Title: Power analysis for latent variable models
Abstract: Power analyses are now requested by most granting agencies. In their introductory
statistics courses, researchers are often taught how to conduct power analyses for hypothesis tests
involving means, correlation coefficients, regression coefficients and/or population R-squared.
However, when researchers' analysis goals require a latent variable model--such as a factor
analysis, structural equation, or latent growth curve model--they may be unsure how to proceed
with a power analysis. In latent variable models, hypotheses could be tested about different
things: (1) individual parameters of a given model, (2) the overall fit of a single model in
isolation, and (3) differences in fit between competing models. Different power analysis
procedures are suited for each of these three hypothesis testing situations. This pedagogical talk
will review approaches for conducting power analyses involving these three situations. These
approaches will be illustrated for a latent growth curve model.
Topics:
1. Brief refresher on power analysis concepts
2. Power analysis procedures for three different analysis goals in latent variable modeling
3. Illustration of each procedure and brief discussion of software implementation
Goals:
1. To understand the logic of power analyses in a latent variable modeling context
2. To obtain introductory information on how to implement such power analyses in practice
using available software
3. To understand the strengths and limitations of alternative procedures and what resources to
consult for further information
Intended Audience: Researchers who use latent variable models but who are unacquainted with
power analysis procedures for such models.
Speaker Description: Sonya Sterba is an Assistant Professor in the Quantitative Methods and
Evaluation Program within the Department of Psychology and Human Development. She teaches
graduate courses on Latent Growth Curve Modeling (Spring, 2012) and Applied Latent Class
and Mixture Modeling (Fall, 2011). Her research involves evaluating the performance of latent
variable models for longitudinal and cross-sectional data and involves extending such models to
address research questions in developmental psychopathology.