This course will prepare students to conduct quantitative data analysis for a thesis, dissertation, journal article, or policy report. Students should enter it with 2 pre-requisites: an introductory statistics course, and an intermediate statistics course. They will require knowledge of descriptive statistics, inference and basic regression techniques. This course has 2 broad learning goals. First, it will expose students to 3 advanced statistical techniques and procedures: categorical data analysis, with a focus on logistic regression; causal inference, with a focus on propensity score matching, and missing data analysis, with a focus on multiple imputation. Please note course topics will lean towards sociologically-oriented educational research, and will not cover detailed issues in psychometrics or econometrics. Second, students will receive guidance in the management and analysis of large data sets, including administrative and survey data, and will become acquainted with STATA statistical software. The major assignments will be cumulative in nature, with the final assignment consisting of original data analysis written in the format of a journal article, dissertation/thesis chapter, or technical report that applies each of the above-mentioned statistical techniques. Students can use their own data if they wish, but can also get access to several educational data sets that will be available through the Data, Equity and Policy in Education [DEPE] Lab (www.oise.utoronto.ca/depelab/).