Intermediate Quantitative Methods: The General Linear Model
This course is designed to meet the data analytic needs of the doctoral students whose dissertation relies on the analysis of quantitative data. Procedures important to the data analyst are covered including data entry & definition, treating missing data, detecting outliers, & transforming distributions. First term topics include multiple regression, analysis of covariance, repeated measures analysis of variance, & multivariate analysis of variance & covariance. Second term topics emphasize categorical data analysis, odds, rations, standardization, log linear models, logistic regression. Other topics include multinominal logistic models, survival analysis, principle components, & factor analysis. The approach is conceptual with heavy reliance on computer software packages. Appropriate for doctoral students desiring specialized knowledge beyond the introductory sequence.
- Old Course Number: E10.2003