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: 1/14, 1/21, 1/28, 2/4. 2/11
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 underlying subgroups 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 transitions over time 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: Lisa Dierker
Course Dates/Times: The course will meet from 5:15 to 7:45 p.m. in the conference room of The Methodology Center on the 4th floor of the Calder Square II Building on the following days: 2/18, 2/25, 3/4, 3/18, 3/25 (NOTE: No meeting on 3/11).
Prerequisites: Prerequisites are two semesters of graduate-level statistics for the social and behavioral sciences or two semesters of graduate-level statistics and a demonstrated interest in the social and behavioral sciences.
Course Description:The goal of this short course is to compare and contrast established and emerging methods able to identify population subgroups that may be meaningful in the planning of targeted behavioral interventions. Techniques reviewed will include: Finite Growth Mixture Modeling, Classification and Regression Tree Analysis, Functional Hierarchical Linear Modeling, and Time Series Analysis. The focus of the course will not be on the mechanics of modeling, but rather on the adequacy and/or importance of each technique for identifying population subgroups. Considerations regarding the use of each technique will include sample size requirements, the need for longitudinal assessment, frequency and spacing of measurement occasions, accommodations for measurement of independent and dependent variables, model fit, and availability of software. Many examples will be provided in an effort to stimulation critical evaluation of the promise that these techniques hold for research that may transform behavioral policy and/or practice. Class time will be spent in lecture and discussion. Exercises will be provided for students to do outside of class.
Instructor: John Graham
Course Dates: 4/8, 4/15, 4/22, 4/29
Prerequisites: BB H 521 (Structural Equation Modeling) or equivalent
Course description: The course will have lectures and analysis assignments and will cover the following topics.
1. 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
2. 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 mode