Spring 2007 One-Credit Courses in Methodology
If you have a question about a specific course, please contact the instructor of that course directly. If you have a general question about the suite of one-credit methodology courses, please contact Katie Bode-Lang.
For more information about the one-credit courses, please see our frequently asked questions:
Course Dates: 1/17, 1/24, 1/31, 2/7, 2/14
Prerequisites: Prerequisites are three semesters of graduate-level statistics for the social and behavioral sciences, or three semesters of graduate-level statistics and a demonstrated interest in the social and behavioral sciences. Basic familiarity with SAS is helpful, but not a prerequisite.
Course Description: The goal of this short course is to help students gain the background and skills to be able to address interesting research questions using latent class and latent transition analysis. Latent class theory is conceptually similar to factor analysis. However, in latent class theory, latent variables are categorical, and individuals are sorted into mutually exclusive and exhaustive latent classes based on a set of item responses. Latent class analysis identifies latent classes in data and estimates their prevalence, while simultaneously adjusting for measurement error. Latent class models can be used to estimate change over time in latent class membership using longitudinal data, in a variation called latent transition analysis (LTA). In addition, multiple-groups analyses can be performed, and covariates can be introduced to predict latent class membership and changes in latent class membership.
Class time will be spent in lecture, discussion, and software demonstrations. Laboratory exercises will be provided for students to do outside of class. In addition, students will be required to do a small project. The software used in this course will be two downloadable add-on procedures for SAS Version 9 for Windows: PROC LCA (for latent class analysis) and PROC LTA (for latent transition analysis).
Instructor: John W. Graham
Course Dates/Times: 4/2, 4/9, 4/16, 4/23, 4/30
Prerequisites: BBH 521, Structural Equation Modeling (or equivalent)
Course Description: The course will cover the following topics:
Analysis of missing data
- Missing data theory
- Analysis with multiple imputation
- Analysis with Full Information Maximum Likelihood (FIML) procedures
- SEM Multiple-Group Missing Data procedure
Planned Missing Data Designs
- 3-Form design and related designs
- Determining which variant of design is best
- Strategies for implementing the 3-form design
- Two-Method Measurement
- Strategies for estimating benefit of model
The course will have lectures and analysis assignments.