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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on 27 October 2016 by Bas Swaen . Revised on 25 October 2022 by Tegan George.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean – any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs discussion vs conclusion, checklist: research results, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analysed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like ‘appears’ or ‘implies’.
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe shop: first discuss the shoes as a whole, then the trainers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualise trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarise or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

Figure 1: Intention to donate to environmental organisations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

‘I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.’

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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Dissertations 5: findings, analysis and discussion: home.

• Results/Findings

Alternative Structures

The time has come to show and discuss the findings of your research. How to structure this part of your dissertation? 

Dissertations can have different structures, as you can see in the dissertation  structure  guide.

Dissertations organised by sections

Many dissertations are organised by sections. In this case, we suggest three options. Note that, if within your course you have been instructed to use a specific structure, you should do that. Also note that sometimes there is considerable freedom on the structure, so you can come up with other structures too. 

A) More common for scientific dissertations and quantitative methods:

- Results chapter 

- Discussion chapter

Example: 

• Introduction
• Literature review
• Methodology
• (Recommendations)

if you write a scientific dissertation, or anyway using quantitative methods, you will have some  objective  results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter.  

B) More common for qualitative methods

- Analysis chapter. This can have more descriptive/thematic subheadings.

- Discussion chapter. This can have more descriptive/thematic subheadings.

• Case study of Company X (fashion brand) environmental strategies 
• Successful elements
• Lessons learnt
• Criticisms of Company X environmental strategies 
• Possible alternatives

C) More common for qualitative methods

- Analysis and discussion chapter. This can have more descriptive/thematic titles.

• Case study of Company X (fashion brand) environmental strategies 

If your dissertation uses qualitative methods, it is harder to identify and report objective data. Instead, it may be more productive and meaningful to present the findings in the same sections where you also analyse, and possibly discuss, them. You will probably have different sections dealing with different themes. The different themes can be subheadings of the Analysis and Discussion (together or separate) chapter(s). 

Thematic dissertations

If the structure of your dissertation is thematic ,  you will have several chapters analysing and discussing the issues raised by your research. The chapters will have descriptive/thematic titles. 

• Background on the conflict in Yemen (2004-present day)
• Classification of the conflict in international law  
• International law violations
• Options for enforcement of international law
• Next: Results/Findings >>
• Last Updated: Aug 4, 2023 2:17 PM
• URL: https://libguides.westminster.ac.uk/c.php?g=696975

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How to Write the Dissertation Findings or Results – Tips

Published by Grace Graffin at August 11th, 2021 , Revised On August 13, 2024

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

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1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired Samples Correlation

Paired Samples Test

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

The last question of the interview focused on the need for improvement in Thai ready-to-eat products and the industry at large, emphasizing the need for enhancement in the current products being offered in the market. When asked if there was any particular need for Thai ready-to-eat meals to be improved and how to improve them in case of ‘yes,’ the males replied mainly by saying that the current products need improvement in terms of the use of healthier raw materials and preservatives or additives. There was an agreement amongst all males concerning the need to improve the industry for ready-to-eat meals and the use of more healthy items to prepare such meals. The females were also of the opinion that the fast-food items needed to be improved in the sense that more healthy raw materials such as vegetable oil and unsaturated fats, including whole-wheat products, to overcome risks associated with trans fat leading to obesity and hypertension should be used for the production of RTE products. The frozen RTE meals and packaged snacks included many preservatives and chemical-based flavouring enhancers that harmed human health and needed to be reduced. The industry is said to be aware of this fact and should try to produce RTE products that benefit the community in terms of healthy consumption.

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What to Avoid in Dissertation Findings Chapter

• Avoid using interpretive and subjective phrases and terms such as “confirms,” “reveals,” “suggests,” or “validates.” These terms are more suitable for the discussion chapter , where you will be expected to interpret the results in detail.
• Only briefly explain findings in relation to the key themes, hypothesis, and research questions. You don’t want to write a detailed subjective explanation for any research questions at this stage.

The Do’s of Writing the Findings or Results Section

• Ensure you are not presenting results from other research studies in your findings.
• Observe whether or not your hypothesis is tested or research questions answered.
• Illustrations and tables present data and are labelled to help your readers understand what they relate to.
• Use software such as Excel, STATA, and SPSS to analyse results and important trends.

Essential Guidelines on How to Write Dissertation Findings

The dissertation findings chapter should provide the context for understanding the results. The research problem should be repeated, and the research goals should be stated briefly.

This approach helps to gain the reader’s attention toward the research problem. The first step towards writing the findings is identifying which results will be presented in this section.

The results relevant to the questions must be presented, considering whether the results support the hypothesis. You do not need to include every result in the findings section. The next step is ensuring the data can be appropriately organized and accurate.

You will need to have a basic idea about writing the findings of a dissertation because this will provide you with the knowledge to arrange the data chronologically.

Start each paragraph by writing about the most important results and concluding the section with the most negligible actual results.

A short paragraph can conclude the findings section, summarising the findings so readers will remember as they transition to the next chapter. This is essential if findings are unexpected or unfamiliar or impact the study.

Our writers can help you with all parts of your dissertation, including statistical analysis of your results . To obtain free non-binding quotes, please complete our online quote form here .

Be Impartial in your Writing

When crafting your findings, knowing how you will organize the work is important. The findings are the story that needs to be told in response to the research questions that have been answered.

Therefore, the story needs to be organized to make sense to you and the reader. The findings must be compelling and responsive to be linked to the research questions being answered.

Always ensure that the size and direction of any changes, including percentage change, can be mentioned in the section. The details of p values or confidence intervals and limits should be included.

The findings sections only have the relevant parts of the primary evidence mentioned. Still, it is a good practice to include all the primary evidence in an appendix that can be referred to later.

The results should always be written neutrally without speculation or implication. The statement of the results mustn’t have any form of evaluation or interpretation.

Negative results should be added in the findings section because they validate the results and provide high neutrality levels.

The length of the dissertation findings chapter is an important question that must be addressed. It should be noted that the length of the section is directly related to the total word count of your dissertation paper.

The writer should use their discretion in deciding the length of the findings section or refer to the dissertation handbook or structure guidelines.

It should neither belong nor be short nor concise and comprehensive to highlight the reader’s main findings.

Ethically, you should be confident in the findings and provide counter-evidence. Anything that does not have sufficient evidence should be discarded. The findings should respond to the problem presented and provide a solution to those questions.

Structure of the Findings Chapter

The chapter should use appropriate words and phrases to present the results to the readers. Logical sentences should be used, while paragraphs should be linked to produce cohesive work.

You must ensure all the significant results have been added in the section. Recheck after completing the section to ensure no mistakes have been made.

The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work and ensure that the discussions section can be written clearly and proficiently.

One way to arrange the results is to provide a brief synopsis and then explain the essential findings. However, there should be no speculation or explanation of the results, as this will be done in the discussion section.

Another way to arrange the section is to present and explain a result. This can be done for all the results while the section is concluded with an overall synopsis.

This is the preferred method when you are writing more extended dissertations. It can be helpful when multiple results are equally significant. A brief conclusion should be written to link all the results and transition to the discussion section.

Numerous data analysis dissertation examples are available on the Internet, which will help you improve your understanding of writing the dissertation’s findings.

Problems to Avoid When Writing Dissertation Findings

One of the problems to avoid while writing the dissertation findings is reporting background information or explaining the findings. This should be done in the introduction section .

You can always revise the introduction chapter based on the data you have collected if that seems an appropriate thing to do.

Raw data or intermediate calculations should not be added in the findings section. Always ask your professor if raw data needs to be included.

If the data is to be included, then use an appendix or a set of appendices referred to in the text of the findings chapter.

Do not use vague or non-specific phrases in the findings section. It is important to be factual and concise for the reader’s benefit.

The findings section presents the crucial data collected during the research process. It should be presented concisely and clearly to the reader. There should be no interpretation, speculation, or analysis of the data.

The significant results should be categorized systematically with the text used with charts, figures, and tables. Furthermore, avoiding using vague and non-specific words in this section is essential.

It is essential to label the tables and visual material properly. You should also check and proofread the section to avoid mistakes.

The dissertation findings chapter is a critical part of your overall dissertation paper. If you struggle with presenting your results and statistical analysis, our expert dissertation writers can help you get things right. Whether you need help with the entire dissertation paper or individual chapters, our dissertation experts can provide customized dissertation support .

FAQs About Findings of a Dissertation

How do i report quantitative findings.

The best way to present your quantitative findings is to structure them around the research hypothesis or research questions you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the qualitative research results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your research question or hypothesis should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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Dissertation findings and discussion sections

(Last updated: 2 March 2020)

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We have helped 10,000s of undergraduate, Masters and PhD students to maximise their grades in essays, dissertations, model-exam answers, applications and other materials. If you would like a free chat about your project with one of our UK staff, then please just reach out on one of the methods below.

Granted that at some point in the discussion you are going to have to link back to this previous research. But you still have the opportunity to demonstrate how you have met that coveted gap in the research and generally made a useful contribution to knowledge.

There are many ways to write up both your findings and discussion. In shorter dissertations, it might make sense to have both of these comprise one section. In longer pieces of work, these chapters are usually separate.

Information contained in this section will highlight the finer details of writing up your findings and discussion sections. We will use the model of Description – Analysis – Synthesis , which are typically the three components readers expect to see in these two sections.

Preparing to write

We also assume that you have used some sort of software program to help you with the organisation of your findings. If you have not completed this process, you must do so before beginning to write. If not, your findings chapter may end up a confusing and unorganised mess of random information. If you need help in this area, make sure to seek it out before beginning to put your findings down on paper.

One of the main issues that students tend to encounter when writing up their findings is the amount of data to include. By the end of the research process, you've probably collected very large amounts of data . Not all of this can possibly appear in your dissertation without completely overwhelming the reader. As a result, you need to be able to make smart decisions about what to include and what to leave out.

One of the easiest ways to approach this task is to create an outline. In approaching the outline, it is in your best interest to focus on two key points. Firstly, you need to focus on answering your research questions. Secondly, you must include any particularly interesting findings that have cropped up as you completed your research.

An outline will give you the structure you need, and should make the whole process of presenting your findings easier. We realise that it is going to be a difficult process to pick and choose pieces of data to include. But you must be diligent in the work that you cut out. A findings chapter that is long and confusing is going to put the reader off reading the rest of your work.

Introducing your findings

It can be up to 40% of the total word count within your dissertation writing . This is a huge chunk of information, so it's essential that it is clearly organised and that the reader knows what is supposed to be happening. One of the ways you can achieve this is through a logical and organised introduction.

There are four main components that your introduction should include:

Reminding the reader of what you set out to do

A brief description of how you intend approaching the write up of the results

Placing the research in context

Letting the reader know where they can find the research instruments (i.e. the Appendix)

With a findings chapter, there should be no suspense for the reader. You need to tell them what they need to know right from the beginning. This way, they'll have a clear idea about what is still to come. A good introduction will start by telling the reader where you have come from in the research process and what the outcome was (in a couple of paragraphs or less).

You need to highlight the structure of the chapter (as you generally will do with all chapters) and where the reader might find any further information (e.g. in the appendices).

Organisation of data

This is really going to depend on the type of project you have created .

For example, if you have completed a qualitative research project, you might have identified some key themes within the software program you used to organise your data. In this case, highlighting these themes in your findings chapter may be the most appropriate way to proceed. Not only are you using information that you have already documented, you are telling a story in each of your sections (which can be useful in qualitative research).

But what if you undertook a more quantitative type study? You might be better off structuring your findings chapter in relation to your research questions or your hypotheses. This assumes, of course, that you have more than one research question or hypothesis. Otherwise you would end up just having one really long section.

This brings us to our next student mistake – trying to do too much within one section.

Subheadings are ultimately going to be your friend throughout your dissertation writing . Not only do they organise your information into logical pieces, they give the reader guidelines for where your research might be going. This is also a break for the reader. Looking at pages and pages of text without any breaks can be daunting and overwhelming for a reader. You don't want to overwhelm someone who is going to mark your work and who is responsible for your success (or failure).

When writing your introduction, be clear, organised and methodical. Tell the reader what they need to know and try to organise the information in a way that makes the most sense to you and your project. If in doubt, discuss this with your supervisor before you start writing.

Presentation of qualitative data

If you have conducted things like interviews or observations, you are likely to have transcripts that encompass pages and pages of work.

Putting this all together cohesively within one chapter can be particularly challenging. This is true for two reasons. First, it is always difficult to determine what you are going to cut and/or include. Secondly, unlike quantitative data, it can often be difficult to represent qualitative data through figures and tables, so condensing the information into a visual representation is simply not possible. As a writer, it is important to address both these challenges.

When considering how to present your qualitative data, it may be helpful to begin with the initial outline you have created (and the one described above). Within each of your subsections, you are going to have themes or headings that represent impactful talking points that you want to focus on.

Once you have these headings, it might be helpful to go back to your data and highlight specific lines that can/might be used as examples in your writing. If you have used multiple different instruments to collect data (e.g. interviews and observations), you are going to want to ensure that you are using both examples within each section (if possible). This is so that you can demonstrate to more well-rounded perspective of the points you are trying to make. Once you have identified some key examples for each section, you might still have to do some further cutting/editing.

Once you have your examples firmly selected for each subsection, you want to ensure that you are including enough information. This way, the reader will understand the context and circumstances around what you are trying to ‘prove’. You must set up the examples you have chosen in a clear and coherent way.

Students often make the mistake of including quotations without any other information. It is important that you embed your quotes/examples within your own thoughts. Usually this means writing about the example both before and after. So you might say something like, “One of the main topics that my participants highlighted was the need for more teachers in elementary schools. This was a focal point for 7 of my 12 participants, and examples of their responses included: [insert example] by participant 3 and [insert example] by participant 9. The reoccurring focus by participants on the need for more teachers demonstrates [insert critical thought here]. By embedding your examples in the context, you are essentially highlighting to the reader what you want them to remember.

Aside from determining what to include, the presentation of such data is also essential. Participants, when speaking in an interview might not do so in a linear way. Instead they might jump from one thought to another and might go off topic here and there.

It is your job to present the reader with information on your theme/heading without including all the extra information. So the quotes need to be paired down to incorporate enough information for the reader to be able to understand, while removing the excess.

Finding this balance can be challenging. You have likely worked with the data for a long time and so it might make sense to you. Try to see your writing through the eyes of someone else, which should help you write more clearly.

Presentation of quantitative data

Something to consider first with numeric data is that presentation style depends what department you are submitting to. In the hard sciences, there is likely an expectation of heavy numeric input and corresponding statistics to accompany the findings. In the arts and humanities, however, such a detailed analysis might not be as common. Therefore as you write out your quantitative findings, take your audience into consideration.

Just like with the qualitative data, you must ensure that your data is appropriately organised. Again, you've likely used a software program to run your statistical analysis, and you have an outline and subheadings where you can focus your findings. There are many software programs available and it is important that you have used one that is most relevant to your field of study.

For some, Microsoft Excel may be sufficient for basic analysis. Others may rely on SPSS, Stata, R, or any of the other programs available through your institution or online. Whatever program you have used, make sure that you document what you have done and the variables that have affected your analysis.

One common mistake found in student writing is the presentation of the statistical analysis. During your analysis of the data, you are likely to have run multiple different analyses from regressions to correlations. Often, we see students presenting multiple different statistical analyses without any real understanding of what the tests mean.

Presentation of quantitative data is more than just about numbers and tables. You must explain your findings and justify why you have run/presented the tests that you have. You could also explain how they relate to the research question. However, depending on how you have organised your work, this might end up in the discussion section.

Students who are not confident with statistical analysis often have a tendency to revert back to their secondary school mathematics skills. They commonly document the mean, median, and mode for all of their results. Now, these three outcomes can be important. But having a good understanding of why you are proceeding with this strategy of analysis is going to be essential in a primarily quantitative study.

That noted, there are different expectations for an undergraduate dissertation and a PhD thesis, so knowing what these expectations are can be really helpful before you begin.

Presentation of graphs, tables, and figures

The first is the use of colour and/or variables. Depending on the presentation of your dissertation, you may be required to print out a final copy for the marker(s). In many cases, this final copy must be printed in black and white. This means that any figures or graphs that you create must be readable in a black and white (or greyscale) format.

This can be challenging because there are only so many distinct shades of grey. In a pie chart, you might show one section as purple and the other as green. Yet when printed, both the purple and the green translate to approximately the same shade of grey, making your graph suddenly unreadable.

Another common error is overwhelming the reader with graphs and tables. Let's think about your outline and subheadings. If you're including a table under each subheadings, it needs to be relevant to the information that is being discussed in that chapter. There is no correct or incorrect number of graphs that should exist within the section, but you should use your judgement about what looks appropriate.

The final mistake we see is the duplication of writing (or absence of writing) when presenting a graph. Some students will present their findings in a graph or table and then write out this information again below the graph. This defeats the entire purpose of using the graph in the first place. So avoid this at all times.

Conversely, other students sometimes include a graph or figure but nothing else. Doing this denies the reader of context or purpose of said graph or figure. At some point, a balance needs to be struck where the reader has the information they require to really understand the point being made within the section.

Analysis and synthesis in a discussion

The purpose of a discussion chapter.

The structure of your discussion chapter is really going to depend on what you are trying to do and how you have structured your findings. If you chose to structure your findings by theme, it might make sense to continue this into the analysis chapter.

Other people might structure it according to the research questions. This clearly indicates to the reader how you have addressed your study. Marking a dissertation usually requires the marker to comment on the extent to which the research questions have been addressed. So by structuring a dissertation that lays out each research question for the marker, you are making their job easier. Needless to say, this a great thing.

Like any other chapter in your thesis, an introduction is an essential component of your discussion. By this point, the reader has gone through your findings and is now looking for your interpretation. Therefore, at the end of your discussion introduction you should highlight the content that each of the subsections will cover.

A conclusion to your discussion section (or a chapter summary) is also going to be beneficial. The length of the analysis chapter is usually quite long, so a wrap up of the key points at the end can help the reader digest your work. It can also help ensure that the reader actually understands the points you are trying to highlight within your project.

Critical thinking

Without any critical thinking, you are really doing yourself a disservice. It will affect the mark that you obtain on your overall dissertation. This is why the analysis chapter is usually weighted quite heavily on the marking rubric.

We tell students about critical thinking and the importance of it on a daily basis. And yet, there does seem to be a general confusion about what critical thinking entails, i.e. what constitutes critical thinking versus what is a simple description.

Critical thinking asks you to provide your own opinion on your topic, which can be daunting at first. For much of your academic career, you've likely been asked to use research to justify a position that has already been set. Unlike critical thinking, this requires you to use other people’s ideas. But even if you're new to it, try and get to grips with what critical thinking entails and use it in your work.

Creating sub-sections

Subheadings need to be informative but not too long. It is possible to layer your subheadings, so you might have a Chapter 2, a Section 2.1 and then a 2.1.1 and 2.2.2. Usually anything after 3 numerical points does not get a number and would not appear in your table of contents.

When creating titles for your subheadings, consider how they are going to look in the table of contents. They need to fit on one line, ideally, so putting your research question as the subheading might end up being too long. Conversely, one- or two-word subheadings usually doesn't give enough information about the purpose of the section.

Finding this balance is important. But remember you can always edit your subheadings retrospectively.

Linking to previous chapters

Ideally, you will be able to concisely and effectively link your research to what has been researched previously. But this can be a challenge. You don't want to repeat what has been said in your literature review or the findings . But you need to pull examples from both of these sections in order to make the points that you need to.

So, how do you tackle this?

One way is by referring the reader back to previous chapters, sections, or subsections. This process can generally be done at the end. You can put in a place holder until you know how your sections will be numbered. For example you might write: “In Section XYZ, the theme of … was discussed. Findings from this study indicate…. (see Section XYZ for details)”. While ‘XYZ’ is obviously not going to be the same section, by using the same abbreviation, you can then search ‘XYZ’ after you have completed writing and replace each term with the appropriate number. This also makes the proofreading process easier.

If you are submitting an electronic version of this document, you may also consider hyperlinks to take the reader to the different sections. But be aware that this can be considerably more work, so you should allow for this in your timescale if it's something you wish to implement.

Let's outline the main takeaway points:

It is essential that you keep in mind the ‘describe, analyse, synthesise’ model.

The findings chapter is essentially the describe part. You need to ensure that you have clearly identified data that relates to your research questions, hypotheses, or themes of your study.

For the ‘describe’ component, you are not looking to support your work with other research, but rather to present your contribution. It is also important to consider your data in the ‘describe’ section. If you have qualitative data, ensure that you have edited the quotes and examples to a reasonable length. Pick quotes that accurately represent your theme. Try not to focus solely on one or two participants (if possible). Ensure that you are demonstrating links between multiple instruments, if you used them.

If you are using quantitative data, be careful about how many statistical tests you run. Make sure you can justify why you chose one particular test over another. When presenting graphs, use a colour scheme that's appropriate for the reader when printing in black and white. Ensure that graphs and tables are appropriately explained, but that the information provided is not duplicated.

From the ‘describe’ element, you move into the 'analysis' and 'synthesis'. These parts usually appear in the discussion and ask you to employ your critical thinking skills to demonstrate how your research fits into the bigger picture. It is often the case that your analysis holds the most weight in the marking scheme. So you should spend considerable time ensuring this section is appropriate. It needs to demonstrate how you have attempted to answer your research questions.

Finally, create an outline before you begin. While this might seem tedious at first, filling in the sections with the appropriate information will mean that you are not writing things over and over again. It'll also make sure you do not go wildly off topic. It is always beneficial to have a second set of eyes assess your work for any errors or omissions. Many students choose to contact professional editors to help with this as they hold the relevant expertise to guide you on the correct path to creating a perfect discussion section that is ready for submission.

In terms of presentation, both the findings and discussion chapters will benefit from a clear and logical introduction and chapter summary. Remember that both of these chapters are meant to inform. You are leading the reader on a journey, so make sure they stay on the path and arrive at the final destination with you!

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Home » Dissertation Methodology – Structure, Example and Writing Guide

Dissertation Methodology – Structure, Example and Writing Guide

• Table of Contents

Dissertation Methodology

In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.

Here are the basic elements that are typically included in a dissertation methodology:

• Introduction : This section should explain the importance and goals of your research .
• Research Design : Outline your research approach and why it’s appropriate for your study. You might be conducting an experimental research, a qualitative research, a quantitative research, or a mixed-methods research.
• Data Collection : This section should detail the methods you used to collect your data. Did you use surveys, interviews, observations, etc.? Why did you choose these methods? You should also include who your participants were, how you recruited them, and any ethical considerations.
• Data Analysis : Explain how you intend to analyze the data you collected. This could include statistical analysis, thematic analysis, content analysis, etc., depending on the nature of your study.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your study. For instance, you could discuss measures taken to reduce bias, how you ensured that your measures accurately capture what they were intended to, or how you will handle any limitations in your study.
• Ethical Considerations : This is where you state how you have considered ethical issues related to your research, how you have protected the participants’ rights, and how you have complied with the relevant ethical guidelines.
• Limitations : Acknowledge any limitations of your methodology, including any biases and constraints that might have affected your study.
• Summary : Recap the key points of your methodology chapter, highlighting the overall approach and rationalization of your research.

Types of Dissertation Methodology

The type of methodology you choose for your dissertation will depend on the nature of your research question and the field you’re working in. Here are some of the most common types of methodologies used in dissertations:

Experimental Research

This involves creating an experiment that will test your hypothesis. You’ll need to design an experiment, manipulate variables, collect data, and analyze that data to draw conclusions. This is commonly used in fields like psychology, biology, and physics.

Survey Research

This type of research involves gathering data from a large number of participants using tools like questionnaires or surveys. It can be used to collect a large amount of data and is often used in fields like sociology, marketing, and public health.

Qualitative Research

This type of research is used to explore complex phenomena that can’t be easily quantified. Methods include interviews, focus groups, and observations. This methodology is common in fields like anthropology, sociology, and education.

Quantitative Research

Quantitative research uses numerical data to answer research questions. This can include statistical, mathematical, or computational techniques. It’s common in fields like economics, psychology, and health sciences.

Case Study Research

This type of research involves in-depth investigation of a particular case, such as an individual, group, or event. This methodology is often used in psychology, social sciences, and business.

Mixed Methods Research

This combines qualitative and quantitative research methods in a single study. It’s used to answer more complex research questions and is becoming more popular in fields like social sciences, health sciences, and education.

Action Research

This type of research involves taking action and then reflecting upon the results. This cycle of action-reflection-action continues throughout the study. It’s often used in fields like education and organizational development.

Longitudinal Research

This type of research involves studying the same group of individuals over an extended period of time. This could involve surveys, observations, or experiments. It’s common in fields like psychology, sociology, and medicine.

Ethnographic Research

This type of research involves the in-depth study of people and cultures. Researchers immerse themselves in the culture they’re studying to collect data. This is often used in fields like anthropology and social sciences.

Structure of Dissertation Methodology

The structure of a dissertation methodology can vary depending on your field of study, the nature of your research, and the guidelines of your institution. However, a standard structure typically includes the following elements:

• Introduction : Briefly introduce your overall approach to the research. Explain what you plan to explore and why it’s important.
• Research Design/Approach : Describe your overall research design. This can be qualitative, quantitative, or mixed methods. Explain the rationale behind your chosen design and why it is suitable for your research questions or hypotheses.
• Data Collection Methods : Detail the methods you used to collect your data. You should include what type of data you collected, how you collected it, and why you chose this method. If relevant, you can also include information about your sample population, such as how many people participated, how they were chosen, and any relevant demographic information.
• Data Analysis Methods : Explain how you plan to analyze your collected data. This will depend on the nature of your data. For example, if you collected quantitative data, you might discuss statistical analysis techniques. If you collected qualitative data, you might discuss coding strategies, thematic analysis, or narrative analysis.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your research. This might include steps you took to reduce bias or increase the accuracy of your measurements.
• Ethical Considerations : If relevant, discuss any ethical issues associated with your research. This might include how you obtained informed consent from participants, how you ensured participants’ privacy and confidentiality, or any potential conflicts of interest.
• Limitations : Acknowledge any limitations in your research methodology. This could include potential sources of bias, difficulties with data collection, or limitations in your analysis methods.
• Summary/Conclusion : Briefly summarize the key points of your methodology, emphasizing how it helps answer your research questions or hypotheses.

How to Write Dissertation Methodology

Writing a dissertation methodology requires you to be clear and precise about the way you’ve carried out your research. It’s an opportunity to convince your readers of the appropriateness and reliability of your approach to your research question. Here is a basic guideline on how to write your methodology section:

1. Introduction

Start your methodology section by restating your research question(s) or objective(s). This ensures your methodology directly ties into the aim of your research.

2. Approach

Identify your overall approach: qualitative, quantitative, or mixed methods. Explain why you have chosen this approach.

• Qualitative methods are typically used for exploratory research and involve collecting non-numerical data. This might involve interviews, observations, or analysis of texts.
• Quantitative methods are used for research that relies on numerical data. This might involve surveys, experiments, or statistical analysis.
• Mixed methods use a combination of both qualitative and quantitative research methods.

3. Research Design

Describe the overall design of your research. This could involve explaining the type of study (e.g., case study, ethnography, experimental research, etc.), how you’ve defined and measured your variables, and any control measures you’ve implemented.

4. Data Collection

Explain in detail how you collected your data.

• If you’ve used qualitative methods, you might detail how you selected participants for interviews or focus groups, how you conducted observations, or how you analyzed existing texts.
• If you’ve used quantitative methods, you might detail how you designed your survey or experiment, how you collected responses, and how you ensured your data is reliable and valid.

5. Data Analysis

Describe how you analyzed your data.

• If you’re doing qualitative research, this might involve thematic analysis, discourse analysis, or grounded theory.
• If you’re doing quantitative research, you might be conducting statistical tests, regression analysis, or factor analysis.

Discuss any ethical issues related to your research. This might involve explaining how you obtained informed consent, how you’re protecting participants’ privacy, or how you’re managing any potential harms to participants.

7. Reliability and Validity

Discuss the steps you’ve taken to ensure the reliability and validity of your data.

• Reliability refers to the consistency of your measurements, and you might discuss how you’ve piloted your instruments or used standardized measures.
• Validity refers to the accuracy of your measurements, and you might discuss how you’ve ensured your measures reflect the concepts they’re supposed to measure.

8. Limitations

Every study has its limitations. Discuss the potential weaknesses of your chosen methods and explain any obstacles you faced in your research.

9. Conclusion

Summarize the key points of your methodology, emphasizing how it helps to address your research question or objective.

Example of Dissertation Methodology

An Example of Dissertation Methodology is as follows:

Chapter 3: Methodology

• Introduction

This chapter details the methodology adopted in this research. The study aimed to explore the relationship between stress and productivity in the workplace. A mixed-methods research design was used to collect and analyze data.

Research Design

This study adopted a mixed-methods approach, combining quantitative surveys with qualitative interviews to provide a comprehensive understanding of the research problem. The rationale for this approach is that while quantitative data can provide a broad overview of the relationships between variables, qualitative data can provide deeper insights into the nuances of these relationships.

Data Collection Methods

Quantitative Data Collection : An online self-report questionnaire was used to collect data from participants. The questionnaire consisted of two standardized scales: the Perceived Stress Scale (PSS) to measure stress levels and the Individual Work Productivity Questionnaire (IWPQ) to measure productivity. The sample consisted of 200 office workers randomly selected from various companies in the city.

Qualitative Data Collection : Semi-structured interviews were conducted with 20 participants chosen from the initial sample. The interview guide included questions about participants’ experiences with stress and how they perceived its impact on their productivity.

Data Analysis Methods

Quantitative Data Analysis : Descriptive and inferential statistics were used to analyze the survey data. Pearson’s correlation was used to examine the relationship between stress and productivity.

Qualitative Data Analysis : Interviews were transcribed and subjected to thematic analysis using NVivo software. This process allowed for identifying and analyzing patterns and themes regarding the impact of stress on productivity.

Reliability and Validity

To ensure reliability and validity, standardized measures with good psychometric properties were used. In qualitative data analysis, triangulation was employed by having two researchers independently analyze the data and then compare findings.

Ethical Considerations

All participants provided informed consent prior to their involvement in the study. They were informed about the purpose of the study, their rights as participants, and the confidentiality of their responses.

Limitations

The main limitation of this study is its reliance on self-report measures, which can be subject to biases such as social desirability bias. Moreover, the sample was drawn from a single city, which may limit the generalizability of the findings.

Where to Write Dissertation Methodology

In a dissertation or thesis, the Methodology section usually follows the Literature Review. This placement allows the Methodology to build upon the theoretical framework and existing research outlined in the Literature Review, and precedes the Results or Findings section. Here’s a basic outline of how most dissertations are structured:

• Acknowledgements
• Literature Review (or it may be interspersed throughout the dissertation)
• Methodology
• Results/Findings
• References/Bibliography

In the Methodology chapter, you will discuss the research design, data collection methods, data analysis methods, and any ethical considerations pertaining to your study. This allows your readers to understand how your research was conducted and how you arrived at your results.

Advantages of Dissertation Methodology

The dissertation methodology section plays an important role in a dissertation for several reasons. Here are some of the advantages of having a well-crafted methodology section in your dissertation:

• Clarifies Your Research Approach : The methodology section explains how you plan to tackle your research question, providing a clear plan for data collection and analysis.
• Enables Replication : A detailed methodology allows other researchers to replicate your study. Replication is an important aspect of scientific research because it provides validation of the study’s results.
• Demonstrates Rigor : A well-written methodology shows that you’ve thought critically about your research methods and have chosen the most appropriate ones for your research question. This adds credibility to your study.
• Enhances Transparency : Detailing your methods allows readers to understand the steps you took in your research. This increases the transparency of your study and allows readers to evaluate potential biases or limitations.
• Helps in Addressing Research Limitations : In your methodology section, you can acknowledge and explain the limitations of your research. This is important as it shows you understand that no research method is perfect and there are always potential weaknesses.
• Facilitates Peer Review : A detailed methodology helps peer reviewers assess the soundness of your research design. This is an important part of the publication process if you aim to publish your dissertation in a peer-reviewed journal.
• Establishes the Validity and Reliability : Your methodology section should also include a discussion of the steps you took to ensure the validity and reliability of your measurements, which is crucial for establishing the overall quality of your research.

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Dissertation

Chapter 3: Method

This chapter presents the methods and research design for this dissertation study. It begins by presenting the research questions and settings, the LibraryThing and Goodreads digital libraries. This is followed by an overview of the mixed methods research design used, incorporating a sequence of three phases. Each of the three methods—qualitative content analysis, a quantitative survey questionnaire, and qualitative interviews—are then presented in detail. The codes and themes used for analysis during the qualitative phases are discussed next. The chapter continues with sections on the management of the research data for this study; the validity, reliability, and trustworthiness of study findings; and ethical considerations. The invitation letters and informed consent statement; survey instrument; interview questions; a quick reference guide used for coding and analysis; and documentation of approval from LibraryThing, Goodreads, and the FSU Human Subjects Committee are included in appendices.

3.1. Research Questions

As stated in Chapter 1 the purpose of this research, taking a social perspective on digital libraries, is to improve understanding of the organizational, cultural, institutional, collaborative, and social contexts of digital libraries. The following two research questions satisfy the purpose of the proposed study within the approach, setting, and framework introduced in Chapter 1 :

• RQ1: What roles do LibraryThing and Goodreads play, as boundary objects, in translation and coherence between the existing social and information worlds they are used within?
• RQ2: What roles do LibraryThing and Goodreads play, as boundary objects, in coherence and convergence of new social and information worlds around their use?

These two questions explore the existing and emergent worlds that may surround digital libraries in social, collaborative use and behavior. RQ1 focuses on examining how LibraryThing and Goodreads may support existing collaboration, communities, and other social activities and behaviors across social and information worlds, with a specific eye to translation, characteristics indicating coherence of existing worlds, and uses of the digital libraries as boundary objects. RQ2 focuses on examining how LibraryThing and Goodreads may support coherence and convergence of new, emergent social and information worlds and their characteristics, as indicated by use of the digital libraries (as boundary objects) as new, localized standards. The questions focus on the roles of each digital library, be there one role, multiple roles, or possibly no role played by LibraryThing and Goodreads. These roles may or may not include explicit support for collaboration, communities, or social contexts. The research questions use and incorporate the definitions, concepts, and propositions of social digital libraries (see section 2.4.3 ), the social worlds perspective (see sections 2.7.1.1 and 2.8.1 ), the theory of information worlds (see section 2.8.2 ), and the synthesized theoretical framework for social digital libraries (developed in section 2.8.3 ). Coherence and convergence are seen as the same concept in boundary object theory (see section 2.7.1.4 ), leading to overlap between the concepts—and the two research questions—in operational data collection and analysis. The connotations of the two indicate convergence will lead to new, emergent worlds, and this meaning is indicated by its use in RQ2, but not RQ1.

3.2. Setting: Case Studies of LibraryThing and Goodreads

In this dissertation study, the boundary objects of interest are defined and given as two digital libraries: LibraryThing and Goodreads (see sections 3.2.2 and 3.2.3 below). This approach is opposite the procedure used by Star and Griesemer (1989), who first identified the populations of communities, users, and stakeholders in their study, then examined the boundary objects they used. Starting with the boundary objects is in line with Star’s later work (Bowker & Star, 1999; Star et al., 2003). Bødker and Christiansen (1997); Gal, Yoo, and Boland (2004); Henderson (1991); and Pawlowski, Robey, and Raven (2000) have used this approach to varying extents, proving its validity and usefulness as an approach to take for studying social digital libraries as boundary objects.

3.2.1. Case Study Approach

This research takes a case study approach, where "a detailed" and intensive "analysis of … individual case[s]"—LibraryThing and Goodreads—will be performed (Fidel, 1984, p. 274). The research looked to generate "a comprehensive understanding of the event under study"—uses of these digital libraries as boundary objects within and across existing and emergent social and information worlds—and develop "more general theoretical statements about regularities in the observed phenomena" surrounding social digital libraries (p. 274). Case studies often focus on the cycle of research methods which inform each other through a longer, more detailed research process than using a single exploratory method. A case study approach fosters multiple opportunities to revisit and reanalyze data collected earlier in the study, revise the research design as new facets and factors emerge, and combine multiple methods and data sources into a holistic description of each case. The research design used here, employing two qualitative and one quantitative method in a cycle (see section 3.3 ), follows this approach.

Yin (2003) breaks the process of conducting a case study into five phases. The phases "effectively force [the researcher] to begin constructing a preliminary theory" prior to data collection (p. 28), as done in Chapter 2 . Each of Yin’s five steps can be found in sections of this dissertation. First, one must determine the research questions to be asked; these were included in section 3.1 above. Second, one must identify what Yin calls the "propositions," statements "direct[ing] attention to something that should be examined within the scope of study" (p. 22). The theoretical framework developed earlier (see section 2.8) and the purpose of this research as stated in Chapter 1 provide this necessary focus from a conceptual perspective. The operationalization of this focus is discussed for each method in sections 3.4.4 , 3.5.3 , 3.6.4 , and 3.7 . Third, Yin says one must determine the unit of analysis, based on the research questions. In this study, the overall units of analysis are the two social digital libraries under consideration, LibraryThing and Goodreads; other units of interest include communities, groups, and individuals. The specific unit of analysis for each method of data collection is discussed in sections 3.4.1 , 3.5.1 , and 3.6.2 . Fourth, one must connect "data to [theoretical] propositions," matching patterns with theories (p. 26). Using the theoretical framework developed in section 2.8 in data analysis (see sections 3.4.4 , 3.5.5 , 3.6.6 , and 3.7 ) provides for this matching process. For the final step, Yin says one must determine "the criteria for interpreting [the] findings" (p. 27); the criteria chosen for this research are discussed in the data analysis sections ( 3.4.4 , 3.5.5 , 3.6.6 , and 3.7 ) and are considered in light of concerns of validity, reliability, and trustworthiness ( section 3.9 ) and the benefits ( section 1.7 and Chapter 5 ) and limitations ( section 5.6 ) of the study.

This research employed a multiple-case, "holistic" design at the highest level, focusing on LibraryThing and Goodreads as units, but what Yin (2003, p. 42) calls an "embedded" design, with multiple units of analysis considered in each method, at lower levels. Examining two social digital libraries allows them to be compared and contrasted, but commonalities were expected to emerge—and did—across the two cases to allow theoretical and practical conclusions to be drawn (see Chapter 5 ). Yin stated case study designs must be flexible and may change as a result of research not turning out as expected, and subtle changes were made to what was intended to be a flexible plan for case studies of LibraryThing and Goodreads and their use as boundary objects within and across existing and emergent social and information worlds.

3.2.2. LibraryThing

LibraryThing (LT) is a social digital library and web site founded in August 2005 (LibraryThing, n.d.-a), with over 1.8 million members as of June 2014 (LibraryThing, 2014). It allows users to catalog books they own, have read, or want to read (LibraryThing, n.d.-b); these serve as Functional Requirements for Bibliographic Records (FRBR) items (International Federation of Library Associations and Institutions, 2009). Users can assign tags to books, mark their favorites, and create and share collections of books with others; these collections are searchable and sortable. LT suggests books to users based on the similarity of collections. Users can provide reviews, ratings, or other metadata (termed "Common Knowledge"; LibraryThing, 2013) for editions of books (FRBR’s manifestations and expressions) and works (as in FRBR); this metadata and users’ tags are shared across the site (LibraryThing, n.d.-c). LT provides groups (administered by users or staff), which include shared library collection searching, forums, and statistics on the books collected by members of the group (LibraryThing, n.d.-d). Discussions from these forums about individual books are included on each book’s page, as are tags, ratings, and reviews. Each user has a profile page which links to their collections, tags, reviews, and ratings, and lists other user-provided information such as homepage, social networks used (Facebook, Twitter, etc.), and a short biography (LibraryThing, n.d.-c).

Examining LibraryThing in light of the definition of social digital libraries (see sections 1.1 and 2.4.3 ) shows the following:

• LT features one or more collections of digital content collected for its users, who can be considered a community as a whole and part of many smaller communities formed by the groups feature. This content includes book data and metadata sourced from Amazon.com and libraries using the Z39.50 protocol (LibraryThing, n.d.-b); and user-contributed data, metadata, and content in many forms: tags, favorites, collections, reviews, posts in discussions, and profile information.
• LT features services relating to the content and serving its user communities, including the ability to catalog books; create collections; discuss with others; and search for and browse books, reviews, tags, and other content.
• LT is managed by a formal organization and company, and draws on the resources of other formal organizations (Amazon.com, libraries) and informal groupings (LT users) for providing and managing content and services.

As a large social digital library and web site, open to the public and with multiple facets, LibraryThing is well-suited as a setting and case for examining the role of digital libraries within and across communities. The existing research literature on LibraryThing has focused on its roles for social tagging and classification (e.g. Chang, 2009; Lu, Park, & Hu, 2010; Zubiaga, Körner, & Strohmaier, 2011) and in recommendation and readers’ advisory (e.g. Naughton & Lin, 2010; Stover, 2009). This study adds an additional view of the site as an online community and social digital library.

3.2.3. Goodreads

Goodreads (GR), similar to LibraryThing, is a social digital library and web site founded in January 2007 (Goodreads, 2014a). As of June 2014, it has 25 million members. Users can "recommend books" via ratings and reviews, "see which books [their] friends are reading; track the books [they are] reading, have read, and want to read; … find out if a book is a good fit for [them] from [the] community’s reviews" (para. 2); and join discussion groups "to discuss literature" (Goodreads, 2014b, para. 11). As with LibraryThing, Goodreads users can create lists of books (called "shelves"), which act as site-wide tags anyone can search on (para. 5). Searching and sorting are possible for other metadata and content types; metadata can apply to editions (manifestations or expressions) of a book or to whole works (in FRBR terms; International Federation of Library Associations and Institutions, 2009). Groups can be created, joined, and moderated by users (including Goodreads staff); they can include group shelves, discussion forums, events, photos, videos, and polling features. Users have profile pages, which may include demographic information, favorite quotes, writing samples, and events. Users who have greater than 50 books on their shelves can apply to become a Goodreads librarian , which allows them to edit and update metadata for books and authors (Goodreads, 2012d, "What can librarians do?" section). In March 2013—during the early stages of this dissertation research—Amazon.com acquired Goodreads (Chandler, 2013).

Examining GR in light of the definition of social digital libraries (see sections 1.1 and 2.4.3 ) shows the following:

• GR features one or more collections of digital content collected for its users, who can be considered a community as a whole and part of many smaller communities formed by the groups feature. This content includes book data and metadata previously sourced from Ingram (a book wholesaler), libraries (via WorldCat and the catalogs of the American, British, and German national libraries), and publishers (Chandler, 2012), and now from Amazon since their purchase (Chandler, 2013); and user-contributed metadata and content, including shelves, lists, forum posts, events, photos, videos, polls, profile information, and book trivia.
• GR features services relating to the content and serve its user communities, including the ability to catalog books; create shelves; discuss with others; and search for and browse books, reviews, lists, and other content.
• GR is managed by a formal organization and company—Goodreads Inc., although now owned by Amazon—and draws on the resources of other formal organizations (Amazon, Ingram, OCLC via WorldCat, libraries, and publishers) and informal groupings (GR users, the librarians group) for providing and managing content and services.

As with LibraryThing, Goodreads is well-suited as a setting and case for examining the role of digital libraries within and across communities, because it is a large social digital library and web site that is open to the public and has multiple facets. There is little existing research literature on Goodreads, limited to its use in recommendation and readers’ advisory (e.g. Naik, 2012; Stover, 2009) and examining its impact on the practice of reading (Nakamura, 2013). This study adds an additional view of the site as an online community and social digital library.

3.3. Research Design

Use of a mixed methods research design combines qualitative and quantitative methods together to emphasize their strengths; minimize their weaknesses; improve validity, reliability, and trustworthiness; and obtain a fuller understanding of uses of social digital libraries as boundary objects within and across social and information worlds. Definitions of mixed methods research vary but core characteristics can be identified, which Creswell and Plano Clark (2011, p. 5) summarize as

• collection and analysis of both qualitative and quantitative data;
• integration of the two forms of data at the same time, in sequence, or in an embedded design;
• prioritizing one or both forms of data;
• combining methods within a single study or multiple phases of a larger research program;
• framing the study, data collection, and analysis within philosophical, epistemological, and theoretical lenses; and
• conducting the study according to a specific research design meting the other criteria.

This study meets all of these criteria. Qualitative and quantitative data were collected and integrated in sequence; qualitative data was prioritized, but not at the expense of quantitative data collection; multiple methods were used within this one study; and the study was based on the theoretical framework developed and the tenets of social informatics and social constructionism explained in Chapter 2 .

This study took a philosophical view of mixed methods research similar to the view of Ridenour and Newman (2008), who "reject[ed] the [standard] dichotomy" between qualitative and quantitative research methods, believing there to be an "interactive continuum" between the two (p. xi). They stated "both paradigms have their own contributions to building a knowledge base" (p. xii), suggesting a holistic approach to research design incorporating theory building and theory testing in a self-correcting cycle. Qualitative methods, Ridenour and Newman argued, should inform the research questions and purpose for quantitative phases, and vice versa; they termed this process an "interactive" one (p. xi). Research designs should come from the basis of "the research purpose and the research question" (p. 1), what "evidence [is] needed," and what epistemological stance should be taken "to address the question" (p. 18).

Greene (2007) presented a similar argument, stating "a mixed methods way of thinking actively engages with epistemological differences" (p. 27); multiple viewpoints are respected, understood, and applied within a given study. She acknowledged the tensions and contradictions that will exist in such thought, but believed this would produce the best "conversation" and allow the researcher to learn the most from their study and data (p. 27). Creswell and Plano Clark (2011) encompassed multiple viewpoints and potential designs in their chapter on choosing a mixed methods design (pp. 53–104). They considered six prototypical designs: (a) convergent parallel; (b) explanatory sequential; (c) exploratory sequential; (d) embedded; (e) transformative; and (f) multiphase.

The research design for this dissertation study is a variation on a multiphase design incorporating elements of the explanatory sequential and exploratory sequential designs of Creswell and Plano Clark. Three methods were use for data collection, following the process proposed by Ridenour and Newman (2008) and taking the approach to thought suggested by these authors, Creswell and Plano Clark (2011), and Greene (2007). The selection of this design and these methods was based on the research purpose discussed in Chapter 1 , the research questions introduced in section 3.1 , and the research setting explained in section 3.2 . The methods used were

• content analysis of messages in LibraryThing and Goodreads groups ( section 3.4 );
• a structured survey of LibraryThing and Goodreads users ( section 3.5 ); and
• semi-structured qualitative interviews with users of LibraryThing and Goodreads ( section 3.6 ).

The holistic combination of these methods, interrelated in a multiphase design, has allowed for exploratory and descriptive research on social digital libraries as boundary objects incorporating the strengths of quantitative and qualitative methods and the viewpoints of multiple perspectives.

3.3.1. Integrated Design

A sequential, multiphase research design was employed for two reasons. First, each of the methods above required focus on data collection and analysis by the researcher. Trying to use a parallel or concurrent design, conducting content analysis alongside a survey or a survey alongside interviews, could have caused excess strain; a sequential design improved the chances of success, the quality of data collected and analyzed, and the significance of and level of insight in the study’s conclusions. Second, each method built on the methods before it. The design of the survey and interview instruments was influenced by ideas drawn from the literature and theories for the study and by elements of interest uncovered during the content analysis phase. The interviews focused on gathering further detail on and insight into findings from the survey results and the content analysis. This combination of methods allowed for exploring each case through content analysis, obtaining summary explanatory data through surveys, and then detailed descriptive and explanatory data through the interviews, achieving the benefits of both the exploratory and explanatory research designs presented by Creswell and Plano Clark (2011, pp. 81–90).

Creswell and Plano Clark (2011) expressed caution, noting multiphase research designs often require substantial time, effort, and multi-researcher teams. The three phases used here were not lengthy or intensive enough to cause lengthy delays in the completion of this dissertation. This is one coherent dissertation study, instead of the long-term, multi-project research program Creswell and Plano Clark cite as the prototypical multiphase design. While it was known in advance this would not be the speediest dissertation research project, using a sequential design allowed for the results from each phase to emerge as the research proceeded, instead of having to wait for all phases to complete as in a concurrent design. A complete and insightful picture of the findings and conclusions of the dissertation came within a reasonable amount of time and with a good level of effort.

3.4. Content Analysis

Content analysis has been defined as "a technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use" (Krippendorff, 2004a, p. 19), with emphasis often placed on "the content of communication" (Holsti, 1969, p. 2)—specific "characteristics of messages" (p. 14)—"as the basis of inference" (p. 2). Early forms of content analysis required objectivity and highly systematic procedures (see Holsti, 1969, pp. 3–5, 14). The form of content analysis used in this study considers the meaning and understanding of content to "emerge in the process of a researcher analyzing a text relative to a particular context" (Krippendorff, 2004a, p. 19), a subjective and less rigid approach. Such text or content may have multiple, socially constructed meanings, speaking to more "than the given texts" (p. 23); they are indicative of the "contexts, discourses, or purposes" surrounding the content (p. 24).

There are at least three categories of content analysis, which Ahuvia (2001) labels traditional , interpretive , and reception-based ; other authors and researchers (e.g. Babbie, 2007, p. 325; Holsti, 1969, pp. 12–14) break content analysis into latent (subjective and qualitative) and manifest (objective and quantitative) categories of analysis. Early content analysis was purely objective and generated quantitative summaries and enumerations of manifest content, but qualitative and latent analysis have found greater acceptance over time (Ahuvia, 2001; Holsti, 1969, pp. 5–14; Krippendorff, 2004a). This study used the interpretive approach and focused coding on the latent content—the underlying meaning—of the data gathered. This section discusses the application of content analysis in the first phase of this dissertation research, including (a) the choice of the unit of analysis; (b) the population and sampling method chosen; (c) the sampling and data collection procedures followed, including a pilot test; and (d) how the data was analyzed.

3.4.1. Unit of Analysis

The unit of analysis chosen for the content analysis in this study was the message . LibraryThing’s and Goodreads’ group discussion boards are organized into threads, each of which may contain multiple individual messages. Analysis of these individual messages was aimed at uncovering indications of the roles the two digital libraries play in existing and emergent social and information worlds. Analysis began with the individual messages to ensure details and phenomena at that level were captured, but over time went beyond individual messages to the thread or group levels, since these phenomena served as instantiations of social and information worlds or as sites for interaction and translation.

3.4.2. Population and Sampling

The broader population of messages could be defined as all messages posted in public LibraryThing and Goodreads groups, but the logistics of constructing a sampling frame for such a population were and are all but impossible; it is improbable the two sites would provide data on all messages posted if it is not required of them by law. Recent messages from active groups were of most interest and use for this study. The population of messages was defined as all messages from the most active LibraryThing groups in the past week (taken from http://www.librarything.com/groups/active ) and the most recently active Goodreads groups (taken from http://www.goodreads.com/group/active ) as of April 30, 2013, the day data collection began for the content analysis phase of the study. The sampling frames were restricted to as close to but no more than 100 groups as possible, based on LibraryThing’s list claiming to list the 100 most active groups; the actual frames consisted of 91 LibraryThing groups and 93 Goodreads groups once duplicates were removed. During the planning and design of this study, Goodreads provided a list of "recently popular" groups (at http://www.goodreads.com/group/recently_popular ) that was akin to LibraryThing’s list in nature; that list was taken down sometime in early 2013 due to it causing a server slowdown (Jack & Finley, 2013). Using the most recently active groups did not guarantee consistent popularity or activity over a recent time period (such as a week), but did address the need to collect recent messages from active groups and was deemed the most acceptable source for a sampling frame still available.

To obtain a sample of messages from this population, a stratified random sampling method using the levels of group, thread, and message was employed. From the lists identified above, five groups were selected at random from each digital library (for a total of ten), but with the following inclusion and exclusion criteria applied to help ensure representativeness and allow for meaningful analysis:

(a) At least one group from each digital library with over 100 messages posted in the last week was selected. (b) At least one group from each digital library with under 100 messages posted in the last week was selected. (c) Any group with fewer than 60 messages total was removed and a new group selected. (d) Any group with fewer than two members was removed and a new group selected. (e) Any group used in the pilot study (see below) was removed and a new group selected.

Due to constraints placed on this research by Goodreads and the nature of this digital library, all group selections for Goodreads required approval from at least one group moderator per group. Prior to the collection of any data, such moderators were messaged via the site using the invitation letter found in Appendix A , section A.1.1 , and provided their consent for their group to be included in the research by agreeing to an informed consent statement (see Appendix A , section A.1.2 ). Any groups for which the moderator did not provide consent within two weeks were removed from the sample and a new group selected, using the same procedures and initial list of groups.

Two additional groups, one from LibraryThing and one from Goodreads, were used for a pilot study of the content analysis procedures, selected at random using the same procedure as above but with only criteria (c) and (d) applied. As with the main sample, the moderator for the Goodreads group selected was contacted to obtain his approval and consent prior to data collection; the moderator of the first group did not respond within two weeks, so a new group was selected. These two groups were selected in December 2012, earlier than the main sample, using the two lists of groups as they were at that time. For the pilot, threads were selected systematically and at random from the threads shown on the group’s front page (i.e. the most recent and active threads) until the total messages per group reached between 50 and 60; in both cases only one thread was selected containing 60 messages. Any thread with fewer than two messages was to be excluded from selection. All messages in the selected threads, up to the 60-message limit, were part of the sample for the pilot test, which totaled 120 messages. At 20% the size of the intended sample for the main content analysis phase, the pilot sample provided sufficient data to assess if the proposed procedures were appropriate and how long this phase of the study would take. The pilot study allowed adjustments to be made for the main content analysis phase, based on problems and difficulties observed.

For the main content analysis phase, the ten groups were selected on April 30, 2013, a later date than the two for the pilot test, using the two lists of groups as they were as of that day. A few weeks later, threads were systematically selected at random from the threads shown on each group’s front page (i.e. the most recent and active threads) until the total messages per group reaches between 50 and 60. As with the pilot, any thread with fewer than two messages was excluded from selection. No more than the first 20 messages in each thread selected were part of the sample, a change from the pilot test made to ensure at least three threads per group were selected and improve the representativeness of the sample. This was intended to lead to a total sample of between 500 and 600 messages, about half from LibraryThing and half from Goodreads. The samples in practice consisted of 286 messages from LibraryThing and 233 from Goodreads, for a total of 519 messages (see also Chapter 4 , section 4.1 ). For all random and systematic sampling in the pilot and main data collection stages, the starting point and interval was chosen by generating random numbers using Microsoft Excel’s RANDBETWEEN function.

This stratified random sampling procedure was chosen to encourage representativeness of the resulting sample while ensuring data allowing for meaningful analysis was selected. Messages, threads, or groups could be selected purposively, but such a method could result in a sample biased towards a given type of message, thread, or group. Random sampling of groups and threads from the population deemed useful for analysis produced a sample of messages from LibraryThing and Goodreads that can be judged to be quite representative, if not quite equivalent to one generated from simple random sampling since the sampling frames did not include the entire population of groups. The sizes of the sample at each stratum were chosen to balance representativeness against the time and resources necessary to complete content analysis.

3.4.3. Data Collection Procedures

Messages were collected by using a Web browser to access the LibraryThing and Goodreads web sites, following the sampling procedures discussed above. Once a thread was displayed on the screen, up to 20 messages from the thread—starting with the earliest messages—were copied and pasted into a Microsoft Word document; one such file was maintained per thread. As found in the digital libraries, each message’s author, date/time posted, and message content was saved to that file. Images or other media included were saved in their original context as best as possible. Members’ identities, as indicated by their usernames, were used to allow for identifying common message authors in a thread, for analysis of the flow of conversation, and for identifying potential participants for later phases of the study. Identities remained confidential and were not be part of further analysis, results, or publications; psuedonyms are used in this dissertation (see section 4.1 ). Avatars from Goodreads were discarded, as members’ usernames were sufficient for this purpose. These documents were stored as discussed in section 3.8 on data management.

3.4.4. Data Analysis

For analysis, the documents were imported into NVivo qualitative analysis software, version 10, running on a MacBook Pro via a virtualized Windows 7 installation. Each message was examined and codes were assigned based on its latent meaning and interpretation. The codes to be assigned drew from boundary object theory, the social worlds perspective, and the theory of information worlds, which served as an interpretive and theoretical framework for the content analysis (cf. Ahuvia, 2001). These codes were common to multiple phases of this study, and can be found in section 3.7 below. So-called "open" codes, not included in the list but judged by the researcher to be emergent in the data and relevant to the study’s purpose and research questions, could be assigned during the content analysis and coding process, as recommended by Ahuvia (2001) for interpretive content analyses and others for general qualitative data analysis (e.g. Charmaz, 2006). Findings from the data as coded and analyzed, including open codes, are included in Chapter 4 , section 4.1 .

3.4.4.1. Pilot test

These coding and analysis procedures were piloted first, using data from two of the groups, prior to their use in the main content analysis phase. Two volunteer coders, doctoral students at the FSU School of Information [1] , applied the coding scheme and procedures developed for analyzing qualitative data in this study, presented in greater detail in section 3.7 below. The researcher applied the same scheme and procedures. Measures were in place to ensure the validity, reliability, and trustworthiness of the data and analysis, as discussed in section 3.9 below. Both intercoder reliability statistics and holistic, qualitative analysis of the results were used to clarify the scheme and procedures after each round of coding. Changes that were made to procedures and the coding scheme, and issues encountered with intercoder reliability statistics, are discussed at length in section 3.7 below.

3.5. Survey

Surveys are a common research method in the social sciences, including library and information science. They allow characteristics of a population to be estimated, via statistics, through analysis of the quantified responses given to questions by a small sample of the population (Fowler, 2002; Hank, Jordan, & Wildemuth, 2009; Sapsford, 1999). Surveys consist of "a set of items, formulated as statements or questions, used to generate a response to each stated item" (Hank et al., 2009, p. 257). The data collected may describe the beliefs, opinions, attitudes, or behaviors of participants on varied topics, although most research surveys have a special purpose and focus (Fowler, 2002). This is true in the case of the survey used here, which focused on obtaining data on uses of LibraryThing and Goodreads by a sample of its users, in the specific context of their usage as boundary objects within and across social and information worlds.

The following sections cover the components of survey research methods cited by Fowler (2002, pp. 4–8) and Hank et al. (2009) as they apply to the survey used in this study. These include discussion of the unit of analysis, population, and sampling (sections 3.5.1 and 3.5.2 ); concept operationalization and survey question design (sections 3.5.3 ); pretesting and data collection ( section 3.5.4 ); and data analysis ( section 3.5.5 ). The survey was designed as a coherent whole—as recommended by Fowler (2002, p. 7)—and in relation to the content analysis and interview methods used in other phases of the study.

3.5.1. Unit of Analysis

For the survey phase of this dissertation study, the unit of analysis was the individual LibraryThing or Goodreads user . These users were—and are—understood to be members of one or more communities, social worlds, or information worlds, and to be members of or frequent one or more LibraryThing or Goodreads groups. Analysis of their responses to questions about these groups and other communities they were part of allowed for greater understanding of the roles the digital library plays for them in context of these worlds. Tentative conclusions could be made about the nine groups from which users were surveyed and about the communities associated with these groups, but generalization to LibraryThing and Goodreads as a whole was not possible, as explained in section 3.5.2 below.

3.5.2. Population and Sampling

The broader population of LibraryThing and Goodreads users totals over 26 million people, and the logistics of constructing anything resembling a sampling frame—i.e. a complete list of all users of the two sites—are all but impossible. Given the focus in the content analysis phase on nine groups (five from LibraryThing, four from Goodreads), narrowing the population to include any user who visits, frequents, or is a member of one or more of these groups made the task of sampling possible and the population compatible with the population of messages used in the content analysis phase. This narrowing of population led to a less representative population than that of all LibraryThing and Goodreads users, limiting the kinds of analysis that could be done of the survey (further details below and in Chapter 4 , section 4.2 ,).

Two sampling methods were used to select potential survey participants from this population:

• A purposive sample, consisting of all LibraryThing users who posted a message within the five LibraryThing groups selected for the content analysis phase. The pool of messages included the messages selected for the main sample in the content analysis phase. (Goodreads did not consent to messaging of Goodreads users for this purpose, so Goodreads users were excluded from this sample.)
• A convenience sample, consisting of all LibraryThing and Goodreads users who responded to an invitation to participate posted to each of the nine groups selected for the content analysis phase (procedures detailed in section 3.5.5 below).

All users who met the criteria (having posted a message or responded to the invitation) and human subjects requirements for age (between 18 and 65) were allowed to participate, helping to increase the responses collected and the representativeness (as best as possible) of the results obtained.

A true random sample, even from the narrower population, could not be drawn because the researcher could not generate a complete list of visitors to and members of the selected groups. Obtaining such a list from LibraryThing and Goodreads—or the group moderators, should they have access to one for their group—would have placed an unreasonable burden on the digital libraries and could have jeopardized their cooperation in and the successful completion of this study. Such a list would have violated the privacy rights of the members of these groups. A random element is included in the sampling process by using the random groups selected during the content analysis phase, but the sample still lacks much of the representativeness of a true random sample. Users could choose to participate or not and not all users of the nine groups were guaranteed to see the invitation, making it impossible to infer beyond the sample due to selection bias. One may assume survey respondents are at least moderately representative of the population of users of the nine LibraryThing and Goodreads groups, and so conclusions can be inferred about those users through nonparametric statistics. Further details are given in Chapter 4 , section 4.2 .

3.5.3. Operationalization of Concepts and Instrument Design

The phenomena of interest for the survey were similar to the phenomena of interest in the content analysis and interview phases of the study: the concepts of boundary objects, translation, coherence, information worlds, social norms, social types, information values, information behaviors or activities, social worlds, organizations, sites, and technologies. Conceptual definitions for these are found in boundary object theory, the social world perspective, the theory of information worlds, and the synthesized theoretical framework for social digital libraries (see Chapter 2 ). For the purposes of the survey and in the context of answering the research questions of this study, these concepts were operationalized through a set of Likert scaled questions (Brill, 2008; McIver & Carmines, 1981), adapted from the conceptual definitions found in the literature, theories, and synthesis thereof. These questions can be found as part of the survey instrument in Appendix B , section B.1 .

Four to six Likert items (Brill, 2008; McIver & Carmines, 1981) for each of the concepts and phenomena of interest were included in the survey. A symmetric five-point scale was used for each item, as is traditional for Likert items (Brill, 2008); five response choices provides for higher levels of reliability without offering respondents too many choices (Brill, 2008), and questions can be re-scaled without significant loss of statistical validity (Dawes, 2008). Each item used the following labels for response choices: Strongly Agree(5), Agree, Neutral, Disagree, and Strongly Disagree(1). In analysis, each of the items was assigned a numeric rating (5–1) and summed to form Likert scales for each phenomenon (Brill, 2008; McIver & Carmines, 1981). Statistical analysis checked the internal consistency and reliability of each scale, with items dropped that contributed to lower levels of reliability (see sections 3.5.5 and 3.9 below, and Chapter 4 , section 4.2.1 ). Using at least four items per scale allowed for appropriate statistical analysis to proceed.

Questions were developed, based on the literature and theoretical framework reviewed in Chapter 2 , to measure each of the phenomena of interest. Hank et al. (2009, pp. 257–258) provided a list of suggestions for constructing survey instruments and writing questions: ensure questions are answerable, stated in complete sentences, use neutral and unbiased language, are at an appropriate level of specificity, and are not double-barreled. They suggested participants should not be forced to answer any one question. Fowler (2002, pp. 76–103) included a chapter on designing questions that are good measures in his book on survey research methods. He cautioned researchers to be careful questions are worded adequately; mean the same to and can be understood by all respondents; can be answered given the respondents’ knowledge and memory; and do not make respondents feel uncomfortable and desire not to give a true, accurate answer. According to Fowler, researchers should not ask two questions at once. Sapsford (1999, pp. 119–122) agreed and suggested care should be taken to ensure questions are precise, lack ambiguity, and are easy to understand and in colloquial language. The questions developed for the survey in this study, found in Appendix B , section B.1 , were developed by the researcher and reviewed by the researcher and his supervisory committee in light of this advice.

An additional set of demographic and usage questions was part of the survey instrument, in a separate section at the end as recommended by Peterson (2000, as cited in Hank et al., 2009, p. 258). These questions allowed for collection of data on other variables of potential relevance to and having possible impact on the phenomena of interest, including use of the Internet, LibraryThing and Goodreads, the groups feature of the sites, and other social media and social networking web sites; and demographic factors such as age and gender. These demographic questions can be found in Appendix B , section B.1 .

3.5.4. Data Collection Procedures

3.5.4.1. pretest.

The first stage of data collection was to pretest the survey instrument to help ensure its reliability and validity (Hank et al., 2009, p. 259). A convenience sample of graduate students and graduate alumni of Florida State University was invited to pretest the survey and answer a few short, open-ended questions about their experience. Recruitment took place via face-to-face discussion, e-mail, and Facebook messages. All pretesters came from the School of Information; initial attempts were made to have this sample represent multiple departments from the university, but no students from other departments contacted (Business and Communication) volunteered. Flyers were posted later in the pretest period and the survey opened up via a direct link, to see if undergraduate or graduate students from other departments would be interested, but no responses were received through the link. One School of Information faculty member did volunteer his time to pretest the survey, and his input was welcomed alongside the students. Minor changes were made as a result, reducing the number of questions slightly to reduce perceived repetitiveness and clarifying other questions that pretesters reported getting stuck on. The pretest helped confirm the length of time for completion of the survey.

3.5.4.2. Main survey

The second stage of data collection was to select the samples discussed in section 3.5.2 and send invitations to participate to them. A couple of weeks before this began, the researcher contacted LibraryThing and the moderators of each Goodreads group to inform them of the beginning of the survey. A staff member from LibraryThing posted a short message in each group to let users know that the research would be taking place and had been given LibraryThing’s approval, to ensure invitations were not seen as spam. (LibraryThing required this step as part of their approval of the research; see Appendix E , section E.1 .) Goodreads moderators were welcome to inform their groups of the upcoming research.

The purposive sample was drawn from LibraryThing users who posted messages collected during the content analysis phase. Each of these users was sent an invitation letter, included in Appendix A , section A.2.1.1 . The private message features of LibraryThing were used to send the invitations to the selected users; while LibraryThing users can include an e-mail address in their profile, not all did so. Reminder invitation letters ( Appendix A , section A.2.1.2 ) were re-sent two weeks and four weeks after the beginning of data collection to remind individuals who had not completed the survey and thanking users who had. The convenience sample was drawn by posting an invitation, included in Appendix A section A.2.2 , to each of the LibraryThing and Goodreads groups selected during the content analysis phase. This invitation was re-posted to the same groups two weeks and four weeks after the beginning of data collection, to help ensure as many group members and visitors as possible saw it and had a chance to respond. Permission was granted by LibraryThing and Goodreads staff for this method of data collection (see Appendix E , sections E.1 and E.2 ).

Participants were given a total of six weeks to complete the survey from August 26th, 2013, the date data collection first began for this phase of the study. The survey was expected to take users about 15 to 20 minutes, an estimate confirmed by the pretesters—with more subject knowledge—taking between 7 and 16 minutes. The reminders at two and four weeks, number of visitors to and members of the nine groups, and number of users directly invited on LibraryThing led to sufficient data for analysis (see Chapter 4 , section 4.2 ), although snowball sampling and other techniques were held in reserve in case they were necessary.

3.5.4.3. Compensation

To encourage participation, compensation was offered in the form of a drawing for one of ten $25 Amazon.com, Barnes and Noble, or Books-A-Million gift cards. These stores were selected since they include the most popular online bookstore—Amazon.com, who after this selection was made acquired Goodreads—and the two most popular brick-and-mortar bookstores (which also have an online presence). Participants were given a choice of which store they would prefer, increasing the potential usefulness of the gift card to them and reducing potential bias created by supporting only one store. Other bookstores are smaller, do not offer online gift cards, or have few locations; offering gift cards from every possible store would present logistical challenges. The e-mail addresses of all participants who completed the survey and included an e-mail address in their response were entered into a Microsoft Excel spreadsheet (maintained under the data management procedures detailed in section 3.8 ). Gift card codes were e-mailed to 10 random e-mail addresses—selected by using Excel’s RANDBETWEEN function to generate 10 random numbers between 1 and the number of users who took the survey, then selecting those users from the spreadsheet—for the store they selected as preferred; these were sent on November 9 th , about one month after the survey was closed. Funds for the gift cards came from a Beta Phi Mu Eugene Garfield Doctoral Dissertation Fellowship, which I acknowledge and am thankful for.

3.5.4.4. Online hosting

The survey instrument was hosted online using Qualtrics online survey software, made available by FSU to all students and faculty. An online, Internet-based survey provided the greatest chance of reaching users of LibraryThing and Goodreads in the context of their use of the site and their interactions with other users. It cost less—survey hosting for a questionnaire of any length is provided free by Qualtrics in association with FSU—and took less time than a self-administered paper survey was expected to, while providing for honest answers and requiring less direct researcher involvement compared with an administered paper or telephone survey (Fowler, 2002, pp. 71–74). Participants completed the survey by following a link in the invitation letters; two separate links were used for users of LibraryThing and Goodreads, so that the survey could be personalized to refer to each digital library by name.

3.5.4.5. Consent and follow-up

The first page of the survey included an informed consent statement, included in Appendix A , section A.2.3 , which participants had to agree to before they could begin answering the survey questions. As seen by the last few questions in Appendix B section B.1 , participants were asked for their e-mail address for purposes of compensation, if they were interested in participating in a follow-up interview, and if they desired a report of the findings of the research once the study was complete. These e-mail addresses are being kept confidential and are stored in a secure, password-protected encrypted volume, the password known to the researcher but no one else. Details of data management are discussed in section 3.8 .

3.5.5. Data Analysis

The survey results were analyzed using SPSS statistical analysis software running on Windows, accessed through a virtual lab environment supported by FSU. First, the Likert scales were analyzed to determine the internal consistency and reliability of the scales via Cronbach’s alpha, following the procedures related by George and Mallery (2010). Individual items were dropped from a scale if their removal would increase the Cronbach’s alpha (and the reliability) of the overall scale. This procedure and its results are detailed in Chapter 4 , section 4.2.1 . The average of the remaining items in the scale was then taken, resulting in one value ranging from one to five for each of the concepts being measured. Combined with the demographic variables collected in the second half of the study, these were analyzed using appropriate, mostly nonparametric statistics including chi-square analysis, Mann-Whitney U tests, median tests, Kruskal-Wallis tests, Wilcoxon signed rank tests, and Kendall’s τ correlations (see Chapter 4 , section 4.2 for details).

3.6. Interviews

Qualitative interviewing, used in the third phase of this study, is a descriptive and interpretive research method that seeks meaning (Kvale & Brinkmann, 2009). While interviewers may seek basic facts, explanations, and statistics, nuanced explorations and descriptions of phenomena are of core interest. Interviews in qualitative and mixed-methods research projects are used "to understand themes of the lived daily world from the [participants’] own perspectives" (p. 24), through researcher interpretation of "the meaning of the described phenomena" (p. 27). Interviews for research purposes are often seen as a form of "professional conversation" (p. 2; see also Lincoln & Guba, 1985a, p. 268; Sutton, 2010, p. 4388) between the interviewer and the interviewee, on given themes introduced by the interviewer but assumed to be of mutual interest to the interviewee. The two "act in relation to each other and reciprocally influence each other" (Kvale & Brinkmann, 2009, p. 32). Interviewees choose specific instances, examples, or areas within the chosen theme(s) to discuss with the interviewer.

Interviews serve as a source of data on phenomena from the past, present, or (potential) future of interviewees, including "persons, events, activities, organizations, feelings, motivations, claims, concerns, … other entities" (Lincoln & Guba, 1985a, p. 268), and the complex interrelations between all of these. Interviews can help to verify ("member check"), extend, and triangulate data and information already obtained via other methods (Creswell & Plano Clark, 2011; Lincoln & Guba, 1985a). They allow for the gathering of research data when the researcher or his/her colleagues cannot conduct an ethnographic participant observation due to time, location, language, or other constraints (Sutton, 2010).

This dissertation study used semi-structured qualitative interviews employing the critical incident technique (Fisher & Oulton, 1999; Flanagan, 1954; Woolsey, 1986) to explore and describe the phenomena surrounding the roles of LibraryThing and Goodreads, as boundary objects, within and across social and information worlds. Interviews helped find nuances and details that were not possible to determine through the survey questionnaire and were missed, glossed over, or not observable during content analysis. The following sections discuss the strengths of interviews for this study, the chosen unit of analysis, population and sampling procedures, design of the interview instrument, procedures used for conducting the interviews, and data analysis.

3.6.1. Strengths of Interviews

The strengths of qualitative interviews are a good fit with the framework and perspective taken in this dissertation. These strengths are evidenced by many of the studies of social digital libraries reviewed in Chapter 2 using interviews (Bishop, 1999; Bishop et al., 2000; Chu, 2008; Farrell et al., 2009; Marchionini et al., 2003; Star et al., 2003; Van House, 2003; You, 2010) and the frequent use of interviews in studies of social and information worlds and of boundary objects (see Burnett, Burnett, et al., 2009; Burnett, Subramaniam, et al., 2009; Chatman, 1992; Clarke & Star, 2008; Gal et al., 2004; Gibson, 2011, 2013; Kazmer & Haythornthwaite, 2001). Thick, nuanced description of meanings, close to users’ thoughts (Forsythe, 2001; Geertz, 1973; Kvale & Brinkmann, 2009), was intended to help expose the social construction of these meanings and of the phenomena of social and information worlds, which happened (see Chapter 4 , section 4.3 ). Since true ethnographic observation would be difficult to arrange and could miss the social elements of interest, qualitative interviews were the best choice for returning rich, descriptive data on participants’ social and information worlds and the roles LibraryThing and Goodreads play in them. The qualitative interviewing literature states that its flexibility as a technique addresses the different contexts interviewees—with varying interests and backgrounds—come from, allowing the interviewer to adjust (Kvale & Brinkmann, 2009; Westbrook, 1997); this was true in practice in this case. The development of rapport can build opportunities for future follow-up, longitudinal research with the same participants, exploring the results of this study in greater detail (Westbrook, 1997). The understanding of participants of the roles of LibraryThing and Goodreads in the social and information worlds they are part of is at the core of this study, and the obtaining of descriptions and perspectives of participants’ "lived worlds" and their "understanding of the meanings in their lived world" was an appropriate use of interviews and played to their strengths (Kvale & Brinkmann, 2009, p. 116).

3.6.2. Unit of Analysis

The unit of analysis chosen for the interview phase of the study was the individual user of LibraryThing or Goodreads. These users were understood, as in the survey phase, to be part of one or more social or information worlds, and their participation in and responses to the interview informed analysis of the roles of LibraryThing and Goodreads in their experiences, in these existing worlds, and in the potential emergence of new worlds. As discussed above and in Chapter 2 , while individuals were interviewed the theoretical framework underlying this proposed study allowed for multi-leveled analysis, taking advantage of the strengths of interviews over other methods while minimizing their weaknesses.

3.6.3. Population and Sampling

The broader population of LibraryThing and Goodreads users totals over 26 million people; as with the survey phase of the study, sampling from this large population would present major logistical challenges. Given the existing sample of users selected to take the survey, restricting the sample of potential interview participants to this subgroup of the population—a ready-made sampling frame—provides a manageable task, if perhaps not anything approaching a true random sample. This method of sampling is appropriate in this case since data is available from the survey about these users, their social and information worlds, and the roles LibraryThing and Goodreads may play in them, leading to more insightful interview data.

The interview phase used purposive sampling of users whose survey responses indicated they could provide insightful data on the roles of LibraryThing and Goodreads in existing and emergent social and information worlds. Determination of this indication was done by looking at the content analysis and survey findings and prioritizing which scores on which variables were most of interest. Users who indicated they would be willing to participate in follow-up research served as the sampling frame, from which participants were sampled and chosen with an eye towards obtaining thick description (Geertz, 1973) of the picture of the phenomena under study, given other constraints such as time and availability. As interviews continued towards saturation, these criteria were reviewed and revised, and ensuring that interviewees were at least moderately representative of the group of survey participants became a concern. True and complete representativeness is not necessary when using qualitative interviewing, but saturation of findings is a necessary requirement (Bauer & Aarts, 2000; Gaskell & Bauer, 2000; Westbrook, 1997), and so sampling continued "until further exemplars"—interviewees in this study—"fail[ed] to add new nuances or to contradict what is understood" from the existing collected data (Westbrook, 1997, p. 147). This sampling method was chosen to obtain data to answer the research questions—from the interviews and in combination with findings from the other two methods—and to provide an accurate representation of LibraryThing and Goodreads in the context of the communities of users from the nine groups selected at the beginning of the content analysis phase.

Participants who were selected due to expectations they would provide insightful data through an interview were invited to take part via the e-mail addresses they provided when confirming their willingness to participate in an interview. The letter prospective interviewees were sent is in Appendix A , section A.3.1 . An initial sample of six prospective interviewees—three from each digital library—was e-mailed at first, to allow interviews to be arranged within a week or two of the contact date and not be forgotten about by participants if scheduled too far in advance. Further prospective participants were invited every week or two thereafter, when necessary to increase the sample size. If and when selected users did not respond to the initial request, a second request was made one to two weeks later, except in the cases at the end of the interview data collection when saturation had been reached. New users replaced the original ones in the sample if the latter did not respond after two to three weeks.

3.6.3.1. Pretest

Prior to collection of actual interview data, the interview instrument and procedures (as discussed in the next two sections) were pretested with an additional convenience sample of two FSU School of Information alumni and one FSU School of Information faculty member who helped pretest the survey. The procedures for this were identical to the procedures discussed below for the main interview phase. Pretesting allowed for potential refinement of the instrument and procedures, ensuring questions are understandable by a broader population, and making any necessary adjustments to the sampling method for the main interviewing process. No transcriptions or data analysis from this pretest took place, and audio recordings that were made to test procedures were only used to refine the interview instrument and procedures; they were deleted once the main interviews began. No specific changes were made to the instrument, although the potential need for additional prompting in association with a few questions was observed; quirks and foibles of the recording software were discovered, leading to tighter and more careful following of recording steps for the main set of interviews.

3.6.4. Instrument Design

The interviews were semi-structured; they used an instrument as a guide, but were treated as a conversation guided by the interviewer’s questions and the interviewees’ personal responses and reflections (Kvale & Brinkmann, 2009; Lincoln & Guba, 1985a). The instrument, included in Appendix C, provided pre-planned questions and themes, but additional follow-up questions and prompts not included in the instrument emerged from the conversation and its natural progression. This allowed key themes related to the research questions to be discussed and focused on without restricting the interview to no more than a given set of questions in advance (cf. Suchman & Jordan, 1990).

Key themes explored in the interviews included

• participants’ use of LibraryThing or Goodreads, focusing on use as a boundary object;
• the social and information worlds of participants, and their relationship to LibraryThing or Goodreads;
• the characteristics of these social and information worlds—their social norms, social types, information values, information behaviors, activities, organizations, sites, and technologies—and their impact on the user and their use of LibraryThing or Goodreads;
• translation between, coherence across, and convergence of social and information worlds, via LibraryThing or Goodreads; and
• the emergence of new social or information worlds through translation, convergence, or related activities and behaviors of LibraryThing or Goodreads users.

Focusing on critical incidents (Fisher & Oulton, 1999; Flanagan, 1954; Woolsey, 1986) of times when users interacted with others using the LibraryThing or Goodreads digital libraries helped provide a rich environment and context for exploration of these themes in detail with each interviewee. Among the interviews the degree of focus by individuals on the critical incident versus the broader spectrum of their use varied, but this was accepted as a natural, emergent element of the interviews, and follow-up questions and prompts were used to ensure sufficient data was elucidated on the incidents. The questions included in the instrument and in prompts and follow-ups used drew from the advice set down by Kvale and Brinkmann (2009, pp. 130–140) in their discussion of scripting interviews and types of interview questions, including

• introducing themes before asking detailed questions;
• focusing on descriptions of what occurred and how during critical incidents, instead of why it happened (at least to begin with);
• following up on responses as appropriate;
• seeking projection of interviewees’ opinions or the opinions of others in their social and information worlds; and
• checking the researcher’s interpretation of previous findings and interview responses.

3.6.5. Data Collection Procedures

As mentioned above, prior to collection of actual interview data the interview instrument and procedures was pretested with two FSU iSchool graduate alumni and one FSU iSchool faculty member.

3.6.5.1. Preparation and recording

After participants agreed to be interviewed by replying to the invitation discussed in section 3.6.3 , a specific date and time was arranged for the interview to take place. Since no participants were at locations close to Tallahassee (and few were expected to be), face-to-face interviews would have been difficult to accomplish. For this reason, it was planned that interviews would take place using online audiovisual media, as popular in studies of "Internet-based activity … where the research participants are already comfortable with online interactions" (Kazmer & Xie, 2008, pp. 257–258). Interviewees were offered a choice of Skype (skype.com), Google Hangouts (accessible via plus.google.com), Apple FaceTime (apple.com), or telephone. Interviews were audio recorded, with interviewee permission; GarageBand (apple.com/ilife/garageband) and Soundflower (cycling74.com/products/soundflower) software were used to record Skype and Apple FaceTime calls, while telephone calls were recorded via Google Hangouts, Google Voice (voice.google.com), GarageBand, and Soundflower software. No users chose Google Hangouts, and more than expected chose telephone calls; while online audiovisual media were the intended plan, interviewees’ preferences were attended to, and this did not cause any major issues with collecting interview data.

The interviewer took any notes he felt necessary on his impressions of the interview as soon as the interview has concluded, to not distract the interviewee with note taking but help ensure an accurate capturing of the interview process. Most interviews took between 40 and 55 minutes; full details are given in Chapter 4 , section 4.3 . These interview procedures allowed for a level of data equivalent to or greater than face-to-face interviews to be gathered, eliminating any potential weaknesses from a non-traditional interview setting while maintaining the strengths of synchronous interviews (Kazmer & Xie, 2008).

3.6.5.2. Introduction and informed consent

The interview process began with introductions, thanking the interviewees for participating, explaining the logistics of the interview, and ensuring that informed consent was obtained. Since obtaining written consent in person was not possible, participants were e-mailed a link to a page (the content for which is shown in Appendix A , section A.3.2 ) requesting their consent for the interviews, including the interview informed consent form, a couple of days before the interview. (This used the same FSU-partnered Qualtrics system as for the survey.) I requested interviewees to review this page and ask any questions they had. Before the interview recording began, consenting participants clicked an "I consent" button at the bottom of the page; some did this before audio or video contact was made, others waited until I directed them there just before the interview began. I then reviewed "the nature and purpose of the interview" with the interviewee, to ensure they knew the overall theme and topic of discussion (Lincoln & Guba, 1985a, p. 270). Prior to the critical incident portion of the interview, I asked a general, "grand tour"-type question (with follow-up prompts as necessary) to explore participants’ use of LibraryThing or Goodreads, the reasons for this use, and the groups they participate in.

3.6.5.3. Critical incident technique

The biggest portion of the interview employed the critical incident technique, a flexible interviewing technique intended to obtain "certain important facts concerning behavior in defined situations" (Flanagan, 1954, p. 335). First developed for use in aviation psychology, it has become a popular interviewing technique in the social sciences, education, and business, including LIS (Butterfield, Borgen, Amundson, & Maglio, 2005; Fisher & Oulton, 1999; Urquhart et al., 2003; Woolsey, 1986). It is often used in exploratory research to build theories, models, or frameworks for later testing and refinement, as typified by Savolainen’s (1995) research establishing his Everyday Life Information Seeking (ELIS) model. Flanagan (1954) outlined five main stages in the technique. The first two stages are to provide further operational definitions and structure for interviews, which have been discussed in the sections above. The fourth and fifth, procedures for analysis and interpretation of data gathered from interviews, are discussed in sections 3.6.6 and 3.7 below.

The third stage is the actual collection of a critical incident from each interviewee. In a critical incident interview, after initial introductions and formalities, the interviewer asks the interviewee to recall an incident where given situation(s) or behavior(s) occurred, as defined during the previous stages. Per Flanagan (1954), these incidents should be recent enough to ensure participants have not forgotten the details of them. Specific language is used to get interviewees to think of such an incident. In this study, the following language was used, with slight changes incorporated in the context of a given interview:

Now I’d like you to think of a time within the past few weeks where you interacted with others, either people you already knew or people you did not know, while using [LibraryThing / Goodreads]. (Pause until such an incident is in mind, or gently prompt the interviewee if they have trouble recollecting one.) Could you tell me about this interaction and how it came about?

This initial question allowed interviewees to refresh their memory of the incident by going over it in their mind, and provided data on their overall impressions of the interaction and how it came about. After this initial discussion, I guided the conversation with gentle prompts and follow-up questions designed to steer the conversation about the incident to the themes mentioned in section 3.6.4 above. Main questions were included in the interview instrument (see Appendix C ); prompts were not. All questions and prompts were aimed at eliciting "the beliefs, opinions, … suggestions … thoughts, feelings, and [reasons] why participants behaved" that way during their interaction (Butterfield et al., 2005, p. 490), in the context of LibraryThing or Goodreads and the social and information worlds at play in the incident.

3.6.5.4. Finishing up

Once the critical incident had been explored at length, the interview concluded with final questions intended to help validate and generalize the findings obtained from the critical incident portion of the interview, a process often called "member checking" (Lincoln & Guba, 1985a). I gave an overall impression of the role or roles I felt LibraryThing or Goodreads played in the incident and in the interviewee’s overall use of the site, and would ask if the impression seemed correct to the interviewee or—if they responded before I could get to that part—engaged them in further reflective conversation. Interviews confirmed if the incidents participants shared matched their overall experiences. The interview concluded by me thanking interviewees for their time and participation, and answering any questions they had (as a couple did about where the research was going or when they would hear about the overall findings). As mentioned above, as soon as the interview was over I took time to write up any notes I felt were necessary, to capture any elements of the experience that risked being lost due to fading memory. Interviewees were then thanked again for their participation and help via e-mail follow-ups a few days to a week later.

3.6.6. Data Analysis

All interview audio was transcribed by the researcher, who used Audacity software (audacity.sourceforge.net) to play back the interview and Microsoft Word to enter the transcription. Parts found to be difficult to understand could be slowed down or amplified in volume using the built-in features of the Audacity software; it provided noise reduction features that were helpful for one or two interview recordings. Any notes taken not already in digital form were transcribed. All notes, audio, and transcriptions were stored as discussed in section 3.8 .

Data analysis proceeded in a similar fashion to the content analysis phase of the study. Transcripts and notes were imported into NVivo 10 qualitative analysis software, which was used to look over each file and assign codes to sentences and passages. As with the earlier qualitative method, the codes assigned draw from boundary object theory, the social worlds perspective, and the theory of information worlds, which served as an interpretive and theoretical framework for analyzing the meaning of interview responses. They can be found in section 3.7 below. Open codes not included in the list but judged to be emergent in the data and relevant to the study’s purpose and research questions could be assigned during the coding process, as recommended by Charmaz (2006) and Kvale and Brinkmann (2009, p. 202), among others; these codes included open codes from the content analysis phase. Measures to ensure the trustworthiness of the data and analysis were taken as discussed in section 3.9 .

3.7. Qualitative Data Analysis

All qualitative data—consisting of the messages collected for the content analysis and transcripts and notes from the interviews—were imported into NVivo 10 qualitative analysis software, which was used to look over each transcript and assign codes.

For analysis, an approach similar to grounded theory (Charmaz, 2006; Strauss & Corbin, 1994) and its constant comparative method was taken, but without the same focus on open coding. Codes were first applied to sentences in messages or in participants’ interview responses (as transcribed). Only the lowest, most detailed level of codes, as presented in the codebook (as 3.7.2 and 3.7.3 below), were applied. Two exceptions to sentence-level coding were allowed. For the content analysis phase, no more than two codes could be applied to an entire message if there was clear evidence for them throughout the message. For the interview phase, no more than two codes could be applied to a paragraph, answer to a question, or short exchange (no more than half a page) if there was clear evidence for them throughout the paragraph, answer, or exchange. No other exceptions were allowed to this rule; codes could not be applied to units smaller than sentences (to provide sufficient context), and were required to be applied individually to multiple messages, answers, or exchanges. Memos and annotations were made to explain any cases where code(s) were applied across multiple sentences within a message or interview transcript at once, and to explain codes in greater detail where deemed necessary; a general rule of "if in doubt, add an annotation" was followed throughout analysis. These rules were refined and clarified after initial pilot testing, details of which are given in section 3.7.1 below.

After initial analysis, higher levels of analysis looked at the coding in the context of paragraphs, entire messages, message threads, and larger portions of interview transcripts, considering these in light of other threads, messages, and interviews. Throughout the coding and analysis process, consideration of the social and information worlds was explicitly multi-leveled: worlds of multiple sizes, shapes, and types were considered throughout the processes of collecting and analyzing data. The boundaries of these worlds, and where these worlds fell on the continuum of existing and emergent worlds, was considered emergent from the data, based on the conceptual, theoretical, and operational definitions given in earlier sections and in the coding scheme below. Memos and annotations were provided to explain the levels of social and information worlds under consideration, especially when boundary-related codes were applied.

The search, query, and report features of NVivo were used in further analysis and the writing of sections 4.1 and 4.3 of Chapter 4 . While messages and individual interviews (as the units of analysis) and sentences within them were coded as individual units, higher level units—passages, threads, groups, social and information worlds, and LibraryThing and Goodreads—were considered as the analysis proceeded. This allowed findings and conclusions to be drawn at multiple levels, as can be seen in Chapters 4 and 5 .

3.7.1. Pilot Testing and Resulting Changes

Pilot testing of the coding scheme and analysis procedures was conducted prior to the content analysis phase. Two fellow FSU iSchool doctoral students, having basic familiarity with the theories incorporated into the theoretical framework used here, were recruited to test intercoder reliability. Each student volunteer was provided with a "quick reference" version of the coding scheme in sections 3.7.2 and 3.7.3 below, with the final version used by the researcher as a guide for analysis included in Appendix D . Pilot test coders were given a summary of the coding rules and guidelines discussed herein. The second volunteer discussed the coding scheme, rules, and guidelines at some length with the researcher—including some brief practice coding—before coding began, and both volunteers took part in debriefing sessions with the researcher after coding had been completed. The researcher and the first volunteer coded the messages selected for the pilot test of the content analysis phase—120 messages, 60 each from one LibraryThing and Goodreads group. Changes were made after this coding cycle based on intercoder reliability statistics—using Cohen’s (1960) kappa as calculated by NVivo—and qualitative and holistic analysis of the results, and a second cycle proceeded. Further changes were made after this second cycle.

Changes were made to address weaknesses identified in the original procedures, coding scheme, and theoretical framework, to help ensure theoretical and operational clarity. Changes made after the first cycle were as follows:

Codes were only to be applied at the sentence level, with two exceptions as mentioned earlier.

Memos and annotations were stressed, especially to explain codes applied at levels higher than the sentence level and to explain coding in greater detail where deemed necessary.

Boundaries of worlds were to be considered emergent from the data, with memos and annotations recommended to explain the level of social and information worlds under consideration.

Definitions for all concepts were refined and tightened.

Cases where social norms or information value had broad application, across substantial parts of a thread or interview, were to be memoed or annotated instead of coded, since the latter was seen to be of less use for later analysis.

Information behavior was tightened, to consider only behavior that was normative at some level and to exclude general occurrences of information behavior, since under the latter interpretation whole threads and interviews could be coded.

If it was unclear whether a new world—of any size or scale—had truly emerged, memos and annotations were recommended to express the degree of confidence.

Three subcodes were added to account for different cases of LibraryThing or Goodreads acting as a standard boundary object: as an emergent site, an emergent technology / ICT, or another type of emergent boundary object.

Changes were made after the second cycle of coding and discussion among the researcher and multiple committee members, as follows:

The distinction between existing and emergent was stressed to be along a continuum, and to be a phenomenon that would emerge from the research data, similar to the size and shape of the worlds and their boundaries. Memos and annotations were further stressed to elaborate on where given cases fall on this continuum.

Codes and procedures were acknowledged to be complex, and to be using theories that had not been combined in previous research; the theoretical framework is emergent. As such, intercoder reliability statistics—as run using Cohen’s (1960) kappa after each coding cycle of the pilot test and initially planned for a portion of the interview data—were considered a less appropriate measure of the potential trustworthiness, credibility, transferability, dependability, and confirmability of the findings than originally thought. Both pilot tests showed that reaching high statistical levels of intercoder reliability would require extensive training of other coders—difficult if not impossible in dissertation research—and much fine-tuning of rules and procedures, fine-tuning that does not fit the interpretive and social constructionist paradigms in use for this research. Other techniques for ensuring qualitative trustworthiness (Gaskell & Bauer, 2000; Lincoln & Guba, 1985), already built into the study (see section 3.9.3 ), would now be emphasized alongside intracoder reliability checking at the conclusion of the study; results of the latter are included in Chapter 4 .

The following sections present the coding scheme used for each research question, as revised after the pilot testing. Section 3.7.2 includes the codes focusing on existing social and information worlds (RQ1), while section 3.7.3 includes the codes focusing on emergent social and information worlds (RQ2). The distinction between existing and emergent was treated as along a continuum, where the degree to which a world is existing or emergent was allowed to emerge from the research data. Frequent memos and annotations were made on this during analysis. An operational definition is given for the concept each code represents, as used in the coding and analysis of data from the content analysis and interviews phases. These definitions come from the literature review presented in Chapter 2 and the theories and theoretical framework described therein, with contributions from definitions in the Oxford English Dictionary’s online version (oed.com) where necessary and appropriate. A summarized version of the coding scheme, used as a quick reference during coding and analysis, is included as Appendix D.

3.7.2. Existing Worlds

3.7.2.1. translation.

Star and Griesemer (1989) defined translation as "the task of reconciling [the] meanings" of objects, methods, and concepts across social worlds (p. 388) so people can "work together" (p. 389). Multiple translations, gatekeepers, or "passage points" can exist between different social worlds (p. 390). This was operationalized as the process of reconciliation and translation of meanings—taken to include understandings—between different people, social worlds, or information worlds.

3.7.2.2. Coherence

While Star and Griesemer (1989) never gave coherence an explicit, glossary-style definition, it can be conceptualized as the degree of consistency between different translations and social or information worlds. Boundary objects play a critical role "in developing and maintaining coherence across intersecting social worlds" (p. 393). Coherence was operationalized using the common characteristics of social and information worlds, coded under the definitions given below. Coding took place at the level of these characteristics, not for coherence in general.

Social norms : Burnett, Besant, and Chatman (2001, p. 537) defined social norms as the "standards of ‘rightness’ and ‘wrongness’ in social appearances" that apply in an information world. Jaeger and Burnett (2010, p. 22) restated this as "a world’s shared sense of the appropriateness—the rightness or *wrongness—*of social appearances and observable behaviors." Drawing from these, social norms were operationally defined as the common standards and sense of appropriate (right or wrong) behaviors, activities, and social appearances in an information world. In some cases, a substantial part of or an entire thread or interview could be seen as socially normative, but it was decided that in those cases the social norms code would not be applied to every message or sentence, as doing so would not be of much use for later analysis. Instead, a memo or annotation was made to note and discuss the application of social norms to large parts of a thread or interview.

Social types : Burnett et al. (2001, p. 537) defined social types as "the [social] classification of a person." Jaeger and Burnett (2010, p. 22) elaborated on this, stating social types are "the ways in which individuals are perceived and defined within the context of their [information] world." This was operationalized following the latter definition and to include explicit and implicit roles, status, and hierarchy.

Information value : Jaeger and Burnett (2010, p. 35) defined information value as "a shared sense of a relative scale of the importance of information, of whether particular kinds of information are worth one’s attention or not." Such values may include, but are not limited to, "emotional, spiritual, cultural, political, or economic value—or some combination" (p. 35). Values may be explicit and acknowledged, or implicit within message content or interview responses. A succinct operational definition, used in this study for coding, is that information value is a shared sense, explicit or implicit, of the relative scale of the importance—emotionally, spiritually, culturally, politically, and/or economically—of information and whether it is worth attention. As with social norms, if a substantial part of or an entire thread or interview was seen as expressing the shared information values of a world, the code was not applied to every message or sentence; instead a memo or annotation was used.

Information behavior and activities : Burnett and Jaeger (2008, "Small worlds" section, para. 8) defined information behavior as "the full spectrum of normative [information] behavior … available to members of a … world"; this was restated in different words by Jaeger and Burnett (2010, p. 23). Information behavior can include seeking, searching, sharing, or use of data, information, or knowledge; communication and interaction; and avoidance of data, information, or knowledge. Strauss (1978) did not provide an explicit definition of activities, but his use of the word within the social worlds perspective corresponds with one of its senses in the Oxford English Dictionary: "something which a person, animal, or group chooses to do; an occupation, a pursuit" ("Activity," 2012). A slight restriction was placed on this operationally, that the "something" should have an informational component (with information construed to include data and knowledge). Operationally, this code was used to identify occurrences of normative, chosen information behavior and information-based occupations or pursuits—defined broadly—by members of a world. Such behavior had to be normative at some level to be coded, and general occurrence of information behavior were not coded, since under such an interpretation whole threads and interviews could be construed as such.

Organizations : Strauss (1978) stated social worlds may have "temporary divisions of labor" at first, but "organizations inevitably evolve to further one aspect or another of the world’s activities." This sense is similar to the definition of an organization as "an organized body of people with a particular purpose" found in the Oxford English Dictionary ("Organization," 2012). A combination of the two was used for operational coding: organizations are organized, but possibly temporary bodies with the particular purpose of furthering one aspect or another of the world’s activities.

3.7.2.3. Boundary object

Codes were applied for treatment of the digital library as a boundary object. This was operationalized by coding passages where the digital libraries cross the boundaries between multiple existing social or information worlds and are used within and adapted to many of them "simultaneously" (Star & Griesemer, 1989, p. 408) while "maintain[ing] a common identity across sites" (Star, 1989, p. 46). Instances of the boundary object’s use as a common site and information and communication technology (ICT) were coded using the definitions below. Coding took place at the level of these characteristics, not for boundary objects in general.

Common site : Strauss (1978) related sites to "space and shaped landscape"; the term’s use under the social worlds perspective corresponds to this sense given in the Oxford English Dictionary: "a position or location in or on something, esp. one where some activity happens or is done" ("Site," 2012). This location may be a physical, virtual, or metaphorical space, as seen in many of the concepts of community reviewed in Section 2.2. A succinct operational definition, used for coding, is that sites are spaces, positions, or locations—physical, virtual, or metaphorical—where information-related activities and behaviors take place.

Common information and communication technologies (ICTs) : Strauss (1978) defined technology as "inherited or innovative modes of carrying out the social world’s activities" (p. 122). ICTs are often referred to in the literature of LIS, knowledge management, education, and other fields without explicit definition, and there is no one historical source all uses stem from. Remaining compatible with most of this literature and adapting from the definitions of Strauss (1978) and the Oxford English Dictionary ("Technology," 2012), ICTs were operationalized for coding purposes as inherited or innovative processes, methods, techniques, equipment, or systems—developed from the practical application of knowledge—used for carrying out information or communication-related behaviors and activities.

3.7.3. Emergent Worlds

3.7.3.1. convergence.

Convergence is seen in similar light to coherence, defined above as the degree of consistency between different translations and social or information worlds. Convergence was operationalized through the emergence of common characteristics in new social and information worlds (or proto-worlds), to be coded under the definitions given in section 3.7.2.2 above for social norms , social types , information value , information behaviors / activities , and organizations . Coding took place at the level of these characteristics, not for convergence in general; coding was kept separate from that for these characteristics under coherence. If it was unclear whether a new world—of any size or scale—had truly emerged, memos and annotations were made to express the degree of emergence seen in the data.

3.7.3.2. Boundary object as standard

Treatment of LibraryThing and Goodreads as a new, local standard for a new, emergent social or information world was coded in this category, to distinguish it from treatment of the digital libraries as boundary objects within and across existing information worlds ( section 3.7.2.3 ). This will be operationalized under three subcodes, where all coding would take place:

Emergent site : Under the definition of sites given above, cases of LibraryThing or Goodreads serving as an emergent, standard, and influential space, position, or location for information-related activities and behaviors were coded here. Clear evidence of the digital library serving as a new standard site for an emergent world was necessary. This code could be applied alongside the "emergent technology" code below, and in many cases this happened.

Emergent technology / ICT : Under the definition of technologies given above, cases of LibraryThing or Goodreads providing emergent and standard processes, methods, techniques, equipment, or systems—developed from the practical application of knowledge—used for carrying out information or communication-related behaviors and activities in an emergent world were coded here. Clear evidence of the digital library providing or serving as a new standard technology within an emergent world was necessary. This code could be applied alongside the "emergent site" code above.

Emergent boundary object : Cases where LibraryThing or Goodreads served as an emergent, standard boundary object, but not as a site or technology, were coded here. Clear evidence of the digital library serving as such a role was necessary, and clear evidence that it was not serving as a site or technology was required. This code was expected to be rare and in reality was; it was applied only a few times in the content analysis and not at all in the analysis of the interviews. It was included to ensure all cases of LibraryThing or Goodreads serving as a new, standardized boundary object wer captured. This code was considered mutually exclusive with the "emergent site" and "emergent technology / ICT" codes above.

3.8. Data Management

I have kept all data from this study in digital format on my personal laptop computer. Survey data was kept in Microsoft Excel (.xls/.xlsx) format, interview audio in .mp3 format, and messages and interview transcripts in Microsoft Word (.doc/.docx) format. A password protected and encrypted disk image was created and used for all dissertation data, the password known to the researcher but no one else. Within this image, separate folders were created for each phase of the study. All data analyzed using the coding scheme discussed in section 3.7 above—including messages, interview transcripts, and notes—was also kept in an NVivo project (.nvp) file at the top level within the image. This disk image will be kept until the date arrives for destruction of records from this dissertation.

Filenames for data served and continue to serve as metadata, reflecting the source of the data (participant pseudonym or group name for individual data, phase name for collated results), the date it was collected, the digital library the data refers to (LibraryThing or Goodreads), and the type of data it represents (e.g. thread, survey response, interview transcript, interview notes, preliminary analysis). For example, bob_GR_transcript_022914 . doc could be the filename for the transcript—in Microsoft Word format—of an interview with "Bob," a Goodreads user, conducted on the fictional date February 29, 2014. Three additional spreadsheets (in Microsoft Excel format) were created to provide metadata. Two—one for LibraryThing and one for Goodreads—link participants’ names and e-mail addresses to their psuedonyms; the other has kept track of survey data for interviewees, and was used during interview recruitment to help determine who would be invited to participate.

Encrypted and password-protected backups of all research data have been made on a weekly basis (with rare exceptions due to travel) onto an external hard drive kept at the researcher’s home. Additional encrypted and password-protected backups have and will be made onto recordable CDs or DVDs, to be kept in a filing cabinet belonging to the researcher in the Shores Building on FSU’s main campus or, once the researcher leaves FSU, in a similar secure work location. All research data for this study, including backups, will be deleted and destroyed by April 30 th , 2019 (this date being fewer than five years from the completion of the study). Appropriate excerpts from the data (using pseudonyms) and synthesized data analysis, findings, and conclusions—including the completed dissertation, journal articles, and conference papers—may be shared with other researchers, scholars, and the general public up to and beyond the date given above. Future research data and findings building on the data collected and conclusions drawn during this study may be shared with other researchers, scholars, and the general public, subject to restrictions put in place by the researcher’s home institution and funding source(s) at the time of such research.

3.9. Validity, Reliability, and Trustworthiness

3.9.1. holistic: mixed methods, case studies.

The validity and reliability of mixed methods studies can be assessed in two ways (Creswell & Plano Clark, 2011). One can look at the research as a whole, considering the study’s design, interrelations, and how everything fits together to ensure high levels of validity and reliability. Towards this view, Creswell and Plano Clark provided a list of potential validity threats in mixed methods research and strategies for minimizing these threats (pp. 242–243), which have been followed throughout the design and execution of this research.

Yin (2003) provided similar guidance for case study designs, summarized in his Figure 2.3 (p. 34). Each of these has been implemented in this study as follows:

"Use multiple sources of evidence": Three different methods of data collection have been used, each sampling across different groups and users from LibraryThing and Goodreads.

"Establish chain of evidence": The methods were linked together and informed each other. Data from content analysis helped inform the survey instrument, while the content analysis and survey data helped inform the interview instrument, process, and analysis. Data from all three methods has been tied together in the overall findings and conclusions from the study (see Chapter 5 ).

"Have key informants review draft case study report": While this specific technique was not used, I confirmed with interviewees that my impression of the critical incident they shared was accurate prior to the conclusion of each interview. Participants who requested a report of the findings on completion will receive one within a few weeks after defense of this dissertation.

"Do pattern-matching": Here Yin refers to looking for "several pieces of information from the same case [that] may be related to some theoretical proposition" (p. 26). This study achieved this by maintaining a consistent focus on the same phenomena throughout all three phases and using the same themes—based on the theoretical framework developed in section 2.8 —for coding the messages (in the content analysis phase) and interview transcripts (in the interview phase).

"Do explanation-building": Here Yin refers to establishing a cause-and-effect relationship between patterns in data and theoretical propositions. The pattern-matching above, combined with the theoretical framework discussed in section 2.8 and the philosophical and epistemological viewpoint provided by social informatics and social constructionism, allowed such explanations to be developed through synthesis of data from all three phases (see Chapter 5 , sections 5.1 and 5.2 ).

"Address rival explanations": While I admit favoring the theories used in the theoretical framework developed in section 2.8 , other theories related to communities, collaboration, information behavior, and knowledge management—reviewed elsewhere in Chapter 2 —could have provided a better explanation. The existing literature in these areas and my knowledge of them is used in later sections of Chapter 5 to address possibilities beyond the theoretical framework that relate to the findings seen here.

"Use logic models": Due to limitations of this study (see Chapter 5 , section 5.7 ), a visual model may be premature at this point. I may develop figures, diagrams, and other visual aids to help present the findings as part of posters, conference papers, journal articles, and research presentations.

"Use theory in single-case studies; use replication logic in multiple-case studies": While this is a multiple-case design, only two cases are considered here. Theory—the theoretical framework in section 2.8 —and replication logic—multiple groups and two digital libraries—have played important roles in the design and execution of this dissertation study.

"Use case study protocol": Constraints placed on procedures by the two sites were unavoidable, but where possible the same procedures were used for LibraryThing and Goodreads. Messages were collected and analyzed the same way; surveys distributed, collected, and analyzed the same way; and interviews followed the same themes and procedures. The extra requirement to obtain the consent of group moderators put in place by Goodreads prior to collecting messages and survey responses from users of that digital library did not cause great differences in the data collected or its comparability with that from LibraryThing groups. The researcher took care to document the study as it proceeded, including deviations in procedures that became necessary; the most notable of these was the need to vary the intended statistics and accept greater limitations on the survey results than were at first intended, as discussed above and in Chapter 4 , section 4.2 .

"Develop case study database": Given few cases in this study, a formal database was not constructed. The data management procedures discussed in section 3.8 and NVivo qualitative analysis software—which runs on a Microsoft SQL Server database—provided similar benefits to Yin’s recommendation here.

While holistic consideration of validity and reliability is useful, a second approach is necessary: examining the validity and reliability of each phase of a mixed-methods study—quantitative and qualitative—as an individual method. Each type of research has "specific types of validity checks" to perform (Creswell & Plano Clark, 2011, p. 239), since—despite the continuum mentioned by Ridenour and Newman (2008)—different methods require different measures of their reliability and validity. The two sections below take this approach and apply it to the quantitative—survey—and qualitative—content analysis and interview—phases of the dissertation study conducted here.

3.9.2. Quantitative: Survey

Validity and reliability for quantitative research are given substantial treatment in research methods textbooks, such as Schutt (2009, pp. 130–141) and Babbie (2007, pp. 143–149). The validity of the survey data can be broken down by the different types of validity these and other authors identify as used for quantitative research:

Face validity (Babbie, 2007, p. 146; Schutt, 2009, p. 132): Given that the survey questions were developed from the theories discussed in Chapter 2 and the theoretical framework developed in section 2.8 , each of which have face validity, the questions are judged to have met face validity for measuring the phenomena in question.

Measurement validity (Schutt, 2009, pp. 130–132): The survey questions were looked over by the researcher and his supervisory committee to ensure they did not suffer from idiosyncratic errors due to lack of understanding or unique feelings; from generic errors caused by outside factors; and from method factors such as unbalanced response choices or unclear questions. Attention paid to other kinds of validity helps improve measurement validity.

Content validity (Babbie, 2007, p. 147; Schutt, 2009, p. 132): Using multiple scales and multiple questions per scale helped the questions cover "the full range of [each] concept’s meaning" (p. 132) and the full range of the roles of LibraryThing and Goodreads in the social and information worlds of their users. The content analysis and interviews provided data from fewer users, but much thicker description of the phenomena of interest, as one would expect from qualitative research methods.

Criterion validity (Babbie, 2007, pp. 146–147; Schutt, 2009, pp. 132–134): This is difficult to measure here because no survey-based measures are known to have been developed for the theory of information worlds or boundary object theory prior to this study, and the social worlds perspective makes rare use of surveys. Schutt stated that "for many concepts of interest to social scientists, no other variable can reasonably be considered a criterion" (p. 134); Babbie (2007, p. 147) advocated using construct validity in these cases instead. Fowler (2002, p. 89) made a similar argument for questions "about subjective states, feelings, attitudes, and opinions," believing "there is no objective way of validating the answers … [they] can be assessed only by their correlations with other answers," through construct validity.

Construct validity (Babbie, 2007, p. 147; Schutt, 2009, pp. 134–135): Most of the measures used in the survey significantly correlated with each other, as one would expect given their relations to each other in the social worlds perspective and the theory of information worlds.

Reliability (Babbie, 2007, pp. 143–146; Schutt, 2009, pp. 135–138): While the survey was not repeated by each participant, using multiple measures of each concept and triangulation of the findings via the content analysis and interview phases of the study served a similar role to measures of test-retest or pre- and post-test reliability in an experimental design. The reliability of the scales was analyzed, while the randomization of survey questions (except the demographic questions) helped improve reliability.

3.9.3. Qualitative: Content Analysis and Interviews

A few qualitative and mixed methods researchers hold to positivistic treatments of validity and reliability, requiring use of quantitative measures such as intercoder percentage agreement, Holsti’s (1969) coefficient of reliability, Cohen’s (1960) kappa, or Krippendorf’s (2004b) alpha. Most qualitative researchers, however, argue validity and reliability should not be ported over from quantitative to qualitative research with no changes, nor ignored; instead they must be adapted and changed to fit the naturalistic and ethnographic nature of most qualitative research (Gaskell & Bauer, 2000; Golafshani, 2003; Kvale & Brinkmann, 2009; Lincoln & Guba, 1985b; Ridenour & Newman, 2008). Which adaptations and changes should be put into place for qualitative research is the subject of debate (Golafshani, 2003). Golafshani found "credibility, … confirmability, … dependability … transferability," and "trustworthiness"—the last term preferred by Lincoln and Guba (1985b)—to be the most often terms used to describe the validity of qualitative research. No matter what term is chosen, validity is "inescapably grounded in the processes and intentions of particular [qualitative] research methodologies and projects" (Winter, 2000, p. 1, as cited in Golafshani, 2003, p. 602). Dependability and trustworthiness were the closest linked to reliability in qualitative research by Golafshani (p. 601) and Lincoln and Guba (1985b).

This dissertation research study, while drawing from all of the sources cited above, adapted the criteria and techniques cited by Gaskell and Bauer (2000) and Lincoln and Guba (1985b) for ensuring the validity and reliability of the qualitative phases of the study. These are discussed below, following four broader categories of trustworthiness outlined by Lincoln and Guba.

3.9.3.1. Credibility

The sequential, multiphase design allowed for prolonged engagement with the environment—19 months from prospectus defense to dissertation defense—and persistent, detailed observation of the phenomena under consideration. Using an approach for coding and analysis similar to the constant comparative method of grounded theory (Charmaz, 2006; Strauss & Corbin, 1994) helped ensure breadth and depth. Methods were triangulated via the sequential, multiphase design, where each method reflexively informed and was informed by the others and the theoretical framework developed in section 2.8 . The theoretical framework provides two perspectives—the lenses of the social worlds perspective and the theory of information worlds—that were triangulated in analysis, and the researcher was and is familiar with other social theories, models, and concepts of information and information behavior, some of which apply to the findings (see the later sections of Chapter 5 ). Triangulation of multiple investigators was difficult given the individual nature of a dissertation project, but the input of the dissertation committee and the researcher’s colleagues was considered and welcomed at appropriate stages. Using member checking in the interview process and later methods in the sequential design to check earlier ones led to greater credibility for the study and produced a high level of communicative validity.

Statistical intercoder reliability testing, while used during the pilot testing of the content analysis procedures, was later and is now considered less appropriate for this study; the combination of theories incorporated in the theoretical framework was being used for the first time, and as such the coding scheme and framework should be considered at least somewhat emergent. The coding scheme and procedures are acknowledged to have been quite complex. Statistics such as Cohen’s (1960) kappa or Krippendorff’s (2004) alpha are not very compatible with this exploratory study, using an emergent framework, and following an interpretive approach to analysis (Ahuvia, 2001). The pilot testing of the content analysis procedures, incorporating intercoder reliability testing with Cohen’s kappa, showed that reaching high statistical levels of intercoder reliability would require extensive training of other coders—difficult if not impossible in dissertation research—and much fine-tuning of rules and procedures, fine-tuning that might be appropriate for a non-dissertation, post-positivistic study, but does not mesh with the interpretive and social constructionist paradigms in use here nor fit with the nature and resources of dissertation research. Intracoder reliability testing was performed, using percent agreement and Cohen’s kappa, for the content analysis and interviews; this is reported in Chapter 4 at the beginning of each section of findings. Stressing of the other measures discussed here to address credibility and qualitative trustworthiness is believed to have been enough to overcome any limitations caused by not using intercoder reliability statistics.

3.9.3.2. Transferability

Every effort was made in the prospectus to be transparent in how the research would be conducted, and such transparency carried over to the research and to writing this dissertation. The data collection for the content analysis and interview phases was constructed to provide valid and complete results, from reaching saturation, leading to insightful analysis; this has occurred. As seen in Chapters 4 and 5, the data allow for thick description (Geertz, 1973) of the phenomena in context, taken from messages and interview transcripts, which can allow other researchers to assess the potential transferability of the research findings to other settings.

3.9.3.3. Dependability

As discussed above, every effort has been made to be transparent in the conduct of this research. The data collection for the content analysis and interview phases provides valid and complete results, having reached saturation, leading to insightful analysis. I remained transparent with users who were surveyed and interviewed, disclosing the full and true purpose of the study and not engaging in deception. Using participants whose survey or content analysis data indicated they would provide interest and insight in an interview helped satisfy Gaskell and Bauer’s call for revealing and relevant findings, and I feel what is found in Chapters 4 and 5 also fits. By ensuring saturation was reached in the interviews, the dependability of the study is increased further. While the inquiry audit suggested by Lincoln and Guba was not implemented for this study, the process of defending the prospectus and dissertation and the guidance of the dissertation committee throughout the process has served a similar purpose.

3.9.3.4. Confirmability

The data analysis process included memoing, annotating, and note taking at appropriate moments, including reflective comments on the data and the researcher’s experience. The researcher noted any and all reflective comments on the research study, theoretical framework, data collection process, and data analysis process during all phases of the project. Triangulation (as discussed above) helped ensure confirmability. While the formal confirmability audit suggested by Lincoln and Guba—examining if findings, interpretations, and recommendations are supported by the data—was not implemented for this study, the process of defending the dissertation serves a similar purpose.

3.10. Ethical Considerations

This study is not known to have violated any ethical principles or procedures. The content analysis phase used messages accessible to the public, posted in LibraryThing and Goodreads groups, as its source of data. The identities of the users who posted each message remains confidential. Usernames have been used to allow for identifying common message authors in a thread, for analysis of the flow of conversation, and for identifying potential participants for later phases of the study, but have not been and will not be part of further analysis, results, and publications. Identities have remained confidential throughout the survey and interview phases of the study, and will continue to do so after a defended dissertation. Psuedonyms have been and will continue to be used in any published or unpublished reports of the results and conclusions, and any other data or information with the potential to identify participants to people familiar with them has been altered for the purposes of this dissertation and future presentation and publication.

Informed consent was obtained from participants in the survey and interview phases, before they completed the survey instrument or participated in the main portion of the interview, and—as required by Goodreads for use of their digital library as a setting for this research (see Appendix A , section A.1 )—from the moderators of Goodreads groups. Their participation was voluntary; any participant who wished not to complete the survey or be interviewed, or wanted to request an interview be stopped or their survey data be deleted, would have been accommodated and allowed to not take part in or withdraw from the study. Moderators had the same right when it came to deciding if their group would take part in the study as a whole. No users or moderators who had previously consented expressed feeling uncomfortable and wishing to withdraw. Some moderators and potential interviewees did not respond to invitations, and one potential interviewee did not show up for her interview time and never responded to inquiries, but it is unclear why she chose to withdraw or why others were not interested in—in some cases further—participation. If any participants wish to withdraw their data from the study in the future, after already completing the survey or having been interviewed, their survey results, interview transcript, interview audio recording, and notes taken by the researcher after their interview will be removed from the data collected and analyzed as best as is possible, although their data will have already been analyzed and affected the conclusions drawn from data analysis (seen in Chapter 5 ). This is an unavoidable consequence and will be dealt with as best as possible by the researcher, should it occur.

On the opposite end of the research lifecycle, in two of the LibraryThing groups—which will not be named to maintain confidentiality and not "rock the boat" where it is unnecessary—a small number of users (five to ten) responded to the survey invitation post with comments disliking the survey instrument or facing confusion over the questions asked. I answered the questions and queries as best as possible without causing excessive bias in the survey results, but there was not much that could be done to please some users. They were, strictly speaking, not expressing any uncomfortable feelings—if anything they made me more uncomfortable than my survey had done to them—but this is worth noting as a negative reaction. It was not the norm; most participants were happy to complete the survey without incident, and no harm or risks occurred to any participants, greater than those experienced in everyday life, as a result of viewing or completing the survey or participating in the research in other ways.

The study was explained to participants in all letters they received, at the beginning of the survey in the informed consent statement, in the interview informed consent statement, and in verbal form at the beginning of the interview; see Appendix A for the letter and consent forms. As such, participants should have had complete awareness of the potential risks (or lack thereof) and benefits, that their participation was and is voluntary, and of the compensation provided, before giving their informed consent for each phase of the data collection. Participants were not deceived in any way at any point during this study. The potential benefits to the participants, as users of the LibraryThing or Goodreads digital libraries, were great enough to outweigh any small possibility of harm or any risks discussed above. The identity and affiliation of the researcher was known to all prospective participants via the invitation letters and informed consent statements, and the purpose of the interview and reasoning behind it was reiterated to each interview participant at the start of their interview. There were no issues seen with the researcher (as interviewer) maintaining appropriate boundaries with participants during the interview phase of the study.

The FSU Human Subjects Committee, an institutional review board (IRB), approved this study, including the pilot test of the content analysis phase. Documentation of this approval can be found in Appendix E , section E.3 .

3.11. Conclusion

This chapter has presented the details of the method and procedures for this dissertation research study. The use of content analysis, a survey questionnaire, and semi-structured interviews in sequence within a mixed methods research design addressed the purpose of the research: to improve understanding of the organizational, cultural, institutional, collaborative, and social contexts of digital libraries. As stated in Chapter 1 and shown in Chapter 2 , these contexts have important effects on users, communities, and information behavior. There is a clear need for theoretical and practical research into the roles digital libraries play within, between, and across communities, social worlds, and information worlds. This study helps satisfy that need.

The research design is well-grounded in epistemology and theory, previous research, and previous and existing practice; Chapter 2 provides this necessary context. The study operates under the tenets of the social paradigm, social informatics, and social constructionism, and incorporates boundary object theory, the social worlds perspective, and the theory of information worlds into its theoretical framework. This design has allowed for data to be collected and analyzed, at multiple levels and using multiple methods, on the roles that LibraryThing and Goodreads, two cases of social digital libraries, play as boundary objects in translation, coherence, and convergence between existing and of emergent social and information worlds. Chapter 4 turns to presenting the findings from this data and analysis of it, with Chapter 5 providing greater synthesis and discussion of the findings, implications, and conclusions of this research.

The FSU iSchool was known at the time as the School of Library and Information Studies; for simplicity the newer name (which took effect in early 2014) will be used to refer to this entity in this dissertation. The older name is still present on the invitation letters and consent forms as approved by FSU’s Human Subjects Committee in Appendix A . ↩︎

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Dissertation & Thesis Outline | Example & Free Templates

Published on June 7, 2022 by Tegan George . Revised on November 21, 2023.

A thesis or dissertation outline is one of the most critical early steps in your writing process . It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding the specifics of your dissertation topic and showcasing its relevance to your field.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

In the final product, you can also provide a chapter outline for your readers. This is a short paragraph at the end of your introduction to inform readers about the organizational structure of your thesis or dissertation. This chapter outline is also known as a reading guide or summary outline.

Table of contents

How to outline your thesis or dissertation, dissertation and thesis outline templates, chapter outline example, sample sentences for your chapter outline, sample verbs for variation in your chapter outline, other interesting articles, frequently asked questions about thesis and dissertation outlines.

While there are some inter-institutional differences, many outlines proceed in a fairly similar fashion.

• Working Title
• “Elevator pitch” of your work (often written last).
• Introduce your area of study, sharing details about your research question, problem statement , and hypotheses . Situate your research within an existing paradigm or conceptual or theoretical framework .
• Subdivide as you see fit into main topics and sub-topics.
• Describe your research methods (e.g., your scope , population , and data collection ).
• Present your research findings and share about your data analysis methods.
• Answer the research question in a concise way.
• Interpret your findings, discuss potential limitations of your own research and speculate about future implications or related opportunities.

For a more detailed overview of chapters and other elements, be sure to check out our article on the structure of a dissertation or download our template .

To help you get started, we’ve created a full thesis or dissertation template in Word or Google Docs format. It’s easy adapt it to your own requirements.

 Download Word template    Download Google Docs template

It can be easy to fall into a pattern of overusing the same words or sentence constructions, which can make your work monotonous and repetitive for your readers. Consider utilizing some of the alternative constructions presented below.

Example 1: Passive construction

The passive voice is a common choice for outlines and overviews because the context makes it clear who is carrying out the action (e.g., you are conducting the research ). However, overuse of the passive voice can make your text vague and imprecise.

Example 2: IS-AV construction

You can also present your information using the “IS-AV” (inanimate subject with an active verb ) construction.

A chapter is an inanimate object, so it is not capable of taking an action itself (e.g., presenting or discussing). However, the meaning of the sentence is still easily understandable, so the IS-AV construction can be a good way to add variety to your text.

Example 3: The “I” construction

Another option is to use the “I” construction, which is often recommended by style manuals (e.g., APA Style and Chicago style ). However, depending on your field of study, this construction is not always considered professional or academic. Ask your supervisor if you’re not sure.

Example 4: Mix-and-match

To truly make the most of these options, consider mixing and matching the passive voice , IS-AV construction , and “I” construction .This can help the flow of your argument and improve the readability of your text.

As you draft the chapter outline, you may also find yourself frequently repeating the same words, such as “discuss,” “present,” “prove,” or “show.” Consider branching out to add richness and nuance to your writing. Here are some examples of synonyms you can use.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, November 21). Dissertation & Thesis Outline | Example & Free Templates. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/dissertation/dissertation-thesis-outline/

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Dissertation examples

Listed below are some of the best examples of research projects and dissertations from undergraduate and taught postgraduate students at the University of Leeds We have not been able to gather examples from all schools. The module requirements for research projects may have changed since these examples were written. Refer to your module guidelines to make sure that you address all of the current assessment criteria. Some of the examples below are only available to access on campus.

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How To Write A Dissertation Introduction

A Simple Explainer With Examples + Free Template

By: Derek Jansen (MBA) | Reviewed By Dr Eunice Rautenbach (D. Tech) | March 2020

If you’re reading this, you’re probably at the daunting early phases of writing up the introduction chapter of your dissertation or thesis. It can be intimidating, I know. 

In this post, we’ll look at the 7 essential ingredients of a strong dissertation or thesis introduction chapter, as well as the essential things you need to keep in mind as you craft each section. We’ll also share some useful tips to help you optimize your approach.

Overview: Writing An Introduction Chapter

• The purpose and function of the intro chapter
• Craft an enticing and engaging opening section
• Provide a background and context to the study
• Clearly define the research problem
• State your research aims, objectives and questions
• Explain the significance of your study
• Identify the limitations of your research
• Outline the structure of your dissertation or thesis

A quick sidenote:

You’ll notice that I’ve used the words dissertation and thesis interchangeably. While these terms reflect different levels of research – for example, Masters vs PhD-level research – the introduction chapter generally contains the same 7 essential ingredients regardless of level. So, in this post, dissertation introduction equals thesis introduction.

Start with why.

To craft a high-quality dissertation or thesis introduction chapter, you need to understand exactly what this chapter needs to achieve. In other words, what’s its purpose ? As the name suggests, the introduction chapter needs to introduce the reader to your research so that they understand what you’re trying to figure out, or what problem you’re trying to solve. More specifically, you need to answer four important questions in your introduction chapter.

These questions are:

• What will you be researching? (in other words, your research topic)
• Why is that worthwhile? (in other words, your justification)
• What will the scope of your research be? (in other words, what will you cover and what won’t you cover)
• What will the limitations of your research be? (in other words, what will the potential shortcomings of your research be?)

Simply put, your dissertation’s introduction chapter needs to provide an overview of your planned research , as well as a clear rationale for it. In other words, this chapter has to explain the “what” and the “why” of your research – what’s it all about and why’s that important.

Simple enough, right?

Well, the trick is finding the appropriate depth of information. As the researcher, you’ll be extremely close to your topic and this makes it easy to get caught up in the minor details. While these intricate details might be interesting, you need to write your introduction chapter on more of a “need-to-know” type basis, or it will end up way too lengthy and dense. You need to balance painting a clear picture with keeping things concise. Don’t worry though – you’ll be able to explore all the intricate details in later chapters.

Now that you understand what you need to achieve from your introduction chapter, we can get into the details. While the exact requirements for this chapter can vary from university to university, there are seven core components that most universities will require. We call these the seven essential ingredients . 

The 7 Essential Ingredients

• The opening section – where you’ll introduce the reader to your research in high-level terms
• The background to the study – where you’ll explain the context of your project
• The research problem – where you’ll explain the “gap” that exists in the current research
• The research aims , objectives and questions – where you’ll clearly state what your research will aim to achieve
• The significance (or justification) – where you’ll explain why your research is worth doing and the value it will provide to the world
• The limitations – where you’ll acknowledge the potential limitations of your project and approach
• The structure – where you’ll briefly outline the structure of your dissertation or thesis to help orient the reader

By incorporating these seven essential ingredients into your introduction chapter, you’ll comprehensively cover both the “ what ” and the “ why ” I mentioned earlier – in other words, you’ll achieve the purpose of the chapter.

Side note – you can also use these 7 ingredients in this order as the structure for your chapter to ensure a smooth, logical flow. This isn’t essential, but, generally speaking, it helps create an engaging narrative that’s easy for your reader to understand. If you’d like, you can also download our free introduction chapter template here.

Alright – let’s look at each of the ingredients now.

#1 – The Opening Section

The very first essential ingredient for your dissertation introduction is, well, an introduction or opening section. Just like every other chapter, your introduction chapter needs to start by providing a brief overview of what you’ll be covering in the chapter.

This section needs to engage the reader with clear, concise language that can be easily understood and digested. If the reader (your marker!) has to struggle through it, they’ll lose interest, which will make it harder for you to earn marks. Just because you’re writing an academic paper doesn’t mean you can ignore the basic principles of engaging writing used by marketers, bloggers, and journalists. At the end of the day, you’re all trying to sell an idea – yours is just a research idea.

So, what goes into this opening section?

Well, while there’s no set formula, it’s a good idea to include the following four foundational sentences in your opening section:

1 – A sentence or two introducing the overall field of your research.

For example:

“Organisational skills development involves identifying current or potential skills gaps within a business and developing programs to resolve these gaps. Management research, including X, Y and Z, has clearly established that organisational skills development is an essential contributor to business growth.”

2 – A sentence introducing your specific research problem.

“However, there are conflicting views and an overall lack of research regarding how best to manage skills development initiatives in highly dynamic environments where subject knowledge is rapidly and continuously evolving – for example, in the website development industry.”

3 – A sentence stating your research aims and objectives.

“This research aims to identify and evaluate skills development approaches and strategies for highly dynamic industries in which subject knowledge is continuously evolving.”.

4 – A sentence outlining the layout of the chapter.

“This chapter will provide an introduction to the study by first discussing the background and context, followed by the research problem, the research aims, objectives and questions, the significance and finally, the limitations.”

As I mentioned, this opening section of your introduction chapter shouldn’t be lengthy . Typically, these four sentences should fit neatly into one or two paragraphs, max. What you’re aiming for here is a clear, concise introduction to your research – not a detailed account.

PS – If some of this terminology sounds unfamiliar, don’t stress – I’ll explain each of the concepts later in this post.

#2 – Background to the study

Now that you’ve provided a high-level overview of your dissertation or thesis, it’s time to go a little deeper and lay a foundation for your research topic. This foundation is what the second ingredient is all about – the background to your study.

So, what is the background section all about?

Well, this section of your introduction chapter should provide a broad overview of the topic area that you’ll be researching, as well as the current contextual factors . This could include, for example, a brief history of the topic, recent developments in the area, key pieces of research in the area and so on. In other words, in this section, you need to provide the relevant background information to give the reader a decent foundational understanding of your research area.

Let’s look at an example to make this a little more concrete.

If we stick with the skills development topic I mentioned earlier, the background to the study section would start by providing an overview of the skills development area and outline the key existing research. Then, it would go on to discuss how the modern-day context has created a new challenge for traditional skills development strategies and approaches. Specifically, that in many industries, technical knowledge is constantly and rapidly evolving, and traditional education providers struggle to keep up with the pace of new technologies.

Importantly, you need to write this section with the assumption that the reader is not an expert in your topic area. So, if there are industry-specific jargon and complex terminology, you should briefly explain that here , so that the reader can understand the rest of your document.

Don’t make assumptions about the reader’s knowledge – in most cases, your markers will not be able to ask you questions if they don’t understand something. So, always err on the safe side and explain anything that’s not common knowledge.

#3 – The research problem

Now that you’ve given your reader an overview of your research area, it’s time to get specific about the research problem that you’ll address in your dissertation or thesis. While the background section would have alluded to a potential research problem (or even multiple research problems), the purpose of this section is to narrow the focus and highlight the specific research problem you’ll focus on.

But, what exactly is a research problem, you ask?

Well, a research problem can be any issue or question for which there isn’t already a well-established and agreed-upon answer in the existing research. In other words, a research problem exists when there’s a need to answer a question (or set of questions), but there’s a gap in the existing literature , or the existing research is conflicting and/or inconsistent.

So, to present your research problem, you need to make it clear what exactly is missing in the current literature and why this is a problem . It’s usually a good idea to structure this discussion into three sections – specifically:

• What’s already well-established in the literature (in other words, the current state of research)
• What’s missing in the literature (in other words, the literature gap)
• Why this is a problem (in other words, why it’s important to fill this gap)

Let’s look at an example of this structure using the skills development topic.

Organisational skills development is critically important for employee satisfaction and company performance (reference). Numerous studies have investigated strategies and approaches to manage skills development programs within organisations (reference).

(this paragraph explains what’s already well-established in the literature)

However, these studies have traditionally focused on relatively slow-paced industries where key skills and knowledge do not change particularly often. This body of theory presents a problem for industries that face a rapidly changing skills landscape – for example, the website development industry – where new platforms, languages and best practices emerge on an extremely frequent basis.

(this paragraph explains what’s missing from the literature)

As a result, the existing research is inadequate for industries in which essential knowledge and skills are constantly and rapidly evolving, as it assumes a slow pace of knowledge development. Industries in such environments, therefore, find themselves ill-equipped in terms of skills development strategies and approaches.

(this paragraph explains why the research gap is problematic)

As you can see in this example, in a few lines, we’ve explained (1) the current state of research, (2) the literature gap and (3) why that gap is problematic. By doing this, the research problem is made crystal clear, which lays the foundation for the next ingredient.

#4 – The research aims, objectives and questions

Now that you’ve clearly identified your research problem, it’s time to identify your research aims and objectives , as well as your research questions . In other words, it’s time to explain what you’re going to do about the research problem.

So, what do you need to do here?

Well, the starting point is to clearly state your research aim (or aims) . The research aim is the main goal or the overarching purpose of your dissertation or thesis. In other words, it’s a high-level statement of what you’re aiming to achieve.

Let’s look at an example, sticking with the skills development topic:

“Given the lack of research regarding organisational skills development in fast-moving industries, this study will aim to identify and evaluate the skills development approaches utilised by web development companies in the UK”.

As you can see in this example, the research aim is clearly outlined, as well as the specific context in which the research will be undertaken (in other words, web development companies in the UK).

Next up is the research objective (or objectives) . While the research aims cover the high-level “what”, the research objectives are a bit more practically oriented, looking at specific things you’ll be doing to achieve those research aims.

Let’s take a look at an example of some research objectives (ROs) to fit the research aim.

• RO1 – To identify common skills development strategies and approaches utilised by web development companies in the UK.
• RO2 – To evaluate the effectiveness of these strategies and approaches.
• RO3 – To compare and contrast these strategies and approaches in terms of their strengths and weaknesses.

As you can see from this example, these objectives describe the actions you’ll take and the specific things you’ll investigate in order to achieve your research aims. They break down the research aims into more specific, actionable objectives.

The final step is to state your research questions . Your research questions bring the aims and objectives another level “down to earth”. These are the specific questions that your dissertation or theses will seek to answer. They’re not fluffy, ambiguous or conceptual – they’re very specific and you’ll need to directly answer them in your conclusions chapter .

The research questions typically relate directly to the research objectives and sometimes can look a bit obvious, but they are still extremely important. Let’s take a look at an example of the research questions (RQs) that would flow from the research objectives I mentioned earlier.

• RQ1 – What skills development strategies and approaches are currently being used by web development companies in the UK?
• RQ2 – How effective are each of these strategies and approaches?
• RQ3 – What are the strengths and weaknesses of each of these strategies and approaches?

As you can see, the research questions mimic the research objectives , but they are presented in question format. These questions will act as the driving force throughout your dissertation or thesis – from the literature review to the methodology and onward – so they’re really important.

A final note about this section – it’s really important to be clear about the scope of your study (more technically, the delimitations ). In other words, what you WILL cover and what you WON’T cover. If your research aims, objectives and questions are too broad, you’ll risk losing focus or investigating a problem that is too big to solve within a single dissertation.

Simply put, you need to establish clear boundaries in your research. You can do this, for example, by limiting it to a specific industry, country or time period. That way, you’ll ringfence your research, which will allow you to investigate your topic deeply and thoroughly – which is what earns marks!

Need a helping hand?

#5 – Significance

Now that you’ve made it clear what you’ll be researching, it’s time to make a strong argument regarding your study’s importance and significance . In other words, now that you’ve covered the what, it’s time to cover the why – enter essential ingredient number 5 – significance.

Of course, by this stage, you’ve already briefly alluded to the importance of your study in your background and research problem sections, but you haven’t explicitly stated how your research findings will benefit the world . So, now’s your chance to clearly state how your study will benefit either industry , academia , or – ideally – both . In other words, you need to explain how your research will make a difference and what implications it will have .

Let’s take a look at an example.

“This study will contribute to the body of knowledge on skills development by incorporating skills development strategies and approaches for industries in which knowledge and skills are rapidly and constantly changing. This will help address the current shortage of research in this area and provide real-world value to organisations operating in such dynamic environments.”

As you can see in this example, the paragraph clearly explains how the research will help fill a gap in the literature and also provide practical real-world value to organisations.

This section doesn’t need to be particularly lengthy, but it does need to be convincing . You need to “sell” the value of your research here so that the reader understands why it’s worth committing an entire dissertation or thesis to it. This section needs to be the salesman of your research. So, spend some time thinking about the ways in which your research will make a unique contribution to the world and how the knowledge you create could benefit both academia and industry – and then “sell it” in this section.

#6 – The limitations

Now that you’ve “sold” your research to the reader and hopefully got them excited about what’s coming up in the rest of your dissertation, it’s time to briefly discuss the potential limitations of your research.

But you’re probably thinking, hold up – what limitations? My research is well thought out and carefully designed – why would there be limitations?

Well, no piece of research is perfect . This is especially true for a dissertation or thesis – which typically has a very low or zero budget, tight time constraints and limited researcher experience. Generally, your dissertation will be the first or second formal research project you’ve ever undertaken, so it’s unlikely to win any research awards…

Simply put, your research will invariably have limitations. Don’t stress yourself out though – this is completely acceptable (and expected). Even “professional” research has limitations – as I said, no piece of research is perfect. The key is to recognise the limitations upfront and be completely transparent about them, so that future researchers are aware of them and can improve the study’s design to minimise the limitations and strengthen the findings.

Generally, you’ll want to consider at least the following four common limitations. These are:

• Your scope – for example, perhaps your focus is very narrow and doesn’t consider how certain variables interact with each other.
• Your research methodology – for example, a qualitative methodology could be criticised for being overly subjective, or a quantitative methodology could be criticised for oversimplifying the situation (learn more about methodologies here ).
• Your resources – for example, a lack of time, money, equipment and your own research experience.
• The generalisability of your findings – for example, the findings from the study of a specific industry or country can’t necessarily be generalised to other industries or countries.

Don’t be shy here. There’s no use trying to hide the limitations or weaknesses of your research. In fact, the more critical you can be of your study, the better. The markers want to see that you are aware of the limitations as this demonstrates your understanding of research design – so be brutal.

#7 – The structural outline

Now that you’ve clearly communicated what your research is going to be about, why it’s important and what the limitations of your research will be, the final ingredient is the structural outline.The purpose of this section is simply to provide your reader with a roadmap of what to expect in terms of the structure of your dissertation or thesis.

In this section, you’ll need to provide a brief summary of each chapter’s purpose and contents (including the introduction chapter). A sentence or two explaining what you’ll do in each chapter is generally enough to orient the reader. You don’t want to get too detailed here – it’s purely an outline, not a summary of your research.

Let’s look at an example:

In Chapter One, the context of the study has been introduced. The research objectives and questions have been identified, and the value of such research argued. The limitations of the study have also been discussed.

In Chapter Two, the existing literature will be reviewed and a foundation of theory will be laid out to identify key skills development approaches and strategies within the context of fast-moving industries, especially technology-intensive industries.

In Chapter Three, the methodological choices will be explored. Specifically, the adoption of a qualitative, inductive research approach will be justified, and the broader research design will be discussed, including the limitations thereof.

So, as you can see from the example, this section is simply an outline of the chapter structure, allocating a short paragraph to each chapter. Done correctly, the outline will help your reader understand what to expect and reassure them that you’ll address the multiple facets of the study.

By the way – if you’re unsure of how to structure your dissertation or thesis, be sure to check out our video post which explains dissertation structure .

Keep calm and carry on.

Hopefully you feel a bit more prepared for this challenge of crafting your dissertation or thesis introduction chapter now. Take a deep breath and remember that Rome wasn’t built in a day – conquer one ingredient at a time and you’ll be firmly on the path to success.

Let’s quickly recap – the 7 ingredients are:

• The opening section – where you give a brief, high-level overview of what your research will be about.
• The study background – where you introduce the reader to key theory, concepts and terminology, as well as the context of your study.
• The research problem – where you explain what the problem with the current research is. In other words, the research gap.
• The research aims , objectives and questions – where you clearly state what your dissertation will investigate.
• The significance – where you explain what value your research will provide to the world.
• The limitations – where you explain what the potential shortcomings and limitations of your research may be.
• The structural outline – where you provide a high-level overview of the structure of your document

If you bake these ingredients into your dissertation introduction chapter, you’ll be well on your way to building an engaging introduction chapter that lays a rock-solid foundation for the rest of your document.

Remember, while we’ve covered the essential ingredients here, there may be some additional components that your university requires, so be sure to double-check your project brief!

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

45 Comments

Thanks very much for such an insight. I feel confident enough in undertaking my thesis on the survey;The future of facial recognition and learning non verbal interaction

Glad to hear that. Good luck with your thesis!

Thanks very much for such an insight. I feel confident now undertaking my thesis; The future of facial recognition and learning non verbal interaction.

Thanks so much for this article. I found myself struggling and wasting a lot of time in my thesis writing but after reading this article and watching some of your youtube videos, I now have a clear understanding of what is required for a thesis.

Thank you Derek, i find your each post so useful. Keep it up.

Thank you so much Derek ,for shedding the light and making it easier for me to handle the daunting task of academic writing .

Thanks do much Dereck for the comprehensive guide. It will assist me queit a lot in my thesis.

thanks a lot for helping

i LOVE the gifs, such a fun way to engage readers. thanks for the advice, much appreciated

Thanks a lot Derek! It will be really useful to the beginner in research!

You’re welcome

This is a well written, easily comprehensible, simple introduction to the basics of a Research Dissertation../the need to keep the reader in mind while writing the dissertation is an important point that is covered../ I appreciate the efforts of the author../

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Thank you very much

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I was looking for some good blogs related to Education hopefully your article will help. Thanks for sharing.

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You just saved me from going astray in writing a Dissertation for my undergraduate studies. I could not be more grateful for such a relevant guide like this. Thank you so much.

Thank you so much Derek, this has been extremely helpful!!

I do have one question though, in the limitations part do you refer to the scope as the focus of the research on a specific industry/country/chronological period? I assume that in order to talk about whether or not the research could be generalized, the above would need to be already presented and described in the introduction.

Thank you again!

Phew! You have genuinely rescued me. I was stuck how to go about my thesis. Now l have started. Thank you.

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Thanks a lot for this masterpiece!

Powerful insight. I can now take a step

Thank you very much for these valuable introductions to thesis chapters. I saw all your videos about writing the introduction, discussion, and conclusion chapter. Then, I am wondering if we need to explain our research limitations in all three chapters, introduction, discussion, and conclusion? Isn’t it a bit redundant? If not, could you please explain how can we write in different ways? Thank you.

Excellent!!! Thank you…

Thanks for this informative content. I have a question. The research gap is mentioned in both the introduction and literature section. I would like to know how can I demonstrate the research gap in both sections without repeating the contents?

I’m incredibly grateful for this invaluable content. I’ve been dreading compiling my postgrad thesis but breaking each chapter down into sections has made it so much easier for me to engage with the material without feeling overwhelmed. After relying on your guidance, I’m really happy with how I’ve laid out my introduction.

Thank you for the informative content you provided

Hi Derrick and Team, thank you so much for the comprehensive guide on how to write a dissertation or a thesis introduction section. For some of us first-timers, it is a daunting task. However, the instruction with relevant examples makes it clear and easy to follow through. Much appreciated.

It was so helpful. God Bless you. Thanks very much

I thank you Grad coach for your priceless help. I have two questions I have learned from your video the limitations of the research presented in chapter one. but in another video also presented in chapter five. which chapter limitation should be included? If possible, I need your answer since I am doing my thesis. how can I explain If I am asked what is my motivation for this research?

You explain what moment in life caused you to have a peaked interest in the thesis topic. Personal experiences? Or something that had an impact on your life, or others. Something would have caused your drive of topic. Dig deep inside, the answer is within you!

Thank you guys for the great work you are doing. Honestly, you have made the research to be interesting and simplified. Even a novice will easily grasp the ideas you put forward, Thank you once again.

Excellent piece!

I feel like just settling for a good topic is usually the hardest part.

Thank you so much. My confidence has been completely destroyed during my first year of PhD and you have helped me pull myself together again

Happy to help 🙂

I am so glad I ran into your resources and did not waste time doing the wrong this. Research is now making so much sense now.

Gratitude to Derrick and the team I was looking for a solid article that would aid me in drafting the thesis’ introduction. I felt quite happy when I came across the piece you wrote because it was so well-written and insightful. I wish you success in the future.

thank you so much. God Bless you

Thank you so much Grad Coach for these helpful insights. Now I can get started, with a great deal of confidence.

It’s ‘alluded to’ not ‘eluded to’.

This is great!

Thank you for all this information. I feel very confident to complete my dissertation with all the help given. This is awesome and very helpful; I was studying alone with very little supervision and feedback of my thoughts. feelings. aspirations and experiences, with my topic or Kaupapa. It is a topic that very little or few researchers have written a thesis about (from personal experiences). As John Burke said ” unless you are sitting in the front seat and row, up close and personal, you will not understand the difficulties of growing up and living with hearing loss (caused by swimmer’s ears infection, resulting in burst eardrums, unless one denies having a hearing loss. This is from a Māori woman’s cultural perspective. Nga mihi nui kia koutou.

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