Library Research Support

Deciding on a topic for your thesis, dissertation or research project is the first step in making sure your research goes as smoothly as possible. When choosing a topic, it’s important to consider:

• Your institution and department’s requirements

• Your areas of knowledge and interest

• The scientific, social, or practical relevance

• The availability of data and sources

• The length and time frame of your dissertation

If you have no dissertation ideas yet, it can be hard to know where to start. Follow these steps to begin narrowing down your ideas.

1. Check the requirements

2. Choose a broad field of research

3. Look for books and articles

4. Find a niche

5. Consider the type of research

6. Determine the relevance

7. Make sure it's plausible

8. Get your topic approved

The process of developing your research question follows several steps:

• Choose a broad topic

• Do some preliminary reading to find out about topical debates and issues

• Narrow down a specific niche that you want to focus on

• Identify a practical or theoretical research problem that you will address

When you have a clearly-defined problem, you need to formulate one or more questions. Think about exactly what you want to know and how it will contribute to resolving the problem.

The way you frame your question depends on what your research aims to achieve.

Describing and exploring:

• What are the characteristics of X?

• How has X changed over time?

• What are the main factors in X?

• How does X experience Y?

• How has X dealt with Y?

Explaining and testing:

• What is the relationship between X and Y?

• What is the role of X in Y?

• What is the impact of X on Y?

• How does X influence Y?

• What are the causes of X?

Evaluating and acting:

• What are the advantages and disadvantages of X?

• How effective is X?

• How can X be achieved?

• What are the most effective strategies to improve X?

• How can X be used in Y?

Depending on the scope of your research, you may identify just one question or several. You may also have one primary research question and several secondary questions or sub-questions that relate to the same problem.

The research design is a framework for planning your research and answering your research questions. Creating a research design means making decisions about:

• The type of data you need

• The location and timescale of the research

• The participants and sources

• The variables and hypotheses (if relevant)

• The methods for collecting and analyzing data

The research design sets the parameters of your project: it determines exactly what will and will not be included. It also defines the criteria by which you will evaluate your results and draw your conclusions. The reliability and validity of your study depends on how you collect, measure, analyze, and interpret your data.

A strong research design is crucial to a successful research proposal, scientific paper, or dissertation.

Step 1: Consider your priorities and practicalities

For most research problems, there is not just one possible research design, but a range of possibilities to choose from. The choices you make depend on your priorities in the research, and often involve some tradeoffs – a research design that is strong in one area might be weaker in another.

Examples

• A qualitative case study is good for gaining in-depth understanding of a specific context, but it does not allow you to generalize to a wider population.

• A laboratory experiment allows you to investigate causes and effects with high internal validity, but it might not accurately represent how things work in the real world (external validity).

As well as scientific considerations, you also need to think practically when designing your research.

• 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)?

If you realize it is not practically feasible to do the kind of research needed to answer your research questions, you will have to refine your questions further.

Step 2: Determine the type of data you need

You probably already have an idea of the type of research you need to do based on your problem statement and research questions. There are two main choices that you need to start with.

Primary vs Secondary data

Primary data

• You will directly collect original data (e.g. through surveys, interviews, or experiments) and then analyze it.

• This makes your research more original, but it requires more time and effort, and relies on participants being available and accessible.

Secondary data

• You will analyze data that someone else already collected (e.g. in national statistics, official records archives, publications, and previous studies).

• This saves time and can expand the scope of your research, but it means you don’t have control over the content or reliability of the data.

Qualitative vs Quantitative data

Qualitative data

• If your objectives involve describing subjective experiences, interpreting meanings, and understanding concepts, you will need to do qualitative research.

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

Quantitative data

• If your objectives involve measuring variables, finding frequencies or correlations, and testing hypotheses, you will need to do quantitative research.

• Quantitative research designs tend to be more fixed, with variables and methods determined in advance of data collection.

Note that these pairs are not mutually exclusive choices: you can create a research design that combines primary and secondary data and uses mixed methods (both qualitative and quantitative).

Step 3: Decide how you will collect the data

Once you know what kind of data you need, you need to decide how, where and when you will collect it.

This means you need to determine your research methods – the specific tools, procedures, materials and techniques you will use. You also need to specify what criteria you’ll use to select participants or sources, and how you will recruit or access them.

Research methods

Surveys

• How many respondents do you need and what sampling method will you use (e.g. simple random sampling or stratified sampling)?

• How will you distribute the survey (e.g. in person, by post, online)?

• How will you design the questionnaire (e.g. open or closed questions)?

Interviews

• How will you select participants?

• Where and when will the interviews take place?

• Will the interviews be structured, semi-structured or unstructured?

Experiments

• Will you conduct the experiment in a laboratory setting or in the field?

• How will you measure and control the variables?

• How will you design the experiment (e.g. between-subjects or within-subjects)?

Secondary data

• Where will you get your sources from (e.g. online or a physical archive)?

• What criteria will you use to select sources (e.g. date range, content)?

Step 4: Decide how you will analyze the data

To answer your research questions, you will have to analyze the data you collected. The final step in designing the research is to consider your data analysis methods.

Quantitative data analysis

To analyze numerical data, you will probably use statistical methods. These generally require applications such as Excel, SPSS or SAS.

Statistical methods can be used to analyze averages, frequencies, patterns, and correlations between variables. When creating your research design, you should clearly define your variables and formulate hypotheses about the relations between them. Then you can choose appropriate statistical methods to test these hypotheses.

Qualitative data analysis

Analyzing words or images is often a more flexible process that involves the researcher’s subjective judgement. You might focus on identifying and categorizing key themes, interpreting patterns and narratives, or understanding social context and meaning.

When creating your research design, you should consider what approach you will take to analyzing the data. The main themes and categories might only emerge after you have collected the data, but you need to decide what you want to achieve in the analysis.

For example, do you simply want to describe participants’ perceptions and experiences, or will you analyze the meaning of their responses in relation to a social context? Will your analysis focus only on what is said or also on how it is said?

Step 5: Write your research proposal

The research design is an important component of your dissertation or thesis proposal. It describes exactly what you plan to do and how you plan to do it, showing your supervisor that your project is both practically feasible and capable of answering your research questions.

Note that, in a proposal, the steps of your research that have yet to be completed should be written in the future tense. The research design or methodology section of your completed paper, on the other hand, describes the research steps in the past tense.

A research problem is a specific issue, difficulty, contradiction, or gap in knowledge that you will aim to address in your research. You might look for practical problems aimed at contributing to change, or theoretical problems aimed at expanding knowledge.

Bear in mind that some research will do both of these things, but usually the research problem focuses on one or the other. The type of research problem you choose depends on your broad topic of interest and the type of research you want to do.

When writing your research proposal or introduction, you will have to formulate it as a problem statement and/or research questions.

Why is the research problem important?

Your topic is interesting and you have lots to say about it, but this isn’t a strong enough basis for academic research. Without a well-defined research problem, you are likely to end up with an unfocused and unmanageable project.

You might end up repeating what other people have already said, trying to say too much, or doing research without a clear purpose and justification. You need a problem in order to do research that contributes new and relevant insights.

Whether you’re planning your thesis, starting a research paper or writing a research proposal, the research problem is the first step towards knowing exactly what you’ll do and why.

Step 1: Identify a broad problem area

As you discuss and read about your topic, look for under-explored aspects and areas of concern, conflict or controversy. Your goal is to find a gap that your research project can fill.

Practical research problems

If you are doing practical research, you can identify a problem by reading reports, following up on previous research, and talking to people who work in the relevant field or organization. You might look for:

• Issues with performance or efficiency in an organization

• Processes that could be improved in an institution

• Areas of concern among practitioners in a field

• Difficulties faced by specific groups of people in society

If your research is connected to a job or internship, you will need to find a research problem that has practical relevance for the organization.

Examples of practical research problems

• Voter turnout in region X has been decreasing, in contrast to the rest of the country.

• Department A of Company B has a high staff turnover rate, affecting productivity and team cohesion.

• Non-profit organization Y faces a funding gap that means some of its programs will have to be cut.

Theoretical research problems

Theoretical research focuses on expanding knowledge and understanding rather than directly contributing to change. You can identify a research problem by reading recent research, theory and debates on your topic to find a gap in what is currently known about it. You might look for:

• A phenomenon or context that has not been closely studied

• A contradiction between two or more perspectives

• A situation or relationship that is not well understood

• A troubling question that has yet to be resolved

Theoretical problems often have practical consequences, but they are not focused on solving an immediate issue in a specific place (though you might take a case study approach to the research).

Examples of theoretical research problems

• The effects of long-term Vitamin D deficiency on cardiovascular health are not well understood.

• The relationship between gender, race and income inequality has yet to be closely studied in the context of the millennial gig economy.

• Historians of Scottish nationalism disagree about the role of the British Empire in the development of Scotland’s national identity.

Step 2: Learn more about the problem

Next, you have to find out what is already known about the problem, and pinpoint the exact aspect that your research will address.

Context and background

• Who does the problem affect?

• Has it been an issue for a long time, or is it a newly discovered problem?

• What research has already been done?

• Have any solutions been proposed?

• What are the current debates about the problem, and what do you think is missing from them?

Specificity and relevance

• What particular place, time and/or people will you focus on?

• What aspects will you not be able to tackle?

• What will be the consequences if the problem is not resolved?

• Whose will benefit from resolving the problem (e.g. the management of an organization or future researchers)?

Example of a specific research problem

• Non-profit organization X has been focused on retaining its existing support base, but lacks understanding of how best to target potential new donors. To be able to continue its work, the organization requires research into more effective fundraising strategies.

When you have narrowed down your problem, the next step is to formulate a problem statement and research questions or hypotheses.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more things, you need to write hypotheses before you start your experiment or data collection.

What is a hypothesis?

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables. An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

• Daily apple consumption leads to fewer doctor’s visits.

In this example, the independent variable is apple consumption — the assumed cause. The dependent variable is the frequency of doctor’s visits — the assumed effect.

Developing a hypothesis

1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and re searchable within the constraints of your project.

• Do students who attend more lectures get better exam results?

2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them.

3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

• Attending more lectures leads to better exam results.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables

• The specific group being studied

• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

• If a first-year student starts attending more lectures, then their exam scores will improve.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

• The number of lectures attended by first-year students has a positive effect on their exam scores.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

• First-year students who attended most lectures will have better exam scores than those who attended few lectures.

6. Write a null hypothesis

If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H₀, while the alternative hypothesis is H₁ or H_a.

• H₀: The number of lectures attended by first-year students has no effect on their final exam scores.

• H₁: The number of lectures attended by first-year students has a positive effect on their final exam scores.

After you have identified a research problem for your project, the next step is to write a problem statement. An effective problem statement is concise and concrete. It should:

• Put the problem in context (what do we already know?)

• Describe the precise issue that the research will address (what do we need to know?)

• Show the relevance of the problem (why do we need to know it?)

• Set the objectives of the research (what will you do to find out?)

When should you write a problem statement?

There are various situations in which you might have to write a problem statement.

In businesses and other organizations, writing a problem statement is an important step in improvement projects. A clearly defined and well-understood problem is crucial to finding and implementing effective solutions. In this case, the problem statement is usually a stand-alone document.

In academic research, writing a problem statement can help you contextualize and understand the significance of your research problem. A problem statement can be several paragraphs long and serve as the basis for your research proposal, or it can be condensed into just a few sentences in the introduction of your paper or thesis.

The problem statement will look different depending on whether you’re dealing with a practical real-world problem or a theoretical scientific issue. But all problem statements follow a similar process.

Step 1: Contextualize the problem

The problem statement should frame your research problem in its particular context and give some background on what is already known about it.

Practical research problems

For practical research, focus on the concrete details of the situation:

• Where and when does the problem arise?

• Who does the problem affect?

• What attempts have been made to solve the problem?

Theoretical research problems

For theoretical research, think about the scientific, social, geographical and/or historical background:

• What is already known about the problem?

• Is the problem limited to a certain time period or geographical area?

• How has the problem been defined and debated in the scholarly literature?

Step 2: Show why it matters

The problem statement should also address the relevance of the research: why is it important that the problem is solved?

This doesn’t mean you have to do something groundbreaking or world-changing. It’s more important that the problem is re searchable, feasible, and clearly addresses a relevant issue in your field.

Practical research problems

Practical research is directly relevant to a specific problem that affects an organization, institution, social group, or society more broadly. To make it clear why your research problem matters, you can ask yourself:

• What will happen if the problem is not solved?

• Who will feel the consequences?

• Does the problem have wider relevance (e.g. are similar issues found in other contexts)?

Theoretical research problems

Sometimes theoretical issues have clear practical consequences, but sometimes their relevance is less immediately obvious. To identify why the problem matters, ask:

• How will resolving the problem advance understanding of the topic?

• What benefits will it have for future research?

• Does the problem have direct or indirect consequences for society?

Step 3: Set your aims and objectives

Finally, the problem statement should frame how you intend to address the problem. Your goal should not be to find a conclusive solution, but to seek out the reasons behind the problem and propose more effective approaches to tackling or understanding it.

The aim is the overall purpose of your research. It is generally written in the infinitive form:

• The aim of this study is to determine…

• This project aims to explore…

• I aim to investigate…

The objectives are the concrete steps you will take to achieve the aim:

• Qualitative methods will be used to identify…

• I will use surveys to collect…

• Using statistical analysis, the research will measure…

A research proposal describes what you will investigate, why it’s important, and how you will do the research. The format of a research proposal varies between fields, but most proposals should contain at least these elements:

• Cover page

• Introduction

• Literature review

• Research design

• Reference list

There may be some variation in how the sections are named or divided, but the overall goals are always the same.

Purpose of a research proposal

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal to get your thesis or dissertation plan approved.

All research proposals are designed to persuade someone — such as a funding body, educational institution, or supervisor — that your project is worthwhile.

Research proposal aims

Relevance: Convince the reader that your project is interesting, original and important

Context: Show that you are familiar with the field, you understand the current state of research on the topic, and your ideas have a strong academic basis

Approach: Make a case for your methodology, showing that you have carefully thought about the data, tools and procedures you will need to conduct the research

Feasibility: Confirm that the project is possible within the practical constraints of the programme, institution or funding

How long is a research proposal?

The length of a research proposal varies dramatically. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations and research funding are often very long and detailed.

Although you write it before you begin the research, the proposal’s structure usually looks like a shorter version of a thesis or dissertation (but without the results and discussion sections).

Title page

Like your dissertation or thesis, the proposal will usually have a title page that includes:

• The proposed title of your project

• Your name

• Your supervisor’s name

• The institution and department

Check with the department or funding body to see if there are any specific formatting requirements.

Abstract and table of contents

If your proposal is very long, you might also have to include an abstract and a table of contents to help the reader navigate the document.

Introduction

The first part of your proposal is the initial pitch for your project, so make sure it succinctly explains what you want to do and why. It should:

• Introduce the topic

• Give background and context

• Outline your problem statement and research question(s)

Some important questions to guide your introduction include:

• Who has an interest in the topic (e.g. scientists, practitioners, policymakers, particular members of society)?

• How much is already known about the problem?

• What is missing from current knowledge?

• What new insights will your research contribute?

• Why is this research worth doing?

If your proposal is very long, you might include separate sections with more detailed information on the background and context, problem statement, aims and objectives, and importance of the research.

Literature review

It’s important to show that you’re familiar with the most important research on your topic. A strong literature review convinces the reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said.

In this section, aim to demonstrate exactly how your project will contribute to conversations in the field.

• Compare and contrast: what are the main theories, methods, debates and controversies?

• Be critical: what are the strengths and weaknesses of different approaches?

• Show how your research fits in: how will you build on, challenge, or synthesize the work of others?

Research design and methods

Following the literature review, it’s a good idea to restate your main objectives, bringing the focus back to your own project. The research design or methodology section should describe the overall approach and practical steps you will take to answer your research questions.

Methodology in a research proposal

Research type

• Will you do qualitative or quantitative research?

• Will you collect original data or work with primary or secondary sources?

• Is your research design descriptive, correlational, or experimental?

Sources

• Exactly what or who will you study (e.g. high school students in New York; Scottish newspaper archives 1976-80)?

• How will you select subjects or sources (e.g. random sampling, case studies)?

• When and where will you collect the data?

Research methods

• What tools and procedures will you use (e.g. surveys, interviews, observations, experiments) to collect and analyze data?

• Why are these the best methods to answer your research questions?

Practicalities

• How much time will you need to collect the data?

• How will you gain access to participants or sources?

• Do you foresee any potential obstacles, and how will you address them?

Make sure not to simply write a list of methods. Aim to make an argument for why this is the most appropriate, valid and reliable approach to answering your questions.

Implications and contribution to knowledge

To finish your proposal on a strong note, you can explore the potential implications of the research for theory or practice, and emphasize again what you aim to contribute to existing knowledge on the topic. For example, your results might have implications for:

• Improving processes in a specific location or field

• Informing policy objectives

• Strengthening a theory or model

• Challenging popular or scientific assumptions

• Creating a basis for further research

Reference list or bibliography

Your research proposal must include proper citations for every source you have used, and full publication details should always be included in the reference list.

In some cases, you might be asked to include a bibliography. This is a list of all the sources you consulted in preparing the proposal, even ones you did not cite in the text, and sometimes also other relevant sources that you plan to read. The aim is to show the full range of literature that will support your research project.

Research schedule

In some cases, you might have to include a detailed timeline of the project, explaining exactly what you will do at each stage and how long it will take. Check the requirements of your programme or funding body to see if this is required.

Research phase and objective

1. Background research and literature review

• Meet with supervisor for initial discussion

• Conduct a more extensive review of relevant literature

• Refine the research questions

• Develop a theoretical framework

2. Research design planning

• Design questionnaires

• Identify online and offline channels for recruiting participants

• Finalize sampling methods and data analysis methods

3. Data collection and preparation

• Recruit participants and send out questionnaires

• Conduct semi-structured interviews with selected participants

• Transcribe and code interviews and clean survey data

4. Data analysis

• Statistically analyze survey data

• Conduct thematic analysis of interview transcripts

• Draft the results and discussion chapters

5. Writing

• Complete a full thesis draft

• Meet with supervisor to discuss feedback and revisions

6. Revision

• Redraft based on feedback

• Get supervisor approval for final draft

• Proofread

• Print, bind and submit

Budget

If you are applying for research funding, you will probably also have to include a detailed budget that shows how much each part of the project will cost.

Make sure to check what type of costs the funding body will agree to cover, and only include relevant items in your budget. For each item, include:

• Cost: exactly how much money do you need?

• Justification: why is this cost necessary to complete the research?

• Source: how did you calculate the amount?

To determine your budget, think about:

• Travel costs: do you need to go to specific locations to collect data? How will you get there, how long will you spend there, and what will you do there (e.g. interviews, archival research)?

• Materials: do you need access to any tools or technologies? Are there training or installation costs?

• Assistance: do you need to hire research assistants for the project? What will they do and how much will you pay them? Will you outsource any other tasks such as transcription?

• Time: do you need to take leave from regular duties such as teaching? How much will you need to cover the time spent on the research?

Revisions and Proofreading

As in any other piece of academic writing, it’s essential to redraft, edit and proofread your research proposal before you submit it. If you have the opportunity, ask a supervisor or colleague for feedback.

For the best chance of approval, you might want to consider using a professional proofreading service to get rid of language errors, check your proposal’s structure, and improve your academic style.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods, but quantitative methods are sometimes also used. Case studies are good for describing, comparing, evaluating and understanding different aspects of a research problem.

When to do a case study

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation. They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Step 1: Select a case

Once you have developed your problem statement and research questions, you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject

• Challenge or complicate existing assumptions and theories

• Propose practical courses of action to resolve a problem

• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Step 2: Build a theoretical framework

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation

• Expand on a theory by uncovering new concepts and ideas that need to be incorporated

• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework. This means identifying key concepts and theories to guide your analysis and interpretation.

Step 3: Collect your data

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g. newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Step 4: Describe and analyze the case

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods, results and discussion.

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

1. Determine who will participate in the survey

2. Decide the type of survey (mail, online, or in-person)

3. Design the survey questions and layout

4. Distribute the survey

5. Analyze the responses

6. Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research.

What are surveys used for?

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: investigating the experiences and characteristics of different social groups

• Market research: finding out what customers think about products, services, and companies

• Health research: collecting data from patients about symptoms and treatments

• Politics: measuring public opinion about parties and policies

• Psychology: researching personality traits, preferences and behaviors

Surveys can be used in both cross-sectional studies, where you collect data just once, and in longitudinal studies, where you survey the same sample several times over an extended period.

Step 1: Define the population and sample

Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of India

• Maharashtra college students

• Second-generation immigrants in the Netherlands

• Customers of a specific company aged 24-30

• British transgender women over the age of 55

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Samples

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Maharashtra or every college student in the India. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

Step 2: Decide on the type of survey

There are two main types of survey:

• A questionnaire, where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.

• An interview, where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Mail:

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.

• You have some control over who is included in the sample (e.g. residents of a specific region).

• The response rate is often low.

Online:

Online surveys are a popular choice for students doing dissertation research, due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms.

• You can quickly access a large sample without constraints on time or location.

• The data is easy to process and analyze.

• The anonymity and accessibility of online surveys mean you have less control over who responds.

In-person:

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.

• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).

• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Interviews

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.

• You can clarify questions and ask for follow-up information when necessary.

• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data: the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Step 3: Design the survey questions

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions

• The content of the questions

• The phrasing of the questions

• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree)

• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree)

• A list of options with a single answer possible (e.g. age categories)

• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research. They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations.

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Step 4: Distribute the survey and collect responses

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

Step 5: Analyze the survey results

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis, which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Step 6: Write up the survey results

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation, or research paper.

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion, you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

Thematic analysis is a method of analyzing qualitative data. It is usually applied to a set of texts, such as interview transcripts. The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process:

1. Familiarization

2. Coding

3. Generating themes

4. Reviewing themes

5. Defining and naming themes

6. Writing up

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke. However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

When to use thematic analysis

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts, social media profiles, or survey responses.

Some types of research questions you might use thematic analysis to answer:

• How do patients perceive doctors in a hospital setting?

• What are young women’s experiences on dating sites?

• What are non-experts’ ideas and opinions about climate change?

• How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

Different approaches to thematic analysis

Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

• An inductive approach involves allowing the data to determine your themes.

• A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

There’s also the distinction between a semantic and a latent approach:

• A semantic approach involves analyzing the explicit content of the data.

• A latent approach involves reading into the subtext and assumptions underlying the data.

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke.

Step 1: Familiarization

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio, reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Step 2: Coding

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

Coding qualitative data

Interview extract

Personally, I’m not sure. I think the climate is changing, sure, but I don’t know why or how. People say you should trust the experts, but who’s to say they don’t have their own reasons for pushing this narrative? I’m not saying they’re wrong, I’m just saying there’s reasons not to 100% trust them. The facts keep changing – it used to be called global warming.

Codes

• Uncertainty

• Acknowledgement of climate change

• Distrust of experts

• Changing terminology

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

Step 3: Generating themes

Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

Turning codes into themes

Codes:

• Uncertainty (Theme: Uncertainty)

• Leave it to the experts (Theme: Uncertainty)

• Alternative explanations (Theme: Uncertainty)

• Changing terminology (Distrust of experts)

• Distrust of scientists (Distrust of experts)

• Resentment toward experts (Distrust of experts)

• Fear of government control (Distrust of experts)

• Incorrect facts (Misinformation)

• Misunderstanding of science (Misinformation)

• Biased media sources (Misinformation)

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Step 4: Reviewing themes

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Step 5: Defining and naming themes

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Step 6: Writing up

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

Systematic sampling is a probability sampling method in which researchers select members of the population at a regular interval (or k) determined in advance.

If the population order is random or random-like (e.g., alphabetical), then this method will give you a representative sample that can be used to draw conclusions about the population.

When to use systematic sampling

Systematic sampling is a method that imitates many of the randomization benefits of simple random sampling, but is slightly easier to conduct.

You can use systematic sampling with a list of the entire population, as in simple random sampling. However, unlike with simple random sampling, you can also use this method when you’re unable to access a list of your population in advance.

Order of the population

When using systematic sampling with a population list, it’s essential to consider the order in which your population is listed to ensure that your sample is valid.

If your population is in ascending or descending order, using systematic sampling should still give you a fairly representative sample, as it will include participants from both the bottom and top ends of the population.

For example, if you are sampling from a list of individuals ordered by age, systematic sampling will result in a population drawn from the entire age spectrum. If you instead used simple random sampling, it is possible (although unlikely) that you would end up with only younger or older individuals.

You should not use systematic sampling if your population is ordered cyclically or periodically, as your resulting sample cannot be guaranteed to be representative.

Example: Alternating list

Your population list alternates between men (on the even numbers) and women (on the odd numbers). You choose to sample every tenth individual, which will therefore result in only men being included in your sample. This would obviously be unrepresentative of the population.

Example: Cyclically ordered list

You are sampling from a population list of approximately 1000 hospital patients. The list is divided into 50 departments of around 20 patients each. Within each department, the list is ordered by age, from youngest to oldest. This results in a list of 20 repeated age cycles.

If you sample every 20th individual, because each department is ordered by age, your population will consist of the oldest person in each one. This will most likely not provide a representative sample of the entire hospital population.

Systematic sampling without a population list

You can use systematic sampling to imitate the randomization of simple random sampling when you don’t have access to a full list of the population in advance.

Research example

You run a department store and are interested in how you can improve the store experience for your customers. To investigate this question, you ask an employee to stand by the store entrance and survey every 20th visitor who leaves, every day for a week.

Although you do not necessarily have a list of all your customers ahead of time, this method should still provide you with a representative sample of your customers since their order of exit is essentially random.

Step 1: Define your population

Like other methods of sampling, you must decide upon the population that you are studying.

In systematic sampling, you have two choices for data collection:

• You can select your sample ahead of time from a list and then approach the selected subjects to collect data, or

• You can approach every kth member of your target population to ask them to participate in your study.

Listing the population in advance

Ensure that your list contains the entire population and is not in a periodic or cyclic order. Ideally, it should be in a random or random-like (such as alphabetical) order, which will allow you to imitate the randomization benefits of simple random sampling.

Example: Listing the population

In your department store study, your customers make up your target population. To create your sample ahead of time, you would need to create a list of every customer who visited your store in the last week.

However, creating such a list would be difficult, if not entirely impossible. You could choose to use receipts to create your list, but this would exclude any non-buying customers, which would most likely bias your results.

Selecting your sample on the spot

If you cannot access a list in advance, but you are able to physically observe the population, you can also use systematic sampling to select subjects at the moment of data collection.

In this case, ensure that the timing and location of your sampling procedure covers the full population to avoid bias in the results.

Example: Sampling on the spot

As you cannot get a complete list of your store’s customers, you instead choose to sample every kth customer as they exit the

store.This allows you to include both those who buy items and those who do not.

You must ensure that you are sampling throughout the entire week to ensure a representative sample, because different types of customers enter at different times and days: Teenagers usually shop after school and on the weekends, while working professionals might shop later in the evening and stay-at-home parents during the day.

Step 2: Decide on your sample size and sampling interval

Before you choose your interval, you must first decide on your sample size. There are several different ways to choose a sample size, but one of the most common involves using a sample size calculator.

Once you have chosen your desired margin of error and confidence level, estimated total size of the population, and the standard deviation of the variables you are attempting to measure, this calculator will provide you with the sample size you should aim for.

When you know your target sample size, you can calculate your interval, k, by dividing your total estimated population size by your sample size. This can be a rough estimate rather than an exact calculation.

Sample size and sampling interval

Although you do not know exactly how many people will visit your store ahead of time, you can estimate the total population by using average of the prior few weeks’ foot traffic.

You estimate that around 7500 people visit your store each week, and based on this estimate you calculate an ideal sample size of 366. Your sampling interval k thus equals 7500/366 = 20.49, which you round to 20.

Step 3: Select the sample and collect data

If you already have a list of your population, randomly select a starting point on your list, and from there, select every kth member of the population to include in your sample.

If you don’t have a list, you choose every kth member of the population for your sample at the same time as collecting the data for your study.

As in simple random sampling, you should try to make sure every individual you have chosen for your sample actually participates in your study. If those who decide to participate do so for reasons connected with the variables that you are collecting, this could bias your study.

Example: Data collection

You choose an employee to stand by the door and survey every 20th customer who leaves. It is important that as many as possible of those chosen for the sample decide to participate; otherwise, your results may not properly reflect the opinions of the overall population.

For instance, those with particularly good or bad opinions of the store may be more willing to participate than the general customer population, thus biasing the results of your survey.