Title

The Latent Curve Arma (P, Q) Panel Model: Longitudinal Data Analysis In Educational Research And Evaluation

Keywords

Autoregressive-moving average; Latent curve ARMA; Longitudinal data; Structural equation modeling; Time series

Abstract

Autocorrelated residuals in longitudinal data are widely reported as common to longitudinal data. Yet few, if any, researchers modeling growth processes evaluate a priori whether their data have this feature. Sivo, Fan, and Witta (2005) found that not modeling autocorrelated residuals present in longitudinal data severely biases latent curve parameter estimates. The purpose of this article is to explain how educational researchers and evaluators analyzing longitudinal data might approach longitudinal data in which change is hypothesized to occur over time. Specification of the latent curve ARMA model (i.e., the growth curve ARMA model) is introduced as an approach to filtering out the effects of autocorrelation on latent curve parameter estimates by modeling this nuisance condition so that the estimates of primary interest are more accurate. © 2008 Taylor & Francis.

Publication Date

8-1-2008

Publication Title

Educational Research and Evaluation

Volume

14

Issue

4

Number of Pages

363-376

Document Type

Review

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/13803610802249670

Socpus ID

49449099983 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/49449099983

This document is currently not available here.

Share

COinS