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This course introduces categorical data analysis and multilevel analysis methods. The goals are applications of the methods to social science research. The classes will provide an intuitive understanding of the basics of categorical and multilevel data analysis including probability distributions, model assumptions, estimators, model checking, and model inference, followed by intensive instruction using empirical data analysis, Stata code, and substantive interpretations. The first seven weeks cover binary, count, and multiple-category dependent variables (logit, probit, Poisson, negative binomial, loglinear, multinomial logit, ordered logit, and conditional logit). The 8th week is devoted to multiple imputation. The last five weeks cover latent class analysis from sample-based cluster analysis to model-based cluster analysis. The statistical software is Stata18 and R.