41  Bibliography

Abramson, Scott F, Korhan Kocak, Asya Magazinnik, and Anton Strezhnev. 2024. “Detecting Preference Cycles in Forced-Choice Conjoint Experiments.” SocArXiv. https://doi.org/10.31235/osf.io/xjre9.

Alexander, Rohan. 2023. Telling Stories with Data: With Applications in r. Chapman; Hall/CRC.

Angelopoulos, Anastasios N., and Stephen Bates. 2022. “A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification,” no. arXiv:2107.07511 (September). https://doi.org/10.48550/arXiv.2107.07511.

Angrist, Joshua D, and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton university press.

Arel-Bundock, Vincent, Noah Greifer, and Andrew Heiss. Forthcoming. “How to Interpret Statistical Models Using marginaleffects in R and Python.” Journal of Statistical Software, Forthcoming.

———. Forthcoming. “How to Interpret Statistical Models Using marginaleffects in R and Python.” Journal of Statistical Software, Forthcoming. https://marginaleffects.com.

Aronow, Peter M., and Benjamin T. Miller. 2019. Foundations of Agnostic Statistics. Cambridge: Cambridge University Press. https://doi.org/10.1017/9781316831762.

Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. 2015. “Fitting Linear Mixed-Effects Models Using lme4.” Journal of Statistical Software 67 (1): 1–48. https://doi.org/10.18637/jss.v067.i01.

Berger, Roger, and George Casella. 2024. Statistical Inference. 2nd ed. CRC Press.

Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14 (1): 63–82.

Brooks, Mollie E., Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Maechler, and Benjamin M. Bolker. 2017. “glmmTMB Balances Speed and Flexibility Among Packages for Zero-Inflated Generalized Linear Mixed Modeling.” The R Journal 9 (2): 378–400. https://doi.org/10.32614/RJ-2017-066.

Bürkner, Paul C. 2024. The Brms Book: Applied Bayesian Regression Modelling Using r and Stan (Early Draft). https://paulbuerkner.com/software/brms-book.

Cameron, A Colin, and Pravin K Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge university press.

Clark, William Roberts, and Matt Golder. 2023. Interaction Models: Specification and Interpretation. Methodological Tools in the Social Sciences. Cambridge University Press.

Efron, Bradley, and R. J. Tibshirani. 1994. An Introduction to the Bootstrap. New York: Chapman; Hall/CRC. https://doi.org/10.1201/9780429246593.

Fair, Ray C. 1978. “A Theory of Extramarital Affairs.” Journal of Political Economy 86: 45–61.

Finch, W Holmes, Jocelyn E Bolin, and Ken Kelley. 2019. Multilevel Modeling Using r. Chapman; Hall/CRC.

Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis. 3rd ed. New York: Chapman; Hall/CRC. https://doi.org/10.1201/b16018.

Gelman, Andrew, and Jennifer Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. 1st ed. Cambridge University Press. http://www.stat.columbia.edu/~gelman/arm/.

Gelman, Andrew, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák. 2020. “Bayesian Workflow.” https://arxiv.org/abs/2011.01808.

Goldsmith-Pinkham, Paul, Peter Hull, and Michal Kolesár. Forthcoming. “Contamination Bias in Linear Regressions.” American Economic Review, Forthcoming.

Hainmueller, Jens, Daniel Hopkins, and Teppei Yamamoto. 2014. “Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments.” Political Analysis 22 (1): 1–30.

Hainmueller, Jens, Jonathan Mummolo, and Yiqing Xu. 2019. “How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice.” Political Analysis 27 (2): 163–92.

Hansen, Bruce. 2022a. Econometrics. 1st ed. Princeton, NJ: Princeton University Press. https://press.princeton.edu/books/hardcover/9780691223248/econometrics .

———. 2022b. Probability and Statistics for Economists. 1st ed. Princeton, NJ: Princeton University Press. https://press.princeton.edu/books/hardcover/9780691235899/probability-and-statistics-for-economists .

Harrell, Frank. 2021. “Statistical Thinking - Avoiding One-Number Summaries of Treatment Effects for RCTs with Binary Outcomes.” https://www.fharrell.com/post/rdist/.

Heiss, Andrew. 2022. “Marginalia: A Guide to Figuring Out What the Heck Marginal Effects, Marginal Slopes, Average Marginal Effects, Marginal Effects at the Mean, and All These Other Marginal Things Are.” May 20, 2022. https://doi.org/10.59350/40xaj-4e562.

Hernán, Miguel A. 2018. “The c-Word: Scientific Euphemisms Do Not Improve Causal Inference from Observational Data.” American Journal of Public Health 108 (5): 616–19.

Hernán, Miguel A, and James M Robins. 2020. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

Hyndman, Rob J, and George Athanasopoulos. 2018. Forecasting: Principles and Practice. OTexts.

Imbens, Guido W, and Donald B Rubin. 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge university press.

Kahneman, Daniel, and Amos Tversky. 1979. “Prospect Theory: An Analysis of Decision Under Risk.” Econometrica 47 (2): 263–91. http://www.jstor.org/stable/1914185.

Kam, Cindy D., and Robert J. Franzese Jr. 2009. Modeling and Interpreting Interactive Hypotheses in Regression Analysis. University of Michigan Press. https://doi.org/10.3998/mpub.206871.

King, Gary, Michael Tomz, and Jason Wittenberg. 2000. “Making the Most of Statistical Analyses: Improving Interpretation and Presentation.” American Journal of Political Science, 347–61.

Krinsky, I., and A. L. Robb. 1986. “On Approximating the Statistical Properties of Elasticities.” Review of Economics and Statistics 68 (4): 715–19.

Lakens, Daniël, Anne M. Scheel, and Peder M. Isager. 2018. “Equivalence Testing for Psychological Research: A Tutorial.” Advances in Methods and Practices in Psychological Science 1 (2): 259–69. https://doi.org/10.1177/2515245918770963.

Leeper, Thomas J., Sara B. Hobolt, and James Tilley. 2020. “Measuring Subgroup Preferences in Conjoint Experiments.” Political Analysis 28 (2): 207–21. https://doi.org/10.1017/pan.2019.30.

Lenth, Russell V. 2024. emmeans: Estimated Marginal Means, Aka Least-Squares Means. https://cran.r-project.org/package=emmeans.

Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021. “What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory.” American Sociological Review 86 (3): 532–65. https://doi.org/10.1177/00031224211004187.

McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in r and STAN. 2nd ed. New York: Chapman; Hall/CRC. https://doi.org/10.1201/9780429029608.

McKinney, Wes. 2022. Python for Data Analysis. " O’Reilly Media, Inc.".

Morgan, Stephen L, and Christopher Winship. 2015. Counterfactuals and Causal Inference. Cambridge University Press.

Nickerson, Raymond S. 2004. Cognition and Chance: The Psychology of Probabilistic Reasoning. 1st ed. Psychology Press. https://doi.org/10.4324/9781410610836.

Ornstein, Joseph T. 2023. “Getting the Most Out of Surveys: Multilevel Regression and Poststratification.” In Causality in Policy Studies: A Pluralist Toolbox, edited by Alessia Damonte and Fedra Negri, 99–122. Springer. https://doi.org/10.1007/978-3-031-12982-7_5.

Pearl, Judea. 2009. Causality. Cambridge university press.

Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic books.

Pustejovsky, James. 2023. clubSandwich: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections. https://CRAN.R-project.org/package=clubSandwich.

Rainey, Carlisle. 2014. “Arguing for a Negligible Effect.” American Journal of Political Science 58 (4): 1083–91.

———. 2024. “A Careful Consideration of CLARIFY: Simulation-Induced Bias in Point Estimates of Quantities of Interest.” Political Science Research and Methods 12 (3): 614–23. https://doi.org/10.1017/psrm.2023.8.

Thornton, Rebecca L. 2008. “The Demand for, and Impact of, Learning HIV Status.” American Economic Review 98 (5): 1829–63.

Tukey, John W. 1977. Exploratory Data Analysis. Reading, MA: Addison-Wesley.

Wasserman, Larry. 2004. All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics. New York, NY: Springer. https://doi.org/10.1007/978-0-387-21736-9.

———. 2006. All of Nonparametric Statistics. Springer Texts in Statistics. New York, NY: Springer.

Wellek, Stefan. 2010. Testing Statistical Hypotheses of Equivalence and Noninferiority. CRC Press. https://www.taylorfrancis.com/books/mono/10.1201/EBK1439808184/testing-statistical-hypotheses-equivalence-noninferiority-stefan-wellek .

Westreich, Daniel, and Sander Greenland. 2013. “The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients.” American Journal of Epidemiology 177 (4): 292–98. https://doi.org/10.1093/aje/kws412.

Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. Sebastopol, CA: O’Reilly Media. https://www.amazon.ca/dp/1492097403.

Zeileis, Achim, Susanne Köll, and Nathaniel Graham. 2020. “Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R.” Journal of Statistical Software 95 (1): 1–36. https://doi.org/10.18637/jss.v095.i01.

Zhao, Jinhui, Tim Stockwell, Tim Naimi, Sam Churchill, James Clay, and Adam Sherk. 2023. “Association Between Daily Alcohol Intake and Risk of All-Cause Mortality: A Systematic Review and Meta-analyses.” JAMA Network Open 6 (3): e236185–85. https://doi.org/10.1001/jamanetworkopen.2023.6185.