for Choosing a Research Method

Copies of Tool 2.3: PAR Menu of Methods

Post-it notes

Quantitative Data

Qualitative Data

Focus Groups

Community Mapping

Community Visioning

Secondary Data

Media Review

Literature Review

Community Members or Organizers

Part I: Nailing Down your Research Goals and Questions  (15 minutes)

• Frame the activity in the context of your campaign: now that we’ve decided to do participatory research we need to dig into how to do it. There are a bunch of different ways we can conduct research so we need to explore these different research methods.
• Put up the butcher paper with “Research Goals” and “Research Questions” from Section 2, Activity 1.
• Ask the group, is anything missing?
• Wrap it up: Summarize what has been said and explain that these goals and questions will help to determine which methods you will use to conduct your research.

Part II:  Brainstorm as a Big Group  (25 minutes)

• Explain that now that we have determined some of our goals and research questions, we need to dig into how to do the research.
• Next, facilitate a discussion that answers the questions: how do we do the research, when do we do it and where? Record responses on butcher paper, and keep butcher paper for Part 3 (This can also be done in break-out groups).

…can you document or better understand the issue? Do you need “hard” numbers (quantitative data) and/or stories of personal experience (qualitative data) or both?

…are you going to give legs to your research? What action strategies could you employ to make the research and report as impactful as possible?

…are the stakeholders in the issue? Who has interest? Who is affected?

…needs to have their voice be heard?

…are you trying to influence? Who has power over the issue?

…is your target audience (community members, elected officials, media)?

…will collect your data?

…can you find the people you need to talk to get your data?

…can you find existing information that is relevant to your research?

…can you go for support and assistance (non-profits, universities, government agencies)?

Part II:  Understanding the Research Methods  (35 minutes)

• Choose 3-4 methods that you think are the most relevant to your project (from Tool T2.1 PAR Menu of Methods).
• Break the participants into 3-4 groups and assign one method that you’ve chosen to each group.
• Pass out Tool T2.1 “PAR Menu of Methods” to each group.
• Tell each group to read over the description for the method they have been assigned and give them 5-7 minutes to make up a skit for that method.  Encourage them to be creative.
• Have each small group perform their skit.
• After each skit, facilitate a discussion with the full group.  Ask the group: what did you see in the skit? What do you think are the pros and cons of that method for our work?  Record the pros and cons list on butcher paper.

Part III:  Decide Your Research Method  (20 Minutes)

• Place the butcher papers from each A2.2 activity next to each other at the focal point of the room.
• First, ask a volunteer to read your responses to the “How” “Who” and “Where” questions from the first activity to remind everyone of your initial conversations.
• Facilitate a discussion: now that we know more about each of the possible research methods, which methods align with the groups responses to the “How”, “Who” and “Where” questions?
• Make a decision about which method(s) make the most sense for your project. Record the methods you choose to put into your research workplan (see Tool 2.3).

Download Activity 2.3

Activity: 2.3 Developing Your Research Timeline

This activity is designed to enable your research team to sit together and plan out the remaining steps of your research project. Through the activity, participants will devise a timeline that will map out all of the necessary steps in your project, and will specify who is going to be responsible for each step of the project. By the end of the activity you will have created a research timeline that you can use to guide the rest of your project.

By the End of this Activity You Will:

• Map out all of the steps of your research project in a timeline
• Decide who is going to do what and when they are going to do it
• Create a system of accountability for your research project

Have been introduced to the basics of Participatory Action Research (PAR)

Have created the research goals and questions for your project

Have decided on your research method

Tool 2.4: Research Timeline Template

Data Report Back

Policy Recommendation

Members and Organizers that will be active in research process

Part I:  Creating Your Research Plan and Timeline  (15 minutes)

1.  Before the meeting prepare the room.

• Prepare two pieces of butcher paper in advance; Butcher Paper 1:  a list of the main steps in PAR (listed below), Butcher Paper 2: recreate the table below on large sheets of butcher paper big enough so that you can write in each box. Depending on the specifics of your project you may need to modify this table.

• Place the two pieces of butcher paper next to each other at the front of the room with the PAR steps to the left of the table.
• Fill out the first three steps (Organizing Goal, Research Question, and Research Plan) in the table if you have already done them. Fill out any other steps that you have already discussed or figured out (for example you might have chosen someone to design the research instruments).

2.  Introduce the activity; today we are going to create our research plan. By the end of the meeting we will have completed a timeline of the research steps and will have split up who will do what. 3.  Describe the butcher paper sheets you have created. Describe that you will be using these sheets to create your timeline. 4.  Go through each of the PAR steps that you will use for your project and fill out the what, when and who of each step with participants. 5.  After you’ve completed the table, take a moment to congratulate everyone as you have now finished the planning stages of your research project! 6.  Keep all of the Butcher Paper sheets you created and use them to type up your Research Plan (see Tool 2.4 and T2.5:  Template for Research Work Plan and Research Timeline Template).

Download Tool 2.1

Tool: 2.1 Guiding Questions for Developing Research Goals and Questions

Descarga Herramienta 2.1 En Espanol

WHAT…

…is the social or policy change you want to bring about at the end of the day?________________________________________________________________________________________________________________

…are your organizing goals, and how can this research be helpful achieving these goals? ________________________________________________________________________________________________________________

…information do you need to better understand and document the issues you are addressing? ________________________________________________________________________________________________________________

…is research useful or important for your organization? ________________________________________________________________________________________________________________

… internally, to inform and assess needs in the community?          YES          NO

Explain:_________________________________________________________________________________________________________

… externally, to mobilize and educate community members around an  issue?

YES        NO

…to support a specific policy campaign or influence policy and public debate around an issue?

YES           NO

Download Tool 2.2

Tool: 2.2 Guiding Questions for Choosing a Research Method

Descarga Herramienta 2.2 En Espanol

…  can you document or better understand the issue?  Do you need “hard” numbers (quantitative data) or stories of personal experience (qualitative data)?

Quantitative         Qualitative           Both

________________________________________________________

…  are you going to give legs to your research? What action strategies could you employ to make the research and report as effective as possible? ________________________________________________________

… are the stakeholders in the issue? Who has interest, who is affected? ________________________________________________________

…needs their voice to be heard? ________________________________________________________

…are you trying to influence? Who has power over the issue? ________________________________________________________

…is your target audience (community members, elected officials, media)? ________________________________________________________

…will collect your data? ________________________________________________________

… can you go to for information and other existing data? ________________________________________________________

…can you go for support and assistance (non-profits, universities, government agencies)? ________________________________________________________

… is the right time to do research? ________________________________________________________

…In your campaign? ________________________________________________________

…In the political context? ________________________________________________________

…In your organization? …In the political context? ________________________________________________________

Download Tool 2.3

Tool: 2.3 Participatory Action Research (PAR) Menu of Methods

Descarga Herramienta 2.3 En Espanol

• Surveys-  Ask specific questions and tend to include short answer, multiple-choice, and scaled-answer questions. Surveys can be done online, through the mail, and can be written and filled out in person.  The most effective way to conduct surveys in support of organizing is in an in person “interview style” so that the surveyor can make personal connections with the respondent. Surveys are helpful for getting information or data from a wider group of people and are better for getting quantitative information like numbers, than they are for getting qualitative information, like people’s stories. Surveys can be helpful when making policy demands because elected officials, policymakers and the media tend to respond to hard numbers.
• Interviews-  Are guided conversations about a specific topic, are often done one-on-one, and tend to use open-ended questions in order to get in-depth explanations.  Interviews are useful when you want to get more specific, detailed information than you would get from a survey and you want to get deeper into people’s experiences and personal stories. Interviews are appropriate when dealing with sensitive or personal information that people may not be comfortable writing on a survey or sharing in a group setting (such as a focus group). Interviews can also assist the organizing outreach process because they facilitate one-to-one interaction, but they can be more time intensive then surveys.
• Focus Groups-  Are small group sessions (7-12 people) that are led by a facilitator in order to obtain opinions based on the research question.  Like interviews, focus groups are good for getting qualitative data, and are an effective way to get people’s personal stories, testimonies, and experiences from a group setting. They can also be useful for delving deeper into a specific issue or research question not fully addressed by another method.  Focus groups can be useful in allowing participants to bounce ideas and stories off of each other.  Due to the group setting, they can also be more challenging than interviews for discussing sensitive topics.
• Community Mapping/Canvassing-  Is a process of documenting and visually presenting trends or patterns in a given community.  Community maps and canvassing can be used to document many physical, spatial dynamics of a neighborhood from new construction sites, to new luxury condos, to green spaces, to new businesses, to vacant lots, etc. This is an effective tool for tracking physical changes in a neighborhood, and specifically as a way to document the impact of gentrification on a neighborhood.
• Community Visioning-  Is a process where group of community members come together to develop an alternative vision or proposal for the future of their community.  Visioning can be used to develop public policy demands and can be particularly useful when communities are working to impact the physical development of their community.  This can also be useful for groups working to influence a particular issue or policy.
• Mystery Shopping-  Is a process where community members posing as customers call or visit businesses and document their experience and observations.  Usually mystery shoppers have a specific set of criteria they are looking for when they visit or call a business.  This is a good way to document employment practices, compliance with labor laws, and consumer fraud.
• Secondary data-  Is data that comes from someone else’s research.  This is distinct from “primary data” which is original data that you collect through your own research in the field.  Secondary data is helpful for getting background information that will complement the ground-level information that comes from people’s experiences (primary data). It can also be helpful to do a bit of secondary data collection before you begin your primary data collection in order to focus your research questions and help you to develop your research instruments (such as surveys and interview guides). Secondary data can come from a variety of public and private sources, such as the U.S. Census Bureau, city and state agencies, research organizations and academic institutions.
• Media Review-  Is a systematic review of a certain number of news articles or clips from a variety of sources about a specific topic to uncover the most common words or themes that emerge.  This can be used as background research to help inform your research design and can also be used on its own to give you data about how a specific issue is being presented or framed in the media.
• Literature Review-  Is a review of existing articles, academic studies or reports in order to find out what information already exists about the topic you are exploring.  This can be part of your secondary research; can help inform your research questions and can help you identify gaps in research and information on a given issue.

Download Tool 2.4 as a Word Doc

Tool: 2.4 Research Work Plan Template

Why is This Tool Useful?

Descarga Herramienta 2.4 En Espanol

This tool will help to document your research plan and methodology.  It is also useful in developing a workplan, timeline and accountability mechanism for your project to make sure that each member of your research team is doing the work they have committed to doing and are keeping up with deadlines.   This can also be helpful in putting together proposals for funding or other support because you will have all the information about your project in one place. Below is a template for a research plan.  Sections can be shifted and deleted as needed.

Name of Organization(s):

Name of Research Project:

This section should include some background information about the social issue that your research will address and/ or the campaign that your research will support.

Overview of project

This section should provide a brief overview of the research project including what issue you are addressing and why, what information you plan to collect, whom you are collecting the information from and how you are collecting information (See Tools 2.1 and 2.2).

Goals of project

This section should include a bulleted list of what you hope to achieve through doing this research project.  Some examples include:

• To gather current and detailed data from our community.
• To develop skills and leadership of members.
• To build the base of members in our organization.
• To educate elected officials about our organization’s campaign.

Research Questions

This should include a bulleted list of the overarching questions you hope to answer through your research.  Research questions are different from survey or interview questions because they are broad and can help to guide the more specific questions you will ask in your surveys, interviews, focus groups, canvassing Tool, etc. Some examples include:

• What is the impact of poor housing conditions on residents of Chinatown?
• What types of benefits are workers getting and what are they not getting from their employers?
• How do various policies and procedures at methadone programs affect participant’s access to methadone?
• What is the current state of luxury housing development in low-income communities of color in NYC?

Methodology/Research Components

This section should include all the methods you will use to answer your research questions along with a short description for each method. Below are some examples, but you should feel free to chose other methods (see Tool 2.3)

• Short survey:   This short survey will be focused on collecting updated and detailed data on x, y and z.  The goal will be to collect 500 surveys.  The surveys will be translated into Spanish and French languages and administered by members of our organization.
• In Depth Interviews:  Members and organizers will conduct in depth interviews with 5-10 workers in order to collect qualitative data about x and y and to show z.
• Secondary Research:  Members will conduct an analysis of current literature and data to support the findings from field research.
• Media Review:  Members will review 100 articles found in local newspapers in the last three years that include the word “public housing” in the headline.  Researchers will identify the most prevalent words and themes in these articles.

Project Output

This section should include a few sentences about what you will create at the end of this project.  This could be a report, a 1 or 2 page summary of your findings, a map, a video, etc.

How the PAR Project Will Support Community Organizing

This section should explain how your research will support and be integrated into your organizing campaign.  Will your research help with leadership development? Help to build your base? Help to garner media attention about a policy issue you are fighting for?

This table should include all of the different tasks that you will need to complete for the research project, along with who will be responsible for completing the task and by what date.  The tasks will differ depending on which methods you chose but Tool 2.5 will provide a template as a place to start.

Download Tool 2.5 as a Word Doc

Tool: 2.5 Research Timeline Template

Download Tool 2.5 as a PDF

Download Tool 2.6 as a PDF

Tool: 2.6 Advisory Board Invitation Template

Download Tool 2.6 as a Word Doc

Tool 2.6:  Sample Advisory Board Invitation Template

Why is this tool useful?

Developing a Research Advisory Board can be a great way to bring together a team of resource allies to support and add capacity to your Participatory Action Research. Academics, lawyers or policy analysts that specialize in the issue you are researching are all good examples of potential advisors. We recommend bringing advisors together as a group early in the process and being clear about the role they will play and what they can expect from the process. Below is a sample letter you can send to invite advisors to an initial meeting. We also have a sample agenda for a Research Advisory Board meet (see Tool 2.7).

[INSERT YOUR ORGANIZATION’S LOGO OR PUT ON ORGANIZATIONAL LETTERHEAD]

Dear ________________,

I hope you are well! I am writing to you to ask you to be a part of an exciting new research project of the  [YOUR ORGANIZATION’S NAME]  by serving on our advisory board.

As you may know…  [INSERT BRIEF SUMMARY ABOUT YOUR ORGANIZATION’S OVERARCHING GOALS AND AIMS]

As part of this work,   we are planning to conduct a participatory action research project focusing on [RESEARCH TOPIC].

Because of your familiarity with [ORGANIZATION NAME] and your expertise with these issues or strategies, I am reaching out to you in the hopes that you will serve on a Research Advisory Team to provide feedback on our research.  As an advisor, I am requesting that you participate in one or more of the following:

• Read a draft of our report and provide feedback;
• Participate in one or more conference calls about the report;
• Provide feedback on policy recommendations;
• Provide advice on how to best use the report to advance  [ORGANIZATION’S NAME] ’s advocacy and organizing goals.

Please let me know by  [INSERT DATE]  if you are willing to participate on this Research Advisory Team.  We will be scheduling for a meeting for  [INSERT DATE].  Please don’t hesitate to call  (XXX) XXX- XXXX  or email [ INSERT EMAIL HERE ] if you would like additional information or have further questions.  We hope you will join us in this important work!

[INSERT NAME]

[INSERT POSITION]

Download Tool 2.7 as a PDF

Tool: 2.7 Sample Advisory Board Meeting Agenda Template

Download Tool 2.7 as a Word Doc

Developing a Research Advisory Board can be a great way to bring together a team of resource allies to support and add capacity to your Participatory Action Research. Academics, lawyers or policy analysts that specialize in the issue you are researching are all good examples of potential advisors. Once the Research Advisory Board (see Tool 2.6) is assembled, it is a good idea to bring the Board together as early in the research process as possible. The research plan should be more or less complete by this point (see Tool 2.4), and advisors can give valuable feedback on research goals and questions, methodology, project output and the timeline. The advisors should also walk away with a concrete understanding of their role in the work and what you will be asking of them in participating in the research process. It is also a good idea to make sure the research timeline is mostly complete (see Tool 2.5) because this will make planning next steps with the board easier. Below is a sample meeting agenda for the Research Advisory Board, which can be used to ensure that the meeting is productive and provides crucial feedback on the project.

Research Advisory Board Meeting

Download The Case Study

Case Study: 2.1 Center for Frontline Retail and CDP Report: Pathways to Success: The Need for Accessible, Appropriate Trainings for Retail Workers, 2017

Download the Report

Background on the Organization and Issue

The Center for Frontline Retail (CFR) is a worker-led organization committed to improving the lives of retail workers through community organizing, industry analysis, and leadership development. CFR works to simultaneously elevate workers’ voices and raise standards in the retail sector. CFR’s prior research has shown that retail workers face discrimination and harassment in the workplace, along with unfair scheduling practices.

Through discussions with their members, CFR identified a lack of training opportunities for workers, impacting their ability to advance in the sector.  . CFR also noticed that women and people of color are disproportionately affected by the lack of training from employers and as a result lack opportunities for career advancement.

In order to document the lack of training and advancement opportunities for retail workers, and the disproportionate effect of this on women and people of color, CFR partnered with the Community Development Project on a participatory action research project in order to voice the concerns of retail workers and highlight CFR’s training model as a pathway for advancement. This project ultimately resulted in a report that describes workers’ desire for, and barriers to, training and advancement opportunities in the retail industry, outlines policies that would set aside money to train retail workers, and puts the CFR training model forward to train and educate entry level workers, as well as higher level training to grow within the retail industry.

Below is a description of the Center for Frontline Retail Research Project, based on the Participatory Action Research guiding framework  (see Tools  2.1  and  2.2 ).

Were the Organizing Goals connected to this research?

• To generate data on the training needs of retail workers in NYC.
• To document and generate data on the extent to which retail workers are offered training and education programs by their employers, and distinguish whether workers of color and women are able to access such programs.
• To document the experiences of people of color and women working in the retail industry in accessing appropriate trainings and education programs.
• To explore and document the existing training and education programs that are available to retail workers and their associated costs.

Overall questions did CFR want to answer through their research?

• What is the current training and education landscape for retail workers in NYC?
• What are the training and education needs of retail workers (with focus on women and people of color)?
• What are the experiences of women and people of color working in retail in accessing training and other career advancement opportunities?

Is this research useful or important for CFR?

• INTERNALLY: to base build and educate retail workers; to develop member leaders and their outreach skills.
• EXTERNALLY: to inform a curriculum developed for retail workers that would provide crucial training for career advancement; put together the landscape of barriers that retail workers face in accessing education and training; put forward recommendations for retailers to adopt high road retail strategies.

Are the Stakeholders in this Issue?

• Retail workers in New York City

Was CFR trying to influence?

• New York City Council Members, Mayor’s Office of Workforce Development, retail employers and brands, developers of commercial retail spaces

Did CFR gather information (what methods did they use)?

• SHORT SURVEY :   CFR members administered a survey to 300 retail workers in order to understand the training needs and existing training opportunities of retail workers working in general merchandise stores in New York City, specifically discount, fast fashion and high end stores. Retail workers were targeted during classes at the Center for Frontline Retail and when retail workers were on breaks throughout the work day.
• FOCUS GROUPS:  In order to build and expand on the quantitative data gathered from surveys, CFR also conducted three focus groups with their members in order to collect qualitative data about the experiences and stories of retail workers accessing trainings in the workplace, and to show the barriers and discrimination faced by women and people of color.
• SECONDARY RESEARCH : CDP conducted an analysis of current literature and data to support findings from research, and to document the current landscape of trainings, curriculum and education programs in retail.

Did Research support CFR’s organizing efforts?

• The survey project provided opportunities to base build and educate community members. The focus groups provided member leaders with the opportunity to learn facilitation skills and a deepened understanding of the landscape of barriers facing workers.
• The data collected through the research was written into a report and presented to key stakeholders in the retail sector, such as retail employers, the New York City Mayor’s Office of Workforce Development, and developers of commercial work spaces who could partner with CFR to provide training to potential retail workers.

Read the report  here . Read coverage of the report release in  Crains NY  and the  Associated Press .

• Designing Your Research project
• Developing Research Goals and Questions
• Choosing Your Research Method
• Developing Your Research Timeline
• Guiding Questions for Developing Research Goals and Questions
• Guiding Questions for Choosing a Research Method
• Participatory Action Research (PAR) Menu of Methods
• Research Work Plan Template
• Research Timeline Template
• Advisory Board Invitation Template
• Sample Advisory Board Meeting Agenda Template
• Center for Frontline Retail and CDP Report: Pathways to Success: The Need for Accessible, Appropriate Trainings for Retail Workers, 2017

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• Knowledge Base
• Starting the research process
• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

Methodology

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

 Statistics

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

Research bias

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

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McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved September 14, 2024, from https://www.scribbr.com/research-process/research-question-examples/

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Designing Qualitative Studies

• First Online: 11 November 2020

Cite this chapter

• Seyyed-Abdolhamid Mirhosseini 2  

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The qualitative research question provides the conceptual base and the direction for actual research involvements. But the actual process of ‘doing’ research involves gathering evidence (data) and making sense of collected data bodies. The successful handling of these processes of dealing with data—that are multifaceted and quite challenging processes—requires some prior planning. The planning is a complex theoretical undertaking and is compounded by numerous practical considerations. Moreover, in addition to a priori planning for the project, you need to constantly assess the early plan and modify it to meet the emerging conditions of the inquiry process. This vibrant and challenging process of planning for your qualitative research is called ‘designing’. This chapter is about designing qualitative language education research as the link between the theoretical considerations and the practical side of the research endeavor.

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Mirhosseini, SA. (2020). Designing Qualitative Studies. In: Doing Qualitative Research in Language Education. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-56492-6_3

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