The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Modeling and Interpreting Interactive Hypotheses in Regression Analysis
Abstract
Social scientists study complex phenomena about which they often propose intricate hypotheses tested with linear-interactive or multiplicative terms. While interaction terms are hardly new to social science research, researchers have yet to develop a common methodology for using and interpreting them. Modeling and Interpreting Interactive Hypotheses in Regression Analysis provides step-by-step guidance on how to connect substantive theories to statistical models and how to interpret and present the results. "Kam and Franzese is a must-have for all empirical social scientists interested in teasing out the complexities of their data." ---Janet M. Box-Steffensmeier, Ohio State University "Kam and Franzese have written what will become the definitive source on dealing with interaction terms and testing interactive hypotheses. It will serve as the standard reference for political scientists and will be one of those books that everyone will turn to when helping our students or doing our work. But more than that, this book is the best text I have seen for getting students to really think about the importance of careful specification and testing of their hypotheses." ---David A. M. Peterson, Texas A&M University "Kam and Franzese have given scholars and teachers of regression models something they've needed for years: a clear, concise guide to understanding multiplicative interactions. Motivated by real substantive examples and packed with valuable examples and graphs, their book belongs on the shelf of every working social scientist." ---Christopher Zorn, University of South Carolina "Kam and Franzese make it easy to model what good researchers have known for a long time: many important and interesting causal effects depend on the presence of other conditions. Their book shows how to explore interactive hypotheses in your own research and how to present your results. The book is straightforward yet technically sophisticated. There are no more excuses for misunderstanding, misrepresenting, or simply missing out on interaction effects!" ---Andrew Gould, University of Notre Dame Cindy D. Kam is Assistant Professor, Department of Political Science, University of California, Davis. Robert J. Franzese Jr. is Associate Professor, Department of Political Science, University of Michigan, and Research Associate Professor, Center for Political Studies, Institute for Social Research, University of Michigan. For datasets, syntax, and worksheets to help readers work through the examples covered in the book, visit: www.press.umich.edu/KamFranzese/Interactions.html
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modelling Approach
Western, B. (1998) · American Journal of Political Science · doi:10.2307/2991856 · Cited by 207
Cited by
Understanding Interaction Models: Improving Empirical Analyses
Brambor, T., Clark, W. R., & Golder, M. (2006) · Political Analysis · doi:10.1093/pan/mpi014 · Cited by 6,223
Hainmueller, J., Mummolo, J., & Xu, Y. (2019) · Political Analysis · doi:10.1017/pan.2018.46 · Cited by 959
Affective polarization, local contexts and public opinion in America
Druckman, J. N., Klar, S., Krupnikov, Y., Levendusky, M., & Ryan, J. B. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-01012-5 · Cited by 463
Franzese, R. J., & Hays, J. C. (2007) · Political Analysis · doi:10.1093/pan/mpm005 · Cited by 473
Self-citationStudents as Experimental Participants: A Defense of the “Narrow Data Base”
Druckman, J. N., & Kam, C. D. (2011) · Cambridge Handbook of Experimental Political Science · doi:10.1017/cbo9780511921452.004 · Cited by 454
Self-citation
Metadata
- Authors
- Franzese, R., & Kam, C.
- Venue
- University of Michigan Press eBooks
- Publisher
- University of Michigan Press
- Published
- 2007
- OpenAlex
- W148167203
- Cited by
- 531
- References
- 1
- FWCI
- 11.09
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Modeling and Interpreting Interactive Hypotheses in Regression Analysis. https://app.cochat.ai/papers/10.3998/mpub.206871
- The paper
- Franzese, R., & Kam, C. (2007). Modeling and Interpreting Interactive Hypotheses in Regression Analysis. University of Michigan Press eBooks. https://doi.org/10.3998/mpub.206871
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.