Title

The Biasing Effects Of Unmodeled Arma Time Series Processes On Latent Growth Curve Model Estimates

Abstract

The purpose of this study was to evaluate the robustness of estimated growth curve models when there is stationary autocorrelation among manifest variable errors. The results suggest that when, in practice, growth curve models are fitted to longitudinal data, alternative rival hypotheses to consider would include growth models that also specify autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. AR (i.e., simplex) processes are commonly found in longitudinal data and may diminish the ability of a researcher to detect growth if not explicitly modeled. MA and ARMA processes do not affect the fit of growth models, but do notably bias some of the parameters. Copyright © 2005, Lawrence Erlbaum Associates, Inc.

Publication Date

5-23-2005

Publication Title

Structural Equation Modeling

Volume

12

Issue

2

Number of Pages

215-231

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1207/s15328007sem1202_2

Socpus ID

18444404054 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/18444404054

This document is currently not available here.

Share

COinS