References

Agresti, A. (2012). Analysis of ordinal categorical data. Wiley.

Agresti, A. (2018). An introduction to categorical data analysis. Wiley.

Bartholomew, D. J., Knott, M., & Moustaki, I. (2011). Latent variable models and factor analysis: A unified approach. Wiley.

Bhattacharya, P. K., & Burman, P. (2016). Theory and methods of statistics. Academic Press.

Brumback, B. A. (2022). Fundamentals of causal inference: With R. Chapman and Hall/CRC.

Collett, D. (2023). Modelling survival data in medical research. Chapman and Hall/CRC.

Demidenko, E. (2007). Sample size determination for logistic regression revisited. In Statistics in Medicine.

Fagerland, M. W., & Hosmer, D. W. (2017). How to test for goodness of fit in ordinal logistic regression models. In The Stata Journal.

Fagerland, M. W., Hosmer, D. W., & Bofin, A. M. (2008). Multinomial goodness‐of‐fit tests for logistic regression models. In Statistics in Medicine.

Friendly, M., & Meyer, D. (2016). Discrete data analysis with R: Visualization and modeling techniques for categorical and count data. Chapman and Hall/CRC.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.

Hilbe, J. M. (2014). Modeling count data. Cambridge University Press.

Holland, P. W. (1986). Statistics and causal inference. In Journal of the American Statistical Association.

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. Wiley.

Imbens, G. W., & Rubin, D. B. (2015). Causal inference for statistics, social, and biomedical sciences: An introduction. Cambridge University Press.

Jeffreys, H. (1961). The theory of probability (3rd edition). Oxford University Press.

Jiang, J. (2021). Linear and generalized linear mixed models and their applications. Springer.

Kruschke, J. K. (2018). Rejecting or accepting parameter values in bayesian estimation. In Advances in Methods and Practices in Psychological Science.

Lindley, D. V. (1972). Bayesian statistics: A review. Society for Industrial and Applied Mathematics.

Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets. In Annu Rev Public Health.

McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.

Menard, S. (2010). Logistic regression: From introductory to advanced concepts and applications. SAGE.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis. Wiley.

Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.

Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. Wiley.

Pearl, J., & Mackenzie, D. (2019). The book of why: The new science of cause and effect. Penguin.

Rao, C. R., Shalabh, Toutenburg, H., & Heumann, C. (2008). The multiple linear regression model and its extensions. Springer.

Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. In Journal of Educational Psychology.

Senn, S. (2011). Francis Galton and regression to the mean. In Significance.

Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Routledge.

Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S. Springer.

Wickham, H., Grolemund, G., & Çetinkaya-Rundel, M. (2023). R for data science. O’Reilly. https://r4ds.hadley.nz/

Wickham, H., Navarro, D., & Pedersen, T. L. (2023). ggplot2: Elegant graphics for data analysis. Springer. https://ggplot2-book.org/