231A. Toolkit for Quantitative Methods Research. (4)

Lecture, four hours. Requisites: courses 230A, 230B, 230C. Elementary probability. Working knowledge with calculus. Mathematical and statistical results useful for advanced quantitative methodology research. Matrix algebra. Random vectors. Multivariate distribution theory. Likelihood and Bayesian estimation and inference. Linear and generalized linear models. Simulation. S/U or letter grading.

M231B. Factor Analysis. (4)

(Formerly numbered 231B.) (Same as Psychology M253.) Lecture, four hours. Requisites: courses 211B, 231A. Exploratory factor analysis, rotations, confirmatory factor analysis, multiple-group analysis. S/U or letter grading.

231BL. Factor Analysis: Computer Laboratory. (1)

Laboratory, one hour. Corequisite: course 231B. Computer data analysis laboratory for exploratory and confirmatory factor analysis. Instruction in CEFA, LISREL, and other relevant statistical analysis packages. S/U grading.

231C. Analysis of Categorical and Other Nonnormal Data. (4)

Lecture, four hours. Requisites: courses 230B, 230C. Regression analysis with dichotomous and polytomous dependent variables, log-linear modeling, coefficients of association for categorical variables, factor analysis, and structural equation modeling. Letter grading.

231D. Advanced Quantitative Models in Nonexperimental Research: Multilevel Analysis. (4)

Lecture, four hours. Requisites: courses 230B, 230C. Examination of conceptual, substantive, and methodological issues in analyzing multilevel data (i.e., on individuals in organizational settings such as schools, corporations, hospitals, communities); consideration of alternative analytical models. Letter grading.

M231E. Statistical Analysis with Latent Variables. (4)

(Same as Statistics M244.) Lecture, three hours. Requisites: courses 231A, 231B. Extends path analysis (causal modeling) by considering models with measurement errors and multiple indicators of latent variables. Confirmatory factor analysis, covariance structure modeling, and multiple-group analysis. Identification, estimation, testing, and model building considerations. Letter grading.

231EL. Latent Variable Modeling: Computer Laboratory. (1)

Laboratory, one hour. Corequisite: course M231E. Computer data analysis laboratory for latent variable modeling. Instruction in LISREL and other relevant statistical analysis packages. S/U grading.