quantitative findings and analysis dissertation example

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On October 9, 2023

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;

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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Not sure how to start your dissertation and get it right the first time? Here are some tips and guidelines for you to kick start your dissertation project.

Table of contents is an essential part of dissertation paper. Here is all you need to know about how to create the best table of contents for dissertation.

If your dissertation includes many abbreviations, it would make sense to define all these abbreviations in a list of abbreviations in alphabetical order.

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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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Quantitative Research is a "means for testing objective theories by examining the relationships among variables.  These variables, in turn, can be measured, typically on instruments, so that numbered data can be analyzed using statistical procedures.  The final written report has a set structure consisting of introduction, literature and theory, methods, results and discussion"  ( Creswell, 2007 ) .

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This post will discuss a quantitative dissertation, its types, structure, and complete examples. A quantitative dissertation involves collecting and analyzing numerical data to answer a research question.

As a student, it is important to understand the options available and determine the best fit for the research. We hope this blog helps you navigate the process of selecting a data analysis approach for your quantitative dissertation.

For maximum knowledge and complete understanding, check out our quantitative dissertation examples alongside the strategies described below;

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What is a Quantitative Dissertation?

The quantitative dissertation is a type of research design that generates numeric data. The subject of analysis is usually a phenomenon that can be measured and assigned numerical values (i.e., how often do people attend religious services?).

Statistical methods are commonly employed to examine relationships or trends in the data. These may include inferential statistics, which allow the researcher to make conclusions about a population based on a sample, or descriptive statistics, which are used to summarize the data.

Types of Quantitative Analysis Dissertation

The quantitative dissertation usually takes one of two forms:

• An empirical study, in which the researcher collects data through surveys or experiments; or
• A secondary analysis, in which the researcher uses an existing dataset.

The most important thing to remember about the quantitative dissertation is that it is not a collection of freestanding studies that happen to use numbers. Instead, it should be a coherent piece of work in which each chapter leads logically to the next.

How to Structure Your Quantitative Dissertation

Chapter 1: Introduction

In Chapter 1, you will need to introduce your topic and explain why it is important. You will also need to state your research question(s) and objectives and describe your methodology. This chapter should end with a clear statement of what you hope to achieve in your study.

Chapter 2: Review of Literature

In Chapter 2, you will review the existing literature on your topic to situate your research within the larger field. This chapter should include a comprehensive review of scholarly articles, journals, books, and other sources relevant to your topic. Keep in mind that this is not simply a list of everything written on your topic; it should be a well-organized synthesis of the literature that highlights gaps and opportunities for further research.

Chapter 3: Methodology

In Chapter 3, you will describe your research design and methods in detail. It includes specifying your participants (or describing how you will select them), explaining how you will collect data from them (e.g., surveys, interviews, experiments), and outlining your data analysis plan. Remember to align your methodology with the research questions you formulated in Chapter 1!

Chapter 4: Findings/Results

Broadly speaking, Chapter 4 presents the findings of your study. However, because different dissertation designs call for different data types (quantitative vs qualitative), this chapter will take on different forms depending on your methodology.

If you conducted an experiment or survey, this would be where you present descriptive statistics such as means, frequencies, and correlations; if you utilized existing data (e.g., census records), this would be where you conduct inferential statistical analyses to answer your research questions; if you relied on qualitative data (e.g., interviews), this would be where you present themes or patterns that emerged from your analysis.

Regardless of its form, Chapter 4 should provide readers with a clear understanding of what happened in your study and why it happened.

Chapter 5: Discussion/Implications

Chapter 5 interprets the findings presented in Chapter 4 and links them back to the literature reviewed in Chapter 2. Here you will discuss what these findings mean concerning what was already known about your topic (as summarized in Chapter 2). This chapter should also address any limitations inherent in your study and explain their implications for future research on this topic. 

Some Quantitative Dissertation Examples

Here are some examples of quantifiable research questions that could be answered using a quantitative dissertation: 

• How does the level of anxiety experienced by first-year college students affect their academic performance? 
• What is the relationship between parental income and child obesity rates in the United States? 
• Does exposure to violent media significantly impact aggressive behaviour in children?

As you can see, these questions can be answered using numerical data. And that's all there is to it! A quantitative dissertation is a research project using numerical data to answer a particular research question. It's not as intimidating as it sounds.

We hope this article has helped to demystify the quantitative dissertation for you. Remember, a quantitative dissertation is a research project using numerical data to answer a particular research question.

Choosing the right data analysis approach for your dissertation is a critical decision that will greatly impact the quality of your results. When selecting, consider the nature of your research project, the type of data you'll be collecting, and the logistics of your data collection process.

With careful consideration of these factors, you can be confident in choosing an approach that will set you up for success.

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What’s Included: Results Chapter Template

This template covers all the core components required in the results chapter of a typical dissertation, thesis or research project:

• The opening /overview section
• The body section for qualitative studies
• The body section for quantitative studies
• Concluding summary

The purpose of each section is explained in plain language, followed by an overview of the key elements that you need to cover. The template also includes practical examples to help you understand exactly what’s required, along with links to additional free resources (articles, videos, etc.) to help you along your research journey.

The cleanly formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

PS – if you’d like a high-level template for the entire thesis, you can we’ve got that too .

FAQ - Thesis Results Chapter

What types of dissertations/theses can this be used for.

The chapter template follows the standard format for academic research projects, which means it will be suitable for the majority of dissertations, theses and research projects (especially those within the sciences). Recommended structures are provided for both qualitative and quantitative studies.

Keep in mind that the exact requirements for the results chapter/section will vary between universities and degree programs. For example, your university may require that the results chapter and discussion chapter are merged into one (this is more common for qualitative studies). So, be sure to double-check your university’s requirements before you finalize your structure.

Is this template for an undergrad, Master or PhD-level thesis?

This template can be used for a dissertation, thesis or research project at any level of study. Doctoral-level projects typically require a lengthier write-up, but the structure will typically remain the same. Again, be sure to check your university’s requirements and norms in terms of document structure.

How long should the results chapter be?

This can vary a fair deal, depending on the level of study (undergrad, Master or Doctoral), the field of research, as well as your university’s specific requirements. Therefore, it’s best to check with your university or review past dissertations from your program to get an accurate estimate.

Can I share this template with my friends/colleagues?

Yes, you’re welcome to share this template in its original format (no editing allowed). If you want to post about it on your blog or social media, please reference this page as your source.

What format is the template (DOC, PDF, PPT, etc.)?

The thesis results chapter template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

Do you have templates for the other chapters?

Yes, we do. We are constantly developing our collection of free resources to help students complete their dissertations and theses. You can view all of our template resources here .

Can Grad Coach help me with my results and/or analysis?

Yes, we can provide coaching-based assistance with your qualitative, quantitative or mixed-method analysis. Get in touch to discuss our private coaching services .

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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• FUNDAMENTALS

Qualitative, quantitative and mixed methods dissertations

What are they and which one should i choose.

In the sections that follow, we briefly describe the main characteristics of qualitative, quantitative and mixed methods dissertations. Rather than being exhaustive, the main goal is to highlight what these types of research are and what they involve. Whilst you read through each section, try and think about your own dissertation, and whether you think that one of these types of dissertation might be right for you. After reading about these three types of dissertation, we highlight some of the academic, personal and practical reasons why you may choose to take on one type over another.

• Types of dissertation: Qualitative, quantitative and mixed methods dissertations
• Choosing between types: Academic, personal and practical justifications

Types of dissertation

Whilst we describe the main characteristics of qualitative, quantitative and mixed methods dissertations, the Lærd Dissertation site currently focuses on helping guide you through quantitative dissertations , whether you are a student of the social sciences, psychology, education or business, or are studying medical or biological sciences, sports science, or another science-based degree. Nonetheless, you may still find our introductions to qualitative dissertations and mixed methods dissertations useful, if only to decide whether these types of dissertation are for you. We discuss quantitative dissertations , qualitative dissertations and mixed methods dissertations in turn:

Quantitative dissertations

When we use the word quantitative to describe quantitative dissertations , we do not simply mean that the dissertation will draw on quantitative research methods or statistical analysis techniques . Quantitative research takes a particular approach to theory , answering research questions and/or hypotheses , setting up a research strategy , making conclusions from results , and so forth. Classic routes that you can follow include replication-based studies , theory-driven research and data-driven dissertations . However, irrespective of the particular route that you adopt when taking on a quantitative dissertation, there are a number of core characteristics to quantitative dissertations:

They typically attempt to build on and/or test theories , whether adopting an original approach or an approach based on some kind of replication or extension .

They answer quantitative research questions and/or research (or null ) hypotheses .

They are mainly underpinned by positivist or post-positivist research paradigms .

They draw on one of four broad quantitative research designs (i.e., descriptive , experimental , quasi-experimental or relationship-based research designs).

They try to use probability sampling techniques , with the goal of making generalisations from the sample being studied to a wider population , although often end up applying non-probability sampling techniques .

They use research methods that generate quantitative data (e.g., data sets , laboratory-based methods , questionnaires/surveys , structured interviews , structured observation , etc.).

They draw heavily on statistical analysis techniques to examine the data collected, whether descriptive or inferential in nature.

They assess the quality of their findings in terms of their reliability , internal and external validity , and construct validity .

They report their findings using statements , data , tables and graphs that address each research question and/or hypothesis.

They make conclusions in line with the findings , research questions and/or hypotheses , and theories discussed in order to test and/or expand on existing theories, or providing insight for future theories.

If you choose to take on a quantitative dissertation , go to the Quantitative Dissertations part of Lærd Dissertation now. You will learn more about the characteristics of quantitative dissertations, as well as being able to choose between the three classic routes that are pursued in quantitative research: replication-based studies , theory-driven research and data-driven dissertations . Upon choosing your route, the Quantitative Dissertations part of Lærd Dissertation will help guide you through these routes, from topic idea to completed dissertation, as well as showing you how to write up quantitative dissertations.

Qualitative dissertations

Qualitative dissertations , like qualitative research in general, are often associated with qualitative research methods such as unstructured interviews, focus groups and participant observation. Whilst they do use a set of research methods that are not used in quantitative dissertations, qualitative research is much more than a choice between research methods. Qualitative research takes a particular approach towards the research process , the setting of research questions , the development and use of theory , the choice of research strategy , the way that findings are presented and discussed, and so forth. Overall, qualitative dissertations will be very different in approach, depending on the particular route that you adopt (e.g., case study research compared to ethnographies). Classic routes that you can follow include autoethnographies , case study research , ethnographies , grounded theory , narrative research and phenomenological research . However, irrespective of the route that you choose to follow, there are a number of broad characteristics to qualitative dissertations:

They follow an emergent design , meaning that the research process , and sometimes even the qualitative research questions that you tackle, often evolve during the dissertation process.

They use theory in a variety of ways - sometimes drawing on theory to help the research process; on other occasions, using theory to develop new theoretical insights ; sometimes both - but the goal is infrequently to test a particular theory from the outset.

They can be underpinned by one of a number of research paradigms (e.g., interpretivism , constructivism , critical theory , amongst many other research paradigms).

They follow research designs that heavily influence the choices you make throughout the research process, as well as the analysis and discussion of 'findings' (i.e., such research designs differ considerably depending on the route that is being followed, whether an autoethnography , case study research , ethnography , grounded theory , narrative research , phenomenological research , etc.).

They try to use theoretical sampling - a group of non-probability sampling techniques - with the goal of studying cases (i.e., people or organisations) that are most appropriate to answering their research questions.

They study people in-the-field (i.e., in natural settings ), often using multiple research methods , each of which generate qualitative data (e.g., unstructured interviews , focus groups , participant observation , etc.).

They interpret the qualitative data through the eyes and biases of the researcher , going back-and-forth through the data (i.e., an inductive process ) to identify themes or abstractions that build a holistic/gestalt picture of what is being studied.

They assess the quality of their findings in terms of their dependability , confirmability , conformability and transferability .

They present (and discuss ) their findings through personal accounts , case studies , narratives , and other means that identify themes or abstracts , processes , observations and contradictions , which help to address their research questions.

They discuss the theoretical insights arising from the findings in light of the research questions, from which tentative conclusions are made.

If you choose to take on a qualitative dissertation , you will be able to learn a little about appropriate research methods and sampling techniques in the Fundamentals section of Lærd Dissertation. However, we have not yet launched a dedicated section to qualitative dissertations within Lærd Dissertation. If this is something that you would like us to do sooner than later, please leave feedback .

Mixed methods dissertations

Mixed methods dissertations combine qualitative and quantitative approaches to research. Whilst they are increasingly used and have gained greater legitimacy, much less has been written about their components parts. There are a number of reasons why mixed methods dissertations are used, including the feeling that a research question can be better addressed by:

Collecting qualitative and quantitative data , and then analysing or interpreting that data, whether separately or by mixing it.

Conducting more than one research phase ; perhaps conducting qualitative research to explore an issue and uncover major themes, before using quantitative research to measure the relationships between the themes.

One of the problems (or challenges) of mixed methods dissertations is that qualitative and quantitative research, as you will have seen from the two previous sections, are very different in approach. In many respects, they are opposing approaches to research. Therefore, when taking on a mixed methods dissertation, you need to think particularly carefully about the goals of your research, and whether the qualitative or quantitative components (a) are more important in philosophical, theoretical and practical terms, and (b) should be combined or kept separate.

Again, as with qualitative dissertations, we have yet to launch a dedicated section of Lærd Dissertation to mixed methods dissertations . However, you will be able to learn about many of the quantitative aspects of doing a mixed methods dissertation in the Quantitative Dissertations part of Lærd Dissertation. You may even be able to follow this part of our site entirely if the only qualitative aspect of your mixed methods dissertation is the use of qualitative methods to help you explore an issue or uncover major themes, before performing quantitative research to examine such themes further. Nonetheless, if you would like to see a dedicated section to mixed methods dissertations sooner than later, please leave feedback .

Library Guides

Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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SYSTEMATIC REVIEW article

The impact of boarding school on student development in primary and secondary schools: a meta-analysis.

• School of Education, Minzu University of China, Beijing, China

As a long-established model of schooling, the boarding system is commonly practiced in countries around the world. Numerous scholars have conducted a great deal of research on the relationship between the boarding school and student development, but the results of the research are quite divergent. In order to clarify the real effects of boarding school on students’ development, this study used meta-analysis to quantify 49 (91 effect sizes) experimental or quasi-experimental studies on related topics at home and abroad. The results find that: (1) Overall, boarding school has no significant predictive effect on student development, with a combined effect size of 0.002 ( p  > 0.05); (2) Specifically, boarding school has a significant positive predictive effect on students’ cognitive development ( g  = 0.248, p  < 0.001), a significant negative predictive effect on students’ affective and attitudinal development ( g  = −0.159, p  < 0.05), and no significant predictive effect on students’ behavioral development ( g  = −0.115, p  > 0.05) and physical development ( g  = −0.038, p  > 0.05); (3) The relationship between the two is moderated by the school stage and the type of boarding school, but not by the instruments; (4) Compared with primary school students, senior high school students and urban boarding students, the negative predictive effect of boarding system on junior middle school students and rural boarding students is more significant. In addition, there are some limitations in the study, such as the limited number of moderator variables included, the results of the study are easily affected by the quality of the included literature, and the dimensionality of the core variable “student development” is not comprehensive enough. In the future, further validation should be conducted through in-depth longitudinal or experimental studies.

Introduction

Boarding school, which began in British public schools, is a common form of schooling that provides students with accommodation and food, and integrates personal lives of students with their academic lives ( Dong, 2012 ). In boarding schools, a relatively closed school management model is generally adopted, and dormitories, canteens and other related living facilities are equipped to meet the basic living needs of students. The boarding school, as a mode of schooling, not only has a relatively long history in the West, but also has been practiced in China for nearly 40 years or so, covering all stages from kindergarten to university. There has been a great deal of academic research around boarding school, mainly including studies on the functions of boarding school ( White, 2004 ), the internal management problems of schools ( Zhang, 2006 ), the impact of boarding school on the physical and mental development of students ( Kahane, 1988 ), and the relationship between boarding school and families ( Ben-David and Erez-Darvish, 1997 ). With the increasing size of boarding school and the younger age of boarding students, boarding school has become an important and unique part of the school system. In recent years, research on the boarding school has gradually shifted from exploring the value implications to promoting students’ development, such as the impact of boarding on students’ academic performance ( Foliano et al., 2019 ) and the impact of boarding on students’ mental health ( Yang and Yan, 2022 ). However, these studies only discuss the relationship between boarding school and one aspect of student development. Indeed, student development encompasses multiple aspects of the educational process and developmental content ( Pan, 2019 ). At the same time, some studies have pointed out that although boarding school helps students accept multiculturalism, promote students’ socialization ( White, 2004 ) and enhance students’ academic performance ( Zhou and Xu, 2021 ), there are also some negative effects, such as affecting the formation of students’ personality ( Schaverien, 2010 ) causing social and emotional distress to students ( Kleinfeld and Bloom, 1977 ), and affecting physical development ( Xu et al., 2014 ). So, how does boarding actually affect the overall development of students? Are there differences in the role of different aspects of student development in a boarding environment? It is not only a summary of the effectiveness of the boarding school that has been implemented for a long time, but also an important question that needs to be answered urgently in order to promote the normalization and under-aging of boarding school.

The correlation between boarding school and the development of students

Many studies have centered on the impact of boarding school on student development at different school stages, types of boarding school and instruments. However, there are some differences in the findings of the studies, which are broadly divided into three categories.

The first view is that boarding school has a significant positive predictive effect on student development. On the one hand, boarding school increases and standardizes the study time of students by providing a collectivized learning and living environment ( Yao et al., 2018 ), which in turn improves students’ academic achievement ( Curto and Fryer, 2014 ; Behaghel et al., 2017 ; Foliano et al., 2019 ). At the same time, boarding school also reduces students’ undesirable behaviors, such as a decline in absenteeism ( Martin et al., 2014 ), and has a positive impact on students’ cognitive development. A survey by the American Association of Boarding Schools (2013) found that 68% of boarding school students believed that boarding school had helped them improve self-discipline, maturity, independence, cooperative learning, and critical thinking. On the other hand, group home living increases contact between students and promotes emotional communication and companionship among peers ( Martin et al., 2014 ; Bosmans and Kerns, 2015 ). This close peer relationship not only helps boarding students better adapt to school life ( Segal, 2013 ) and enhance their ability to live independently ( Ma, 2012 ), but also increases student satisfaction with school and life, and promotes the development of students’ healthy personality ( Wu et al., 2011 ). In addition, good peer relationships also serve as role models that can continuously stimulate students’ motivation and promote their interest in learning ( Kennedy, 2010 ). Multi-subject attachment theory suggests that the scope of the attachment relationship is not limited to the parent–child relationship, and that teachers, as one of the important attachment objects for boarding students, can to some extent “substitute for the parents” and “compensate” for the lack of parent–child relationship of boarding students ( Verschueren and Koomen, 2012 ). Supported by the theory of humanities and sociology and with the help of students’ autobiographies, White (2004) also amply substantiated the important role of boarding school in the development of students.

The second view is that boarding school has a significant negative predictive effect on student development. First of all, boarding school adopts a relatively closed management mode, which weakens the influence of the family and society in the growth of students, and causes certain harm to the physical and mental development of students ( Schaverien, 2010 ). Especially for younger students, they are more dependent on their families, so the role of family environment is more important for their socialization ( Yan et al., 2013 ). Secondly, boarding school is strictly regulated and competition within schools is fierce ( Yao et al., 2018 ). Coupled with the dilution of parent–child relationship, students lack effective emotional support ( Ye and Pan, 2007 ). As a result, boarding students are more likely to develop aversion to studying, leading to a decline in academic performance ( Lu and Du, 2010 ), which in turn leads to undesirable behaviors, such as truancy, school bullying and dropping out of school ( Pfeiffer and Pinquart, 2014 ; Shi and Zhao, 2016 ). Finally, the boarding environment increases the density of interactions between students, which tends to produce the contagion of negative emotions among peers ( Li and Lin, 2019 ). It usually manifests itself in the form of interpersonal hypersensitivity, accompanied by depression, anxiety, paranoia and various other negative emotions and psychological problems ( Niknami et al., 2011 ; Mander et al., 2014 ).

The third view is that boarding does not show significant differences in learning goals, learning engagement and mental health of students ( Li, 2007 ; Martin et al., 2014 ). On the one hand, although boarding students have more psychological problems at the time of admission, as they move up the grades, they become more resilient to school life and their psychological problems gradually decrease ( Liu et al., 2004 ; Xiao et al., 2010 ). On the other hand, boarding students can only communicate with their parents by phone as well as at home on weekends, which can not only dilute parent–child conflicts, but also satisfy students’ psychology of freedom and independence. Therefore, it is conducive to the development of parent–child relationship ( Shen, 2021 ). Additionally, the problem of parental attachment is mitigated due to the growing influence of teacher-student and peer relationships on students ( Wu et al., 2021 ).

Potential moderators of the association between boarding school and the development of students

Different school stages can affect the effectiveness of boarding school on student development. Most studies identify age characteristics as the main factor influencing students’ mental health ( Papworth, 2014 ; Wang and Mao, 2015 ). Primary school boarding students are young and have an imperfect level of physical and mental development. When primary school students are faced with an unfamiliar living environment, they often experience psychological maladaptation and difficulties in interpersonal interactions ( Wang, 2015 ). Due to their relatively complete physical and mental development, junior middle school boarding students have basically formed psychological qualities such as cooperation, self-discipline and freedom, and have a relatively favorable psychological environment. It further supports the negative effects of underage boarding on children’s emotions and socialization ( Wang, 2015 ). In addition, research is more divergent when it comes to academic development. Some scholars believed that there is no significant difference in the impact of boarding school on the academic performance of students in different grades ( Bozdoğan et al., 2014 ), and at the same time, boarding has the same degree of positive impact on students in all grades ( Gao, 2017 ). However, some scholars used instrumental variable regression to show that boarding has a more significant impact on the academic performance of primary school students, but not on junior middle school students ( Qiao and Di, 2014 ). Thus, the effect of boarding school on student development may be moderated by different school stages.

Different types of boarding school affect the effectiveness of boarding on student development. In general, boarding school can be categorized into rural boarding school and urban boarding school. Studies with rural boarding students concluded that boarding school has a positive impact on the academic performance of rural students ( Gao, 2017 ), which is consistent with the findings of numerous studies ( Du et al., 2010 ; Kennedy, 2010 ); but studies with urban boarding students found that urban boarding students have a significant advantage in academic performance ( Xu, 2019 ) and a better psychological condition than rural boarding students ( Luo, 2013 ). Compared to rural boarding students, urban boarding students have better access to social resources, boarding environment, faculty, and more advanced concept of family education ( Tan, 2020 ). In summary, there are some differences in the impact that different types of boarding school have on student development.

In terms of instruments, standardized scales, standardized tests, and self-administered questionnaires are widely used at present. Therefore, they can be divided into two categories: standardized and non-standardized instruments. The use of different instruments may affect the effectiveness of boarding on student development. For example, a self-administered questionnaire, the Mental Health Questionnaire for Junior Middle School Students, was used to measure the mental health level of students, and the results showed that the mental health of boarding students is significantly higher than that of non-boarding students ( Zhang, 2020 ); the results measured using the Diagnostic Test of Mental Health (MHT) is the opposite of the former, showing that the mental health of non-boarding students is significantly better than that of boarding students ( Chen, 2016 ). It follows that the effect of boarding school on student development may be moderated by the instruments.

Current study

In summary, the overall effect of boarding school on student development needs to be further tested. In addition, factors such as different school stages, types of boarding school, and instruments may moderate the relationship between boarding school and student development. Established research mainly discusses one aspect of student development and the findings are not consistent. Therefore, this study adopts the meta-analytical approach to integrate, evaluate and analyze the existing empirical studies on boarding school and student development in order to draw general and generalized conclusions.

Materials and methods

Data retrieval strategies.

This study utilized a variety of sources to collect literature related to the impact of boarding school on student development over the past three decades, both domestically and internationally. Specifically, firstly, the foreign language databases “Web of Science,” “Springer” and “Google Scholar” were searched with “boarding school,” “boarding” and “effect” and “impact” as the subject words, and a total of 1,325 foreign language documents were obtained. Secondly, in the Chinese databases of “CNKI,” “Wanfang Data” and “VIP “, a total of 1,524 Chinese literature was obtained by searching “boarding” and “boarding school” as the titles. The date of the search was 21 October 2023.

Inclusion criteria

In this study, the Endnote20 literature management tool was used to screen the included literature according to the following criteria: (a) The topic of the study was the effect of boarding on students’ development; (b) The research subjects were primary and secondary school students; (c) The study needs to take boarding school as the independent variable; (d) The type of the study is an experiment or quasi-experiment comparing the differences in the development of boarding and non-boarding students, in which a single group of experiments need to provide pre- and post-tests data; (e) The study provides complete data that can calculate the effect size, such as the sample size (N), the mean (Means), the standard deviation (SD), or the p -value, t-value, and the correlation coefficient (r), and so forth; (f) Identical studies that had been published in a different format are excluded. After several rounds of literature screening and elimination of literature that did not meet the criteria, 49 papers were finally included and a total of 91 effect sizes were generated that could be used for meta-analysis. Among them, there were 35 articles in Chinese and 14 articles in foreign languages. The literature span from 1986 to 2023, but it was primarily focused on the last decade ( Figure 1 ).

Figure 1 . Flowchart of the inclusion protocol.

Coding procedure

To further explore and analyze the impact of boarding school on students’ development, the key information was extracted and features coded from the included literature. In this study, 49 articles were independently coded by two coders to ensure reliability and consistency of the coding. There are three main aspects of coding:

The first is the basic information aspect of the literature, including the names of the authors, the time of publication, and data about the effect sizes.

Secondly, in terms of the dependent variable, this study used student development as the dependent variable. According to Benjamin Bloom’s Taxonomy of Educational Objectives ( Anderson, 2009 ), student development is categorized into three dimensions: cognition, behavior, and affect and attitude. In addition, the dimension of physical development has been added in line with the boarding schools’ provision of food and accommodation. Among them, the cognitive dimension mainly consists of students’ academic performance and cognitive ability. Academic performance is a sufficient but not necessary condition for the cognitive development, thus a distinction is made here between academic performance and cognitive ability. The behavioral dimension includes both pro-social behaviors, etc., as well as problematic behaviors such as school bullying and absenteeism. The affective and attitudinal dimension includes students’ emotions, self-esteem, and motivation, etc. The physical development dimension includes the student’s BMI, nutrition, etc.

The third is the moderator variables, including three variables: school stage, the type of boarding school and instruments. First of all, the development of students is stage-specific and sequential, and the impact of choosing boarding at different school stages is also different, mainly including three stages: primary school, junior middle school and senior high school. Secondly, boarding schools can be divided into different types according to different classification criteria. In order to harmonize the definition of boarding school in domestic and foreign studies, this study mainly categorized boarding school into urban boarding school and rural boarding school according to geography. Finally, according to the degree of standardization of the instruments, they are divided into standardized and non-standardized instruments, where standardized instruments refer to the use of standardized questionnaires, scales, etc. to measure student development.

The included literature were coded according to the above characteristics, including author information, year of publication, dependent variable dimensions, school stages, school types, instruments, and effect size. The effect sizes d reported in the collected literature were transformed by the following equation: g  =  d [1−(3/(4 df−1)), df = n 1  + n 2 -2. If the included studies did not report an effect size d, they were calculated from raw data such as sample size, mean, and standard deviation: d = (M1–M2)/Spooled, Spooled = [(n 1 –1) s 1 2  + (n 2 –1) s 2 2 /n1 + n 2 -2] 1/2 . In addition, if the included studies did not fully report raw data such as sample size, mean, standard deviation, etc., they were transformed by the χ 2 value, F value or t value of the raw data: d  = 2[ χ 2 /(N− χ 2 ) 1/2 ; d  = 2/F (n 1  + n 2 )/n 1 n 2 ] 1/2; d  =  t /(n 1  + n 2 /n 1 n 2 ) 1/2 .

Effect size

Due to the small sample size of this study, Hedges’ g -value was selected to measure the impact of boarding school on students’ development. According to Cohen’s criterion for judging the effect size: when the effect size is less than 0.2, its influence is small; when the effect size is more than 0.2 and less than 0.5, there is a moderate influence; when the effect size is more than 0.8, it has a large influence ( Figure 2 ).

Figure 2 . Meta-analytic framework diagram.

Statistical analysis

The concept of meta-analysis was pioneered by Glass, an American psychologist. Meta-analysis, which aims to synthesize existing research, is a research process and systematic method for quantitatively combining and analyzing the effects of multiple conflicting studies on a given topic ( Glass, 1976 ). In this study, the meta-analysis software Comprehensive Meta-Analysis Version 3.0 was used for data processing and analysis, and relevant data from the literature, such as the values of sample size, standard deviation, and mean, were entered into CMA for relevant calculations.

Publication bias analysis

A publication bias analysis is first required before any specific data analysis of the sample literature can be conducted ( Viechtbauer, 2007 ). Qualitative funnel plots and quantitative Egger’s were used for publication bias tests. Based on the funnel plot indicating ( Figure 3 ) that the effect sizes of the study sample were focused on the upper middle region and more evenly distributed on both sides of the axis, it is initially judged that there is less likelihood of publication bias in the data. The study further utilized Egger’s method and the results of the data showed that t  = 0.914 < 1.96 and p  = 0.182 > 0.05, which satisfied the conditions of no publication bias ( Egger et al., 1997 ). In summary, the results of meta-analysis were less likely to be biased for publication ( Tables 1 , 2 ).

Figure 3 . Funnel plot.

Table 1 . Coding table for meta-analytic variables.

Table 2 . Summary of studies included in the meta-analysis.

Heterogeneity analysis

There may be differences between the different studies included due to a number of factors. To avoid the inability to combine effect sizes due to the presence of heterogeneity in the study, the I 2 statistic is generally used to determine the degree of heterogeneity in the sample, and thus to determine an effect model that is more appropriate for the study ( Higgins, 2003 ). When I 2  < 75%, a fixed effects model is used; when I 2  > 75%, a random effects model is used. According to the test results, I 2  = 99.309% > 75% and Q  = 13020.408 ( p  < 0.001), the study had high heterogeneity ( Table 3 ). Therefore, the random effect model would be chosen to analyze the effect of boarding school on student development in this study.

Table 3 . Heterogeneity test results.

Main effects test

The results of the study indicated that boarding school was not a significant predictor of overall student development ( g  = 0.002, 95%CI = [−0.073, 0.078], Z  = 0.053, p  > 0.05). The study further examined the effect of boarding school on different dimensions of student development. According to the results of Table 4 , the effect sizes from large to small were cognitive dimension ( g  = 0.248, p  < 0.001) > affective and attitudinal dimension ( g  = |−0.159|, p  < 0.05) > behavioral dimension ( g  = |−0.115|, p  > 0.05) > physical development dimension ( g  = |−0.038|, p  > 0.05). The results of the meta-analysis showed that boarding school had little effect on students’ overall development, but there were significant differences across the sub-dimensions. Specifically, boarding school has a moderate positive impact on students’ cognitive development and a small negative impact on students’ behavioral development, affective and attitudinal development, and physical development.

Table 4 . The overall impact of boarding school on student development.

Moderating effect test

Although the overall effect of boarding school on student development was small, there was significant heterogeneity in the effect size of different dimensions. Therefore, subgroup analyses are required. Moderating effect test was conducted using random effect model around different school stages, types of boarding school, and instruments.

School stage

This study focuses on the impact of boarding school on the development of students in primary and secondary schools, so the school stages are coded into three groups: primary school, junior middle school and senior high school according to the current classification standards. Overall, there was a significant difference in the overall effect of different school stages on student development ( Q  = 6.895, p  < 0.05), with the effect strengths between school stages in the following order: junior middle school ( g  = |−0.274|) > senior high school ( g  = |−0.158|) > primary school ( g  = 0.007) ( Table 5 ). Specifically, there was a significant difference in the effect of boarding school on students’ physical development in the physical development dimension ( Q  = 13.481, p  < 0.001). Among them, boarding school had a negative effect on the physical development of primary school students ( g  = −1.48, p  < 0.05), while it had a positive effect on the physical development of junior middle school students ( g  = 0.123, p  < 0.001). In addition, there was no significant difference in the cognitive dimension ( Q  = 0.144, p  = 0.931), behavioral dimension ( Q  = 4.389, p  = 0.111) and affective and attitudinal dimension ( Q  = 0.792, p  = 0.673) ( Table 6 ).

Table 5 . The moderating effect of school stages on boarding school and student development.

Table 6 . The moderating effect of sub-dimension across school stages.

Type of boarding school

In this study, boarding schools were categorized into two types, rural boarding schools and urban boarding schools according to geography. Overall, there was a significant difference in the overall effect of different school types on student development ( Q  = 4.819, p  < 0.05), with effect strengths in the following order: urban boarding ( g  = 0.126) > rural boarding ( g  = |−0.077|) ( Table 7 ). Specifically, on the cognitive development dimension, there was a significant difference in the effect of boarding school on students’ cognitive development ( Q  = 5.903, p  < 0.05). In this case, boarding school had no significant effect on the cognitive development of rural boarding students ( g  = 0.040, p  < 0.05), while it produced a significant positive effect on the cognitive development of urban boarding students ( g  = 0.289, p  < 0.001). In addition, there was no significant difference in the development of students across school types by boarding school on either the behavioral dimension ( Q  = 0.360, p  = 0.549) or the affective and attitudinal dimension (Q = 0.251, p  = 0.617) ( Table 8 ).

Table 7 . The moderating effect of school types on boarding school and student development.

Table 8 . The moderating effect of sub-dimension across school type.

Instruments

The reliability and scientificity of the findings of quantitative research will be affected to some extent by the research tool. As can be seen from the sample of literature, most of the studies used standardized tests or maturity scales to measure student development, while a small number of studies developed self-administered questionnaires to report students’ development. Therefore, the instruments were categorized into standardized and non-standardized instruments to further explore the moderating effect of instruments on the relationship between boarding school and student development. Overall, there was no significant difference in the overall impact of the different instruments on student development ( Q  = 0.128, p  > 0.05). Specifically, on the behavioral dimension, there was a significant difference in the effect of boarding school on students’ behavioral development ( Q  = 4.274, p  < 0.05). In particular, there was no significant negative effect of boarding school on students’ behavioral development when standardized instruments were used ( g  = −0.029, p  > 0.05), while boarding school had a significant negative effect on students’ behavioral development when non-standardized instruments were used ( g  = −0.319, p  < 0.001; Table 9 ). In addition, there were no significant differences between the boarding school on the cognitive dimension ( Q  = 0.004, p  = 0.951), the affective and attitudinal dimension ( Q  = 0.198, p  = 0.657), and the physical development dimension ( Q  = 0.498, p  = 0.481) ( Table 10 ).

Table 9 . The moderating effect of instruments on boarding school and student development.

Table 10 . The moderating effect of sub-dimension across instruments.

The association between boarding school and the development of students

Compared with non-boarding school, boarding school has a smaller effect on student development ( g  = 0.002, p  > 0.05), which supports the third view that there is no significant predictive effect of boarding on student development ( Xiao et al., 2010 ; Martin et al., 2014 ; Sparks, 2015 ). The reason for this has much to do with the multidimensional concept of “student development.” There are many theories about the student development, the more typical ones are Social Learning Theory, Person-Environment Theory, Ecosystem Theory and so on. Together, these theories emphasize that student development is influenced by various factors, such as genetic, environmental, educational, and individuals. The boarding school provides students with a relatively closed learning environment, while integrating their studies and lives organically. In boarding schools, the extent to which students can be influenced by the environment in their interactions with it depends not only on the environment itself, but also on the students’ own initiative and motivation, school education, family environment and other factors ( Du et al., 2010 ).

The results of the data show that boarding school reached a statistically significant level on cognitive development and affective and attitudinal development of the students. Therefore, the study only focuses on these two sub-dimensions for discussion. Boarding school has a positive and significant predictive effect on students’ cognitive development ( g  = 0.248, p  < 0.001), which is consistent with previous findings ( Kennedy, 2010 ; Lu and Du, 2010 ; Gao, 2017 ). Boarding life promotes the development of students’ self-awareness and increases their independence and self-discipline ( Ma, 2012 ). These positive psychological qualities can be transferred to students’ learning, which in turn promotes the development of their cognitive abilities ( TABS, 2023 ). Boarding school presents a negative and significant predictive effect on students’ affective and attitudinal development ( g  = −0.159, p  < 0.05), which provides evidence for the second view ( Ye and Pan, 2007 ; Mander et al., 2014 ). When a student enters a boarding school, he or she will be faced with a completely new environment, as well as the stripping away of parental attachments. Attachment theory suggests that stable attachment relationships are critical for students’ academic, emotional, and social development ( Granot and Mayseless, 2001 ), while parents are the most important attachment relationship in students’ development ( Bosmans and Kerns, 2015 ). In addition, boarding schools often have a closed management model, which can easily lead to problems such as academic overload and depression among students ( Schaverien, 2010 ).

School stage as a moderator

The relationship between boarding school and student development is moderated by different school stages ( Q  = 6.895, p  < 0.05). Among them, boarding school has a significant negative effect on the development of junior middle school students, which may be related to the stage of physical and mental development that students are in ( Wang, 2015 ). According to Piaget’s Cognitive-developmental Theory, junior middle school students are in the transition from the stage of concrete operations to the stage of formal operations, a period in which students shift from perceptual thinking to logical thinking. With the increasing difficulty of knowledge acquisition, it is a great challenge for students’ cognitive development. In addition, students’ physical functions and forms continue to develop and improve during this period, but their psychology is in a semi-mature and emotionally unstable stage. Some students will face a crisis of self-identity and a conflict of role confusion ( Chen and Liu, 2019 ). Therefore, teachers should not only help students to stimulate their interest in learning, but also strengthen the support of families for students, and parents should be involved in students’ lives and learning.

Type of boarding school as a moderator

According to the results of the data, the type of boarding school plays a moderating role between boarding school and student development ( Q  = 4.819, p  < 0.05). Among them, rural boarding school has a negative effect on student development, which supports the views of Chen et al. (2018) , Lu et al. (2017) , Jiang and Xu (2020) , and others. The result that urban boarding school has a positive effect on student development supports Luo (2013) , Xu (2019) , Blau and Blau (2021) and others. The main reason for the disparity lies in the economic differences between urban and rural areas. From the students’ point of view, rural boarding students are more likely to come from rural areas, where their families are economically limited and their parents are generally less educated. From the perspective of schools, urban boarding schools have better accommodations, hardware facilities, and teachers than rural boarding schools ( Chen and Qi, 2010 ). Thus, it can be seen that boarding schools create variability in student development through differences in student population and level of schooling. In order to change the negative impact of the boarding school on rural students, the most important thing is to increase the total amount of financial input, and the gap between urban and rural areas is essentially an economic development gap. In addition, it is necessary to constantly expand the sources of funding to ensure the effective operation of the rural boarding school.

Instruments as a moderator

There is no significant difference in the effect of the instruments on student development under the boarding condition, which suggests that the relationship between boarding school and student development is not moderated by the instruments ( Q  = 0.128, p  > 0.05), but it is still of some analytical value. First, in terms of the specific effect size of the instruments, the effect size of using standardized instruments is smaller than that of non-standardized instruments. Although this difference does not reach the statistically significant level, it reflects the development trend of the two, that is, the measurement results of the non-standardized instruments are inflated compared with the standardized instruments. This is because standardized instruments are usually designed to be rigorous and preset the results within a certain range; whereas non-standardized instruments are usually a form of self-assessment and are more subjective, with flexible and open-ended results. Therefore, it can be presumed that standardized instruments are more realistic and reliable. Secondly, in terms of the scientific validity of the instruments, although the non-standardized instruments have not been recognized by the academic community and tested in practice like the mature standardized instruments, the operational procedures have been strictly followed and their reliability and validity tests have been tested, thus guaranteeing the scientificity and effectiveness of the instruments. This may also be one of the reason why the between-group effect failed to reach a statistically significant level.

Limitations and future directions

The study used a meta-analytic approach to systematically analyze the effects of boarding school on the overall development of primary and secondary school students as well as on different sub-dimensions. In addition, the study explored the moderating effects of different school stages, types of boarding school and instruments. However, there are some limitations to this study. First of all, the number of moderating variables included is limited. There are many factors that affect student development, such as gender, family economic situation, peer relationships, etc., and more moderating variables should be included in the future. Secondly, the results of the study are based on the literature sample, which will be affected by factors such as the quality of the literature sample, the sample size and the research period. Finally, student development is a comprehensive and multidimensional concept that should also include the development of students’ skills, literacy, information literacy, etc. ( Pan, 2019 ). Therefore, in the future, the validity of the findings of this study should be further verified by adopting a more scientific and comprehensive dimensionalization of the core concept of “student development.”

This study utilized a meta-analytic research methodology to explore the impact of boarding school on student development in primary and secondary schools. The results showed that boarding school had no significant predictive effect on students’ overall development, but it was a significant positive predictor of cognitive development and a significant negative predictor of affective and attitudinal development. The relationship between boarding school and student development was also moderated by the stage and type school. The conclusions of the study provide some reference significance for the subsequent theoretical research, and provide new insights and suggestions for the implementation and improvement of the boarding school in practice.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

ZZ: Conceptualization, Methodology, Writing – review & editing. YF: Writing – original draft. YX: Project administration, Supervision, Writing – review & editing.

The author (s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Zhang, D. S. (2020). Analysis on boarding and non-boarding students in junior MiddleSchool in Qianxi County . (HB, China: Doctoral dissertation Hebei Normal University).

Zhou, J. Y., and Xu, L. N. (2021). The effect of boarding on Students' academic achievement, cognitive ability and non-cognitive ability in junior high school. Educ. Sci. Res. 1, 53–59. doi: 10.3969/j.issn.1009-718X.2021.05.010

Keywords: boarding school, student development, meta-analysis, primary and secondary school students, effect size

Citation: Zhong Z, Feng Y and Xu Y (2024) The impact of boarding school on student development in primary and secondary schools: a meta-analysis. Front. Psychol . 15:1359626. doi: 10.3389/fpsyg.2024.1359626

Received: 21 December 2023; Accepted: 15 March 2024; Published: 28 March 2024.

Reviewed by:

Copyright © 2024 Zhong, Feng and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yongqi Xu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Research Project Lead for Studies of Postsecondary

How to apply.

To apply for this position, please upload [1] a cover letter, [2] your CV, [3] one or more writing samples demonstrating your research skills (e.g., job market paper, dissertation chapter, recent publication that you sole authored or for which you are first author), [4] evidence of significant experience preparing data for analysis using Stata (e.g.,two or more substantial samples of individually written, carefully commented code that demonstrate your skills with cleaning, coding, organizing, merging, and otherwise preparing data for analysis), and [5] contact information for at least three individuals who are willing and able to serve as references for you. The cover letter should address in detail your fit for the position and the ways in which you meet the required and desirable qualifications for the position, listed below, as well as your professional commitment to diversity, inclusion, justice, and equity. If the writing sample is a co-authored paper, include in your cover letter a detailed explanation of your particular role in the work. Review of applications will begin immediately and continue until the position is filled. Applications received by April 28, 2024, will receive full consideration.

The Research Project Lead takes substantial responsibility for the day-to-day organization and execution of one or more assigned research projects addressing postsecondary students' educational and labor market outcomes, and policies and practices that influence student success and labor market outcomes. The Research Project Lead also collaborates in and provides support to other original research projects and funding proposals led by other team members. The position is part of a research team under the direction of Peter Riley Bahr, Associate Professor in the Center for the Study of Higher and Postsecondary Education, who will determine the scope of work. This is a one-year position with the likelihood of renewal depending on funding. The position may be filled as full-time or part-time, and requests for a flexible schedule will be considered.

Responsibilities*

We are seeking skilled individuals who can take initiative and bring creativity in using administrative data to answer research questions and produce actionable findings for colleges, systems, and states.

A person taking on the role of Research Project Lead is responsible for the following:

• Interpret the objectives and research questions of assigned projects
• Make methodologically sound, defensible decisions about data cleaning, defining key terms conceptually and operationalizing them as variables, specifying an appropriate sample and unit of analysis, adjudicating between and selecting analytic approaches based on relevant literature, and sharing results in an accessible format
• Clean, code, organize, merge, and otherwise prepare and manage complex longitudinal data sets for analysis using Stata
• Perform data quality assurance checks and identify potential problems with data and the sources of the problems
• Write carefully commented and well-organized Stata syntax
• Design and refine methodologies to meet project objectives and answer project research questions
• Conduct advanced statistical analyses
• Prepare data tables, figures, and other visualizations to present research findings for internal team review and external dissemination
• Thoroughly document methodological and operational decisions of data preparation and analysis
• Prepare data codebooks
• Conduct literature reviews
• Write manuscripts, reports, briefs, and funding proposals
• Prepare and deliver presentations
• Collaborate in research activities with other team members
• Participate in and contribute to team meetings
• Guide the work of graduate student research team members, assign tasks, set timelines, ascertain the quality and completeness of work products, and ensure that deadlines are met
• Other duties as assigned

Required Qualifications*

• M.A. (Ph.D. preferred) in Higher Education, Public Policy, Sociology, Economics, or a related field
• Extensive experience with advanced quantitative research methods
• Extensive experience preparing complex longitudinal datasets for analysis, including cleaning, coding, organizing, merging, and managing data using Stata
• Extensive experience analyzing data and displaying results in tabular and graphical form using Stata
• Experience writing manuscripts for peer-reviewed scholarly journals
• Experience translating research findings for presentation to scholarly audiences, such as at research conferences
• Extensive, in-depth knowledge of one or more areas of higher education research
• Familiarity with contemporary research and policy discourse on community colleges and other open-access postsecondary institutions
• Demonstrated ability to work independently and meet deadlines
• Demonstrated ability to work well with others, including both receiving direction and providing direction
• Demonstrated ability to work on multiple projects simultaneously while maintaining exceptional attention to detail
• Experience with project planning and leadership
• Strong written and verbal communication skills

Desired Qualifications*

• Ph.D. in Higher Education, Public Policy, Sociology, Economics, or a related field
• Experience working with administrative course-level, student-level, and institution-level education data
• Experience writing research grant proposals
• Experience writing research reports or research briefs for policymakers and/or practitioners
• Experience translating research findings for presentation to policymakers and/or practitioners
• Experience working in state or national higher education organizations

Additional Information

The School of Education is located at 610 E. University, Ann Arbor, Michigan. This is a 100% remote position, but an on-campus office is available if preferred. The position may be filled as full-time or part-time, and requests for a flexible schedule will be considered. 

Statement on Diversity:

We respect and value individuals from all races, ethnic backgrounds, ages, genders, religions, sexual orientations, disabilities, economic or veteran status, and other diverse perspectives and individual differences. Further, we are committed to tolerance, sensitivity, understanding, and mutual respect everywhere within our community and we affirm our promise to make the School of Education a welcoming place for all.  In seeking new staff members, we are committed to hiring those who share in our reverence and expectation for diversity.

Background Screening

The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks will be performed in compliance with the Fair Credit Reporting Act.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.