how to make chapter 2 in quantitative research

• Contact sales (+234) 08132546417
• Have a questions? [email protected]
• Latest Projects

Project Materials

How to write a good chapter two: literature review.

Click Here to Download Now.

Do You Have New or Fresh Topic? Send Us Your Topic

How to write chapter two of a research pape r.

As is known, within a research paper, there are several types of research and methodologies. One of the most common types used by students is the literature review. In this article, we will be dealing how to write the literature review (Chapter Two) of your research paper.

Although when writing a project, literature review (Chapter Two) seems more straightforward than carrying out experiments or field research, the literature review involves a lot of research and a lot of reading. Also, utmost attention is essential when it comes to developing and referencing the content so that nothing is pointed out as plagiarism.

However, unlike other steps in project writing, it is not necessary to perform the separate theoretical reference part in the review. After all, the work itself will be a theoretical reference, filled with relevant information and views of several authors on the same subject over time.

As it technically has fewer steps and does not need to go to the field or build appraisal projects, the research paper literature review is a great choice for those who have the tightest deadline for delivering the work. But make no mistake, the level of seriousness in research and development itself is as difficult as any other step.

To further facilitate your understanding, we have divided this research methodology into some essential steps and will explain how to do each of them clearly and objectively. Want to know more about it? Read on and check it out!

What is the literature review in a research paper?

To develop a project in any discipline, it is necessary, first, to study everything that other authors have already explored on that subject. This step aims to update the subject for the academic community and to have a basis to support new research. Therefore, it must be done before any other process within the research paper. However, in the literature review (Chapter Two), this step of searching for data and previous work is all the work. That is, you will only develop the theoretical framework.

In general, you will need to choose the topic in question and search for more relevant works and authors that worked around that research idea you want to discuss. As the intention is to make history and update the subject, you will be able to use works from different dates, showing how opinions and views have evolved over time.

Suppose the subject of your research paper is the role of monarchies in 21st-century societies, for example. In that case, you must present a history of how this institution came about, its impact on society, and what roles the institution is currently playing in modern societies. In the end, you can make a more personal conclusion about your vision.

If your topic covered contains a lot of content, you will need to select the most important and relevant and highlight them throughout the work. This is because you will need to reference the entire work. This means that the research paper literature review needs to be filled with citations from other authors. Therefore, it will present references in practically every paragraph.

In order not to make your work uninteresting and repetitive, you should quote differently throughout development. Switching between direct and indirect citations and trying to fit as much content and work as possible will enrich your project and demonstrate to the evaluators how deep you have been in the search.

Within the review, the only part that does not need to be referenced is the conclusion. After all, it will be written as your final and personal view of everything you have read and analyzed.

How to write a literature review?

Here are some practical and easy tips for structuring a quality and compliant research paper literature review!

Introduction

As with any work, the introduction should attract your project readers’ attention and help them understand the basis of the subject that will be worked on. When reading the introduction, you need to be clear to whoever is reading about your research and what it wants to show.

Following the example cited on the theme of monarchies’ roles in the 21st-century societies, the introduction needs to clarify what this type of institution is and why research on it is vital for this area. Also, it would help if you also quoted how the work was developed and the purpose of your literary study.

Basically, you will introduce the subject in such a way that the reader – even without knowing anything about the topic – can read the complete work and grasp the approach, understanding what was done and the meaning of it.

Methodology

Describing the methodology of a literature review is simpler than describing the steps of field research or experiment. In this step, you will need to describe how your research was carried out, where the information was searched, and retrieved.

As you will need to gather a lot of content, searches can be done in books, academic articles, academic publications, old monographs, internet articles, among other reliable sources. The important thing is always to be sure with your supervisor or other teachers about the reliability of each content used. After all, as the entire work is a theoretical reference, choosing unreliable base papers can greatly damage your grade and hinder your approval, putting at risk the quality and integrity of your entire research paper.

Results and conclusion

The results must present clearly and objectively everything that has been observed and collected from studies throughout history on the research’s theme. In this step, you should show the comparisons between authors, like what was the view of the subject before and how it is currently, in an updated way.

You will also be able to show the developments within the theme and the progress of research and discoveries, as well as the conclusions on the issue so far. In the end, you will summarize everything you have read and discovered, and present your final view on the topic.

Also, it is important to demonstrate whether your project objectives have been met and how. The conclusion is the crucial point to convince your reader and examiner of the relevance and importance of all the work you have done for your area or branch, society, or the environment. Therefore, you must present everything clearly and concisely, closing your research paper with a flourish.

In all academic work, bibliographic references are essential. In the academic paper literature review, however, these references will be gathered at the end of the work and throughout the texts.

Citations during the development of the subject must be referenced in accordance with the guideline of your institutions and departments. For each type of reference, there is a rule that depends on the number of words or how you will make it.

Also, in the list of bibliographic references, where you will need to put all the content used, the rules change according to your search source. For internet sources, for example, the way of referencing is different than book sources.

A wrong quote throughout the text or a used work that you forget to put in the references can lead to your project being labeled a plagiarism work, which is a crime and can lead to several consequences. Therefore, studying these standards is essential and determinant for the success of your work’s literature review (Chapter Two).

By adequately studying the rules, dedicating yourself, and putting them into practice, not only will it be easy to develop a successful project, but achieving your dream grade will be closer than you think.

Not What You Were Looking For? Send Us Your Topic

INSTRUCTIONS AFTER PAYMENT

• 1.Your Full name
• 2. Your Active Email Address
• 3. Your Phone Number
• 4. Amount Paid
• 5. Project Topic
• 6. Location you made payment from

» Send the above details to our email; [email protected] or to our support phone number; (+234) 0813 2546 417 . As soon as details are sent and payment is confirmed, your project will be delivered to you within minutes.

Latest Updates

Impact of sand and gravel dredging on the environment, characteristics of solid waste in akure, ondo state, nature and extent of environmental pollution in the tarkwa prestea mining areas in the western region of ghana, leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

This site uses Akismet to reduce spam. Learn how your comment data is processed .

Advertisements

• Hire A Writer
• Plagiarism Research Clinic
• International Students
• Project Categories
• WHY HIRE A PREMIUM RESEARCHER?
• UPGRADE PLAN
• PROFESSIONAL PLAN
• STANDARD PLAN
• MBA MSC STANDARD PLAN
• MBA MSC PROFESSIONAL PLAN

How To Write The Results/Findings Chapter

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point.

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

Learn More About Quantitative:

Triangulation: The Ultimate Credibility Enhancer

Triangulation is one of the best ways to enhance the credibility of your research. Learn about the different options here.

Inferential Statistics 101: Simple Explainer (With Examples)

Learn about the key concepts and tests within inferential statistics, including t-tests, ANOVA, chi-square, correlation and regression.

Descriptive Statistics 101: Simple Explainer (With Examples)

Learn about the key concepts and measures within descriptive statistics, including measures of central tendency and dispersion.

Validity & Reliability: Explained Simply

Learn about validity and reliability within the context of research methodology. Plain-language explainer video with loads of examples.

Research Design 101: Qualitative & Quantitative

Learn about research design for both qualitative and quantitative studies. Includes plain-language explanations and examples.

📄 FREE TEMPLATES

Research Topic Ideation

Proposal Writing

Literature Review

Methodology & Analysis

Academic Writing

Referencing & Citing

Apps, Tools & Tricks

The Grad Coach Podcast

Thank you. I will try my best to write my results.

Awesome content 👏🏾

this was great explaination

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Submit Comment

• Print Friendly

How To Write Chapter 2 Of A PhD Thesis Proposal (A Beginner’s Guide)

The second chapter of a PhD thesis proposal in most cases is the literature review. This article provides a practical guide on how to write chapter 2 of a PhD thesis.

Introduction to the chapter

Theoretical review, empirical review, chronological organisation of empirical literature review, thematic organisation of empirical literature review, developing a conceptual framework, research gaps, chapter summary, final thoughts on how to write chapter 2 of a phd thesis proposal.

The format for the literature review chapter is discussed below:

This section is about a paragraph-long and informs the readers on what the chapter will cover.

The theoretical review follows immediately after the introductory section of the chapter.

In this section, the student is expected to review the theories behind his/her topic under investigation. One should discuss who came up with the theory, the main arguments of the theory, and how the theory has been applied to study the problem under investigation.

A given topic may have several theories explaining it. The student should review all those theories but at the end mention the main theory that informs his study while giving justification for the selection of that theory.

Because of the existence of many theories and models developed by other researchers, the student is expected to do some comparative analysis of the theories and models that are applicable to his study.

After discussing the theories and models that inform your study, the student is expected to review empirical studies related to his problem under investigation. Empirical literature refers to original studies that have been done by other studies through data collection and analysis. The conclusions drawn from such studies are based on data rather than theories.

This section requires critical thinking and analysis rather than just stating what the authors did and what they found. The student is expected to critique the studies he is reviewing, while making reference to other similar studies and their findings.

For instance, if two studies on the same topic arrive at contrary conclusions, the student should be able to analyse why the conclusions are different: e.g. the population of study could be different, the methodology used could be different etc.

There are two ways of organising empirical literature: chronological and thematic:

In this method, the empirical literature review is organised by date of publication, starting with the older literature to the most recent literature.

The advantage of using this method is that it shows how the state of knowledge of the problem under investigation has changed over time.

The disadvantage of chronological empirical review is that the flow of discussion is not smooth, because similar studies are discussed separately depending on when they were published.

In this method, like studies are discussed together.

The studies are organised based on the variables of the study. Each variable has its own section for discussion. All studies that examined a variable are discussed together, highlighting the consensus amongst the studies, as well as the points of disagreement.

The advantage of this method is that it creates a smooth flow of discussion of the literature. It also makes it easier to identify the research gaps in each variable under investigation.

While the choice between chronological and thematic empirical review varies from one institution to another, the thematic synthesis is most preferred especially for PhD-level programs.

After the theoretical and empirical review, the student is expected to develop his own conceptual framework. A conceptual framework is a diagrammatic representation of the variables of a study and the relationship between those variables.

The conceptual framework is informed by the literature review. Developing a conceptual framework involves three main steps:

• Identify all the variables that will be analysed in your study.
• Specify the relationship between the variables, as informed by the literature review.
• Draw a diagram with the variables and the relationship between them.

The main purpose of conducting literature review is to document what is known and what is not known.

Research gaps are what is not yet known about the topic under investigation.

Your contribution to knowledge will come from addressing what is not yet known.

It is therefore important for PhD students to first review existing literature for their area of study before settling on the final topic.

Additionally, when reviewing literature, the student should review all of the most recent studies to avoid duplicating efforts. Originality is important especially for PhD studies.

There are different types of research gaps:

• Gaps in concepts or variables studied e.g. most studies on maternal health focus on pregnancy and delivery but not on post-partum period. So you conduct a study focusing on the post-partum period.
• Geographical coverage: rural vs. urban or rural vs. urban slums; developed vs. developing countries etc
• Time: past vs. recent
• Demographics: middle class vs. poor communities; males vs. females; educated vs. uneducated etc
• Research design: quantitative vs. qualitative or mixed methods
• Data collection: questionnaires vs. interviews and focus group discussions
• Data analysis techniques: descriptive vs. inferential statistics etc

This section provides a summary of what the chapter is about and highlights the main ideas.

This article provided some guidance on how to write chapter 2 of a PhD thesis proposal as well as the format expected of the chapter by many institutions. The format may vary though and students are advised to refer to the dissertation guidelines of their institutions. Writing the literature review chapter can be the most daunting task of a PhD thesis proposal because it informs chapter 1 of the proposal. For instance, writing the contribution to knowledge section of chapter 1 requires the student to have read and reviewed many articles.

Related post

How To Write Chapter 1 Of A PhD Thesis Proposal (A Practical Guide)

How To Write Chapter 3 Of A PhD Thesis Proposal (A Detailed Guide)

Grace Njeri-Otieno

Grace Njeri-Otieno is a Kenyan, a wife, a mom, and currently a PhD student, among many other balls she juggles. She holds a Bachelors' and Masters' degrees in Economics and has more than 7 years' experience with an INGO. She was inspired to start this site so as to share the lessons learned throughout her PhD journey with other PhD students. Her vision for this site is "to become a go-to resource center for PhD students in all their spheres of learning."

Recent Content

SPSS Tutorial #12: Partial Correlation Analysis in SPSS

Partial correlation is almost similar to Pearson product-moment correlation only that it accounts for the influence of another variable, which is thought to be correlated with the two variables of...

SPSS Tutorial #11: Correlation Analysis in SPSS

In this post, I discuss what correlation is, the two most common types of correlation statistics used (Pearson and Spearman), and how to conduct correlation analysis in SPSS. What is correlation...