234649 Power Analysis and Sample Size Estimation for Structural Equation Modeling

Monday, October 31, 2011: 12:50 PM

Jichuan Wang Sr., PhD , School of Medicine, The George Washington University, Bowie, MD
Like in any statistical analysis, determination of appropriate sample size is critical in structural equation modeling (SEM). Unfortunately, in structural equation modeling literature there is no consensus as to what would be the appropriate sample size for SEM. Determination of sample size needed for SEM is complicated, and there is no absolute standard regarding adequate sample size and no rule of thumb that applies to all situations in structural equation modeling. Besides the rules of thumb, some model-based approaches have been increasingly used to determine appropriate sample size needed for specific SEM models. In the presentation, after reviewing the rules of thumb for SEM sample size, the author will discuss and demonstrate how to use different approaches, such as Satorra & Saris's method, Monta Carlo simulation, MacCallum's method, and Kim's method, for SEM power analysis and sample size estimation.

Learning Areas:
Biostatistics, economics
Social and behavioral sciences

Learning Objectives:
1) Brief review of the rules of thumbs for sample size needed for SEM. 2) Discuss and demonstrate the Satorra-Saris’s method for SEM power analysis and sample size estimate. 3) Discuss and demonstrate Monte Carlo simulation for SEM power analysis and sample size estimate. 4) Discuss and demonstrate other methods (e.g., MacCallum’s method and Kim’s method ) for SEM sample size estimate.

Keywords: Statistics, Biostatistics

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am qualified on the content I am responsible for because I have been working in public health studies for over 20 years. I usually do two presentations in the Statistics Sessions of the APHA annual meetings each year in many of the past years.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

See more of: Statistical Modeling in Public Health II
See more of: Statistics