Minor in Quantitative Psychology
Students may pursue a Minor in Quantitative Psychology. Common core requirements for the department require all students to complete Biostatistics I, Quantitative Methods II, and Multivariate Analysis. Clinical science students are required to complete one additional advanced analytic elective. Students who wish to complete a Minor in Quantitative Psychology are required to complete two additional advanced analytic electives, for a total of 6 statistics courses. Students must achieve a minimum of B- in the course in order for it to be counted toward the minor. Electives may be selected from the following list of approved courses. Students may request approval for courses not on the list from Dr. Stefany Coxe in the Department of Psychology. Dr. Coxe is also an Associated Faculty of the clinical science program and affiliated with both the CCF and the Integrated Biostatistics Center.
Introduction to SEM for Psychological Research (PSY 5939): This course is an introduction to structural equation models as applied to problems in the social sciences, broadly defined. The major purpose of the course is to familiarize you with the technique of structural equation modeling and to provide you with working knowledge of AMOS and MPLUS, computer programs designed to execute the analysis of a broad class of structural equation models. We consider numerous advanced topics, described below. The goal of the course is to expose students to a variety of analytical techniques so that they may become proficient in using SEM analyses to answer individual research questions.
Longitudinal Data Analysis (PSY 5939 / PHC 6056): This course covers topics related to statistical analysis of longitudinal data, focusing on methods used in the social sciences and health research. Topics build on a basic ANOVA and regression (general linear model) framework and include ANCOVA, mediation, multilevel modeling of longitudinal data, and latent growth modeling. Students will be able to analyze, interpret, and write up results using these methods.
Categorical Data Analysis (PSY 5939): This course covers topics related to statistical analysis of categorical outcome variables, focusing on methods used in the social sciences. Topics include chi-square and other non-parametric methods for categorical outcomes, the generalized linear model (GLiM, including logistic regression, Poisson regression, and survival analysis), and repeated measures extensions of GLiM (such as GEE and generalized linear mixed models). Students will analyze, interpret, and write up results using these methods.
Systematic Review & Meta-Analysis (PHC 6062): This course is designed to train students in the conduct of a systematic literature review and developing the skills critical for evidence-based clinical and public health practice. This course will provide a detailed description of systematic review process and will combine didactic sessions with in-class laboratory sessions. Students will be provided step-by-step guidance on how to perform a systematic review & meta-analysis. They will be expected to apply all the tools taught in the class to a topic of their choosing. The final deliverable for the course will be a systematic literature review with/without meta-analysis.