Element contents
Bayesian Social Science Statistics
From the Very Beginning
Published online by Cambridge University Press: 24 October 2024
Summary
In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than that in a basic statistics course. At the end of the process, readers will understand the core tenets of Bayesian theory and practice in a way that enables them to specify, implement, and understand models using practical social science data. Chapters will cover theoretical principles and real-world applications that provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in both R and Python is provided throughout.
- Type
- Element
- Information
- Online ISBN: 9781009341189Publisher: Cambridge University PressPrint publication: 24 October 2024
References
Bartels, L. M. (1996). Pooling disparate observations. American Journal of Political Science, 40(3), 905–942.CrossRefGoogle Scholar
Bayes, T. (1763). Lii. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, frs communicated by Mr. Price, in a letter to John Canton, amfr s. Philosophical Transactions of the Royal Society of London, (53), 370–418.Google Scholar
Berk, R. A., Western, B., & Weiss, R. E. (1995). Statistical inference for apparent populations. Sociological Methodology, 421–458.CrossRefGoogle Scholar
Birnbaum, A. (1962). On the foundations of statistical inference. Journal of the American Statistical Association, 57(298), 269–306.CrossRefGoogle Scholar
Canes-Wrone, B., Brady, D. W., & Cogan, J. F. (2002). Out of step, out of office: Electoral accountability and house members’ voting. American Political Science Review, 96(1), 127–140.CrossRefGoogle Scholar
Christensen, W. F., & Florence, L.W. (2008). Predicting presidential and other multistage election outcomes using state-level pre-election polls. The American Statistician, 62(1), 1–10.CrossRefGoogle Scholar
Copas, J. (1969). Compound decisions and empirical bayes. Journal of the Royal Statistical Society: Series B (Methodological), 31(3), 397–417.CrossRefGoogle Scholar
Dale, A. I. (2012). A history of inverse probability: From Thomas Bayes to Karl Pearson. Springer Science & Business Media.Google Scholar
Diaconis, P., & Freedman, D. (1986). On the consistency of bayes estimates. The Annals of Statistics, 14(1), 1–26.Google Scholar
Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 222(594–604), 309–368.Google Scholar
Fisher, R. A. (1925). Theory of Statistical Estimation. Mathematical Proceedings of the Cambridge Philosophical Society, 22(5), 700–725.CrossRefGoogle Scholar
Founta, A.-M., Djouvas, C., Chatzakou, D. et al. (2018). Large scale crowdsourcing and characterization of Twitter abusive behavior. In 11th international conference on web and social media, ICWSM 2018.CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2015). Bayesian data analysis (3rd ed.). CRC Press.Google Scholar
Gelman, A., Hullman, J., Wlezien, C., & Morris, G. E. (2020). Information, incentives, and goals in election forecasts. Judgment and Decision Making, 15(5), 863–880.CrossRefGoogle Scholar
Gill, J. (1999). The insignificance of null hypothesis significance testing. Political Research Quarterly, 52(3), 647–674.CrossRefGoogle Scholar
Gill, J. (2014). Bayesian methods: A social and behavioral sciences approach (Vol. 20). CRC Press.CrossRefGoogle Scholar
Gill, J., & Freeman, J. R. (2013). Dynamic elicited priors for updating covert networks. Network Science, 1(1), 68–94.CrossRefGoogle Scholar
Gill, J., & Torres, M. (2019). Generalized linear models: A unified approach (Vol. 134). Sage.Google Scholar
Gill, J., & Walker, L. D. (2005). Elicited priors for Bayesian model specifications in political science research. The Journal of Politics, 67(3), 841–872.CrossRefGoogle Scholar
Groves, R. M., & Lyberg, L. (2010). Total survey error: Past, present, and future. Public Opinion Quarterly, 74(5), 849–879.CrossRefGoogle Scholar
Isakov, M., & Kuriwaki, S. (2020). Towards principled unskewing: Viewing 2020 election polls through a corrective lens from 2016. Harvard Data Science Review, 2(4).Google Scholar
Kolmogorov, A. N. (1933). Grundbegriffe der wahrscheinlichkeitreichnung. Ergebnisse der Mathematik.Google Scholar
Lauderdale, B. E., Bailey, D., Blumenau, J., & Rivers, D. (2020). Model-based pre-election polling for national and sub-national outcomes in the US and UK. International Journal of Forecasting, 36(2), 399–413.CrossRefGoogle Scholar
Leamer, E. E. (1972). A class of informative priors and distributed lag analysis. Econometrica: Journal of the Econometric Society, 40(6), 1059–1081.CrossRefGoogle Scholar
Madson, G. J., & Hillygus, D. S. (2020). All the best polls agree with me: Bias in evaluations of political polling. Political Behavior, 42(4), 1055–1072.CrossRefGoogle Scholar
Stein, R. M., Mann, C., Stewart III, C. et al. (2020). Waiting to vote in the 2016 presidential election: Evidence fromamulti-county study. Political Research Quarterly, 73(2), 439–453.CrossRefGoogle Scholar
Stigler, S.M. (1982). Thomas Bayes’s Bayesian inference. Journal of the Royal Statistical Society: Series A (General), 145(2), 250–258.CrossRefGoogle Scholar
Stigler, S. M. (1983). Who discovered Bayes’s theorem? The American Statistician, 37(4a), 290–296.Google Scholar
Stoetzer, L. F., Leemann, L., & Traunmueller, R. (2024). Learning from polls during electoral campaigns. Political Behavior, 46(1), 543–564.CrossRefGoogle Scholar
Stunt, J., van Grootel, L., Bouter, L., Trafimow, D., Hoekstra, T., & de Boer, M. (2021). Why we habitually engage in null-hypothesis significance testing: A qualitative study. Plos One, 16(10), e0258330.CrossRefGoogle ScholarPubMed
Wagner, K., & Gill, J. (2005). Bayesian inference in public administration research: Substantive differences from somewhat different assumptions. International Journal of Public Administration, 28(1–2), 5–35.CrossRefGoogle Scholar