ERIC Number: EJ1257828
Record Type: Journal
Publication Date: 2020-Jul
Pages: 12
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0165-0254
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Available Date: N/A
An Illustration of Bayesian Approximate Measurement Invariance with Longitudinal Data and a Small Sample Size
Winter, Sonja D.; Depaoli, Sarah
International Journal of Behavioral Development, v44 n4 p371-382 Jul 2020
This article illustrates the Bayesian approximate measurement invariance (MI) approach in Mplus with longitudinal data and small sample size. Approximate MI incorporates zero-mean small variance prior distributions on the differences between parameter estimates over time. Contrary to traditional invariance testing methods, where exact invariance is tested, this method allows for some "wiggle room" in the parameter estimates over time. The procedure is illustrated using longitudinal data on college students' academic stress as it changes in the period leading up to and right after an important midterm. Results show that traditional invariance testing methods come to a standstill due to the small sample size. Bayesian approximate MI testing was able to identify non-invariant parameters, after which a partially invariant model could be estimated.
Descriptors: Bayesian Statistics, Measurement, Data Analysis, Sample Size, Longitudinal Studies, Goodness of Fit, Undergraduate Students, Stress Variables
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
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