Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R)
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.
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.
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.
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.
I found the text to be consistent in its notation, which is important in statistics texts.
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.
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.
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.
I did not encounter problematic grammatical errors.
I did not find the text to be culturally insensitive in any way.