Graduate Courses in Quantitative Methods
6131 Univariate Analysis I: Analysis of Variance (3): Topics include descriptive statistics and graphics, applied probability, elementary distribution theory, principles of statistical inference, theory and application of analysis of variance (ANOVA) models for between-subjects and repeated measures designs.
6132 Univariate Analysis II: Regression (3): Prerequisite, Psychology 6131, or permission from the instructor. Topics include correlation, simple linear regression, multiple linear regression, regression diagnostics, logistic regression.
6135 Psychology of Data Visualization (3): Prerequisite, Psychology 6130 or Psychology 6131 and Psychology 6132, or permission from the instructor. Topics include varieties of information visualization, history of information visualization, software tools for information visualization, visualization in statistics and human factors research.
6136 Categorical Data Analysis (3): Prerequisite, Psychology 6130 or Psychology 6131 and Psychology 6132, or permission from the instructor. Topics include discrete data, two-way tables of counts, three-way contingency tables, log-linear models, generalized linear models, logit models, logistic regression, polytomous response models and models for correlated categorical responses.
6137 Best Practices in Quantitative Research Methods (3): Prerequisite, Psychology 6130 or Psychology 6131 and Psychology 6132. This course is designed to introduce and train students with research skills aligned with current recommendations on best practices in quantitative research. The course will focus on all stages of research including power analysis and multiplicity control in research design, data management and documentation for reproducibility, and research reporting and presentation graphics.
6138 Computational Methods for Statistical Modeling (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132, or permission from the instructor. This is a course in which students will learn the theory and programming components found in common numerical methods routinely used in statistical analyses. The principal goals of the course are to help students to become familiar with statistical and numerical analysis theory for fitting models and inferring results.
6139 Item Response Theory (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132, or permission from the instructor. The course focuses on understanding and modelling test response data using IRT methods, applying IRT to different measurement problems, and examining the uses of IRT in applied research. Topics related to more advanced IRT investigations, such as computerized adaptive testing, test equating, and differential item functioning will also be discussed.
6155 Statistical Consulting in Psychology (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132, or permission from the instructor. An introduction to the statistical consulting process, emphasizing its nontechnical aspects.
6140 Multivariate Analysis (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132, or permission from the instructor. Topics include a brief introduction to matrix algebra, review of multiple regression, factor analysis, structural equation modelling, methods longitudinal data, multilevel modelling.
6145 Advanced Linear and Nonlinear Models (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132, or permission from the instructor. Topics include extensions to the General Linear Model, including advanced ANOVA strategies (e.g., interactions, mixed model for repeated measures), moderation/mediation (and combinations), discriminant function analysis, generalized linear models, (e.g., logistic/multinomial regression, Poisson regression), nonlinear models, and survival/event-history analysis.
6160 Multilevel Modeling (3): Prerequisite, Psychology 6130 or Psychology 6131 and Psychology 6132, or permission from the instructor. This course will familiarize students with the basic concepts and statistical techniques behind multilevel modeling (MLM), which is also referred to as mixed-effects modeling or simply mixed modeling. A special case of multilevel modeling is known as hierarchical linear modeling (HLM). These models are useful for cross-sectional data with non-independent observations, repeated-measures experimental data, and longitudinal data. Topics include random-effects ANOVA, intraclass correlations, random-slope models, cross-level interactions, and growth curve models.
6176 Structural Equation Modelling (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132, or permission from the instructor. Topics include concepts and methods underlying structural equation models, including causation and correlation, path analysis, confirmatory factor analysis, latent variable models, and practical use with major statistical software.
6180 Psychometric Methods (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132, or permission from the instructor. Topics include validity theory, classical test theory, measurement of latent variables using exploratory and confirmatory factor analysis, and item response theory.
6190 Longitudinal Data Analysis (3): Prerequisite, Psychology 6130, or Psychology 6131 and Psychology 6132 or permission from the instructor. Topics include repeated measures ANOVA designs, or theory and application of multilevel models for repeated measures data.
