Robert Hanneman : Teaching
This page contains links to materials supporting some courses that I teach.  Additional features may be found at the U.C.R. instructional support website:  http://ilearn.ucr.edu  (log in as a guest, my courses are open for viewing).

Undergraduate:

Data Processing (Soc. 109): Levels of measurement, data structures and coding, using the WWW, word processing, spreadsheets, and statistical packages.

Statistical Analysis (Soc. 5): Introduction to univariate and bivariate descriptive and inferential statistics for applied social scientists.

Multivariate Analysis (Soc. 110): Causal models, regression and log-linear analysis for multiple independent variables.

Social Class and Inequality (Soc. 133):  Classes, inequalities, and mobility.

Social Network Analysis (Soc. 157): An introduction to formal methods for the analysis of social network data.

Social Mobility (Soc. 183W):  Who gets Ahead?  Status attainment processes of individuals, groups, and societies [incomplete]

Graduate:

Research Methods: Quantitative Approaches (Soc. 201A):  Introduction to principles of research design, measurement, and highly structured data collection methods.

Intro. to Multivariate Analysis (Soc. 203A): Single equation models for quantitative (regression, ANOVA, ANCOVA) and qualitative (crosstabulation, logit, log-linear) outcomes.

Multi-equation and Measurement Models (Soc. 203B): Recursive path models, Exploratory and confirmatory factor analysis, latent class analysis, cluster analysis.

Social Networks Seminar (offered by Phil Bonacich and Rick Grannis) UCLA Sociology 208A and 208B.

Tutorial Introduction to Linear (regression, GLM, Mixed) Models.

Social Network Analysis with Pajek and UCINET:  Short-course/workshop

Research Practicum (Soc. 250): Support for students completing their "professional" research paper.

Topics in Theory Construction: Simulation Modeling (Soc. 242G): Readings and experiments with agent-based ("bottom up" "complexity") and top down ("systems") models in (mostly) Sociology.

Intermediate Quantitative Analysis (Soc. 271) Extensions of the classical normal linear regression model to non-normal dependent variables, non-linear link functions, non-independent sampling: logit, probit, log-linear, hierarchical models, survival, counts, etc.

Analyzing Crosstabulations with CDAS: Materials supporting Eliason's CDAS software for structured log-linear models (row and column, edge, general log linear, latent class, etc.).