Parsimonious Structural Equation Models for Repeated Measures Data, with Application to the Study of Consumer Preferences

Recent research reflects a growing awareness of the value of using structural equation models to analyze repeated measures data. However, such data, particularly in the presence of covariates, often lead to models that either fit the data poorly, are exceedingly general and hard to interpret, or are...

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Bibliographic Details
Published in:Psychometrika 2012-04, Vol.77 (2), p.358-387
Main Authors: Elrod, Terry, Häubl, Gerald, Tipps, Steven W.
Format: Article
Language:English
Subjects:
Assessment
Behavioral Science and Psychology
Biological and medical sciences
Computer programs
Consumers
Cultural Influences
Data
Data processing
Demography
Factor Analysis
Fundamental and applied biological sciences. Psychology
Growth models
Humanities
Latent Variables
Law
Mathematical models
Personality Traits
Predictor Variables
Preferences
Psychology
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics
Psychometrics. Statistics. Methodology
Regression (Statistics)
Resistance (Psychology)
Software
Statistical Analysis
Statistical power
Statistical Theory and Methods
Statistics for Social Sciences
Statistics. Mathematics
Structural equation modeling
Structural Equation Models
Testing and Evaluation
Variables
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Summary:Recent research reflects a growing awareness of the value of using structural equation models to analyze repeated measures data. However, such data, particularly in the presence of covariates, often lead to models that either fit the data poorly, are exceedingly general and hard to interpret, or are specified in a manner that is highly data dependent. This article introduces methods for developing parsimonious models for such data. The underlying technology uses reduced-rank representations of the variances, covariances and means of observed and latent variables. The value of this approach, which may be implemented using standard structural equation modeling software, is illustrated in an application study aimed at understanding heterogeneous consumer preferences. In this application, the parsimonious representations characterize systematic relationships among consumer demographics, attitudes and preferences that would otherwise be undetected. The result is a model that is parsimonious, illuminating, and fits the data well, while keeping data dependence to a minimum.
ISSN:0033-3123
1860-0980
DOI:10.1007/s11336-012-9260-x