This simulation study examined the performance of the curve-of-factors growth
model when serial correlation and growth processes were present in the first-level factor
structure. As previous research has shown (Ferron, Dailey, & Yi, 2002; Kwok, West, &
Green, 2007; Murphy & Pituch, 2009) estimates of the fixed effects and their standard
errors were unbiased when serial correlation was present in the data but unmodeled.
However, variance components were estimated poorly across the examined serial
correlation conditions. Two new models were also examined: one curve-of-factors model
was fitted with a first-order autoregressive serial correlation parameter, and a second
curve-of-factors model was fitted with first-order autoregressive and moving average
serial correlation parameters. The models were developed in an effort to measure growth
and serial correlation processes within the same data set. Both models fitted with serial
correlation parameters were able to accurately reproduce the serial correlation parameter
and approximate the true growth trajectory. However, estimates of the variance
components and the standard errors of the fixed effects were problematic. The two models also produced inadmissible solutions across all conditions. Of the three models,
the curve-of-factors model had the best overall performance. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/6573 |
| Date | 20 October 2009 |
| Creators | Murphy, Daniel Lee |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. |
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