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

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HD FS 597F - Multiway contingency table analysis using Configural Frequency Analysis (CFA)

CRN: 521398

Instructor: Dr. Mark Stemmler ( mark.stemmler@uni-bielefeld.de), Professor of Psychological Methodology and Quality Management, Bielefeld University, Germany

Course Dates: 8/25, 9/1, 9/8, 9/15, 9/22

Prerequisites: Some knowledge of multivariate statistics and SPSS

Course objective: The goal of this short course is to give you a different perspective on data analysis. This course takes a person-oriented instead of a variable-oriented approach.

Grading: Course grade is based on homework assignments and class participation.

General information: Class time will be spent in lecture, discussion, and software demonstrations. Laboratory exercises will be assigned during the first four weeks of class; it is expected that students will complete the exercise each week and be prepared to discuss it during the next class. Class materials, including lecture notes, readings, and lab materials will be provided on a CD-ROM.

BBH 597A – Missing data: Analysis and design

CRN: 534265

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

Course Dates: 11/3, 11/10, 11/17, 12/1, 12/8

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.

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). I will try to use Amos wherever possible

Primary Reading:

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

Other Key Readings:

Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549-576.

Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.

Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E. (2006). Planned missing data designs in psychological research. Psychological Methods, 11, 323-343.

Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007).  How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory. Prevention Science, 8, 206-213.

Collins, L. M., Schafer, J. L., & Kam, C. M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351.

Graham, J. W., Cumsille, P. E., & Elek-Fisk, E. (2003). Methods for handling missing data. In J. A. Schinka & W. F. Velicer (Eds.). Research Methods in Psychology (pp. 87-114). Volume 2 of Handbook of Psychology (I. B. Weiner, Editor-in-Chief). New York: John Wiley & Sons.

Other Readings:

Graham, J. W., & Donaldson, S. I. (1993). Evaluating interventions with differential attrition: The importance of nonresponse mechanisms and use of followup data. Journal of Applied Psychology, 78, 119-128.

Graham, J. W. (2003). Adding missing-data relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80-100.

Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60.

Graham, J. W., Hofer, S.M., Donaldson, S.I., MacKinnon, D.P., & Schafer, J.L. (1997). Analysis with missing data in prevention research.  In K. Bryant, M. Windle, & S. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research. (pp. 325-366). Washington, D.C.: American Psychological Association.

Graham, J. W., & Schafer, J. L. (1999). On the performance of multiple imputation for multivariate data with small sample size. In R. Hoyle (Ed.) Statistical Strategies for Small Sample Research, (pp. 1-29). Thousand Oaks, CA:  Sage.

Graham, J. W., Taylor, B. J., & Cumsille, P. E. (2001). Planned missing data designs in analysis of change. In L. Collins & A. Sayer (Eds.), New methods for the analysis of change, (pp. 335-353). Washington, DC: American Psychological Association.