quantitative research methods short course

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Quantitative methods (qcr).

Science searches for patterns in data. Quantitative methods are tools for finding and evaluating these patterns. This course introduces foundational concepts in quantitative methods, including data visualization and common statistical tests used in psychological research.

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Target Audience: Early stage postgraduate research students and early career researchers 

This course is for you if you have little or no previous experience of quantitative research methods (i.e. methods involving numbers and basic statistics) but feel that there is potential to apply such methods to your research. The course covers different types of quantitative methods including calculation of summary statistics, an introduction to correlation and regression analysis, hypothesis testing and calculating p-values.

This course will be useful if:

You need to apply these methods in your own discipline

You want to assess whether there is potential to use quantitative methods

You need to interpret the output from other quantitative research

Description

This is a two-day introductory course, and does not assume any previous experience of quantitative research methods.

The aim of the course is an introduction to statistics as a whole, but will focus on several common topics of interest to many disciplines. The course does not aim to teach students how to use particular statistical packages, though there will be some examples of implementing statistical methods in R.

The focus is on understanding the topics; applications are shown by simple examples calculated by hand. Although 'by hand' calculations are not common practice in research, their purpose here is for students to gain an insight into how to obtain statistical results and to appreciate the meaning and implications of their output from statistics software.

The course includes the following topics

A description of different types of quantitative data

Strength sand weaknesses of quantitative data

Discussions on data integrity

Describing data graphically

Calculating summary statistics

Measures of correlation

Conducting hypothesis testing

Calculating and interpreting p-values

Introduction to regression analysis

• Disseminating quantitative research

Aims:  The aim of the course is to provide a brief introduction to the principles of quantitative research methods.

Objectives:

By the end of the course, you will:

Be able to distinguish between different types of quantitative data

Be able to provide basic numerical summaries of data

Be able to perform hypothesis tests and report p-values

• Be able to interpret simple regressions

Process:  A two-day course involving discussion and practical exercises. Course Materials:

You can self-enrol to access the course resources in Moodle by following this link: https://moodle.nottingham.ac.uk/course/view.php?id=140161

Here you will find all the course information for both the online and face to face versions of this course which complement each other, including the slides, pre-recorded tutor videos, workbook and formulae sheet.

You do not need to bring a copy of the workbook, as this is intended to be used as a reference having completed the course. You need to bring a printed copy or have access to the electronic version of the formulae sheet, as this will often be used for exercises during the course.

The R codes used in the class are also available on the course page. The R codes used in the class are also available on the course page. Solutions to exercises can also be checked  after completion of the course.

If you cannot access the course page, please email [email protected] . Please note, attendances for courses will be recorded. There are no training points associated with courses run by the Researcher Academy.

Related courses

'Introduction to SPSS' is an online course designed to teach you the basis of IBM® SPSS® Statistics.

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Additionally, help is available from the Methods and Data Institute (MDI): PGRs engaged in empirical research for their PhD thesis can call on the MDI for advice on their choice and use of methods and data. MDI often host drop-in sessions especially for PGRs  - appointments must be made for this by emailing  [email protected]

Research Integrity - stand-alone online course designed to strengthen your awareness of your own responsibilities and accountability when planning and conducting research and provides guidance on what to do should things go wrong.  Course Accessibility The following table shows a summary of what is needed to participate in the course.  If you feel you will experience any difficulties participating, please let us know via the ‘special requirements’ tab, providing as much information as possible. The special requirements tab can be completed when you book your place. Alternatively, you can contact us directly at [email protected] .

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Researchers should plan to arrive prior to the advertised course start time. Except for exceptional reasons, there will be no admittance to a Researcher Academy or Faculty Training Programme (FTP) course 15 minutes after the advertised course start time.

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When booking on to a Researcher Academy short course you are entering into a commitment to attend. If you find that you are no longer available to attend you MUST cancel your place (on the system if more than three days before the course) or if at short notice by emailing [email protected] . This will ensure that your place can be offered to another researcher on the waiting list. Failure to cancel a place results in other researchers missing out on places through the waiting list process.

It is unacceptable for researchers to just not attend when booked onto a course. The Researcher Academy maintains records of those who repeatedly do not attend courses they have booked. This may affect future eligibility to book onto further Researcher Academy courses and will affect considerations for Researcher Academy funded opportunities.

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A Quick Guide to Quantitative Research in the Social Sciences

(12 reviews)

Christine Davies, Carmarthen, Wales

Copyright Year: 2020

Last Update: 2021

Publisher: University of Wales Trinity Saint David

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Jennifer Taylor, Assistant Professor, Texas A&M University-Corpus Christi on 4/18/24

This resource is a quick guide to quantitative research in the social sciences and not a comprehensive resource. It provides a VERY general overview of quantitative research but offers a good starting place for students new to research. It... read more

Comprehensiveness rating: 4 see less

This resource is a quick guide to quantitative research in the social sciences and not a comprehensive resource. It provides a VERY general overview of quantitative research but offers a good starting place for students new to research. It offers links and references to additional resources that are more comprehensive in nature.

Content Accuracy rating: 4

The content is relatively accurate. The measurement scale section is very sparse. Not all types of research designs or statistical methods are included, but it is a guide, so details are meant to be limited.

Relevance/Longevity rating: 4

The examples were interesting and appropriate. The content is up to date and will be useful for several years.

Clarity rating: 5

The text was clearly written. Tables and figures are not referenced in the text, which would have been nice.

Consistency rating: 5

The framework is consistent across chapters with terminology clearly highlighted and defined.

Modularity rating: 5

The chapters are subdivided into section that can be divided and assigned as reading in a course. Most chapters are brief and concise, unless elaboration is necessary, such as with the data analysis chapter. Again, this is a guide and not a comprehensive text, so sections are shorter and don't always include every subtopic that may be considered.

Organization/Structure/Flow rating: 5

The guide is well organized. I appreciate that the topics are presented in a logical and clear manner. The topics are provided in an order consistent with traditional research methods.

Interface rating: 5

The interface was easy to use and navigate. The images were clear and easy to read.

Grammatical Errors rating: 5

I did not notice any grammatical errors.

Cultural Relevance rating: 5

The materials are not culturally insensitive or offensive in any way.

I teach a Marketing Research course to undergraduates. I would consider using some of the chapters or topics included, especially the overview of the research designs and the analysis of data section.

Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 3/9/24

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers... read more

Comprehensiveness rating: 3 see less

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers references to other resources that can be used to deepen the knowledge. The text does not include a glossary or index. The references in the figures for each chapter are not included in the reference section. It would be helpful to include those.

Overall, the text is accurate. For example, Figure 1 on page 6 provides a clear overview of the research process. It includes general definitions of primary and secondary research. It would be helpful to include more details to explain some of the examples before they are presented. For instance, the example on page 5 was unclear how it pertains to the literature review section.

In general, the text is relevant and up-to-date. The text includes many inferences of moving from qualitative to quantitative analysis. This was surprising to me as a quantitative researcher. The author mentions that moving from a qualitative to quantitative approach should only be done when needed. As a predominantly quantitative researcher, I would not advice those interested in transitioning to using a qualitative approach that qualitative research would enhance their research—not something that should only be done if you have to.

Clarity rating: 4

The text is written in a clear manner. It would be helpful to the reader if there was a description of the tables and figures in the text before they are presented.

Consistency rating: 4

The framework for each chapter and terminology used are consistent.

Modularity rating: 4

The text is clearly divided into sections within each chapter. Overall, the chapters are a similar brief length except for the chapter on data analysis, which is much more comprehensive than others.

Organization/Structure/Flow rating: 4

The topics in the text are presented in a clear and logical order. The order of the text follows the conventional research methodology in social sciences.

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader.

Grammatical Errors rating: 3

There are some grammatical/typographical errors throughout. Of note, for Section 5 in the table of contents. “The” should be capitalized to start the title. In the title for Table 3, the “t” in typical should be capitalized.

Cultural Relevance rating: 4

The examples are culturally relevant. The text is geared towards learners in the UK, but examples are relevant for use in other countries (i.e., United States). I did not see any examples that may be considered culturally insensitive or offensive in any way.

I teach a course on research methods in a Bachelor of Science in Public Health program. I would consider using some of the text, particularly in the analysis chapter to supplement the current textbook in the future.

Reviewed by Finn Bell, Assistant Professor, University of Michigan, Dearborn on 1/3/24

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary. read more

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary.

Content Accuracy rating: 5

As far as I can tell, the text is accurate, error-free and unbiased.

Relevance/Longevity rating: 5

This text is up-to-date, and given the content, unlikely to become obsolete any time soon.

The text is very clear and accessible.

The text is internally consistent.

Given how short the text is, it seems unnecessary to divide it into smaller readings, nonetheless, it is clearly labelled such that an instructor could do so.

The text is well-organized and brings readers through basic quantitative methods in a logical, clear fashion.

Easy to navigate. Only one table that is split between pages, but not in a way that is confusing.

There were no noticeable grammatical errors.

The examples in this book don't give enough information to rate this effectively.

This text is truly a very quick guide at only 26 double-spaced pages. Nonetheless, Davies packs a lot of information on the basics of quantitative research methods into this text, in an engaging way with many examples of the concepts presented. This guide is more of a brief how-to that takes readers as far as how to select statistical tests. While it would be impossible to fully learn quantitative research from such a short text, of course, this resource provides a great introduction, overview, and refresher for program evaluation courses.

Reviewed by Shari Fedorowicz, Adjunct Professor, Bridgewater State University on 12/16/22

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing... read more

Comprehensiveness rating: 5 see less

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing the reader with the ability to distinguish two terms that frequently get confused. In addition, links and outside resources are provided to deepen the understanding as an option for the reader. The use of these links, coupled with diagrams and examples make this text comprehensive.

The content is mostly accurate. Given that it is a quick guide, the author chose a good selection of which types of research designs to include. However, some are not provided. For example, correlational or cross-correlational research is omitted and is not discussed in Section 3, but is used as a statistical example in the last section.

Examples utilized were appropriate and associated with terms adding value to the learning. The tables that included differentiation between types of statistical tests along with a parametric/nonparametric table were useful and relevant.

The purpose to the text and how to use this guide book is stated clearly and is established up front. The author is also very clear regarding the skill level of the user. Adding to the clarity are the tables with terms, definitions, and examples to help the reader unpack the concepts. The content related to the terms was succinct, direct, and clear. Many times examples or figures were used to supplement the narrative.

The text is consistent throughout from contents to references. Within each section of the text, the introductory paragraph under each section provides a clear understanding regarding what will be discussed in each section. The layout is consistent for each section and easy to follow.

The contents are visible and address each section of the text. A total of seven sections, including a reference section, is in the contents. Each section is outlined by what will be discussed in the contents. In addition, within each section, a heading is provided to direct the reader to the subtopic under each section.

The text is well-organized and segues appropriately. I would have liked to have seen an introductory section giving a narrative overview of what is in each section. This would provide the reader with the ability to get a preliminary glimpse into each upcoming sections and topics that are covered.

The book was easy to navigate and well-organized. Examples are presented in one color, links in another and last, figures and tables. The visuals supplemented the reading and placed appropriately. This provides an opportunity for the reader to unpack the reading by use of visuals and examples.

No significant grammatical errors.

The text is not offensive or culturally insensitive. Examples were inclusive of various races, ethnicities, and backgrounds.

This quick guide is a beneficial text to assist in unpacking the learning related to quantitative statistics. I would use this book to complement my instruction and lessons, or use this book as a main text with supplemental statistical problems and formulas. References to statistical programs were appropriate and were useful. The text did exactly what was stated up front in that it is a direct guide to quantitative statistics. It is well-written and to the point with content areas easy to locate by topic.

Reviewed by Sarah Capello, Assistant Professor, Radford University on 1/18/22

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text. read more

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text.

The content is mostly accurate. I would have preferred a few nuances to be hashed out a bit further to avoid potential reader confusion or misunderstanding of the concepts presented.

The content is current; however, some of the references cited in the text are outdated. Newer editions of those texts exist.

The text is very accessible and readable for a variety of audiences. Key terms are well-defined.

There are no content discrepancies within the text. The author even uses similarly shaped graphics for recurring purposes throughout the text (e.g., arrow call outs for further reading, rectangle call outs for examples).

The content is chunked nicely by topics and sections. If it were used for a course, it would be easy to assign different sections of the text for homework, etc. without confusing the reader if the instructor chose to present the content in a different order.

The author follows the structure of the research process. The organization of the text is easy to follow and comprehend.

All of the supplementary images (e.g., tables and figures) were beneficial to the reader and enhanced the text.

There are no significant grammatical errors.

I did not find any culturally offensive or insensitive references in the text.

This text does the difficult job of introducing the complicated concepts and processes of quantitative research in a quick and easy reference guide fairly well. I would not depend solely on this text to teach students about quantitative research, but it could be a good jumping off point for those who have no prior knowledge on this subject or those who need a gentle introduction before diving in to more advanced and complex readings of quantitative research methods.

Reviewed by J. Marlie Henry, Adjunct Faculty, University of Saint Francis on 12/9/21

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of... read more

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of thought. There is no glossary but, for a guide of this length, a glossary does not seem like it would enhance the guide significantly.

The content is relatively accurate. Expanding the content a bit more or explaining that the methods and designs presented are not entirely inclusive would help. As there are different schools of thought regarding what should/should not be included in terms of these designs and methods, simply bringing attention to that and explaining a bit more would help.

Relevance/Longevity rating: 3

This content needs to be updated. Most of the sources cited are seven or more years old. Even more, it would be helpful to see more currently relevant examples. Some of the source authors such as Andy Field provide very interesting and dynamic instruction in general, but they have much more current information available.

The language used is clear and appropriate. Unnecessary jargon is not used. The intent is clear- to communicate simply in a straightforward manner.

The guide seems to be internally consistent in terms of terminology and framework. There do not seem to be issues in this area. Terminology is internally consistent.

For a guide of this length, the author structured this logically into sections. This guide could be adopted in whole or by section with limited modifications. Courses with fewer than seven modules could also logically group some of the sections.

This guide does present with logical organization. The topics presented are conceptually sequenced in a manner that helps learners build logically on prior conceptualization. This also provides a simple conceptual framework for instructors to guide learners through the process.

Interface rating: 4

The visuals themselves are simple, but they are clear and understandable without distracting the learner. The purpose is clear- that of learning rather than visuals for the sake of visuals. Likewise, navigation is clear and without issues beyond a broken link (the last source noted in the references).

This guide seems to be free of grammatical errors.

It would be interesting to see more cultural integration in a guide of this nature, but the guide is not culturally insensitive or offensive in any way. The language used seems to be consistent with APA's guidelines for unbiased language.

Reviewed by Heng Yu-Ku, Professor, University of Northern Colorado on 5/13/21

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive... read more

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive research study as an Appendix after section 7 (page 26) to help readers comprehend information better.

For the most part, the content is accurate and unbiased. However, the author only includes four types of research designs used on the social sciences that contain quantitative elements: 1. Mixed method, 2) Case study, 3) Quasi-experiment, and 3) Action research. I wonder why the correlational research is not included as another type of quantitative research design as it has been introduced and emphasized in section 6 by the author.

I believe the content is up-to-date and that necessary updates will be relatively easy and straightforward to implement.

The text is easy to read and provides adequate context for any technical terminology used. However, the author could provide more detailed information about estimating the minimum sample size but not just refer the readers to use the online sample calculators at a different website.

The text is internally consistent in terms of terminology and framework. The author provides the right amount of information with additional information or resources for the readers.

The text includes seven sections. Therefore, it is easier for the instructor to allocate or divide the content into different weeks of instruction within the course.

Yes, the topics in the text are presented in a logical and clear fashion. The author provides clear and precise terminologies, summarizes important content in Table or Figure forms, and offers examples in each section for readers to check their understanding.

The interface of the book is consistent and clear, and all the images and charts provided in the book are appropriate. However, I did encounter some navigation problems as a couple of links are not working or requires permission to access those (pages 10 and 27).

No grammatical errors were found.

No culturally incentive or offensive in its language and the examples provided were found.

As the book title stated, this book provides “A Quick Guide to Quantitative Research in Social Science. It offers easy-to-read information and introduces the readers to the research process, such as research questions, research paradigms, research process, research designs, research methods, data collection, data analysis, and data discussion. However, some links are not working or need permissions to access them (pages 10 and 27).

Reviewed by Hsiao-Chin Kuo, Assistant Professor, Northeastern Illinois University on 4/26/21, updated 4/28/21

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and... read more

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and process, discusses methods, data collection and analysis, and ends with writing a research report. It also identifies its target readers/users as those begins to explore quantitative research. It would be helpful to include more examples for readers/users who are new to quantitative research.

Its content is mostly accurate and no bias given its nature as a quick guide. Yet, it is also quite simplified, such as its explanations of mixed methods, case study, quasi-experimental research, and action research. It provides resources for extended reading, yet more recent works will be helpful.

The book is relevant given its nature as a quick guide. It would be helpful to provide more recent works in its resources for extended reading, such as the section for Survey Research (p. 12). It would also be helpful to include more information to introduce common tools and software for statistical analysis.

The book is written with clear and understandable language. Important terms and concepts are presented with plain explanations and examples. Figures and tables are also presented to support its clarity. For example, Table 4 (p. 20) gives an easy-to-follow overview of different statistical tests.

The framework is very consistent with key points, further explanations, examples, and resources for extended reading. The sample studies are presented following the layout of the content, such as research questions, design and methods, and analysis. These examples help reinforce readers' understanding of these common research elements.

The book is divided into seven chapters. Each chapter clearly discusses an aspect of quantitative research. It can be easily divided into modules for a class or for a theme in a research method class. Chapters are short and provides additional resources for extended reading.

The topics in the chapters are presented in a logical and clear structure. It is easy to follow to a degree. Though, it would be also helpful to include the chapter number and title in the header next to its page number.

The text is easy to navigate. Most of the figures and tables are displayed clearly. Yet, there are several sections with empty space that is a bit confusing in the beginning. Again, it can be helpful to include the chapter number/title next to its page number.

Grammatical Errors rating: 4

No major grammatical errors were found.

There are no cultural insensitivities noted.

Given the nature and purpose of this book, as a quick guide, it provides readers a quick reference for important concepts and terms related to quantitative research. Because this book is quite short (27 pages), it can be used as an overview/preview about quantitative research. Teacher's facilitation/input and extended readings will be needed for a deeper learning and discussion about aspects of quantitative research.

Reviewed by Yang Cheng, Assistant Professor, North Carolina State University on 1/6/21

It covers the most important topics such as research progress, resources, measurement, and analysis of the data. read more

It covers the most important topics such as research progress, resources, measurement, and analysis of the data.

The book accurately describes the types of research methods such as mixed-method, quasi-experiment, and case study. It talks about the research proposal and key differences between statistical analyses as well.

The book pinpointed the significance of running a quantitative research method and its relevance to the field of social science.

The book clearly tells us the differences between types of quantitative methods and the steps of running quantitative research for students.

The book is consistent in terms of terminologies such as research methods or types of statistical analysis.

It addresses the headlines and subheadlines very well and each subheading should be necessary for readers.

The book was organized very well to illustrate the topic of quantitative methods in the field of social science.

The pictures within the book could be further developed to describe the key concepts vividly.

The textbook contains no grammatical errors.

It is not culturally offensive in any way.

Overall, this is a simple and quick guide for this important topic. It should be valuable for undergraduate students who would like to learn more about research methods.

Reviewed by Pierre Lu, Associate Professor, University of Texas Rio Grande Valley on 11/20/20

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas. read more

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas.

Mostly accurate content.

As a quick guide, content is highly relevant.

Succinct and clear.

Internally, the text is consistent in terms of terminology used.

The text is easily and readily divisible into smaller sections that can be used as assignments.

I like that there are examples throughout the book.

Easy to read. No interface/ navigation problems.

No grammatical errors detected.

I am not aware of the culturally insensitive description. After all, this is a methodology book.

I think the book has potential to be adopted as a foundation for quantitative research courses, or as a review in the first weeks in advanced quantitative course.

Reviewed by Sarah Fischer, Assistant Professor, Marymount University on 7/31/20

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable). read more

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable).

Content Accuracy rating: 1

Contains VERY significant errors, such as saying that one can "accept" a hypothesis. (One of the key aspect of hypothesis testing is that one either rejects or fails to reject a hypothesis, but NEVER accepts a hypothesis.)

Very relevant to those experiencing the research process for the first time. However, it is written by someone working in the natural sciences but is a text for social sciences. This does not explain the errors, but does explain why sometimes the author assumes things about the readers ("hail from more subjectivist territory") that are likely not true.

Clarity rating: 3

Some statistical terminology not explained clearly (or accurately), although the author has made attempts to do both.

Very consistently laid out.

Chapters are very short yet also point readers to outside texts for additional information. Easy to follow.

Generally logically organized.

Easy to navigate, images clear. The additional sources included need to linked to.

Minor grammatical and usage errors throughout the text.

Makes efforts to be inclusive.

The idea of this book is strong--short guides like this are needed. However, this book would likely be strengthened by a revision to reduce inaccuracies and improve the definitions and technical explanations of statistical concepts. Since the book is specifically aimed at the social sciences, it would also improve the text to have more examples that are based in the social sciences (rather than the health sciences or the arts).

Reviewed by Michelle Page, Assistant Professor, Worcester State University on 5/30/20

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new... read more

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new researcher would not be able to use this as a stand alone guide for quantitative pursuits without having a supplemental text that explains the steps in the process more comprehensively. The introduction does provide this caveat.

Content Accuracy rating: 3

There are no biases or errors that could be distinguished; however, it’s simplicity in content, although accurate for an outline of process, may lack a conveyance of the deeper meanings behind the specific processes explained about qualitative research.

The content is outlined in traditional format to highlight quantitative considerations for formatting research foundational pieces. The resources/references used to point the reader to literature sources can be easily updated with future editions.

The jargon in the text is simple to follow and provides adequate context for its purpose. It is simplified for its intention as a guide which is appropriate.

Each section of the text follows a consistent flow. Explanation of the research content or concept is defined and then a connection to literature is provided to expand the readers understanding of the section’s content. Terminology is consistent with the qualitative process.

As an “outline” and guide, this text can be used to quickly identify the critical parts of the quantitative process. Although each section does not provide deeper content for meaningful use as a stand alone text, it’s utility would be excellent as a reference for a course and can be used as an content guide for specific research courses.

The text’s outline and content are aligned and are in a logical flow in terms of the research considerations for quantitative research.

The only issue that the format was not able to provide was linkable articles. These would have to be cut and pasted into a browser. Functional clickable links in a text are very successful at leading the reader to the supplemental material.

No grammatical errors were noted.

This is a very good outline “guide” to help a new or student researcher to demystify the quantitative process. A successful outline of any process helps to guide work in a logical and systematic way. I think this simple guide is a great adjunct to more substantial research context.

Table of Contents

• Section 1: What will this resource do for you?
• Section 2: Why are you thinking about numbers? A discussion of the research question and paradigms.
• Section 3: An overview of the Research Process and Research Designs
• Section 4: Quantitative Research Methods
• Section 5: the data obtained from quantitative research
• Section 6: Analysis of data
• Section 7: Discussing your Results

Ancillary Material

About the book.

This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed to undertake a quantitative research project despite a hatred for maths, then this booklet should be a real help.

The booklet was amended in 2022 to take into account previous review comments.  

About the Contributors

Christine Davies , Ph.D

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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?
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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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Advanced Quantitative Research Methods 7KNIP042

Key information, subject area:.

Nursing, Midwifery & Palliative Care

Course type:

Assessed Module

Credit level:

Credit value:, available course dates:.

From: 08 October 2024 To: 12 November 2024

Course overview

This module forms part of the MRES Clinical Research. The module contributes to your programme of study by providing a solid grounding in quantitative research methods, providing you with skills for designing, conducting and appraising your own and others’ research. This is the first module of the programme and provides a foundation for your research methods training, complemented by the Advanced Qualitative Research Methods module taken in term 2.

The module is for PGR students and MSc students only. Not available as Free Standing Module.

What will I achieve?

Participants will develop:

• Advanced understanding of key approaches in quantitative health research.
• Advanced understanding of the application and use of appropriate quantitative research methods.
• Advanced understanding of stages involved in undertaking quantitative research methods.
• Advanced understanding of stages involved in undertaking quantitative research.
• Advanced critical appraisal skills to interrogate the application of quantitative research approaches and methods.
• Advanced skills in selecting and conducting appropriate statistical tests for quantitative data.

The content of this module will include:

• Research design and data collection approaches.
• Surveys, case-control, and cohort studies.
• Clinical trials.
• Prognostic and diagnostic tests.
• Data types and distribution.
• Access and ethics.
• Hypothesis testing.
• Samples and populations.
• Approaches to analyses: comparisons, regression, correlation.
• Analysis of categorical and continuous data.
• Combining the results of independent studies.

Who will I learn with?

Associate Dean (Postgraduate Research Studies) and Senior Lecturer in Mental Health

Who is this for?

Healthcare professionals

How will I be assessed?

3000 word assignment. Critical appraisal of a quantitative research paper.

What is the teaching schedule?

08/10/2024, 15/10, 22/10, 29/10, 05/11, 12/11

Course status:

Full fee £3400

To find out more about this module, please contact Module lead Vasiliki Tzouvara.

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• Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care

Quantitative Methodology: Measurement and Statistics, M.S.

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June 30, 2025

In-State - $12,540 Out-of-State - $26,490 More Info

This Quantitative Methodology: Measurement and Statistics, Master of Science (M.S.) program provides you with advanced training in quantitative research methods and statistical analysis. You will learn to design and conduct research studies, analyze data using sophisticated statistical techniques, and interpret and present research findings effectively. We emphasize both theoretical knowledge and practical skills, preparing you for careers in any industry. Whether pursuing further graduate studies or entering the workforce directly, you will be well-prepared to contribute to the advancement of knowledge in your chosen field.

Key Features

• Balanced Training : Gain comprehensive skills in quantitative methods suitable for various professional settings.
• Proximity to Washington, D.C. : Access diverse academic and professional opportunities in the nation's capital.
• Rigorous Core Curriculum : Master key concepts in applied measurement, statistical modeling, and evaluation methods.
• Flexibility : Choose from a range of elective courses to deepen your expertise in specific areas of interest.
• Demonstrate proficiency in applied measurement, statistical analysis, and research design.
• Apply quantitative methods to address complex research questions in diverse contexts.
• Evaluate and critique research literature and methodologies in the field of quantitative methodology.
• Communicate quantitative findings effectively to diverse audiences through written reports and presentations.

This program offers a wide range of career pathways, including:

• Research Associate
• Data Analyst
• Policy Analyst 
• Evaluation Specialist

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Admission Requirements           Guide to Applying

You are required to submit all required documents before submitting the application.

Program Specific Requirements

• Letters of Recommendation (3)
• Graduate Record Examination (GRE)
• Writing Sample (1)

Marieh Arnett, student, Quantitative Methodology: Measurement and Statistics

Courses in this program are carefully selected and highly customizable to give you the best possible experience. Your specific program of study will be structured to take into account your background and aspirations. Both thesis and non-thesis options are available. 

QMMS Graduate Student Handbook

There is a common core of courses comprised of:

• EDMS 623 Applied Measurement: Issues and Practices (3) 
• EDMS 646 General Linear Models I (3) 
• EDMS 647 Causal Inference and Evaluation Methods (3)
• EDMS 651 General Linear Models II (3) 
• EDMS 655 Introduction to Multilevel Modeling (3) 
• EDMS 657 Exploratory Latent and Composite Variable Methods (3) 
• EDMS 724 Modern Measurement Theory (3)

Additional elective coursework completes the program. A written comprehensive examination based on the first four courses of the core is required. The Graduate School allows transfer of up to six credits of appropriate prior graduate work. 

Sep 17 Graduate Fair Expo Sep 17, 2024 4:00 – 6:00 pm

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Quantitative Methods and Research Design (854N1)

15 credits, Level 7 (Masters)

Autumn teaching

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We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum. We’ll make sure to let you know of any material changes to modules at the earliest opportunity.

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• 2.1. Seminar
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• 11.2. Linear Regression
• 12. Datasets
• 13. R Resources
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Introduction to Quantitative Methods

About this course.

This course is designed to introduce you to and help you become familiar with quantitative methodologies critical to your development as a social scientist. The introductory methods course has two primary aims. First, students will be introduced to quantitative methodology that researchers and policymakers use in answering social, political and economic questions. Second, the course will equip students to use one or more of the discussed techniques in their MSc dissertation.

By the end of the course, you should be able to understand basic research methods, apply them to real world problems and evaluate their use in published research. Students will also acquire competency in performing statistical analyses using a popular statistical program (R).

The assessment for this course is a 24 hour prior disclosure exam which will be completed in the last week of the term.

*Please note that this module is only available to SPP students. Students taking this module are not permitted to take PUBLG088 Advanced Quantitative Methods.

You can also access this site with a short URL: http://bit.ly/PUBLG100

Psychological resilience and valued living in difficult times: mixed method research in cultural context

• Published: 12 September 2024

Cite this article

• Feyza Topçu   ORCID: orcid.org/0000-0002-5853-2670 1 &
• Mehmet Dinç   ORCID: orcid.org/0000-0001-9544-8999 1  

2 Altmetric

Valued living involves aligning one’s actions with one’s personal values, which can contribute to mental strength and psychological resilience. The present study aimed to examine the importance of living a valued life in maintaining mental strength, particularly during challenging times. In addition, it identifies the culturally relevant values that strengthen adults. Our study used both quantitative and qualitative methods. In the quantitative phase, 493 adults completed the following scales Valued Living Questionnaire (VLQ), Connor-Davidson Resilience Scale Short Form (CD-RISC-10), and Depression, Anxiety, and Stress Scale (DASS-21). We calculated Pearson’s product-moment correlation coefficient and used structural equation modeling (SEM) to conduct the path analysis. For the qualitative phase, we recruited 15 participants from a larger quantitative sample based on their scores obtained on administered scales and conducted in-depth interviews along with researcher’s journal and memos. Based on our quantitative findings, this study indicates that the concept of valued living serves as a mediating factor between psychological resilience and mental health outcomes. In our qualitative analysis, we identified certain values (e.g., acceptance, love, and goodwill) that serve to empower adults during challenging periods. The findings of this study are deemed to serve as a reference for the formulation and implementation of interventions intended to strengthen psychological resilience, from the viewpoints of both mental health specialists and psychological counselors.

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You should have a Kandidaatti / Kandidat (University Bachelor Degree), (Professional Title) (AMK/YH) (Bachelor degree from a University of Applied Sciences), Arkkitehti / Arktitekt / Diplomi-insööri / Diplomingenör / Proviisori / Provisor with a final overall result of at least 3.5 on a 5-point scale.

Chinese qualifications

You should have a four-year Bachelor degree with a final overall score of at least 75-80% depending on the institution attended.

Egyptian qualifications

You should have a Bachelor degree from a university with a final overall result of at least 75% (Very Good) or 3.0 on a 4-point scale.

Vietnamese qualifications

You should have a Bachelor degree (Bằng Tốt Nghiệp Đại Học/Bằng Cử Nhân) of at least four years or a Master's degree (Thạc sĩ) from a recognised degree-awarding institution with a final overall result of at least 7.0 on a 10-point scale.

Estonian qualifications

You should have a Bakalaurusekraad (Bachelor degree), Diplomeeritud spetsialisti ülikoolidiplom/Kraadita Diploma (University Specialist's Diploma) or Rakenduskõrgharidusõppe Diplom (Professional Higher Education Diploma) with a final overall result of at least 4 on a 5-point scale (B).

German qualifications

You should have a Bachelor degree, Erste Staatsprüfung (Primarstufe / Sekundarstufe I), Fachhochschuldiplom / Diplom (FH) or Magister Artium with a final overall result of at least 2.5 (Gut).

Taiwanese qualifications

You should have a Bachelor degree with a final overall result of at least 75%.

Zimbabwean qualifications

You should have a Bachelor Honours degree with a final overall result of at least Upper Second Division (65%).

Turkish qualifications

You should have a Lisans Diplomasi (Bachelor degree) or Mühendis Diplomasi with a final overall score of at least GPA 2.8-3.0 on a 4-point scale, depending on the institution you have attended.

Spanish qualifications

You should have a Título Universitario Oficial de Licenciado en (subject area) (Licenciatura), Título Universitario Oficial de Graduado en (subject area) (Grado), Título de Ingeniero or Título de Arquitecto with a final overall result of at least 7 out of 10 (Notable/Very Good).

Other qualifications

We welcome applications from graduates from all countries so if you can’t see your country in the list, please contact our admissions team for advice about your specific entry requirements.

Tunisian qualifications

You should have a Licence, Maîtrise, Diplôme National d'Ingénieur, Diplôme National d'Architecture with a final overall result of at least 14 out of 20 (Bien).

Tanzanian qualifications

You should have a Bachelor degree with a final overall result of at least Upper Second (Very Good, B+ or GPA 3.5 on a 5-point scale).

Thai qualifications

You should have a Bachelor degree with a final overall result of at least CGPA 3.0.

American qualifications

You should have an Honors Bachelor degree or Bachelor degree with a final overall result of at least CGPA 3.0 on a 4-point scale.

Ugandan qualifications

You should have a Bachelor degree with a final overall result of at least Class II Upper or GPA 4.0 on a 5-point scale.

Ukrainian qualifications

You should have a Bachelor degree (awarded after 2007) or Specialist Diploma with a final result of at least 80% or 3.5 on a 4-point scale. Please contact us if your institution uses a different grading scale.

Emirati qualifications

You should have a Bachelor degree with a final overall result of at least 3 out of 4, 80% or B.

Hungarian qualifications

You should have an Alapfokozt (Bachelor degree) or Egyetemi Oklevél (University Diploma) with an overall final result of 4 out of 5.

Ghanaian qualifications

You should have a Bachelor degree with a final overall result of Upper Second Class (60% or 3.25 on a 4-point scale).

Greek qualifications

You should have a University Bachelor degree (Ptychio) or Diploma with a final overall score of at least 6.5 out of 10.

Indian qualifications

Typically, you should have a Bachelor degree with a final overall result of at least First Class. However, due to the number of different grading scales in use, we ask that you upload a copy of the grading scale used by your institution, along with your transcript, when you submit your application.

If you have graduated within the last 5 years from an Indian university where the medium of instruction is English and have achieved the required level of English in your Indian 12th Standard examinations, we may be able to assess your English. More information can be found here .

• Fees and funding

Fees and funding information for Advanced Quantitative Methods in Social Sciences MRes full-time

Your tuition fees and how you pay them will depend on whether you are a Home or Overseas student.

Tuition fees

See the most recent fees for postgraduate courses .

Tuition fee loans

If you are studying a postgraduate course, you may be able to take out a loan for your tuition fees and living costs.

Scholarships and bursaries

You could be considered for a bursary or scholarship to help you study at Bath. You do not have to pay it back.

Other payment options

You can pay your tuition fees by Direct Debit, debit card, credit card or bank transfer. You may also be eligible for a student loan to help you pay your fees.

You will need to budget at least £100 for the cost of photocopying, printing and binding. You will also need to budget for the cost of textbooks.

Some courses involve visits away from campus and you may be required to pay some or all of the costs of travel, accommodation and food and drink.

If you’re on a placement, you’re responsible for your own travel, accommodation and living costs. You should also consider the financial implications if you go on an unpaid or overseas placement.

• Application information
• Course title Advanced Quantitative Methods in Social Sciences
• Final award MRes
• Mode of study Full-time
• Course code TDUXX-MR65
• Department Department of Education
• Location University of Bath Claverton Down, Bath BA2 7AY

We recommend you apply early as we may close applications before the deadline if a course is full.

We recommend you apply early as we may close applications before the deadline if a course is full. We may consider late applications but if you need a Student Visa to study in the UK, you will need time to apply for and receive your visa to be in the UK by the start of the course.

• Application eligibility Home and Overseas students are eligible to apply
• Regulator The Office for Students (OfS)
• Course enquiries
• Related courses
• Advanced Quantitative Methods in Social Sciences MRes – 2 years part-time
• Statistics and Data Science (Health) MSc – 1 years

On this page

• Course changes for 2025/26
• Recognition of Professional Qualifications

Develop a range of technical skills that cover major experimental and quasi-experimental approaches to data analysis in the social sciences.

This unit aims to introduce you to these approaches, and provide you with:

theoretical understanding of these techniques and practical experience in using them

• the knowledge to critically appraise research findings using these techniques

an insight into how these methods can be applied in your own field of interest

Enhance your understanding of the basic qualitative methods in social sciences research.

You'll develop the conceptual and practical skills to begin to conduct, analyse, interpret, and present qualitative research.

Develop analytical skills that will allow you to:

• understand and analyse data
• develop insights that influence decision-making

You will develop your analytical thinking and problem-solving skills. You'll learn a variety of modern analytics tools and techniques that help decision-making in many business areas like finance, marketing and operations management.

Develop your ability to understand, collect and analyse complex and evolving forms of digital data.

You'll receive:

a foundational overview of the variety of digital data sources used for research across multiple social science disciplines

an introduction to range of computational, quantitative and qualitative methods that can be used to collect and analyse specific forms of digital data.

Throughout this unit, you'll also learn about ethical and research quality issues.

Identify, investigate and present your own piece of original research. You'll do this across 12 months.

During this unit, you'll receive guidance to develop the necessary research skills to produce a pilot study and a dissertation of academic and professional quality.

You'll identify an appropriate methodology and engage with relevant theory and data to answer your research question(s).

• Year 1 - Semester 1
• Year 1 - Semester 2

​​This is your opportunity to develop an in-depth understanding of a key research-related issue of your choice.​

Receive an introduction to research practices and theories, and qualitative and quantitative research approaches.

You'll gain knowledge of:

• qualitative research approaches to a level that will allow you to understand and to interpret a range of phenomenological or textual data
• quantitative approaches to a stage that will give you a base level of statistical literacy

You'll also explore the philosophical foundations of social research.

Gain the advanced-level mathematical skills needed to overcome various types of optimisation problems.

You'll learn to use software that can solve practical optimisation problems within research.

Explore key social sciences data analysis techniques. These techniques aren’t covered in ‘AQM 1 - Experimental and Quasi-experimental Quantitative Methods in Social Science’.

• theoretical understanding of these techniques and practical experience using them
• an insight into how these methods can be applied in your own field

Develop the skills and knowledge to formulate research questions and carry out your own research projects.

You will learn about the epistemological and methodological traditions in social science research and the social science research space, with a focus on the range of quantitative methods used.

You'll then build the necessary skills to

design and organise a research project

conduct critical reviews of published research

effectively disseminate and communnicate research