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The SAGE Handbook of Research Methods in Political Science and International Relations

Luigi Curini - Università degli Studi di Milano
Robert Franzese - University of Michigan
Description

The SAGE Handbook of Research Methods in Political Science and International Relations offers a comprehensive overview of research processes in social science — from the ideation and design of research projects, through the construction of theoretical arguments, to conceptualization, measurement, & data collection, and quantitative & qualitative empirical analysis — exposited through 65 major new contributions from leading international methodologists.

Each chapter surveys, builds upon, and extends the modern state of the art in its area. Following through its six-part organization, undergraduate and graduate students, researchers and practicing academics will be guided through the design, methods, and analysis of issues in Political Science and International Relations:

Part One: Formulating Good Research Questions & Designing Good Research Projects

Part Two: Methods of Theoretical Argumentation

Part Three: Conceptualization & Measurement

Part Four: Large-Scale Data Collection & Representation Methods

Part Five: Quantitative-Empirical Methods

Part Six: Qualitative & “Mixed” Methods

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

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Supplements

For scholars seeking credible research designs, this is an indispensable volume. The methods are wide-ranging and on the cutting edge, and the authors are an all-star cast of leading experts.

This is an extraordinarily comprehensive handbook on the current state of the art in research methods for political science. The roster of authors is both stellar and extensive. No single person knows this much about all this material. So all serious researchers can benefit from having this handbook on their shelves, whether to expand the scope of their own work or to enhance their reading of the work of others.

Since the dawn of the twenty-first century there has been an explosion of methods in the social and natural sciences. As data has gotten bigger and bigger, we have been developing new tools to acquire, analyze, and synthesize all these bits and bytes, and this has led to nothing short of a revolution in political science. The very leaders of this revolution have come together in these volumes to show the way, with both deep insight and engaging connections to the biggest substantive problems of our day. This is literally the dream team of political science, and they are explaining in plain language exactly how to live on the cutting edge. As someone deeply committed to both learning and teaching new methods, I can't think of another book I would rather have on my shelf.

This handbook provides the reader with a very broad overview of research methods in political science. With chapters authored by notable senior and junior methodologists and applicants, it does not only cover a wide range of techniques, but also places methods within their context, such as research designs. This book is an excellent companion for researchers of all steps of their career who are about to find their way through the jungle of methodological offers.

This is a very impressive and broad collection of authors and essays. This book will be my, and my students’, first stop in exploring any topic in political methodology. The editors provide an important service to the discipline.

The Sage Handbook of Research Methods in Political Science and International Relations has wide coverage from leading scholars and practitioners. There is definitely something for everyone to learn while emphasizing accessibility for all as well.

Political Science Research Methods in Action

© 2013
Michael Bruter (Reader in European Political Science) 0 ,
Martin Lodge (Professor of Political Science and Public Policy) 1

Department of Government, London School of Economics and Political Science, UK

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Part of the book series: ECPR Research Methods (REMES)

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15 Citations

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Table of contents (12 chapters)

Front matter, introduction: writer’s block.

Michael Bruter, Martin Lodge

Approaching and Measuring Social Science Objects

Measuring the immeasurable.

Michael Bruter

Decoding Manifestos and Other Political Texts: The Case of Extreme-Right Ideology

Sarah Harrison

Reaction Time Measures in Implicit Attitudes Research

Brad Verhulst, Milton Lodge

Studying Party Choice

Mark N. Franklin, Maja Renko

Into the Archives

Ben Wellings

Making Sense of Data

Euro-visions: an analysis of visual imagery in tv news.

Jessica Bain, Natalia Chaban

When Access Is Restricted: Craftiness and Combining Methods in the Study of a Secretive Elite

Julie Gervais

Semistructured Interviews and Informal Institutions: Getting Inside Executive Government

Martin Lodge

Error-Correction as a Concept and as a Method: Time Series Analysis of Policy-Opinion Responsiveness

Will Jennings

Working Backwards? Using Simulation to Sort Out Empirical Inconsistencies

Robert Erikson, Aaron Strauss, Michael Bruter

Back Matter

Institution
political science
social science
time series

About this book

Editors and affiliations, about the editors, bibliographic information.

Book Title : Political Science Research Methods in Action

Editors : Michael Bruter, Martin Lodge

Series Title : ECPR Research Methods

DOI : https://doi.org/10.1057/9781137318268

Publisher : Palgrave Macmillan London

eBook Packages : Palgrave Political & Intern. Studies Collection , Political Science and International Studies (R0)

Hardcover ISBN : 978-0-230-36775-3 Published: 23 July 2013

Softcover ISBN : 978-1-349-34973-9 Published: 01 January 2013

eBook ISBN : 978-1-137-31826-8 Published: 23 July 2013

Series ISSN : 2947-5201

Series E-ISSN : 2947-521X

Edition Number : 1

Number of Pages : X, 261

Topics : Political History , Political Theory , Political Philosophy , Political Science , Methodology of the Social Sciences , Statistics for Social Sciences, Humanities, Law

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

(8 reviews)

Hank Jenkins-Smith, University of Oklahoma

Joseph Ripberger, University of Oklahoma

Publisher: University of Oklahoma Libraries

Language: English

Formats Available

Conditions of use.

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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

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.

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.

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.

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.

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.

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.

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.

The formatting and interface is problem free.

This book is well edited and free from grammatical errors.

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.

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.

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.

The book is consistent in its approach and terminology.

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.

The book is well-written.

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.

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

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.

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.

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

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.

I saw no inconsistencies.

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.

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

I saw no interface issues.

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

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.

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

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

I saw no glaring inaccuracies in the text.

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.

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.

The book is generally consistent in terms of format.

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.

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.

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.

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.

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

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.

The book is accurate and error-free.

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

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

The text is internally consistent.

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

The topics are presented in a logical and clear fashion.

There are no significant interface issues.

The text is free of grammatical errors.

The books is not culturally offensive in any way.

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.

I Theory and Empirical Social Science

1 Theories and Social Science
2 Research Design
3 Exploring and Visualizing Data
4 Probability
5 Inference
6 Association of Variables

II Simple Regression

7 The Logic of Ordinary Least Squares Estimation
8 Linear Estimation and Minimizing Error
9 Bi-Variate Hypothesis Testing and Model Fit
10 OLS Assumptions and Simple Regression Diagnostics

III Multiple Regression

11 Introduction to Multiple Regression
12 The Logic of Multiple Regression
13 Multiple Regression and Model Building
14 Topics in Multiple Regression
15 The Art of Regression Diagnostic

IV Generalized Linear Model

16 Logit Regression

V Appendices

17 Appendix: Basic

Ancillary Material

About the book.

The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses. The first part of the book introduces the scientific method, then covers research design, measurement, descriptive statistics, probability, inference, and basic measures of association. The second part of the book covers bivariate and multiple linear regression using the ordinary least squares, the calculus and matrix algebra that are necessary for understanding bivariate and multiple linear regression, the assumptions that underlie these methods, and then provides a short introduction to generalized linear models. The book fully embraces the open access and open source philosophies. The book is freely available in the SHAREOK repository; it is written in R Markdown files that are available in a public GitHub repository; it uses and teaches R and RStudio for data analysis, visualization and data management; and it uses publically available survey data (from the Meso-Scale Integrated Socio-geographic Network) to illustrate important concepts and methods. We encourage students to download the data, replicate the examples, and explore further! We also encourage instructors to download the R Markdown files and modify the text for use in different courses.

About the Contributors

Hank Jenkins-Smith earned his PhD in political science from the University of Rochester (1985). He is a George Lynn Cross Research Professor in the Political Science Department at the University of Oklahoma, and serves as a co-Director of the National Institute for Risk and Resilience. Professor Jenkins-Smith has published books and articles on public policy processes, national security, weather, and energy and environmental policy. He has served on National Research Council Committees, as an elected member on the National Council on Radiation Protection and Measurement, and as a member of the governing Council of the American Political Science Association. His current research focuses on theories of the public policy process, with particular emphasis on the management (and mismanagement) of controversial technical issues involving high risk perceptions on the part of the public. In 2012 he and collaborators initiated a series of studies focused on social responses to the risks posed by severe weather. This work continues with a panel survey of Oklahoma households, funded by the National Science Foundation, to track perceptions of and responses to changing weather patterns. In his spare time, Professor Jenkins-Smith engages in personal experiments in risk perception and management via skiing, scuba diving and motorcycling.

Joseph Ripberger currently works at the Center for Risk and Crisis Management, University of Oklahoma. Joseph does research in Public Policy. Their current project is 'Glen Canyon Dam.'

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Introduction to Political Science Research Methods (Franco et al.)

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Page ID 76154

Josue Franco
Cuyamaca College

Introduction to Political Science Research Methods is an Open Education Resource Textbook that surveys the research methods employed in political science. The textbook includes chapters that cover: history and development of the empirical study of politics; the scientific method; theories, hypotheses, variables, and units; conceptualization, operationalization and measurement of political concepts; elements of research design including the logic of sampling; qualitative and quantitative research methods and means of analysis; and research ethics.

ProgramPage
Table of Contents

1.1: Welcome
1.2: The Social Network of Political Science
1.3: Organization of the Book
1.4: Analyzing Journal Articles
1.5: Research Paper Project Management
1.6: Key Terms/Glossary
1.7: Summary
1.8: Review Questions
1.9: Suggestions for Further Study

2.1: Brief History of Empirical Study of Politics
2.2: The Institutional Wave
2.3: The Behavioral Wave
2.4: Currents- Qualitative versus Quantitative
2.5: Currents- Politics- Normative and Positive Views
2.6: Emerging Wave- Experimental Political Science
2.7: Emerging Wave- Big Data and Machine Learning
2.8: Key Terms/Glossary
2.9: Summary
2.10: Review Questions
2.11: Critical Thinking Questions
2.12: Suggestions for Further Study

3.1: Philosophy of Science
3.2: Whats is the Scientific Method?
3.3: Applying the Scientific Method to Political Phenomena
3.4: Key Terms/Glossary
3.5: Summary
3.6: Review Questions
3.7: Critical Thinking Questions
3.8: Suggestions for Further Reading/Study

4.1: Correlation and Causation
4.2: Theory Constrution
4.3: Generating Hypotheses from Theories
4.4: Exploring Variables
4.5: Units of Observation and Units of Analysis
4.6: Casual Modeling
4.7: Key Terms/Glossary
4.8: Critical Thinking Problems
4.9: Review Questions
4.10: Critical Thinking Questions
4.11: Critical Thinking Questions

5.1: Conceptualization in Political Science
5.2: Operationalization
5.3: Measurement
5.4: Key Terms/Glossary
5.5: Summary
5.6: Review Questions
5.7: Critical Thinking Questions
5.8: Suggestions for Further Study

6.1: Introduction- Building with a Blueprint
6.2: Types of Design- Experimental and Nonexperimental Designs
6.3: Components of Design- Sampling
6.4: Components of Design- Observations
6.5: Key Terms/Glossary
6.6: Summary
6.7: Review Questions
6.8: Critical Thinking Questions
6.9: Suggestions for Further Study

7.1: What are Qualitative Methods?
7.2: Interviews
7.3: Exploring Documentary Sources
7.4: Ethnographic Research
7.5: Case Studies
7.6: Key Terms/Glossary
7.7: Summary
7.8: Review Questions
7.9: Critical Thinking Questions
7.10: Suggestions for Further Study

8.1: What are Quantitative Methods
8.2: Making Sense of Data
8.3: Introduction to Statistical Inference and Hypothesis Testing
8.4: Interpreting Statistical Tables in Political Science Articles
8.6: Summary
8.7: Review Questions
8.8: Critical Thinking Questions
8.9: Suggestions for Further Study
8.5: Key Terms

9.1: Ethics in Political Research
9.2: Research Ethics
9.3: Navigating Qualitative Data Collection
9.4: Research Ethics in Quantitative Research
9.5: Ethically Analyzing and Sharing Co-generated Knowledge
9.6: Key Terms/Glossary
9.7: Summary
9.8: Review Questions
9.9: Critical Thinking Questions
9.10: Suggestions for Further Study

10.1: Congratulations!
10.2: The Path Forward
10.3: Frontiers of Political Science Research Methods
10.4: How to Contribute to this OER

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Writing in Political Science - Duke University Writing Studio 4 page introduction to the basics of political science scholarly communication.
Research Methods Knowledge Base The Research Methods Knowledge Base is a comprehensive web-based textbook that addresses all of the topics in a typical introductory undergraduate or graduate course in social research methods. It covers the entire research process including: formulating research questions; sampling (probability and nonprobability); measurement (surveys, scaling, qualitative, unobtrusive); research design (experimental and quasi-experimental); data analysis; and, writing the research paper. It also addresses the major theoretical and philosophical underpinnings of research including: the idea of validity in research; reliability of measures; and ethics.
Choosing a Research Design - PHDStudent.com
Research Basics - Explorable.com (previously www.experiment-resources.com) Includes sections on: -Research Methods: Formulating questions, collecting data, testing hypotheses -Experimental Research: Setting up experiments -Research Designs: Different types of designs used in research -Statistics in Research: A guide to statistics in research.
The Theory and Practice of Field Experiments: An Introduction from the EGAP Learning Days Since 2015, EGAP has conducted week-long workshops on experimental research methods for scholars and evaluation professionals based in Sub-Saharan Africa and Latin America. In these workshops — known as Learning Days — EGAP members from around the world travel to the workshop location to instruct on core topics in causal inference and experimental design, and to work closely one-on-one with participants to develop their research designs. In an effort to create a resource that EGAP members, workshop alumni, and others can use to organize their own workshops or refresh their training, Jake Bowers, Maarten Voors, and Nahomi Ichino have produced an online book. The book is organized around modules on the usual Learning Days topics as well as some new topics. At the heart of each module are slides that workshop facilitators can use directly or adapt for specific audiences. more... less... Authors: Jake Bowers, Maarten Voors, and Nahomi Ichino
UC Library Search Search tip: Use these subject headings and keyword searches to find the latest books, ebooks, etc.
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Research Methods in Political Science (Book Only) 8th Edition

ISBN-10 1133309291
ISBN-13 978-1133309291
Edition 8th
Publisher Cengage Learning
Publication date January 3, 2012
Language English
Dimensions 7.9 x 0.6 x 9.8 inches
Print length 320 pages
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Publisher ‏ : ‎ Cengage Learning; 8th edition (January 3, 2012)
Language ‏ : ‎ English
Paperback ‏ : ‎ 320 pages
ISBN-10 ‏ : ‎ 1133309291
ISBN-13 ‏ : ‎ 978-1133309291
Item Weight ‏ : ‎ 1.2 pounds
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The SAGE Handbook of Research Methods in Political Science and International Relations

Luigi Curini - Università degli Studi di Milano
Robert Franzese - University of Michigan
Description

Part One: Formulating Good Research Questions & Designing Good Research Projects

Part Two: Methods of Theoretical Argumentation

Part Three: Conceptualization & Measurement

Part Four: Large-Scale Data Collection & Representation Methods

Part Five: Quantitative-Empirical Methods

Part Six: Qualitative & “Mixed” Methods

Supplements

For scholars seeking credible research designs, this is an indispensable volume. The methods are wide-ranging and on the cutting edge, and the authors are an all-star cast of leading experts.

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SAGE Knowledge is the premier social sciences platform for SAGE and CQ Press book, reference and video content.

The platform allows researchers to cross-search and seamlessly access a wide breadth of must-have SAGE book and reference content from one source.

SAGE Research Methods is a research methods tool created to help researchers, faculty and students with their research projects. SAGE Research Methods links over 175,000 pages of SAGE’s renowned book, journal and reference content with truly advanced search and discovery tools. Researchers can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and more.

With SAGE Research Methods, researchers can explore their chosen method across the depth and breadth of content, expanding or refining their search as needed; read online, print, or email full-text content; utilize suggested related methods and links to related authors from SAGE Research Methods' robust library and unique features; and even share their own collections of content through Methods Lists. SAGE Research Methods contains content from over 720 books, dictionaries, encyclopedias, and handbooks, the entire “Little Green Book,” and "Little Blue Book” series, two Major Works collating a selection of journal articles, and specially commissioned videos.

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The Oxford Handbook of Political Methodology

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1 Political Science Methodology

Janet M. Box‐Steffensmeier is Vernal Riffe Professor of Political Science and Sociology, Director of the Program in Statistics Methodology, Ohio State University.

Henry E. Brady is Professor of Political Science and Public Policy at University of California, Berkeley. He received his Ph.D. in Economics and Political Science from MIT, and his areas of interest include Quantitative Methodology, American and Canadian Politics, and Political Behavior. He teaches undergraduate courses on political participation and party systems and graduate courses on advanced quantitative methodology.

David Collier is Professor of Political Science at University of California, Berkeley and former President of the American Political Science Association. His fields are comparative politics, Latin American politics, and methodology. His latest book is Rethinking Social Inquiry: Diverse Tools, Shared Standards, of which he is co-editor and co-author with Henry E. Brady.

Published: 02 September 2009
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Political methodology offers techniques for clarifying the theoretical meaning of concepts such as revolution and for developing definitions of revolutions. It also provides descriptive indicators for comparing the scope of revolutionary change, and sample surveys for gauging the support for revolutions. It then presents an array of methods for making causal inferences that provide insights into the causes and consequences of revolutions. An overview of the book is given. Topics addressed include social theory and approaches to social science methodology; concepts and development measurement; causality and explanation in social research; experiments, quasi-experiments, and natural experiments; general methods of quantitative tools for causal and descriptive inference; quantitative tools for causal and descriptive inference; qualitative tools for causal inference; and organizations, institutions, and movements in the field of methodology. In general, the Handbook provides overviews of specific methodologies, but it also emphasizes three things: utility for understanding politics, pluralism of approaches, and cutting across boundaries. This volume discusses interpretive and constructivist methods, along with broader issues of situating alternative analytic tools in relation to an understanding of culture.

“You say you want a revolution Well, you know, We all want to change the world.” The Beatles

People of the 1960s generation did not argue much with the Beatles—we listened to them with rapt attention. But are they right? What do they mean by a revolution? Do we all want to change the world? What would change the world? Would the result be good or bad?

Political methodology provides the practicing political scientist with tools for attacking all these questions, although it leaves to normative political theory the question of what is ultimately good or bad. Methodology provides techniques for clarifying the theoretical meaning of concepts such as revolution and for developing definitions of revolutions. It offers descriptive indicators for comparing the scope of revolutionary change, and sample surveys for gauging the support for revolutions. And it offers an array of methods for making causal inferences that provide insights into the causes and consequences of revolutions. All these tasks are important and strongly interconnected. While causal inference is fundamental in political science, making good inferences depends entirely on adequate conceptualization and measurement of the phenomena under study—tasks that receive substantial attention in this volume. Yet time and again our authors return to the question of what might constitute a valid causal inference using qualitative or quantitative data, small‐ N or large‐ n data, in‐depth interviews or sample surveys, historical narratives or experimental data.

Growth of “causal thinking” in JSTOR articles from 1910 to 1999

Although not all of modern political science is about causal inference, between 1995 and 1999 about 33 percent of the articles in the American Political Science Review (APSR) mentioned these words and 19 percent of all the journal articles in JSTOR for this period mentioned them. The proportions rise to 60 percent for all journals and 67 percent for the APSR if we add the words “cause” or “causes,” but these words do not have the same technical meaning as “causal” or “causality” so we will stick with the narrower measure of our concept, even though it might be an underestimate of the scope of causal thinking. 1 As shown in Figure 1.1 , the concern with causality is increasing, and the mentions of these terms grew rapidly from less than 2 percent of JSTOR articles from 1910 to 1950 to an increasing proportion from 1950 onwards, with the APSR apparently leading the way.

What is behind this dramatic increase in mentions of “causal” or “causality?” Does it tell us something meaningful about political science in the twentieth century? Have we measured a useful concept (i.e. “causal thinking in political science”) with our JSTOR search? Have we described accurately the rise of causal thinking in the twentieth century? Can we explain this rise? The methods contained in this handbook are expressly designed to answer social science questions of this sort. Our discussion of causality may be just a “toy example,” but it does have the virtue that it is familiar to and perhaps interesting to political scientists. And it has the additional virtue that explaining the increasing concern with a new perspective such as “causal thinking” within political science is a miniature and simpler version of explaining the rise of “revolutionary” perspectives in politics—the emergence of eighteenth‐century liberalism, nineteenth‐century socialism, early to mid‐twentieth‐ century New Deal liberalism, late twentieth‐century neoliberalism, and the modern environmental movement. If we can understand the difficulties of explaining the rise of causal thinking within political science, indeed the difficulties of merely describing whether or not causal thinking actually increased during the twentieth century, we will not only provide an overview of this handbook, but we will also learn a lot about what methodologists can contribute to doing political science research. If along the way the reader grimaces over some of our methodological approaches, we hope this reaction has the effect of raising questions about what can and cannot be done with these methods. Perhaps it will also help us all develop some modesty about what our craft can accomplish.

1 Social Theory and Approaches to Social Science Methodology

How do we think about explaining the rise of causal thinking in political science? One place to start is with social theory which asks questions about the ontology and epistemology of our enterprise. Ontology deals with the things that we think exist in the world, and epistemology with how we come to know about those things. Hardin ( Chapter 2 ) suggests that we should start social science inquiry with individuals, their motivations, and the kinds of transactions they undertake with one another. He starts with self-interest (although he quickly suggests that there are other motivations as well), and this provides a useful starting place for understanding the increasing focus on causality in political science. Self‐interest suggests that people publish in order to advance their careers and that they will do what is necessary to achieve that end, but it begs the question of why causal thinking is a common goal of the political science profession.

Hardin describes four basic schools of social theory: conflict, shared‐values, exchange, and coordination theories. Several of these might help to explain why political scientists have adopted causal thinking as a goal for their enterprise. Political scientists might be adhering to shared “scientific” values about understanding the world through the exploration of causal relationships. And this scientific value might have become important to science in the twentieth century because it allowed humans to manipulate their world and to shape it in their self‐interest. According to this explanation, the social sciences simply adopted this value because, well, it was valuable. Alternatively, political scientists might be exchanging their causal knowledge for resources garnered from the larger society. Or they might be coordinating on the topic of causality in order to have a common standard for evaluating research, although this leaves open why they chose this solution to the coordination problem. One answer might be that a focal point was created through the invention of some convenient tool that promised to help political scientists with their research. Two obvious methodological tools of the early twentieth century are correlation analysis ( Pearson 1909 ) and regression analysis ( Pearson 1896 ; Yule 1907 ), although as we shall see, only regression analysis provided at least rhetorical support for causal inference.

Bevir ( Chapter 3 ) provides some explanations for the rise of causal thinking as the “behavioral revolution's” reaction to the nineteenth century's teleological narratives about history (“developmental historicism”) and early twentieth‐century emphasis on classifications, correlations, and systems (“modernist empiricism”). The behavioral revolution took a somewhat different direction and emphasized general theories and the testing of causal hypotheses. Bevir's chapter suggests that the rise of causal thinking might have been a corollary of this development. But Bevir warns that there are new currents in philosophy which have moved beyond behavioralism.

De Marchi and Page ( Chapter 4 ) explore one kind of mathematical modeling, agent‐based modeling, that has become increasingly common in political science. We might have included chapters on other theoretical perspectives (rational choice, social network modeling, historical narratives, etc.) but this one was especially apt for a methodology handbook since agent‐based modeling is not only an approach to modeling; it is also a way of simulating models to generate testable hypotheses and even of generating data that can then be analyzed. Agent‐based models suggest that we should think of political scientists as agents with goals who interact according to some rules—including rule‐changing rules. These “rule‐changing rules” might include changes in what is valued or in how people coordinate—such as a change towards emphasizing causal thinking over other kinds of inquiry.

Three possible causes of the increased emphasis on causality follow from this discussion. Two of them look to the development of a new tool, either regression or correlation, that made it easier to determine causality so that more scholars focused upon that problem. The third suggests value change with the rise of behavioralism. There may be other plausible explanations, but these provide us with a trio of possibilities for developing our example. Indeed, these categories of explanation— new inventions and new values—crop up again and again in social science. The rise of capitalism, for example, has been explained as the result of inventions such as markets, corporations, and industrial processes that made individual accumulation possible, and it has been explained as the result of an emphasis on particular values such as a protestant ethic that valued accumulation and accomplishment.

Growth of mentions of words related to causal thinking in political science in JSTOR articles from 1910 to 1999

2 Concepts and Measurement

To proceed with our investigation of the rise in causal thinking, we must clarify our concepts and develop measures. Our concepts are “the study of causality in political science,” the use of the tools of “regression analysis” or “correlation,” and changes in values due to the “behavioral revolution.” Continuing with what we have already done, we measure them using word searches in JSTOR. For regression and correlation, we look for “regression” or “correlation.” 2 We risk, of course, the possibility that these terms are being used in nonstatistical ways (“regression to his childhood” or “the correlation of forces”), but we assume for the moment that these uses stay relatively constant over time.

In order to determine the utility of this approach, we focus on the definition of “behavioral revolution,” but if we had more space we could have added similar discussions about measuring “the study of causality in political science” or “correlation” or “regression.” To measure the extent of the behavioral revolution, we look for the words “behavior” or “behavioral.” When we count these various terms over the ninety years we get the results in Figure 1.2 .

Goertz ( Chapter 5 ) provides guidance on how to think about our concepts. Not surprisingly, he tells us that we must start by thinking about the theory embedded in the concept, and we must think about the plausible method for aggregating indicators of the concept. For measuring the extent of the “the behavioral revolution” we want to measure those habits and perspectives of inquiry that distinguished those researchers who were concerned with general theories of behavior from those who went before them. Simply counting words may seem like a poor way to do this—at first blush it would seem that we should use a more sophisticated method that codes articles based on whether or not they proposed general hypotheses, collected data to test them, and carried out some tests to do just that. At the very least, we might look for the word “behavioralism” or “behaviorism” to make sure that the authors subscribed to the movement. But from 1910 to 1999, “behavioralism” is only mentioned in 338 articles— out of a total of 78,046 (about 0.4 percent). And “behaviorism” is mentioned in even fewer articles, which is not too surprising since political scientists (although not psychologists) tended to prefer the term “behavioralism.”

The words “behavioral” and “behavior” turn out to be better measures as judged by tests of criterion and convergent validity (Jackman, Chapter 6 ). The word behavioral is mentioned in 8.9 percent of the articles and the word “behavior” in 31.3 percent. These two words (behavior and behavioral) are almost always mentioned when the criterion words of behavioralism or behaviorism are mentioned (about 95 percent of the time). Moreover, in a test of convergent validity, the articles of those people known to be leaders of the behavioral movement used these terms more frequently than the authors of the average article. Between 1930 and 1990, we find that the average article mentioned one or more of the four terms 33 percent of the time, but the articles by the known behavioralists mentioned one or more of the four terms 66 percent of the time. 3 Hence, these words appear to be closely related to the behavioral movement, and we will often refer to mentions of them as indicators of “behavioralism.” Similarly, we will often refer to mentions of “causal” and “causality” as indicators of “causal thinking.”

In our running example, we used JSTOR categories to describe scientific disciplines (political science, sociology, etc.) and to classify journals and items (as articles or book reviews or editorials) according to these categories. Collier, LaPorte, and Seawright ( Chapter 7 ) and Ragin ( Chapter 8 ) remind us that these are important decisions with significant implications for conceptualization and calibration.

Collier, LaPorte, and Seawright ( Chapter 7 ) discuss categories and typologies as an optic for looking at concept formation and measurement. Working with typologies is crucial not only to the creation and refinement of concepts, but it also contributes to constructing categorical variables involving nominal, partially ordered, and ordinal scales. Although typologies might be seen as part of the qualitative tradition of research, in fact they are also employed by quantitative analysts, and this chapter therefore provides one of the many bridges between these two approaches that are crucial to the approach of this handbook.

Ragin ( Chapter 8 ) distinguishes between “measurement” and “calibration,” arguing that with calibration the researcher achieves a tighter integration of measurement and theory. For example, a political science theory about “developed countries” will probably not be the same as a theory about “developing countries,” so that careful thought must be given to how the corresponding categories are conceptualized, and how countries are assigned to them. In our running example, articles in political science will probably be different from those in other disciplines, so care must be taken in defining the scope of the discipline. Yet we have rather cavalierly allowed JSTOR to define this membership, even though by JSTOR's categorization, political science thereby includes the journals Social Science History , the Journal of Comparative Law , and Asian Studies . We have also allowed JSTOR to treat articles as examples of “causal thinking” when they have at least one mention of “causal” or “causality” even though there might be a substantial difference between articles that mention these terms only once versus those that mention them many times. Alternative calibration decisions are certainly possible. Perhaps only journals with political, politics, or some similar word in their titles should be considered political science journals. Perhaps we should require a threshold number of mentions of “causal” or “causality” before considering an article as an example of “causal thinking.” Perhaps we should revisit the question of whether “cause” and “causes” should be used as measures of “causal thinking.” Ragin provides a “fuzzy‐set” framework for thinking about these decisions, and thereby offers both direct and indirect methods for calibration.

Jackman ( Chapter 6 ) also focuses on measurement, starting from the classic test theory model in which an indicator is equal to a latent variable plus some error. He reminds us that good measures must be both valid and reliable, and defines these standards carefully. He demonstrates the dangers of unreliability, and discusses the estimation of various measurement models using Bayesian methods. Jackman's argument reminds us to consider the degree to which our counts of articles that mention specific words represent the underlying concepts, and he presents a picture of measurement in which multiple indicators are combined—typically additively— to get better measures of underlying concepts. Goertz's chapter suggests that there is an alternative approach in which indicators are combined according to some logical formula. Our approach to measuring behavioralism at the article level has more in common with Goertz's approach because it requires that either “behavior” or “behavioral” be present in the article, but it has more in common with Jackman's approach when we assume that our time series of proportions of articles mentioning these terms is a valid and relatively reliable measure of the degree to which behavioralism has infused the discipline.

Poole ( Chapter 9 ), Jackman ( Chapter 6 ), and Bollen et al. ( Chapter 18 ) consider whether concepts are multidimensional. Any measurement effort should consider this possibility, but political scientists must be especially careful because the dimensionality of politics matters a great deal for understanding political contestation. To take just one example, multidimensional voting spaces typically lead to voting cycles ( Arrow 1963 ) and “chaos” theorems ( McKelvey 1979 ; Schofield 1983 ; Saari 1999 ) for voting systems. Poole reviews how the measurement of political issue spaces has developed in the past eighty years through borrowings from psychometrics (scaling, factor analysis, and unfolding), additions from political science theories (the spatial theory of voting and ideal points), and confronting the special problems of political survey, roll‐call, and interest‐group ratings data. Bollen et al. show how factor analysis methods for determining dimensionality can be combined with structural equation modeling (SEM).

Extraction method: principal component analysis. Rotation method: oblimin with Kaiser normalization.

There does not seem to be any obvious need to consider dimensions for our data, but suppose we broaden our inquiry by asking whether there are different dimensions of political science discourse. Based upon our relatively detailed qualitative knowledge of “political science in America,” we chose to search for all articles from 1970 to 1999 on five words that we suspected might have a two‐dimensional structure: the words “narrative,” “interpretive,” “causal or causality,” “hypothesis,” and “explanation.” After obtaining their correlations across articles, 4 we used principal components and an oblimin rotation as described in Jackman ( Chapter 6 ). We found two eigenvalues with values larger than one which suggested a two dimensional principal components solution reported in Table 1.1 . There is a “causal dimension” which applies to roughly one‐third of the articles and an “interpretive” dimension which applies to about 6 percent of the articles. 5 Although we expected this two‐ dimensional structure, we were somewhat surprised to find that the word “explanation” was almost entirely connected with “causal or cauality” and with “hypothesis.” And we were surprised that the two dimensions were completely distinctive, since they are essentially uncorrelated at .077. Moreover, in a separate analysis, we found that whereas the increase in “causal thinking” occurred around 1960 or maybe even 1950 in political science (see Figure 1.1 ), the rise in the use of the terms “narrative” and “interpretive” came in 1980. 6

This result reminds us that “causal thinking” is not the only approach to political science discourse. Our volume recognizes this by including chapters that consider historical narrative (Mahoney and Terrie, Chapter 32 ) and intensive interviewing (Rathbun, Chapter 28 ), but there is also a rich set of chapters in a companion volume, the Oxford Handbook of Contextual Political Analysis , which the interested reader might want to consult.

This discussion leads us to think a bit more about our measure of “causal thinking.” The chapters on “Concepts and Measurement” suggest that we have been a bit cavalier in our definition of concepts. Perhaps we should be thinking about measuring “scientific thinking” instead of just “causal thinking.” How can we do that? In fact, like many researchers, we started with an interesting empirical fact (i.e. the mentions of “causal” and “causality” in political science articles), and worked from there. At this point, some hard thinking and research (which will be mostly qualitative) about our concepts would be useful. Philosophical works about the nature of science and social science should be consulted. Some well‐known exemplars of good social science research should be reviewed. And something like the following can be done.

Based upon our reflections about the nature of scientific thinking (and the factor analysis above), we believe that the words “hypothesis” and “explanation” as well as “causal or causality” might be thought of as indicators of a “scientific” frame of mind. 7 Consider the frequency of these words in all articles in JSTOR in various disciplines from 1990 to 1999. Table 1.2 sorts the results in terms of the discipline with the highest use of any of the words at the top of the table. Note that by these measures, ecology and evolutionary biology, sociology, and economics are most “scientific” while “history,” “film studies,” and “performing arts” are least “scientific.” Also note that the highest figures in each row (excluding the final column) are in bold. Note that we put “scientific” in quotations because we want to emphasize our special and limited definition of the term.

Ecology and evolutionary biology and economics refer to “hypothesis” to a greater degree than other disciplines which mention “explanation” more. But also note that political science (17.2 percent) and sociology (25.2 percent) tend to be high in mentions of “causal” or “causality.” In contrast, “performing arts” has a 3.6 percent rate of mention of “causal” or “causality” and “film studies” has a 5.8 percent rate.

As researchers, we might at this point rethink our dependent variable, but we are going to stay with mentions of “causal or causality” for two reasons. First, these words come closest to measuring the concerns of many authors in our book. Second, the narrowness of this definition (in terms of the numbers of articles mentioning the terms) may make it easier to explain. But the foregoing analysis (including our discussion of narrative and interpretive methods) serves as a warning that we have a narrow definition of what political science is doing.

Source : Searches of JSTOR archive by authors.

3 Causality and Explanation in Social Research

Brady ( Chapter 10 ) presents an overview of causal thinking by characterizing four approaches to causal inference. The Humean regularity approach focuses on “lawlike” constant conjunction and temporal antecedence, and many statistical methods—pre‐ eminently regression analysis—are designed to provide just the kind of information to satisfy the requirements of the Humean model. Regression analysis can be used to determine whether a dependent variable is still correlated (“constantly conjoined”) with an independent variable when other plausible causes of the dependent variable are held constant by being included in the regression; and time‐series regressions can look for temporal antecedence by regressing a dependent variable on lagged independent variables.

In our running example, if the invention of regression analysis actually led to the emphasis upon causality in political science, then we would expect to find two things. First in a regression of “causal thinking” (that is, mentions of “causal or causality”) on mentions of “regression,” mentions of “correlation,” and mentions of “behavioralism,” we expect to find a significant regression coefficient on the “regression” variable. Second, we would expect that the invention of the method of regression and its introduction into political science would pre‐date the onset of “causal thinking” in political science. In addition, in a time‐series regression of mentions of “causal thinking” on lagged values of mentions of “regression,” “correlation,” and “behavioralism” we would expect a significant coefficient on lagged “regression.” We shall discuss this approach in detail later on.

The counterfactual approach to causation asks what would have happened had a putative cause not occurred in the most similar possible world without the cause. It requires either finding a similar situation in which the cause is not present or imagining what such a situation would be like. In our running example, if we want to determine whether or not the introduction of regression analysis led to an efflorescence of causal thinking in political science, we must imagine what would have happened if regression analysis had not been invented by Pearson and Yule. In this imagined world, we would not expect causal thinking to develop to such a great extent as in our present world. Or alternatively, we must find a “similar” world (such as the study of politics in some European country such as France) where regression was not introduced until much later than in the United States. In this most similar world, we would not expect to see mentions of “causal thinking” in the political science literature until much later as well.

The manipulation approach asks what happens when we actively manipulate the cause: Does it lead to the putative effect? In our running example, we might consider what happened when the teaching of regression was introduced into some scholarly venue. When graduate programs introduced regression analysis, do we find that their new Ph.Ds focused on causal issues in their dissertations? Does the manipulation of the curriculum by teaching regression analysis lead to “causal thinking?”

Finally, as we shall see below, the mechanism and capacities approach asks what detailed steps lead from the cause to the effect. In our running example, it asks about the exact steps that could lead from the introduction of regression analysis in a discipline to a concern with causality.

Brady also discusses the INUS model which considers the complexity of causal factors. This model gets beyond simple necessary or sufficient conditions for an effect by arguing that often there are different sufficient pathways (but no pathway is strictly necessary) to causation—each pathway consisting of an insufficient but nonredundant part of an unnecessary but sufficient (INUS) condition for the effect.

Sekhon ( Chapter 11 ) provides a detailed discussion of the Neyman—Rubin model of causal inference that combines counterfactual thinking with the requirement for manipulation in the design of experiments. This model also makes the basic test of a causal relationship a probabilistic one: whether or not the probability of the effect goes up when the cause is present. 8 Sekhon shows how with relatively weak assumptions (but see below) this approach can lead to valid causal inferences. He also discusses under what conditions “matching” approaches can lead to valid inferences, and what kinds of compromises sometimes have to be made with respect to generalizability (external validity) to ensure valid causal inferences (internal validity).

Freedman ( Chapter 12 ) argues that “substantial progress also derives from informal reasoning and qualitative insights.” Although he has written extensively on the Neyman—Rubin framework and believes that it should be employed whenever possible because it sets the gold standard for causal inferences, Freedman knows that in the real world, we must sometimes fall back on observational data. What do we do then? The analysis of large “observational” data‐sets is one approach, but he suggests that another strategy relying upon “causal process observations” (CPOs) might be useful as a complement to them. CPOs rely on detailed observations of situations to look for hints and signs that one or another causal process might be at work. These case studies sometimes manipulate the putative cause, as in Jenner's vaccinations. Or they rule out alternative explanations, as in Semmelweis's rejection of “atmospheric, cosmic, telluric changes” as the causes for puerperal fever. They take advantage of case studies such as the death of Semmelweis's colleague by “cadaveric particles,” Fleming's observation of an anomaly in a bacterial culture in his laboratory that led to the discovery of penicillin, or the death of a poor soul in London who next occupied the same room as a newly arrived and cholera infected seaman. Or a lady's death by cholera from what Snow considered the infected water from the “Broad Street Pump” even though she lived far from the pump but, it turned out, liked the taste of the water from the pump.

Hedström ( Chapter 13 ) suggests that explanation requires understanding mechanisms which are the underlying “cogs and wheels” which connect the cause and the effect. The mechanism, for example, which explains how vaccinations work to provide immunity from an illness is the interaction between a weakened form of a virus and the body's immune system which confers long‐term immunity. In social science, the rise in a candidate's popularity after an advertisement might be explained by a psychological process that works on a cognitive or emotional level to process messages in the advertisement. Hedström inventories various definitions of “mechanism.” He provides examples of how they might work, and he presents a framework for thinking about the mechanisms underlying individual actions.

In our running example, it would be useful to find out how regression might have become a tool for supposedly discovering causality. Some of the mechanisms include the following. Regression is inherently asymmetrical leading to an identification of the “dependent” variable with the effect and the “independent” variables with possible causes. The interpretation of regression coefficients to mean that a unit change in the independent variable would lead to a change in the dependent variable equal to the regression coefficient (everything else equal) strongly suggests that regression coefficients can be treated as causal effects, and it provides a simple and powerful way to describe and quantify the causal effect for someone. The names for regression techniques may have played a role from about 1966 onwards when there was a steady growth for the next twenty‐five years in articles that described regression analyses as “causal models” or “causal modeling” 9 even though some authors would argue that the names were often seriously misleading—even amounting to a “con job” ( Leamer 1983 ; Freedman 2005 ). And the relative ease with which regression could be taught and used (due to the advent of computers) might also explain why it was adopted by political scientists.

4 Experiments, Quasi‐experiments, and Natural Experiments

Experiments are the gold standard for establishing causality. Combining R. A. Fisher's notion of randomized experiment ( 1925 ) with the Neyman—Rubin model ( Neyman 1923 ; Rubin 1974 ; 1978 ; Holland 1986 ) provides a recipe for valid causal inference as long as several assumptions are met. At least one of these, the Stable Unit Treatment Value Assumption (SUTVA), is not trivial, 10 but some of the others are relatively innocuous so that when an experiment can be done, the burden of good inference is to properly implement the experiment. Morton and Williams ( Chapter 14 ) note that the number of experiments has increased dramatically in political science in the last thirty‐five years because of their power for making causal inferences. 11 At the same time, they directly confront the Achilles heel of experiments—their external validity. They argue that external validity can be achieved if a result can be replicated across a variety of data‐sets and situations. In some cases this means trying experiments in the field, in surveys, or on the internet; but they also argue that the control possible in laboratory experimentation can make it possible to induce a wider range of variation than in the field—thus increasing external validity. They link formal models with experimentation by showing how experiments can be designed to test them.

For Gerber and Green ( Chapter 15 ) field experiments and natural experiments are a way to overcome the external validity limitations of laboratory experiments. They show that despite early skepticism about what could be done with experiments, social scientists are increasingly finding ways to experiment in areas such as criminal justice, the provision of social welfare, schooling, and even politics. But they admit that “there remain important domains of political science that lie beyond the reach of randomized experimentation.” Gerber and Green review the Neyman—Rubin framework, discuss SUTVA, and contrast experimental and observational inference. They also discuss the problems of “noncompliance” and “attrition” in experiments. Noncompliance occurs when medical subjects do not take the medicines they are assigned or citizens do not get the phone calls that were supposed to to encourage their participation in politics. Attrition is a problem for experiments when people are more likely to be “lost” in one condition (typically, but not always, the control condition) than another. They end with a discussion of natural experiments where some naturally occurring process such as a lottery for the draft produces a randomized or nearly randomized treatment.

With the advice of these articles in hand, we can return to our running example. We are encouraged to think hard about how we might do an experiment to find out about the impact of new techniques (regression or correlation) or changes in values (the behavioral revolution) on causal thinking. We could, for example, randomly assign students to either a 1970s‐style curriculum in which they learned about “causal modeling” methods such as regression analysis or a 1930s‐style curriculum in which they did not. We could then observe what kinds of dissertations they produced. It would also be interesting to see which group got more jobs, although we suspect that human subjects committees (not to mention graduate students) would look askance at these scientific endeavors. Moreover, there is the great likelihood that SUTVA would be violated as the amount of communication across the two groups might depend on their assignment. All in all, it is hard to think of experiments that can be done in this area. This example reminds us that for some crucial research questions, experiments may be impossible or severely limited in their usefulness.

5 Quantitative Tools for Causal and Descriptive Inference: General Methods

Our discussion of the rise of causal thinking in political science makes use of the JSTOR database. Political science is increasingly using databases that are available on the internet. But scientific surveys provided political scientists with the first opportunities to collect micro‐data on people's attitudes, beliefs, and behaviors, and surveys continue to be an immensely important method of data collection. Other handbooks provide information on some of these other methods of data collection, but the discussion of survey methods provides a template for thinking about data collection issues. Johnston ( Chapter 16 ) considers three dimensions for data collection: mode, space, and time. For sample surveys, the modes include mail, telephone, in‐person, and internet. Space and time involve the methods of data collection (clustered samples versus completely random samples) and the design of the survey (cross‐sectional or panels). Beyond mode, space, and time, Johnston goes on to consider the problems of adequately representing persons by ensuring high response rates and measuring opinions validly and reliably through the design of high‐quality questions.

In our running example, our data come from a computerized database of articles, but we could imagine getting very useful data from other modes such as surveys, in‐ depth interviews, or old college catalogs and reading lists for courses. Our JSTOR data provide a fairly wide cross‐section of extant journals at different locations at any moment in time, and they provide over‐time data extending back to when many journals began publishing. We can think of the data as a series of repeated cross‐sections, or if we wish to consider a number of journals, as a panel with repeated observations on each journal. As for the quality of the data, we can ask, as does Johnston in the survey context about the veracity of question responses, whether our articles and coding methods faithfully represent people's beliefs and attitudes.

The rest of this section and all of the next section of the handbook discuss regression‐like statistical methods and their extensions. These methods can be used for two quite different purposes that are sometimes seriously conflated and unfortunately confused. They can be used for descriptive inferences about phenomena, or they can be used to make causal inferences about them ( King, Keohane, and Verba 1994 ). Establishing the Humean conditions of constant conjunction and temporal precedence with regression‐like methods often takes pride of place when people use these methods, but they can also be thought of as ways to describe complex data‐sets by estimating parameters that tell us important things about the data. For example, Autoregressive Integrated Moving Average (ARIMA) models can quickly tell us a lot about a time series through the standard “p,d,q” parameters which are the order of the autoregression (p), the level of differencing (d) required for stationarity, and the order of the moving average component (q). And a graph of a hazard rate over time derived from an events history model reveals at a glance important facts about the ending of wars or the dissolution of coalition governments. Descriptive inference is often underrated in the social sciences (although survey methodologists proudly focus on this problem), but more worrisome is the tendency for social scientists to mistake description using a statistical technique for valid causal inferences. For example, most regression analyses in the social sciences are probably useful descriptions of the relationships among various variables, but they often cannot properly be used for causal inferences because they omit variables, fail to deal with selection bias and endogeneity, and lack theoretical grounding.

Let us illustrate this with our running example. The classic regression approach to causality suggests estimating a simple regression equation such as the following for cross‐sectional data on all political science articles in JSTOR between 1970 and 1979. For each article we score a mention of either “causality or causal” as a one and no mention of these terms as a zero. We then regress these zero—one values of the “dependent variable” on zero—one values for “independent variables” measuring whether or not the article mentioned “regression,” “correlation,” or “behavioralism.” When we do this, we get the results in column one in Table 1.3 .

Significant at .001 level;

Significant at .01 level;

Significant at .05 level.

If we use the causal interpretation of regression analysis to interpret these results, we might conclude that all three factors led to the emphasis on “causal thinking” in political science because each coefficient is substantively large and statistically highly significant. But this interpretation ignores a multitude of problems.

Given the INUS model of causation which emphasizes the complexity of necessary and sufficient conditions, we might suspect that there is some interaction among these variables so we should include interactions between each pair of variables. These interactions require that both concepts be present in the article so that a “regression × correlation” interaction requires that both regression and correlation are mentioned. The results from estimating this model are in column two of the table. Interestingly, only the “behavior × regression” interaction is significant, suggesting that it is the combination of the behavioral revolution and the development of regression analysis that “explains” the prevalence of causal thinking in political science. (The three‐way interaction is not reported and is insignificant.) Descriptively this result is certainly correct—it appears that a mention of behavioralism alone increases the probability of “causal thinking” in an article by about 11 percent, the mention of regression increases the probability by about 6 percent, the mention of correlation increases the probability by about 15 percent, and the mention of both behavioralism and regression together further increases the probability of causal thinking by about 13.5 percent.

But are these causal effects? This analysis is immediately open to the standard criticisms of the regression approach when it is used to infer causation: Maybe some other factor (or factors) causes these measures (especially “behavioral,” “regression,” and “causality”) to cohere during this period. Maybe these are all spurious relationships which appear to be significant because the true cause is omitted from the equation. Or maybe causality goes both ways and all these variables are endogenous. Perhaps “causal thinking” causes mentions of the words “behavioral or behavior” and “regression” and “correlation.”

Although the problem of spurious relationships challenged the regression approach from the very beginning (see Yule 1907 ), many people (including Yule) thought that it could be overcome by simply adding enough variables to cover all potential causes. The endogeneity problem posed a greater challenge which only became apparent to political scientists in the 1970s. If all variables are endogenous, then there is a serious identification problem with cross‐sectional data that cannot be overcome no matter how much data are collected. For example, in the bivariate case where “causal thinking” may influence “behavioralism” as well as “behavioralism” influencing “causal thinking,” the researcher only observes a single correlation which cannot produce the two distinctive coefficients representing the impact of “behavioralism” on “causal thinking” and the impact of “causal thinking” on “behavioralism.”

The technical solution to this problem is the use of “instrumental variables” known to be exogenous and known to be correlated with the included endogenous variables, but the search for instruments proved elusive in many situations. Jackson ( Chapter 17 ) summarizes the current situation with respect to “endogeneity and structural equation estimation” through his analysis of a simultaneous model of electoral support and congressional voting records. Jackson's chapter covers a fundamental problem with grace and lucidity, and he is especially strong in discussing “Instrumental Variables in Practice” and tests for endogeneity. Jackson's observations on these matters are especially appropriate because he was a member of the group that contributed to the 1973 Goldberger and Duncan volume on Structural Equation Models in the Social Sciences which set the stage for several decades of work using these methods to explore causal relationships.

The most impressive accomplishment of this effort was the synthesis of factor analysis and causal modeling to produce what became known as LISREL, covariance structure, path analysis, or structural equation models. Bollen, Rabe‐Hesketh, and Skrondal ( Chapter 18 ) summarize the results of these efforts which typically used factor analysis types of models to develop measures of latent concepts which were then combined with causal models of the underlying latent concepts. These techniques have been important on two levels. At one level they simply provide a way to estimate more complicated statistical models that take into account both causal and measurement issues. At another level, partly through the vivid process of preparing “path diagrams,” they provide a metaphor for understanding the relationships between concepts and their measurements, latent variables and causation, and the process of going from theory to empirical estimation. Unfortunately, the models have also sometimes led to baroque modeling adventures and a reliance on linearity and additivity that at once complicates and simplifies things too much. Perhaps the biggest problem is the reliance upon “identification” conditions that often require heroic assumptions about instruments.

One way out of the instrumental variables problem is to use time‐series data. At the very least, time series give us a chance to see whether a putative cause “jumps” before a supposed effect. We can also consider values of variables that occur earlier in time to be “predetermined”—not quite exogenous but not endogenous either. Pevehouse and Brozek ( Chapter 19 ) describe time‐series methods such as simple time‐series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). There are two tricky problems in this literature. One is the complex but tractable difficulty of autocorrelation, which typically means that time series have less information in them per observation than cross‐sectional data and which suggest that some variables have been omitted from the specification ( Beck and Katz 1996 ; Beck 2003 ). The second is the more pernicious problem of unit roots and commonly trending (co‐integrated) data which can lead to nonsense correlations. In effect, in time‐series data, time is almost always an “omitted” variable that can lead to spurious relationships which cannot be easily (or sensibly) disentangled by simply adding time to the regression. And thus, the special adaptation of methods designed for these data.

For our running example, we estimate a time‐series autoregressive model for eighteen five‐year periods from 1910 to 1999. The model regresses the proportion of articles mentioning “causal thinking” on the lagged proportions mentioning the words “behavioral or behavior,” “regression,” or “correlation.” Table 1.4 shows that mentions of “correlation” do not seem to matter (the coefficient is negative and the standard error is bigger than the coefficient), but mentions of “regression” or “behavioralism” are substantively large and statistically significant. (Also note that the autoregressive parameter is insignificant.) These results provide further evidence that it might have been the combination of behavioralism and regression that led to an increase in causal thinking in political science.

A time series often throws away lots of cross‐sectional data that might be useful in making inferences. Time‐series cross‐sectional (TSCS) methods try to remedy this problem by using both sorts of information together. Beck ( Chapter 20 ) summarizes this literature nicely. Not surprisingly, TSCS methods encounter all the problems that beset both cross‐sectional and time‐series data. Beck starts by considering the time‐series properties including issues of nonstationarity. He then moves to cross‐sectional issues including heteroskedasticity and spatial autocorrelation. He pays special attention to the ways that TSCS methods deal with heterogeneous units through fixed effects and random coefficient models. He ends with a discussion of binary variables and their relationship to event history models which are discussed in more detail in Golub ( Chapter 23 ).

Significant: .05 ,

Martin ( Chapter 21 ) surveys modern Bayesian methods of estimating statistical models. Before the 1990s, many researchers could write down a plausible model and the likelihood function for what they were studying, but the model presented insuperable estimation problems. Bayesian estimation was often even more daunting because it required not only the evaluation of likelihoods, but the evaluation of posterior distributions that combined likelihoods and prior distributions. In the 1990s, the combination of Bayesian statistics, Markov Chain Monte Carlo (MCMC) methods, and powerful computers provided a technology for overcoming these problems. These methods make it possible to simulate even very complex distributions and to obtain estimates of previously intractable models.

Using the methods in this chapter, we could certainly estimate a complex time‐ series cross‐sectional model with latent variable indicators for the rise of causal thinking in the social sciences. We might, for example, gather yearly data from 1940 onwards on our various indicators for six different political science journals that have existed since then. 12 We could collect yearly indicators for each latent variable that represents a concept (e.g. “causal” or “causality” for “causal thinking” and “behavior” or “behavioral” for “behavioralism”). We could postulate some time‐series cross‐ sectional model for the data which includes fixed effects for each journal and lagged effects of the explanatory variables. We might want to constrain the coefficients on the explanatory variables to be similar across journals or allow them to vary in some way. But we will leave this task to others.

6 Quantitative Tools for Causal and Descriptive Inference: Special Topics

Often our research requires that we use more specially defined methods to answer our research questions. In our running example, we have so far ignored the fact that our dependent variable is sometimes a dichotomous variable (as in Table 1.3 above), but there are good reasons to believe that we should take this into account. Discrete choice modeling ( Chapter 22 ) by Glasgow and Alvarez presents methods for dealing with dichotomous variables and with ordered and unordered choices. These methods are probably especially important for our example because each journal article that we code represents a set of choices by the authors which should be explicitly modeled. Alvarez and Glasgow take readers to the forefront of this methodological research area by discussing how to incorporate heterogeneity into these models.

Golub's discussion of survival analysis ( Chapter 23 ) presents another way to incorporate temporal information into our analysis in ways that provide advantages similar to those from using time series. In our running example, we could consider when various journals began to publish significant numbers of articles mentioning “causality” or “causal” to see how these events are related to the characteristics of the journals (perhaps their editorial boards or editors) and to characteristics of papers (such as the use of regression or behavioral language). As well as being a useful way to model the onset of events, survival analysis, also known as event history analysis, reveals the close ties and interaction that can occur between quantitative and qualitative research. For example, Elliott (2005) brings together narrative and event history analysis in her work on methodology.

A statistical problem that has commanded the attention of scholars for over a hundred years is addressed by Cho and Manski ( Chapter 24 ). Scholars face this problem of “cross‐level inference” whenever they are interested in the behavior of individuals but the data are aggregated at the precinct or census tract level. Cho and Manskid's chapter lays out the main methodological approaches to this problem; they do so by first building up intuitions about the problem. The chapter wraps up by placing the ecological inference problem within the context of the literature on partial identification and by describing recent work generalizing the use of logical bounds to produce solutions that are “regions” instead of point estimates for parameters.

The chapters on spatial analysis ( Chapter 25 ) by Franzese and Hays and hierarchical modeling ( Chapter 26 ) by Jones point to ways we can better capture the spatial and logical structure of data. In our running example, the smallest data unit was the use of words such as “causality” within the article, but these articles were then nested within journals and within years (and even in some of our analysis, within different disciplines). A complete understanding of the development of “causal thinking” within the sciences would certainly require capturing the separate effects of years, journals, and disciplines. It would also require understanding the interdependencies across years, journals, and disciplines.

Franzese and Hayes consider the role of “spatial interdependence” between units of analysis by employing a symmetric weighting matrix for the units of observation whose elements reflect the relative connectivity between unit i and unit j . By including this matrix in estimation in much the same way that we include lagged values of the dependent variable in time series, we can discover the impact of different forms of interdependence. In our example, if we had separate time series for journals, we could consider the impact of the “closeness” of editorial boards within disciplines based upon overlapping membership or overlapping places of training. These interdependencies could be represented by a “spatial” weighting matrix whose entries represent the degree of connection between the journals. The inclusion of this matrix in analyses poses a number of difficult estimation problems, but Franzese and Hayes provide an excellent overview of the problems and their solutions.

Jones considers multilevel models in which units are nested within one another. The classic use of multilevel models is in educational research, where students are in classrooms which are in schools which are in school districts that are in states. Data can be collected at each level: test scores for the students, educational attainment and training for the teachers, student composition for the schools, taxing and spending for the school districts, and so forth. Multilevel methods provide a way of combining these data to determine their separate impacts on outcome variables.

At the moment, spatial and multilevel information cannot be easily incorporated in all types of statistical models. But these two chapters suggest that progress is being made, and that further innovations are on the way.

7 Qualitative Tools for Causal Inference

Throughout this chapter, we have been using our qualitative knowledge of American political science to make decisions regarding our quantitative analysis. We have used this knowledge to choose the time period of our analysis, to choose specific journals for analysis, to name our concepts and to select the words by which we have measured them by searching in JSTOR, to think about our model specifications, and to interpret our results. Now we use qualitative thinking more directly to further dissect our research problem.

Levy ( Chapter 27 ) suggests that counterfactuals can be used along with case studies to make inferences, although strong theories are needed to do this. He argues that game theory is one (but not the only) approach that provides this kind of theory because a game explicitly models all of the actors' options including those possibilities that are not chosen. Game theory assumes that rational actors will choose an equilibrium path through the extensive form of the game, and all other routes are considered “off the equilibrium path”—counterfactual roads not taken. Levy argues that any counterfactual argument requires a detailed and explicit description of the alternative antecedent (i.e. the cause which did not occur in the counterfactual world) which is plausible and involves a minimal rewrite of history, and he suggests that one of the strengths of game theory is its explicitness about alternatives. Levy also argues that any counterfactual argument requires some evidence that the alternative antecedent would have actually led to a world in which the outcome is different from what we observe with the actual antecedent.

Short of developing game theory models to understand the history of political science, Levy tells us that we must at least try to specify some counterfactuals clearly to see what they might entail. One of our explanations for the rise of “causal thinking” is the invention of regression. Hence, one counterfactual is that regression analysis is not invented and therefore not brought into political science. Would there be less emphasis on causality in this case? It seems likely. As noted earlier, regression analysis, much more than correlation analysis, provides a seductive technology for exploring causality. Its asymmetry with a dependent variable that depends on a number of independent variables lends itself to discussions of causes (independent variables) and effects (dependent variables), whereas correlation (even partial correlation) analysis is essentially symmetric. Indeed, path analysis uses diagrams which look just like causal arrows between variables. Econometricians and statisticians provide theorems which show that if the regression model satisfies certain conditions, then the regression coefficients will be an unbiased estimate of the impact of the independent variables on the dependent variables. Regression analysis also provides the capacity to predict that if there is a one‐unit change in some independent variable, then there will be a change in the dependent variable equal to the value of the independent variable's regression coefficient. In short, regression analysis delivers a great deal whereas correlation analysis delivers much less.

Yet, it is hard to believe that regression analysis would have fared so well unless the discipline valued the discussion of causal effects—and this valuation depended on the rise of behavioralism in political science to begin with. It seems likely that be‐ havioralism and regression analysis complemented one another. In fact, if we engage in a counterfactual thought experiment in which behavioralism does not arise, we speculate that regression alone would not have led to an emphasis on causal thinking. After reflection, it seems most likely that behavioralism produced fertile ground for thinking about causality. Regression analysis then took advantage of this fertile soil to push forward a “causal modeling” research agenda. 13

It would be useful to have some additional corroboration of this story. With so many journal articles to hand in JSTOR, it seems foolhardy not to read some of them, but how do we choose cases? We cannot read all 78,046 articles from 1910 to 1999. Gerring ( Chapter 28 ) provides some guidance by cataloging nine different techniques for case selection: typical, diverse, extreme, deviant, influential, crucial, pathway, most similar, and most different. Our judgment is that we should look for influential, crucial, or pathway cases. Influential cases are those with an influential configuration of the independent variables. Gerring suggests that if the researcher is starting from a quantitative database, then methods for finding influential outliers can be used. Crucial cases are those most or least likely to exhibit a given outcome. Pathway cases help to illuminate the mechanisms that connect causes and effects.

To investigate the role of behavioralism, we chose a set of four cases (sorted by JSTOR's relevance algorithm) that had “behavioralism” or “behavioral” in their titles or abstracts and that were written between 1950 and 1969. We chose them on the grounds that they might be pathway cases for behavioralism. The first article, by John P. East (1968) , is a criticism of behavioralism, but in its criticism it notes that the behavioralist's “plea for empirical or causal theory over value theory is well known” (601) and that behavioralism “employs primarily empirical, quantitative, mathematical, and statistical methods” (597). The second article by Norman Luttbeg and Melvin Kahn (1968) reports on a survey of Ph.D. training in political science. The data are cross‐tabulated by “behavioral” versus “traditional” departments with the former being much more likely to offer “behavioral” courses on “Use and Limits of Scientific Method” (60 percent to 20 percent), “Empirically Oriented Political Theory (60 percent to 24 percent), or “Empirical Research Methods” (84 percent to 48 percent) and being much more likely to require “Statistical Competence” (43 percent to 4 percent). The third article (“The Role for Behavioral Science in a University Medical Center”) is irrelevant to our topic, but the fourth is “A Network of Data Archives for the Behavioral Sciences” by Philip Converse (1964) . Converse mentions regression analysis in passing, but the main line of his argument is that with the growing abundance of survey and other forms of data and with the increasing power of computers, it makes sense to have a centralized data repository. The effort described in this article led to the ICPSR whose fortunes are reviewed in a later chapter in this handbook. After reading these four cases, it seems even more likely to us that behavioralism came first, and regression later. More reading might be useful in other areas such as “causal modeling” or “regression analysis” during the 1970s.

Rathbun ( Chapter 29 ) offers still another method for understanding phenomena. He recommends intensive, in‐depth interviews which can help to establish motivations and preferences, even though they must deal with the perils of “strategic reconstruction.” Certainly it seems likely that interviews with those who lived through the crucial period of the 1950s to the 1970s would shed light on the rise of causal thinking in political science. Lacking the time to undertake these interviews, two of us who are old enough to remember at least part of this period offer our own perspectives. We both remember the force with which statistical regression methods pervaded the discipline in the 1970s. There was a palpable sense that statistical methods could uncover important causal truths and that they provided political scientists with real power to understand phenomena. One of us remembers thinking that causal modeling could surely unlock causal mechanisms and explain political phenomena.

Andrew Bennett ( Chapter 30 ) offers an overview of process tracing, understood as an analytic procedure through which scholars make fine‐grained observations to test ideas about causal mechanisms and causal sequences. He argues that the logic of process tracing has important features in common with Bayesian analysis: It requires clear prior expectations linked to the theory under investigation, examines highly detailed evidence relevant to those expectations, and then considers appropriate revisions to the theory in light of observed evidence. With process tracing, the movement from theoretical expectations to evidence takes diverse forms, and Bennett reviews these alternatives and illustrates them with numerous examples.

Benoît Rihoux ( Chapter 31 ) analyzes the tradition of case‐oriented configurational research, focusing specifically on qualitative comparative analysis (QCA) as a tool for causal inference. This methodology employs both conventional set theory and fuzzy‐set analysis, thereby seeking to capture in a systematic framework the more intuitive procedures followed by many scholars as they seek to “make sense of their cases.” Rihoux explores the contrasts between QCA procedures and correlation‐based methods, reviews the diverse forms of QCA, and among these diverse forms presents a valuable discussion of what he sees as the “best practices.”

Much of what we have been doing in our running example in this chapter is to try to fathom the course of history—albeit a rather small political science piece of it. Comparative historical analysis provides an obvious approach to understanding complicated, drawn‐out events. Mahoney and Terrie ( Chapter 32 ) suggest that comparative historical analysis is complementary to statistical analysis because it deals with “causes of effects” rather than “effects of causes.” Whereas statistical analysis starts from some treatment or putative cause and asks whether it has an effect, comparative historical analysis tends to start with a revolution, a war, or a discipline concerned with causal analysis, and asks what caused these outcomes, just as a doctor asks what caused someone's illness. In some cases, these are singular events which pose especially difficult problems—for doctors, patients, and political science researchers.

After providing a diagnosis of the distinctive features of historical research, Ma‐ honey and Terrie go on to provide some ideas about how we can tackle the problems posed by engaging in comparative historical inquiry. In our case, it seems likely that some comparative histories of American and European political science might yield some insights about the role of behavioralism and regression analysis. Another comparative approach would be to compare articles in journals with different kinds of editorial boards. Figure 1.3 suggests that there are substantial differences in the growth of mentions of “causal thinking” in the American Political Science Review (APSR) , Journal of Politics (JOP) , and Review of Politics (ROP) between 1940 and 1999. It would be useful to compare the histories of these journals.

Fearon and Laitin ( Chapter 33 ) discuss how qualitative and quantitative tools can be used jointly to strengthen causal inference. Large‐n correlational analysis offers a valuable point of entry for examining empirical relationships, but if it is not used in conjunction with fully specified statistical models and insight into mechanisms, it makes only a weak contribution to causal inference. While case studies do not play a key role in ascertaining whether these overall empirical relations exist, they are valuable for establishing if the empirical relationships can be interpreted causally. Fearon and Laitin argue that this use of case studies will be far more valuable if the cases are chosen randomly. In our running example, this suggests that we should choose a number of articles in JSTOR at random and read them carefully. We might even stratify our sample so that we get more coverage for some kinds of articles than others.

Growth of “causal thinking” in three journals 1940–1999

8 Organizations, Institutions, and Movements in the Field of Methodology

If nothing else, the preceding pages should convince most people that organizations, institutions, and movements matter in political science. They certainly mattered for the behavioralists, and they have mattered for political methodologists. The final chapters review some of these movements—several of which involved the present authors at first hand. 14

A clear trajectory in our discipline is that more and more attention is being devoted to methodology writ large. There is ample evidence for this assertion. The two methodology sections of the American Political Science Association are two of the largest of thirty‐eight sections. There is an increasing demand for training in methodology. The discipline has expanded its ability to train its own graduate students (instead of sending them to economics or some other discipline), and there is an increasing capacity to better train our undergraduates in methodology as well. Training is now available at the venerable Inter‐University Consortium for Political and Social Research (ICPSR) Summer Training Program in methods, the Empirical Implications of Theoretical Models (EITM) summer programs that link formal models and empirical testing, and the winter Consortium on Qualitative Research Methods (CQRM) training program on qualitative methods. Methodology is taught more and more by political scientists to political scientists. Political methodology is also finding more and more connections with theory. Beck (2000) draws the contrast between statisticians and political methodologists in that “statisticians work hard to get the data to speak, whereas political scientists are more interested in testing theory.” The focus on theory draws both quantitative and qualitative political scientists to the substance of politics, and it helps unite political methodologists to the political science community.

The rapid development of institutions for the study of qualitative methods in the past decade is discussed by Collier and Elman ( Chapter 34 ). The discipline's welcoming response to these institutions reflects the pent‐up need for them and the pluralistic culture of political science which facilitated the development of both the CQRM and the American Political Science Association's organized section on Qualitative Methods, recently renamed the Qualitative and Multi‐Method Research Section.

Franklin ( Chapter 35 ) traces the history of the quantitative methodology institutions, ICPSR, and the American Political Science Association's Political Methodology Section. ICPSR has the longest history, having been established in the 1960s in response to the needs of a newly quantitative field that lacked a tradition of training in statistical techniques. It was not until 1984 that the Political Methodology Section was formed to respond to the intellectual concerns driving the field.

Lewis‐Beck ( Chapter 36 ) discusses the forty‐year history of publications in quantitative political methodology. He shows that the range and scope of outlets now available stands in dramatic contrast to what existed forty years ago.

Finally, Aldrich, Alt, and Lupia ( Chapter 37 ) discuss the National Science Foundation's initiative to close the gap between theory and methods. The original goal of the Empirical Implications of Theoretical Models (EITM) initiative was to create a new generation of scholars who knew enough formal theory and enough about methods to build theories that could be tested, and methods that could test theories. Aldrich, Alt, and Lupia talk about the EITM as currently understood as a way of thinking about causal inference in service to causal reasoning. The empirical tool kit is seen as encompassing statistical approaches, experiments, and qualitative methods.

As Franklin rightly points out, academic institutions develop and are sustained because there are intellectual and professional needs that they serve. And these institutions matter. We know this as political scientists and see it in the development of our methodology field. Based on the vibrancy of our institutions, the future of political methodology looks bright indeed.

9 What Have We Learned?

The field of political methodology has changed dramatically in the past thirty years. Not only have new methods and techniques been developed, but the Political Methodology Society and the Qualitative and Multi‐Method Research Section of the American Political Science Association have engaged in ongoing research and training programs that have advanced both quantitative and qualitative methodology. The Oxford Handbook of Political Methodology is designed to reflect these developments. Like other handbooks, it provides overviews of specific methodologies, but it also emphasizes three things.

Utility for understanding politics —Techniques should be the servants of improved data collection, measurement, and conceptualization and of better understanding of meanings and enhanced identification of causal relationships. The handbook describes techniques with the aim of showing how they contribute to these tasks, and the emphasis is on developing good research designs. The need for strong research designs unites both quantitative and qualitative research and provides the basis upon which to carry out high‐quality research. Solid research design “… ensures that the results have internal, external, and ecological validity” (Educational Psychology).

Pluralism of approaches —There are many different ways that these tasks can be undertaken in the social sciences through description and modeling, case‐study and large‐n designs, and quantitative and qualitative research.

Cutting across boundaries —Techniques can and should cut across boundaries and should be useful for many different kinds of researchers. For example, in this handbook, those describing large‐n statistical techniques provide examples of how their methods inform, or may even be adopted by, those doing case studies or interpretive work. Similarly, authors explaining how to do comparative historical work or process tracing reach out to explain how it could inform those doing time‐series studies.

Despite its length and heft, our volume does not encompass all of methodology. As we indicated earlier, there is a rich set of chapters contained in a companion volume, the Oxford Handbook of Contextual Political Analysis . This volume discusses interpretive and constructivist methods, along with broader issues of situating alternative analytic tools in relation to an understanding of culture. The chapter by Mark Bevir insightfully addresses questions of meta‐methodology, a topic explored more widely in the other volume in discussions of epistemology, ontology, logical positivism, and postmodernism. Another important focus in the other volume is narrative analysis, as both a descriptive and an explanatory tool. Finally, in the traditions of research represented in our volume, the issues of context that arise in achieving measurement validity and establishing causal homogeneity are of great importance. But, corresponding to its title—i.e. contextual political analysis —the companion volume offers considerably more discussion of context and contextualized comparison which can be seen as complementary to our volume.

We hope that our running example on American political science has shown that at least some research problems (and perhaps all of them) can benefit from the use of both quantitative and qualitative methods. We find that quantitative methods provide some important insights about the size and scope of phenomena and about the linkages among variables, but quantitative methods are often maddeningly opaque with respect to the exact causal mechanisms that link our variables. Qualitative methods fill in some of these dark corners, but they sometimes lead to worries about the possibility that we have simply stumbled across an idiosyncratic causal path. We find ourselves oscillating back and forth between the methods, trying to see if insights from one approach can be verified and explicated by the other. But both are certainly helpful.

With respect to our running example, we conclude, with some trepidation given the incompleteness of our analysis, that values and inventions both help explain the rise of “causal thinking” in political science. The behavioral movement furthered “scientific values” like causal thinking, and regression provided an invention that seemingly provided political scientists with estimates of causal effects with minimal fuss and bother. As this handbook shows, however, regression is not the philosopher's stone that can turn base observational studies into gold‐standard experimental studies. And even experimental studies have their limits, so that we are forced to develop an armamentarium of methods, displayed in this handbook, for dragging causal effects out of nature and for explaining political phenomena.

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If we just search for the words “cause” or “causes” alone in all political science articles, we find that the proportion of these words is 55 percent in 1995–9 which is not a very dramatic increase since 1910–19 when it was 50 percent. This suggests that the words “cause” or “causes” measure something different from “causality” and “causal.” As we shall see, political methodology often grapples with questions like this about construct validity.

We might also search for the term “least squares” but almost whenever it appears (88 percent of the time), the term “regression” also appears, so not much is gained by searching for it as well.

Using a list of presidents of the American Political Science Association, we coded those people known to be “behavioralists” from 1950 to 1980—we coded sixteen of the 31 presidents in this way (Odegard, Herring, Lasswell, Schattschneider, Key, Truman, Almond, Dahl, Easton, Deutsch, Lane, Eulau, Leiserson, Ranney, Wahlke, and Miller). Using different time periods yields similar results. (For example, the 1950–80 period yields 35 percent for a general article and 78 percent for those by the famous behavioralists.)

We constructed variables for each word with a zero value if the word was not present in an article and a one if it was mentioned at least once. Then we obtained the ten correlations between pairs of the five variables with articles as the unit of analysis.

Each word appears in a different number of articles, but one or the other or both of the words “narrative” or “interpretive” appear in about 5.9 percent of the articles and the words “hypothesis” or “causal” or “causality” appear in almost one‐third (31.3 percent). “Explanation” alone appears in 35.4 percent of the articles.

In 1980–4, the words “narrative” or “interpretive” were mentioned only 4.1 percent of the time in political science journals; in the succeeding five‐year periods, the words increased in use to 6.1 percent, 8.1 percent, and finally 10.1 percent for 1995–9.

At least two other words might be relevant: “law” and “theory.” The first gets at the notion of the need for “law‐like” statements, but searching for it on JSTOR obviously leads to many false positives—mentions of public laws, the rule of law, the study of law, and the exercise of law. Similarly, “theory” gets at the notion of “theories” lying behind hypotheses, but the subfield of “political theory” uses theory in a much different sense.

Thus if C is cause and E is effect, a necessary condition for causality is that Prob(EǀC) > Prob(Eǀnot C). Of course, this also means that the expectation goes up E(EǀC) > E(Eǀnot C).

Not until the 1960s are there any articles that use the term “regression” and either “causal model” or “causal modeling.” Then the number grows from 25 in the 1960s, to 124 in the 1970s, to 129 in the 1980s. It drops to 103 in the 1990s.

SUTVA means that a subject's response depends only on that subject's assignment, not the assignment of other subjects. SUTVA will be violated if the number of units getting the treatment versus the control status affects the outcome (as in a general equilibrium situation where many people getting the treatment of more education affects the overall value of education more than when just a few people get education), or if there is more communication of treatment to controls depending on the way assignment is done.

The observant reader will note that these authors make a causal claim about the power of an invention (in this case experimental methods) to further causal discourse.

American Political Science Review (1906), Annals of the American Academy of Political and Social Science (1890), Journal of Politics (1939), Political Science Quarterly (1886), Public Opinion Quarterly (1937), and Review of Politics (1939).

The time‐series analysis provides some support for this idea. If we regress the proportion of articles mentioning behavioralism on its lagged value and the lagged values of the proportion of articles mentioning regression, correlation, and causality, only behavioralism lagged has a significant coefficient and causality and correlation have the wrong signs. Behavioralism, it seems, is only predicted by its lagged value. If we do the same analysis by regressing causality on its lagged value and the lagged values of regression, correlation, and behavioralism, we find that only behavioralism is significant and correlation has the wrong sign. If we eliminate correlation, then causality has the wrong sign. If we then eliminate it, we are left with significant coefficients for behavioralism and regression suggesting that mentions of causality come from both sources.

Brady was a founding member and early president of the Political Methodology Society. He was a co‐principal investigator (with PI Paul Sniderman and Phil Tetlock) of the Multi‐Investigator Study which championed the use of experiments in surveys and which provided the base for the TESS program. And he was present at the meeting convened by Jim Granato at NSF which conceived of the EITM idea, and he is a co‐PI of one of the two EITM summer programs. Janet Box‐Steffensmeier was an early graduate student member of the Political Methodology Society and a recent President. David Collier was the founding President of the APSA qualitative methods section, and the Chair of CQRM's Academic Council.

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Political science professor publishes book providing insights from 2020 election

Joseph Coll, assistant professor of political science at The College of Wooster, coedited Lessons Learned from the 2020 U.S. Presidential Election: Hindsight is 2020

Joseph Coll, assistant professor of political science at The College of Wooster, recently published a book providing insights into the historic 2020 election, held during the height of the COVID-19 pandemic, offering best practices scholars and practitioners can turn to when similar challenges arise. Coll coedited Lessons Learned from the 2020 U.S. Presidential Election: Hindsight is 2020 along with Joseph Anthony, assistant professor of political science at State University of New York, after attending a conference in January 2022 with other political science scholars discussing the challenges of administering the election.

“We learned so much about how to operate elections during major pandemics, but just as important, it taught us about how to be adaptable in the election environment whenever these issues pop up,” Coll said, noting that the same insights can be applied not just to health crises but tornadoes, hurricanes, or other events that make it difficult to administer elections. “We know, for example, that if you change your polling site, you decrease how many poll workers are available, and you could really shutter voter turnout. We wrote the book specifically to explain the lessons we learned from the COVID pandemic but also to show how we can transfer these lessons to understanding how to administer elections when uncertainties arise.”

Contributing authors to the book address how states and localities altered their elections because of the pandemic, poll worker motivation for working during a health crisis, and how the changes to elections were viewed by election officials. Further, they examined how changes affected whether a citizen decided to cast a ballot, how they voted, and who they voted for, as well as how these changes influenced evaluations of the election, how long voters waited to cast a ballot, and how confident they were in the outcome. The culmination of the work educates scholars and practitioners about election administration, access, and evaluations from this historic election. For the project, Coll had the opportunity to learn about the publishing process and collaborate with senior scholars to share information about the various aspects of the election process that can affect voter access and voter confidence.

Increasing voter turnout through ballot drop boxes and the promotion and expansion of the ability to vote by mail apply not only when voters can’t get to the polls because of health concerns but could also be used in cases of areas affected by natural disasters or anything that prohibits people from voting in person. In his own chapter, Coll explained how electoral reforms like these “have the potential to reduce confidence in voting and the elections overall” in many years. “That’s really not what happened during the COVID pandemic though,” he said. “People recognized that policies were changed for legitimate reasons. They didn’t think it was partisan warfare. They thought it was trying to add access for those who have health disabilities.”

The changes that were made at polling locations also helped support voter confidence. “Policies that we use to protect voters in person, like cleaning your voting booth, social distance voting—these made people feel a lot safer. They also said that not only did they feel safer, but they thought poll workers performed better; they thought the polling places were better ran, and they had a more positive overall voting experience,” he said. “We can take that out of the COVID context and say, when you have these major issues arise, when you outfit your polling places to prevent any additional issues arising from that problem, you have the potential to make voters feel like the poll workers care more, and they’re trying harder to administer these elections.”

When teaching Wooster students, Coll emphasizes the importance of understanding the voting process and the complex details that go into administering elections. “We have very little understanding in the American public today about how elections are administered or the nuances thereof, and that’s what causes a lot of people to be less confident or think that elections are not legitimate,” he said. Coll calls attention to how elections operate and why they are structured certain ways. “Given that a lot of our students are young adults, just beginning their lives, it’s important to really give them the sense that these elections are secure,” said Coll.

Closing in on the end of his first semester at Wooster, Coll particularly enjoys his close work with students on research, including working with seniors on Independent Study to build their own interest in research. Additionally, he appreciates working closely with sophomore research assistants to offer perspective and support on his research about election policies, voter qualifications, voter confidence, and perceptions about electoral integrity. “I originally got interested in elections because I was wanted to see how we can design our election system to make sure that everybody has an equal access and equal voice in that system,” he said. “A lot of my research revolves around that idea of what are the policies we put in place for elections and how they affect how people access elections.”

Posted in Faculty , News on April 25, 2024.

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Political science professor publishes book providing insights from 2020
Political science professor publishes book providing insights from 2020 election. Joseph Coll, assistant professor of political science at The College of Wooster, recently published a book providing insights into the historic 2020 election, held during the height of the COVID-19 pandemic, offering best practices scholars and practitioners can turn to when similar challenges arise.

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Table of contents (12 chapters)

Approaching and Measuring Social Science Objects

Decoding Manifestos and Other Political Texts: The Case of Extreme-Right Ideology

Reaction Time Measures in Implicit Attitudes Research

Studying Party Choice

Into the Archives

Making Sense of Data

When Access Is Restricted: Craftiness and Combining Methods in the Study of a Secretive Elite

Semistructured Interviews and Informal Institutions: Getting Inside Executive Government

Error-Correction as a Concept and as a Method: Time Series Analysis of Policy-Opinion Responsiveness

Working Backwards? Using Simulation to Sort Out Empirical Inconsistencies

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1 Political Science Methodology

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2 Concepts and Measurement

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4 Experiments, Quasi‐experiments, and Natural Experiments

5 Quantitative Tools for Causal and Descriptive Inference: General Methods

6 Quantitative Tools for Causal and Descriptive Inference: Special Topics

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Political science professor publishes book providing insights from 2020 election

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