Fall 2012 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:

FAQs for Students

FAQs for Faculty

 


 

HD FS 597F - Person-centered methods: Advanced contingency table analysis

Schedule Number: 909307

Instructors: Dr. Mark Stemmler, Professor of Psychological Diagnostics, Department of Psychology and Sports Science, University of Erlangen-Nuremberg, Germany

Credits: 1

Course Dates: August 20-24 (5 days).  Note: This is the week PRIOR to the start of the semester.

Time and Location: 1-4 PM at the Methodology Center

 

Prerequisites: Some knowledge of multivariate statistics and SPSS

 

Enrollment limited to 15 students

 

Course Description: This course takes a person-oriented instead of a variable-oriented approach to data analysis with multiway contingency tables, sometimes referred to as configural frequency analysis.  Class time will be spent in lecture, discussion, and software demonstrations. Course grade is based on homework assignments and class participation.

 

Register through the Office of the Registrar--the schedule of courses is listed here.

 


 

HD FS 597A - Latent Class Analysis for Cross-sectional and Repeated Measures Data

Schedule Number: 909892

Instructors: Dr. Bethany Bray, Assistant Professor, Psychology Department, Virginia Tech University

Credits: 1

Course Dates: 9/26-10/24/12 (5 weeks)

Time and Location: Wednesdays, 5:15-7:45 PM in S127 Henderson (please note the change in location, 8/24/12)

 

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.

 

Enrollment limited to 15 students

 

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). 

 

Register through the Office of the Registrar--the schedule of courses is listed here.

 


 

BBH 597A - Missing data: Analysis and design

Schedule Number: 905977

Instructors: Dr. John W. Graham (JGraham@psu.edu), Professor of Biobehavioral Health, Penn State

Credits: 1

Course Dates: 11/7, 11/14, 11/28, 12/5, 12/12/12 (5 weeks)

Time and Location: Wednesdays, 5:30-8:00 PM in E323 HHHD Bldg. (please note the change in location, 10/10/12)

 

Prerequisites: HDFS 519 or equivalent course on Multiple Regression. Knowledge of Structural Equation Modeling (e.g., BBH 521 or equivalent) is desirable, but not absolutely required. Knowledge of LISREL is best, but knowledge of other SEM programs should work.

 

Enrollment limited to 15 students

 

Tentative Schedule of Topics: Each session will be divided about evenly into lecture and hands-on analysis. There will be weekly readings and assignments.   

Session 1:

Intro to Missing Data. Missing Data Theory According to Graham

Hands-on Session: Imputation with NORM, Analysis with SPSS Regression

 

Session 2:

Attrition: Bias and Power

Hands-on Session: Longitudinal diagnostics.

 

Session 3:

FIML (SEM) Methods; MGSEM; Some Simulation Techniques

Hands-on Session: Analysis with Amos or LISREL (this session is highly dependent on SEM knowledge

 

Session 4:

Planned Missingness Designs (focus on 3-form design)

Hands-on Session: Setting up the 3-form design with certain variables

 

Session 5:

Troubleshooting, Use of Auxiliary Variables, Missing data with Multilevel Data

Hands-on Session: Performing MI with NORM when one has problem data, and cluster data, then using Proc Mixed, SPSS Mixed, HLM.

 

Software Required for this Course: 

NORM 2.03 (free download available)

Automation Utilities (free download)

SPSS (Version 17 or later highly recommended) OR SAS (version 9)

 

Software Highly Recommended for this Course:

Amos is a very intuitive program, and can be very useful for some things even if you have no prior experience with it.

OR

LISREL (version 8.54 or later – student version may be sufficient, but there is no point getting LISREL if you don't already know how to use it).

 

Primary Reading:

Graham, J. W. (2012). Missing data: Analysis and design. New York: Springer.

Register through the Office of the Registrar -- the schedule of courses is listed here.

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