We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

STATISTICS FOR SOCIOLOGISTS III

SOC 362
Course Description

Generalized linear models with selected applications to social science data. Topics: Review of multiple regression; properties of estimators; general linear restrictions; instrumental variables; two-stage least squares; panel data; fixed and random effects; logit, probit, and related models.

Prerequisites

C&ESOC/SOC 361 , STAT 302, ECON 400 , ECON 410 , MATH/STAT 309 , or graduate/professional standing

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.38

-0.59% from Historical

Completion Rate
100%

1.38% from Historical

A Rate
31.03%

-12.57% from Historical

Class Size
29

-5.97% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

Similar Courses

APPLIED ECONOMETRIC ANALYSIS II

AAE 637

Focus on extending the standard regression model. Topics include nonlinear regression models, maximum likelihood estimation, panel data, simultaneous equations, linear and nonlinear systems, analysis of discrete choice, limited dependent variables, empirical economic applications and policy analysis.

STATISTICS FOR SOCIOLOGISTS II

C&ESOC/SOC 361

Applied linear regression modeling for social scientists. Bivariate and multiple regression, dummy variables, interactions, nonlinear relationships, indirect effects and omitted variable bias, outliers, heteroskedasticity, and mulitcollinearity; associated diagnostics and corrections. Use of Stata and/or SAS for dataset creation and analysis.

ECONOMIC STATISTICS AND ECONOMETRICS II

ECON 710

Extensions of the linear regression model; introduction to multiple equation models.

SEMINAR-MATHEMATICAL AND STATISTICAL APPLICATIONS IN SOCIOLOGY

SOC 952

Selected topics in measurement and model construction in social research.

ECONOMETRICS II

ECON 705

Econometric methods, theory, and applications. Topics include instrumental variables, GMM, panel data, limited dependent variables, time series, and vector autoregressions.