The biasing effects of unmodeled ARMA time series processes on latent growth curve model estimates

    Authors

    S. Sivo; X. T. Fan;L. Witta

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Struct. Equ. Modeling

    Keywords

    PARAMETERS; PANEL; Mathematics, Interdisciplinary Applications; Social Sciences, ; Mathematical Methods

    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.

    Journal Title

    Structural Equation Modeling-a Multidisciplinary Journal

    Volume

    12

    Issue/Number

    2

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    215

    Last Page

    231

    WOS Identifier

    WOS:000228626500002

    ISSN

    1070-5511

    Share

    COinS