Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R) - 3rd Edition

Reviewed by Saahir Shafi, Assistant Professor, California State University, Dominguez Hills on 12/13/22

Comprehensiveness rating: 4 see less

This textbook provides a solid introduction to quantitative methods in social science research. It is ideal for introducing early stage researchers to R as a tool of quantitative research. From a broad overview of the scientific method and research design to OLS and logit regression, researchers can expect to become comfortable using R for data analysis. The authors could expand this volume to introduce more intermediate and advanced examples of quantitative methods such as ridge regression, panel regression, etc.

Content Accuracy rating: 5

The content is accurate, error-free, and quite straightforward - R scripts are broken down with clear discussions on what the script is evaluating and how to interpret results.

Relevance/Longevity rating: 5

The content is up-to-date. Although newer R packages continue to be made available, this text provides a foundational knowledge of basic statistical analysis which is unlikely to become obsolete anytime soon.

Clarity rating: 5

The text is highly accessible and may be successfully used by graduate students with little to no prior knowledge of R. A base understanding of research methods and quantitative analysis would be beneficial for students to get the most out of this text.

Consistency rating: 5

The text is consistent in terms of terminology and presentation of material.

Modularity rating: 5

The text is easily divisible into sections and concepts that progressively build upon each other and ideal for college level coursework. The book is split into 16 sections which would fit ideally within the scope of a 16 week course.

Organization/Structure/Flow rating: 5

Topics are presented in a logical progression moving from general research design to variables and model specification.

Interface rating: 5

There are no interface issues. Text is presented in an organized and accessible format.

Grammatical Errors rating: 5

The text contains no grammatical errors.

Cultural Relevance rating: 5

The text is not culturally insensitive or offensive in any way. In future editions, the authors could make efforts to include more diverse demographic groupings within the specified models to demonstrate the best way to evaluate such variables.

Reviewed by Lindsay Benstead, Associate Professor, Portland State University on 9/3/22

Comprehensiveness rating: 4 see less

This book is highly comprehensive in the sense that it effectively marries a discussion of the theoretical foundations of research design and statistics with concrete examples and syntax for applying the concepts in R. Although there is neither an index nor a glossary, the table of contents is detailed and the book itself is effectively organized. Key words are presented in bold.

Content Accuracy rating: 5

In my review of the textbook, I found no evidence of errors of bias. The book appears to be carefully edited with well-chosen examples pertinent to the field of study.

Relevance/Longevity rating: 5

Given the subject matter (i.e., statistics and mathematics), it is unlikely that the material will date quickly. It is plausible but unlikely that the R syntax will no longer work in future iterations of the program. The textbook is on its third edition, suggesting that the authors are attentive to implementing improvements. Additionally, while there are screenshots to pages where students can download resources, the instructions are described in the text without the use of many links which can stop working and create frustration for readers.

Clarity rating: 5

This book contains very clear descriptions of key topics. Specific chapters could be assigned in courses, even if the application in R is not being used in the course, in which case some of the chapters would be less relevant.

Consistency rating: 5

This book is thoroughly edited and presents the material in a clear and well-organized way as one would find in any quality textbook on the subject.

Modularity rating: 5

The early chapters on research design and statistical theory could be assigned on their own, while the same would be true of the latter chapters for a course needing only instructions on how to use R.

Organization/Structure/Flow rating: 5

The organization of the book is effective. I would like to see potential material on how to conduct a literature review and research ethics. Creation of examples using SPSS or Stata would also be welcome.

Interface rating: 5

The formatting and interface is problem free.

Grammatical Errors rating: 5

This book is well edited and free from grammatical errors.

Cultural Relevance rating: 5

This book is free from insensitive or offensive material.

Reviewed by Caitlin Jewitt, Associate Professor, Virginia Tech on 12/14/21

Comprehensiveness rating: 3 see less

This book is very comprehensive, beginning with the scientific method, progressing through research design, data visualization, inference, regression, and culminating with a chapter on generalized linear models (logit). The table of contents is thorough, which is helpful to the reader, especially for a methods textbook, which often is not read cover-to-cover, but is referenced time and time again. There is also an appendix which introduces the reader to utilizing R to implement some of the statistical techniques provided in the text. I would like to see more time spent on interaction terms, as this is an important component of teaching quantitative methods to political scientists. While the book covers a breadth of topics, it provides only a surface coverage of many fundamental aspects of research methods (e.g. independent and dependent variables). In other words, the coverage is broad but not deep. Compared to other methods textbooks, there are not as many examples and there are not problems or questions that are often very helpful to students.

Content Accuracy rating: 5

The content is accurate and unbiased and its presentation is straightforward.
The authors could spend more time explaining how to apply these concepts in R and what version of R they are using, so that they may be more easily replicated.

Relevance/Longevity rating: 5

Due to the content (methods-based), it will remain relevant and not be quickly outdated like many texts in political science. The text is also on its third edition, which demonstrates that the authors are continuing to update and improve the text.

Clarity rating: 4

There are some missed opportunities for the authors to define terms. For instance, they discuss a working hypothesis and null hypothesis in the first paragraph (and put these terms in bold), but do not explicitly define them in the text. In other places, they define terms (such as the definition of theory on page 5). Defining terms consistently throughout the text would be helpful and would improve the text's clarity.

Overall, the language is clear and straightforward. It is written in a manner that is easily accessible to undergraduates. Additional examples, however, would provide a useful supplement to aid in understanding.

The presentation of graphs could be enhanced by using variable labels as opposed to variable names.

Consistency rating: 5

The book is consistent in its approach and terminology.

Modularity rating: 5

There are many short sections within each chapter. This makes it ideal and easy to assign sections of a chapter to students, rather than requiring that they read the whole chapter. Other than understanding the basics of research methods, readers could easily move between sections and read portions out of order.

Organization/Structure/Flow rating: 4

The book is well organized, and progresses in an expected fashion. It begins with theories of social science and the scientific method, discusses fundamentals of research methods, describing and displaying data, discusses inference, then presents bivariate and multivariate OLS regression, and finally general linear models. This coincides with the order in which I would teach these topics.

Interface rating: 4

The book is produced in latex, and so the format (including figures) should be familiar to many political scientists who utilize this software. One revision the authors could make for future revisions would be to include hyperlinks in the table of contents to link the reader to the sections, chapters, and figures.

Grammatical Errors rating: 5

The book is well-written.

Cultural Relevance rating: 5

The book is not culturally insensitive or offensive. The examples are straightforward and brief. In a future revision, the authors could consider discussing the measurement of demographic variables (e.g. gender and sex, race) in greater depth. This would provide the reader with a stronger grasp of the advantages and disadvantages of utilizing different measurement strategies.

Comments

The book is a good, comprehensive overview of research methods. It would be difficult to use it as the sole textbook for undergraduates, due to the lack of examples. It would be a strong choice for a supplemental text or may be more appropriate for a graduate course.

Reviewed by Kimberly Wilson, Assistant Professor, East Tennessee State University on 3/22/20

Comprehensiveness rating: 4 see less

The book's overall approach is great -- framing quantitative methods in terms of social scientific research more broadly. If I was teaching a quantitative methods course, I would most likely use this book, as it covers a nice range of essentials, particularly regression, while the open source nature ensures that students can always return to this book for reference. The book's use of R is similarly ideal. There are a few areas where an instructor may wish to expand upon the book's content, but this can easily be done through lecture or by assigning one or two additional and complementary readings. I do wish the book did a bit more in terms of clearly defining key terms and concepts. For instance, null hypothesis is first mentioned on page 4, but is not defined until page 10, and one only learns this by reading the full chapter. While the book description says that the book is designed for upper level undergraduates and graduate students, I assume that most students do not encounter terms like null hypothesis until their first methods course, which is usually where they are also learning quantitative methods (and where this textbook would be appropriate). In short, a glossary of the terms set in bold would be a strong addition to this book.

Content Accuracy rating: 5

I saw no inaccuracies worthy of note. One always has preferences for the way in which methods are explained, but I saw nothing that would cause me to view this book as inaccurate.

Relevance/Longevity rating: 5

The book tackles fundamentals in social scientific research and quantitative methods, and these will stay relevant.

Clarity rating: 4

As mentioned above, a glossary would be an easy addition that would greatly strengthen the text. Students at all levels can become intimidated by a methods book with unfamiliar terminology. A glossary can help alleviate some anxiety.

Consistency rating: 5

I saw no inconsistencies.

Modularity rating: 5

The book is organized in a consistent and clear manner. The headings and subheadings are easily understood and navigated. Chapters can easily be broken down into smaller sections for class readings.

Organization/Structure/Flow rating: 5

The text builds in a clear and logical fashion, appropriate to the subject matter of this type of course.

Interface rating: 5

I saw no interface issues.

Grammatical Errors rating: 5

There are only trivial grammatical errors, of the kind similar to all textbooks.

Cultural Relevance rating: 5

I did not see anything culturally insensitive or offensive in the text. I have to admit, I only understood the Monty Python reference after googling it, but that's life.

Comments

I have one relatively minor suggestion. In the first two chapters, where theory and social scientific methodology is discussed, it might help to use a consistent, versatile example to illustrate many concepts of those chapters. For instance, when the text discusses the goal of generalization, and uses the example of why a president's approval rating may have dropped, why not also use this example later to discuss independent and dependent variables? The discussion of dependent and independent variables on page 6 doesn't use an example, and I think students would greatly benefit by having an example to illustrate this content.

Reviewed by Christina Ladam, Graduate Part-time Instructor, CU Boulder on 6/5/19, updated 7/1/19

Comprehensiveness rating: 4 see less

This text does a solid job in providing an introduction to statistical analysis with a focus on regression. Additionally, it provides a light introduction to statistical computing in R. This is mostly a tool for teaching regression, with a light introduction to maximum likelihood estimation and generalized linear models through a chapter on logistic regression. The text briefly discusses some other methods, though, for instance, the discussion on experimental research designs is quite minimal. There is no discussion of survey experiments, which are increasingly used by social scientists as research design. Perhaps the text should be more clearly framed as one to teach regression. Additionally, there could be more instruction provided on R, specifically in teaching best practices for conducting analyses in R.

Content Accuracy rating: 4

I found the content in the text to be mostly accurate. The "Inference" section could use some editing in reference to p-values and how we interpret them. This is notoriously difficult, but could be improved.

Relevance/Longevity rating: 4

While I cannot foresee the content regarding regression becoming obsolete any time soon, there are some limitations to the relevance of the text. For instance, many more recent developments in methodology are not included. That is fine, as no one book can address that many streams of quantitative research. However, the framing of the book makes it seem like it would address more than regression. Additionally, the text would be improved by providing an updated, more thorough introduction to R, including a "best practices" approach to analysis in R.

Clarity rating: 5

The text is written quite clearly, and would be very appropriate for its target audience. Complex econometric concepts are written in an approachable way, with illustrative and complementary examples. I can see this text being especially useful for public policy and public administration students. While the text is framed as being designed for graduate students, it also seems appropriate for teaching undergraduate statistics courses.

Consistency rating: 5

I found the text to be consistent in its notation, which is important in statistics texts.

Modularity rating: 5

I really appreciated the way in which chapters were organized. Subjects were broken down to manageable chapter lengths, and the use of headings and subheadings was very clear. I can easily picture assigning readings throughout the semester without much modification to chapters.

Organization/Structure/Flow rating: 4

I appreciate the authors' decision to structure the the text as similar to the way in which scientific research is conducted, beginning with the development of theory, moving to research design, and ending with statistical analyses and model evaluation. It is important to place an emphasis on following the scientific method when conducting statistical analyses. While the Appendix on R is helpful, it may make sense to incorporate some introduction to R in the main text. When R is introduced in the main text, it somewhat assumes a baseline familiarity with R.

Interface rating: 4

The PDF version was mostly free of interface issues. It would be nice to incorporate hyperlinks within the text, so that one can simply click on a page number to navigate to a section rather than being limited to scrolling to find things. There also seems to be some inconsistency in formatting of tables and figures -- while most are center-aligned, some are left-aligned.

Grammatical Errors rating: 5

I did not encounter problematic grammatical errors.

Cultural Relevance rating: 5

I did not find the text to be culturally insensitive in any way.

Reviewed by Chris Garmon, Assistant Professor of Health Administration, University of Missouri - Kansas City on 5/24/19

Comprehensiveness rating: 4 see less

The book's coverage of regression is outstanding. In particular, this is the most comprehensive coverage of regression diagnostics I've seen in a research methods text. There is also an entire chapter on logit regression, whereas most texts may devote a paragraph to it at best. Most texts jump right into inference after descriptive statistics, but the authors add a chapter on probability before discussing inference, which is a nice addition. However, there are certain topics that are not covered or barely covered. There is only a cursory discussion of sampling distributions and only one paragraph on the Central Limit Theorem. The authors fly through the discussion of t tests and there is no coverage of the assumptions needed for independent sample t tests. The only coverage of ANOVA is in the discussion of model fit. I think this is an excellent text for instructors who want to emphasize regressions, but those who like to build up to regressions with t tests and ANOVA might find this text lacking.

Content Accuracy rating: 5

The book is accurate and thorough, particularly regarding regression and regression diagnostics. On a few occasions, the authors talked about "accepting the null hypothesis" if the p value is greater than 0.05, which is too strong, but apart from that, I saw no problems with the analysis or interpretations.

Relevance/Longevity rating: 5

A nice feature of this text is that it is written in open source R markdown, so instructors can adapt and add content as desired, making updates easy to implement.

Clarity rating: 4

The book has the right tone and level of technical information for Ph.D. students in the social sciences, but I think parts of it are too advanced for the typical MPA student. There are entire chapters on calculus (chapter 8) and matrix algebra (chapter 11), which in my opinion are unnecessary for and would likely intimidate most MPA students.

Consistency rating: 5

The terminology and framework are consistent and easy to follow.

Modularity rating: 2

This book is best covered as a whole. I think it would be difficult to use only a subset of chapters as they all build off and reference each other. For instance, there are numerous instances where terminology (e.g., null hypothesis, Likert scale) are briefly introduced with the promise to cover them in more detail in future chapters.

Organization/Structure/Flow rating: 5

The book starts off with an emphasis on theory as the basis and guiding force of quality social science research and the topics are presented with this theme in mind. I applaud the authors for making theory and causality a guiding principle for the organization of the text because too many research methods texts leave the students with the impression that quantitative research involves looking at the data, discerning patterns, and then developing a theory. I think the organization of the text is ideal with the emphasis of theory and testable hypotheses as the starting point of research.

Interface rating: 5

The text has no interface or navigation problems.

Grammatical Errors rating: 4

There are a number of minor grammatical mistakes and typos, but nothing that would cause confusion for the reader.

Cultural Relevance rating: 3

The text uses only one example throughout (an analysis of a survey of perceptions of climate change risk by political ideology and sex). Students outside of political science might not find the example interesting or relevant for the research problems they are likely to face. The title of the text implies that it is designed for public administration students. The text should illustrate at least some of the concepts with research problems public administrators are likely to face.

Comments

Overall, I think this is an excellent text, but I think it is too advanced and technical, and has too much of a political science focus, for MPA students.

Reviewed by Sarah Fisher, Assistant Professor, Emory and Henry College on 3/20/19

Comprehensiveness rating: 5 see less

In terms of content, this text contains nearly everything I generally cover in my introductory statistics class. This book is aimed at graduate students, but I am reviewing it for undergraduate social science majors. Overall, I think this will be a good text. It does seem that the authors assume some level of knowledge of R before beginning the book. There is additional information available online and in the appendix, but I think more of an introduction to R placed at the beginning of the book would have been useful, given how prominently R features in the text. I share the author's frustration with teaching this course-- the cost of these textbooks is high and the relationship between statistics and actual research is sometimes spotty. I think this text does a good job of really connecting statistical techniques to social science research.

Content Accuracy rating: 5

I saw no glaring inaccuracies in the text.

Relevance/Longevity rating: 5

One great thing about statistics books is that the formula for standard deviation is unlikely to change any time soon. I see this text as having a long self life. The only thing that might change would be the R code, but the authors have noted that there is more information available about the R online.

Clarity rating: 4

I found the writing in this text to be very clear. One nice addition would be titles for all of the R code that corresponded with a quick reference list for the code included. Then, if a student was looking for the R code to recode a variable (page 80), for instance, they could quickly find it. Given the online format, one can search for this information in the text, but I think students who print the text might find it useful.

Consistency rating: 5

The book is generally consistent in terms of format.

Modularity rating: 5

This is one of the text's strong points. They cover a lot of information in an efficient manner, and they also include some useful asides. For instance, in section 5.3.3, when discussing statistical inference, they have a header entitled "Some Miscellaneous Notes about Hypothesis Testing." I find this sort of discussion very useful. This section included information on why .05 is a standard, Type I and Type II errors, etc. While these are clearly important, they are secondary compared to general ideas about inference. In this sense, I think the layout of the text is very reader friendly. The bolded terms are also crucial. I also appreciate the "Summary" sections at the end of chapters.

Organization/Structure/Flow rating: 5

I think the organization is very good. In an undergraduate course, I'm not sure I will go in as much depth as is included in some of the later chapters (ex: having students do quartile plots for residuals), but I still find it useful. Moreover, an instructor could easily pick and choose which sections they wanted students to read given the section headers. I might just move the R appendix to the beginning of the text.

Interface rating: 4

I think that the graphics (some in color) are particularly useful. Moreover, I think that the inclusion of R output throughout the text was generally useful. I would like to have seen more presentations of "cleaned" data, to show students how they should present their data output. There are several points in the text where the R code seems to be out of place. For instance, on page 76, part of the code goes outside the grey shaded box.

Grammatical Errors rating: 5

The grammar and writing style of the textbook was good. I saw no major problems.

Cultural Relevance rating: 4

The text has the occasional nerd-culture reference (ex: page 40 contained a Monty Python reference) and sports references (ex: lots of baseball references in the probability chapter). In another example, when talking about sampling strategies, the authors write about how one might observe a potential partner in a variety of circumstances to determine whether they would me a good match. While I find this example a bit odd, I think the impulse to include interesting examples is a good one.

Comments

I am planning on using this for an undergraduate class, and it seems like the authors have pitched this for graduate students. I don't anticipate too many differences, but I'm excited to see how this textbook works for undergrads.

Reviewed by Saleheh Sharifmoghaddam, Adjunct Lecturer, Lehman College, City University of New York on 5/21/18

Comprehensiveness rating: 4 see less

This book definitely tackles many of the issues facing students doing quantitative analysis in social sciences. The authors try to cover the main data analysis techniques, providing readers with ample examples to better appreciate the complexity and dynamism of each model. While no text can attend to all models with detail, this book tries to educate the reader holistically and achieves this breadth, in my opinion, very effectively.

Content Accuracy rating: 4

The book is accurate and error-free.

Relevance/Longevity rating: 5

The book is certainly up-to-date and includes R codes to apply the models in the R interface.

Clarity rating: 5

The forte of the book is explaining complex econometrics models in very simple language with ample examples.

Consistency rating: 5

The text is internally consistent.

Modularity rating: 5

The books has various subheadings and makes the division of material very clear at the outset.

Organization/Structure/Flow rating: 5

The topics are presented in a logical and clear fashion.

Interface rating: 4

There are no significant interface issues.

Grammatical Errors rating: 5

The text is free of grammatical errors.

Cultural Relevance rating: 3

The books is not culturally offensive in any way.

Comments

The authors can improve the teaching capacity of the material by adding a sequel to the book, discussing more complicated models used in social sciences.