what is theme in research paper

What Is the Theme of a Research Paper?

How to Write a Motif Paper

The term "theme" is a small word, but it can intimidate students when they see it on an assignment or test. To overcome the fear and develop confidence, especially with regard to research papers, understand what the word means and see the parallels with any work, including poems, essays, plays, novels and movies.

“Theme” Defined

A theme is a major and sometimes recurring idea, subject or topic that appears in a written work. A dominant theme usually reveals what the work is really about and can be helpful in forming insights and analysis. A theme can consist of one word, two words or more. For example, your teacher might ask you to explore the straightforward ideas of “anger” or “selfishness” or more complex themes of “emotional intelligence” or “conflicted emotions.” Either way, careful reading of the work is vital so that you can marshal examples of where the theme was apparent.

Examples in Research

Themes in research papers might require a little digging, but they are there. Sometimes they are easier to spot when several research papers on the same subject are compared or contrasted, for this is when such subtext emerges. For example, three research papers on the subject of avid TV viewing by teenagers might contain different themes, such as simpler ideas including “passivity” or "grades" or a more complex theme, such as “effects on familial relationships.”

Seize the Opportunity

Once you've identified the theme of a research paper or papers, seize the opportunity and analyze it. Say that you like the idea of exploring how avid TV viewing -- more than four hours per day -- affects teens' grades. Further, suppose that researchers are in general agreement about the correlation but cast a wide net in terms of how they define “passivity.” You might set up a thematic segue for a research paper by saying, “Researchers continue to debate how to define passivity in teens and reach across the spectrum to include the number of hours per day they spend in solitude, the number of people they count as close friends and their lack of interest in hobbies and extracurricular activities.” Then you would take each of these ideas and expound in greater detail.

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With education, health care and small business marketing as her core interests, M.T. Wroblewski has penned pieces for Woman's Day, Family Circle, Ladies Home Journal and many newspapers and magazines. She holds a master's degree in journalism from Northern Illinois University.

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• How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or 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: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

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.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

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.

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See an example

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.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

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.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

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 .

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.

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:

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.

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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:

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.

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.

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”.

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.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Discourse analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

• Great Research/Sources
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If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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• How to Do Thematic Analysis | Guide & Examples

How to Do Thematic Analysis | Guide & Examples

Published on 5 May 2022 by Jack Caulfield . Revised on 7 June 2024.

Thematic analysis is a method of analysing qualitative data . It is usually applied to a set of texts, such as an interview or 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:

• Familiarisation
• Generating themes
• Reviewing themes
• Defining and naming themes

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.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarisation, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up.

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 secondary school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyse it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large datasets 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.

Prevent plagiarism, run a free check.

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 analysing 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 analysing 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 .

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 analysing 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.

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:

In this extract, we’ve highlighted various phrases in different colours 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 condensed overview of the main points and common meanings that recur throughout the data.

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:

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.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the dataset 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.

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’.

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.

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Caulfield, J. (2024, June 07). How to Do Thematic Analysis | Guide & Examples. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/research-methods/thematic-analysis-explained/

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Organizing Your Social Sciences Research Paper

• 4. The Introduction
• Purpose of Guide
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The introduction leads the reader from a general subject area to a particular topic of inquiry. It establishes the scope, context, and significance of the research being conducted by summarizing current understanding and background information about the topic, stating the purpose of the work in the form of the research problem supported by a hypothesis or a set of questions, explaining briefly the methodological approach used to examine the research problem, highlighting the potential outcomes your study can reveal, and outlining the remaining structure and organization of the paper.

Key Elements of the Research Proposal. Prepared under the direction of the Superintendent and by the 2010 Curriculum Design and Writing Team. Baltimore County Public Schools.

Importance of a Good Introduction

Think of the introduction as a mental road map that must answer for the reader these four questions:

• What was I studying?
• Why was this topic important to investigate?
• What did we know about this topic before I did this study?
• How will this study advance new knowledge or new ways of understanding?

According to Reyes, there are three overarching goals of a good introduction: 1) ensure that you summarize prior studies about the topic in a manner that lays a foundation for understanding the research problem; 2) explain how your study specifically addresses gaps in the literature, insufficient consideration of the topic, or other deficiency in the literature; and, 3) note the broader theoretical, empirical, and/or policy contributions and implications of your research.

A well-written introduction is important because, quite simply, you never get a second chance to make a good first impression. The opening paragraphs of your paper will provide your readers with their initial impressions about the logic of your argument, your writing style, the overall quality of your research, and, ultimately, the validity of your findings and conclusions. A vague, disorganized, or error-filled introduction will create a negative impression, whereas, a concise, engaging, and well-written introduction will lead your readers to think highly of your analytical skills, your writing style, and your research approach. All introductions should conclude with a brief paragraph that describes the organization of the rest of the paper.

Hirano, Eliana. “Research Article Introductions in English for Specific Purposes: A Comparison between Brazilian, Portuguese, and English.” English for Specific Purposes 28 (October 2009): 240-250; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide. Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education.

Structure and Writing Style

I.  Structure and Approach

The introduction is the broad beginning of the paper that answers three important questions for the reader:

• What is this?
• Why should I read it?
• What do you want me to think about / consider doing / react to?

Think of the structure of the introduction as an inverted triangle of information that lays a foundation for understanding the research problem. Organize the information so as to present the more general aspects of the topic early in the introduction, then narrow your analysis to more specific topical information that provides context, finally arriving at your research problem and the rationale for studying it [often written as a series of key questions to be addressed or framed as a hypothesis or set of assumptions to be tested] and, whenever possible, a description of the potential outcomes your study can reveal.

These are general phases associated with writing an introduction: 1.  Establish an area to research by:

• Highlighting the importance of the topic, and/or
• Making general statements about the topic, and/or
• Presenting an overview on current research on the subject.

2.  Identify a research niche by:

• Opposing an existing assumption, and/or
• Revealing a gap in existing research, and/or
• Formulating a research question or problem, and/or
• Continuing a disciplinary tradition.

3.  Place your research within the research niche by:

• Stating the intent of your study,
• Outlining the key characteristics of your study,
• Describing important results, and
• Giving a brief overview of the structure of the paper.

NOTE:   It is often useful to review the introduction late in the writing process. This is appropriate because outcomes are unknown until you've completed the study. After you complete writing the body of the paper, go back and review introductory descriptions of the structure of the paper, the method of data gathering, the reporting and analysis of results, and the conclusion. Reviewing and, if necessary, rewriting the introduction ensures that it correctly matches the overall structure of your final paper.

II.  Delimitations of the Study

Delimitations refer to those characteristics that limit the scope and define the conceptual boundaries of your research . This is determined by the conscious exclusionary and inclusionary decisions you make about how to investigate the research problem. In other words, not only should you tell the reader what it is you are studying and why, but you must also acknowledge why you rejected alternative approaches that could have been used to examine the topic.

Obviously, the first limiting step was the choice of research problem itself. However, implicit are other, related problems that could have been chosen but were rejected. These should be noted in the conclusion of your introduction. For example, a delimitating statement could read, "Although many factors can be understood to impact the likelihood young people will vote, this study will focus on socioeconomic factors related to the need to work full-time while in school." The point is not to document every possible delimiting factor, but to highlight why previously researched issues related to the topic were not addressed.

Examples of delimitating choices would be:

• The key aims and objectives of your study,
• The research questions that you address,
• The variables of interest [i.e., the various factors and features of the phenomenon being studied],
• The method(s) of investigation,
• The time period your study covers, and
• Any relevant alternative theoretical frameworks that could have been adopted.

Review each of these decisions. Not only do you clearly establish what you intend to accomplish in your research, but you should also include a declaration of what the study does not intend to cover. In the latter case, your exclusionary decisions should be based upon criteria understood as, "not interesting"; "not directly relevant"; “too problematic because..."; "not feasible," and the like. Make this reasoning explicit!

NOTE:   Delimitations refer to the initial choices made about the broader, overall design of your study and should not be confused with documenting the limitations of your study discovered after the research has been completed.

ANOTHER NOTE: Do not view delimitating statements as admitting to an inherent failing or shortcoming in your research. They are an accepted element of academic writing intended to keep the reader focused on the research problem by explicitly defining the conceptual boundaries and scope of your study. It addresses any critical questions in the reader's mind of, "Why the hell didn't the author examine this?"

III.  The Narrative Flow

Issues to keep in mind that will help the narrative flow in your introduction :

• Your introduction should clearly identify the subject area of interest . A simple strategy to follow is to use key words from your title in the first few sentences of the introduction. This will help focus the introduction on the topic at the appropriate level and ensures that you get to the subject matter quickly without losing focus, or discussing information that is too general.
• Establish context by providing a brief and balanced review of the pertinent published literature that is available on the subject. The key is to summarize for the reader what is known about the specific research problem before you did your analysis. This part of your introduction should not represent a comprehensive literature review--that comes next. It consists of a general review of the important, foundational research literature [with citations] that establishes a foundation for understanding key elements of the research problem. See the drop-down menu under this tab for " Background Information " regarding types of contexts.
• Clearly state the hypothesis that you investigated . When you are first learning to write in this format it is okay, and actually preferable, to use a past statement like, "The purpose of this study was to...." or "We investigated three possible mechanisms to explain the...."
• Why did you choose this kind of research study or design? Provide a clear statement of the rationale for your approach to the problem studied. This will usually follow your statement of purpose in the last paragraph of the introduction.

IV.  Engaging the Reader

A research problem in the social sciences can come across as dry and uninteresting to anyone unfamiliar with the topic . Therefore, one of the goals of your introduction is to make readers want to read your paper. Here are several strategies you can use to grab the reader's attention:

• Open with a compelling story . Almost all research problems in the social sciences, no matter how obscure or esoteric , are really about the lives of people. Telling a story that humanizes an issue can help illuminate the significance of the problem and help the reader empathize with those affected by the condition being studied.
• Include a strong quotation or a vivid, perhaps unexpected, anecdote . During your review of the literature, make note of any quotes or anecdotes that grab your attention because they can used in your introduction to highlight the research problem in a captivating way.
• Pose a provocative or thought-provoking question . Your research problem should be framed by a set of questions to be addressed or hypotheses to be tested. However, a provocative question can be presented in the beginning of your introduction that challenges an existing assumption or compels the reader to consider an alternative viewpoint that helps establish the significance of your study. 
• Describe a puzzling scenario or incongruity . This involves highlighting an interesting quandary concerning the research problem or describing contradictory findings from prior studies about a topic. Posing what is essentially an unresolved intellectual riddle about the problem can engage the reader's interest in the study.
• Cite a stirring example or case study that illustrates why the research problem is important . Draw upon the findings of others to demonstrate the significance of the problem and to describe how your study builds upon or offers alternatives ways of investigating this prior research.

NOTE:   It is important that you choose only one of the suggested strategies for engaging your readers. This avoids giving an impression that your paper is more flash than substance and does not distract from the substance of your study.

Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. University College Writing Centre. University of Toronto; Introduction. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Introductions. The Writing Center. University of North Carolina; Introductions. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Introductions, Body Paragraphs, and Conclusions for an Argument Paper. The Writing Lab and The OWL. Purdue University; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Resources for Writers: Introduction Strategies. Program in Writing and Humanistic Studies. Massachusetts Institute of Technology; Sharpling, Gerald. Writing an Introduction. Centre for Applied Linguistics, University of Warwick; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Swales, John and Christine B. Feak. Academic Writing for Graduate Students: Essential Skills and Tasks . 2nd edition. Ann Arbor, MI: University of Michigan Press, 2004 ; Writing Your Introduction. Department of English Writing Guide. George Mason University.

Writing Tip

Avoid the "Dictionary" Introduction

Giving the dictionary definition of words related to the research problem may appear appropriate because it is important to define specific terminology that readers may be unfamiliar with. However, anyone can look a word up in the dictionary and a general dictionary is not a particularly authoritative source because it doesn't take into account the context of your topic and doesn't offer particularly detailed information. Also, placed in the context of a particular discipline, a term or concept may have a different meaning than what is found in a general dictionary. If you feel that you must seek out an authoritative definition, use a subject specific dictionary or encyclopedia [e.g., if you are a sociology student, search for dictionaries of sociology]. A good database for obtaining definitive definitions of concepts or terms is Credo Reference .

Saba, Robert. The College Research Paper. Florida International University; Introductions. The Writing Center. University of North Carolina.

Another Writing Tip

When Do I Begin?

A common question asked at the start of any paper is, "Where should I begin?" An equally important question to ask yourself is, "When do I begin?" Research problems in the social sciences rarely rest in isolation from history. Therefore, it is important to lay a foundation for understanding the historical context underpinning the research problem. However, this information should be brief and succinct and begin at a point in time that illustrates the study's overall importance. For example, a study that investigates coffee cultivation and export in West Africa as a key stimulus for local economic growth needs to describe the beginning of exporting coffee in the region and establishing why economic growth is important. You do not need to give a long historical explanation about coffee exports in Africa. If a research problem requires a substantial exploration of the historical context, do this in the literature review section. In your introduction, make note of this as part of the "roadmap" [see below] that you use to describe the organization of your paper.

Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70.

Yet Another Writing Tip

Always End with a Roadmap

The final paragraph or sentences of your introduction should forecast your main arguments and conclusions and provide a brief description of the rest of the paper [the "roadmap"] that let's the reader know where you are going and what to expect. A roadmap is important because it helps the reader place the research problem within the context of their own perspectives about the topic. In addition, concluding your introduction with an explicit roadmap tells the reader that you have a clear understanding of the structural purpose of your paper. In this way, the roadmap acts as a type of promise to yourself and to your readers that you will follow a consistent and coherent approach to addressing the topic of inquiry. Refer to it often to help keep your writing focused and organized.

Cassuto, Leonard. “On the Dissertation: How to Write the Introduction.” The Chronicle of Higher Education , May 28, 2018; Radich, Michael. A Student's Guide to Writing in East Asian Studies . (Cambridge, MA: Harvard University Writing n. d.), pp. 35-37.

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Themes, Theories, and Models

Affiliation.

• 1 1 Portland State University, Portland, Oregon, USA.
• PMID: 29313466
• DOI: 10.1177/1049732317750127

Although theory building is often described as the ultimate goal of qualitative research, an examination of articles in Qualitative Health Research ( QHR) shows that themes are actually the typical format for reporting results. In addition, articles that rely on themes often present low-level theories in the form of models that connect these themes. Because models have received less attention than either themes or theories, this article concentrates on summarizing four different kinds of models: hierarchies, timelines, processes, and cycles. In each of these cases, it presents both a general illustration of such a model and a realistic example from a published article in QHR. It concludes with a call for greater recognition of the role that models play in capturing the results of qualitative research.

Keywords: methodology; qualitative analysis; reflexivity; theory development.

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Topical themes and thematic progression: the “picture” of research articles

Leong Ping Alvin lectures at the Language and Communication Centre, Nanyang Technological University, Singapore. He obtained his PhD degree from the National University of Singapore in 2001 under a research scholarship. His book-length publications include Transforming Literacies and Language (co-editor; Continuum, 2011) and Theme and Rheme (Peter Lang, 2004). His research interests are in systemic-functional grammar, discourse analysis, and literacy studies.

Although much has been written about the features of academic writing, there is a lack of research attention on macro issues related to the development of ideas, particularly in the writing of research articles. A concept that is useful in investigating such issues is the Hallidayan notion of theme. However, the thematic structure of research articles has received only modest attention over the years. It is also rare for thematic diagrams to be used even though they can be helpful in clarifying the thematic structure of the text. In this exploratory study, the patterning of topical themes in research articles was investigated using a diagrammatic approach. Twenty biology-related research articles were divided into t-units and analyzed for topical themes. Thematic diagrams were generated for all the articles. The diagrams revealed a progressive thematic pattern in the introduction sections of all the articles. At the whole-text level, an anchored-development pattern was observed in the majority of the articles. These findings suggest that research articles at the macro level share similarities in their thematic structure. They also shed light on how authors achieve focus in the writing through the systematic use of clause-initial elements.

About the author

Appendix: articles analyzed in the study, articles from database.

Hishigaki, Haretsugu & Satoru Kuhara. 2011. hERGAPDbase: A database documenting hERG channel inhibitory potentials and APD-prolongation activities of chemical compounds. Database 2011. doi: 10.1093/database/bar017.

Green, Jason M., Jaturon Harnsomburana, Mary L. Schaeffer, Carolyn J. Lawrence & Chi-Ren Shyu. 2011. Multi-source and ontology-based retrieval engine for maize mutant phenotypes. Database 2011. doi: 10.1093/database/bar012.

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Visendi, Paul, Wanjiku Ng’ang’a, Wallace Bulimo, Richard Bishop, James Ochanda & Etienne P. de Villiers. 2011. TparvaDB: A database to support Theileria parva vaccine development. Database 2011. doi: 10.1093/database/bar015.

Williams, G. W., P. A. Davis, A. S. Rogers, T. Bieri, P. Ozersky & J. Spieth. 2011. Methods and strategies for gene structure curation in WormBase. Database 2011. doi: 10.1093/database/baq039.

Blankenberg, Daniel, Nathan Coraor, Gregory Von Kuster, James Taylor & Anton Nekrutenko. 2011. Integrating diverse databases into an unified analysis framework: A Galaxy approach. Database 2011. doi: 10.1093/database/bar011.

Vellozo, Augusto F., Amélie S. Véron, Patrice Baa-Puyoulet, Jaime Huerta-Cepas, Ludovic Cottret, Gérard Febvay, Federica Calevro, Yvan Rahbé, Angela E. Douglas, Toni Gabaldón, Marie-France Sagot, Hubert Charles & Stefano Colella. 2011. CycADS: An annotation database system to ease the development and update of BioCyc databases. Database 2011. doi: 10.1093/database/bar008.

Magrane, Michele & UniProt Consortium. 2011. UniProt Knowledgebase: A hub of integrated protein data. Database 2011. doi: 10.1093/database/bar009.

Costanzo, Maria C., Julie Park, Rama Balakrishnan, J. Michael Cherry & Eurie L. Hong. 2011. Using computational predictions to improve literature-based Gene Ontology annotations: A feasibility study. Database 2011. doi: 10.1093/database/bar004.

Bluhm, Wolfgang F., Bojan Beran, Chunxiao Bi, Dimitris Dimitropoulos, Andreas Prlić, Gregory B. Quinn, Peter W. Rose, Chaitali Shah, Jasmine Young, Benjamin Yukich, Helen M. Berman & Philip E. Bourne. 2011. Quality assurance for the query and distribution systems of the RCSB protein data bank. Database 2011. doi: 10.1093/database/bar003.

Articles from DNA Research

Chi, Yunhua, Yansong Cheng, Jeevanandam Vanitha, Nadimuthu Kumar, Rengasamy Ramamoorthy, Srinivasan Ramachandran & Shu-Ye Jiang. 2011. Expansion mechanisms and functional divergence of the glutathione s-transferase family in sorghum and other higher plants. DNA Research 18(1). 1–16.

Le, Dung Tien, Rie Nishiyama, Yasuko Watanabe, Keiichi Mochida, Kazuko Yamaguchi-Shinozaki, Kazuo Shinozaki & Lam-Son Phan Tran. 2011. Genome-wide expression profiling of soybean two-component system genes in soybean root and shoot tissues under dehydration stress. DNA Research 18(1). 17–29.

Kenny, Elaine M., Paul Cormican, William P. Gilks, Amy S. Gates, Colm T. O’Dushlaine, Carlos Pinto, Aiden P. Corvin, Michael Gill & Derek W. Morris. 2011. Multiplex target enrichment using DNA indexing for ultra-high throughput SNP detection. DNA Research 18(1). 31–38.

Satbhai, Santosh B., Takafumi Yamashino, Ryo Okada, Yuji Nomoto, Takeshi Mizuno, Yuki Tezuka, Tomonori Itoh, Mitsuru Tomita, Susumu Otsuki & Setsuyuki Aoki. 2011. Pseudo-response regulator (PRR) homologues of the moss physcomitrella patens: Insights into the evolution of the PRR family in land plants. DNA Research 18(1). 39–52.

Garg, Rohini, Ravi K. Patel, Akhilesh K. Tyagi & Mukesh Jain. 2011. De novo assembly of chickpea transcriptome using short reads for gene discovery and marker identification. DNA Research 18(1). 53–63.

Sato, Shusei, Hideki Hirakawa, Sachiko Isobe, Eigo Fukai, Akiko Watanabe, Midori Kato, Kumiko Kawashima, Chiharu Minami, Akiko Muraki, Naomi Nakazaki, Chika Takahashi, Shinobu Nakayama, Yoshie Kishida, Mitsuyo Kohara, Manabu Yamada, Hisano Tsuruoka, Shigemi Sasamoto, Satoshi Tabata, Tomoyuki Aizu, Atsushi Toyoda, Tadasu Shin-i, Yohei Minakuchi, Yuji Kohara, Asao Fujiyama, Suguru Tsuchimoto, Shin’ichiro Kajiyama, Eri Makigano, Nobuko Ohmido, Nakako Shibagaki, Joyce A. Cartagena, Naoki Wada, Tsutomu Kohinata, Alipour Atefeh, Shota Yuasa, Sachihiro Matsunaga & Kiichi Fukui. 2011. Sequence analysis of the genome of an oil-bearing tree, Jatropha Curcas L. DNA Research 18(1). 65–76.

Gourcilleau, Delphine, Catherine Lenne, Claudia Armenise, Bruno Moulia, Jean-Louis Julien, Gisèle Bronner & Nathalie Leblanc-Fournier. 2011. Phylogenetic study of plant q-type C2H2 zinc finger proteins and expression analysis of poplar genes in response to osmotic, cold, and mechanical stresses. DNA Research 18(2). 77–92.

Doorduin, Leonie, Barbara Gravendeel, Youri Lammers, Yavuz Ariyurek, Thomas Chin-A-Woeng & Klaas Vrieling. 2011. The complete chloroplast genome of 17 individuals of pest species Jacobaea vulgaris : SNPs, microsatellites and barcoding markers for population and phylogenetic studies. DNA Research 18(2). 93–105.

Sayama, Takashi, Tae-Young Hwang, Kunihiko Komatsu, Yoshitake Takada, Masakazu Takahashi, Shin Kato, Hiroko Sasama, Ayako Higashi, Yumi Nakamoto, Hideyuki Funatsuki & Masao Ishimoto. 2011. Development and application of a whole-genome simple sequence repeat panel for high-throughput genotyping in soybean. DNA Research 18(2). 107–115.

Lamprea-Burgunder, Estelle, Philipp Ludin & Pascal Mäser. 2011. Species-specific typing of DNA based on palindrome frequency patterns. DNA Research 18(2). 117–124.

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Journal and Issue

Articles in the same issue.

The Lesson Study Group

at Mills College

Develop a Research Theme

Access the teaching-learning plan, choose a topic.

10-30 minutes

• Develop or revisit your your long-term goals for student development
• Develop a draft of your “theory of action”—the approaches you will explore to build your long-term goals

Identify Your Long-Term Goals

The building blocks of your theme.

A research theme expresses the long-term goals of your work. If your team or school has already developed a research theme, revisit it now to refresh your memory about your long-term goals and ideas about how to get there.

Begin by having team members individually jot down qualities in response to the following prompt:

• Ideally, what qualities do we hope students will have when they graduate from our school? ( If we bumped into our students in 5-10 years, what qualities do we hope they would have?)

Now, again working individually, spend a few minutes jotting down a list of qualities in response to a second prompt:

• What are the current qualities of our students? (For example, what qualities of our students inspire us? Anything that concerns us?)

Again, share your individual lists and write all the qualities on a second list labeled “Current.”

Compare the two lists–ideal and current–and notice gaps that really speak to you as educators. Find one or two gaps where you would like to invest your time and energy.

Your research theme positively states the qualities you will work toward. Some examples follow.

• “For students to value friendship, develop their own perspectives and ways of thinking, and enjoy science.”
• “Develop social-emotional skills and…a deeper understanding of mathematics”
• “Across both math and language arts, develop our students’ abilities to use evidence and reasoning to support and critique arguments.”
• “…to take responsibility and initiative as learners.”

A lot of [U.S.] schools develop mission statements, but we don’t do anything with them. The mission statements get put in a drawer and then teachers become cynical…Lesson Study gives guts to a mission statement, makes it real, and brings it to life.

Develop a Theory of Action

Moving from the what to the how.

The second part of your research theme is a “theory of action”—how you will work toward your long-term goals and the specific research questions you will examine. What experiences in school help students move toward a goal such as “students have their own thoughts and can explain them logically?” Teachers addressing this research theme focused their initial theory of action on two classroom routines: students’ presentation of ideas at the board and their use of reflective journals. They actively tested strategies to improve these two classroom routines and posed questions about them. For example, they asked what the features are of effective student presentations and how teachers help students see the power of these strategies (such as using visual models). In order to strengthen the impact of reflective mathematics journals, teachers strategically selected several student journals from the prior day to be read aloud at the beginning of each mathematics lesson, which built students’ interest in each other’s ideas and helped them see the impact of well-explained ideas. The first part of your research theme—your overarching goal—is likely to stay the same for several years. The second part of your research theme—your theory of action—is likely to change as you incorporate effective ideas into your practice and go on to experiment with additional changes designed to achieve your long-term goals. For example, the group that experimented with changes to student presentations and reflective journals went on to experiment with routines for discussion and lesson summarization that further built students’ capacity “to have their own thoughts and explain them logically.”

Developing a Research Theme Presentation

Examples of Research Themes

• What is Lesson Study?
• Why Lesson Study?
• Teacher Learning
• Content Resources
• Teaching Through Problem-solving (TTP)
• School-wide Lesson Study
• U.S. Networks
• International Networks

Examples of research themes, topics and facets in higher education 

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