Latent growth curve (LGC) modeling is emerging as a preferred method of longitudinal analysis, which uses the structural equation modeling (SEM) framework to demonstrate growth or change (Meredith & Tisak, 1990). The purpose of this dissertation was to examine the performance of commonly utilized measures of model fit in LGC modeling data environments. A Monte Carlo simulation was conducted to examine the influence of LGC modeling design characteristics (i.e., sample size, waves of data, and model complexity) on selected fit indexes (i.e., χ2, NNFI, CFI, and RMSEA) estimated in correct LGC models. The CFI performed the best, followed by the NNFI, χ 2, and finally, the RMSEA showed the least desirable characteristics. The RMSEA was found to over-reject correct models (i.e., suggest poor model fit) in conditions of small to moderate sample size (N ≤ 1,000) and few waves of data. The χ 2 over-rejected correct multivariate models with more waves of data and small sample sizes (N = 100). The NNFI over-rejected unvariate and multivariate models with small sample size ( N = 100) and three waves of data. Six guidelines were proposed for LGC modeling researchers, including: maximizing the chance of obtaining a plausible solutions, cautioning the use of the χ2, adopting the novel LGC modeling cutoff values, using multiple fit indexes, and assessing the within-person fit. As LGC modeling applications escalate in the social and behavioral sciences, there is a critical need for additional research regarding LGC model fit, specifically, the sensitivity of fit indexes to relevant types of LGC model misspecification.
|Advisor:||Hutchinson, Susan R.|
|Commitee:||Gilliam, David, Rue, Lisa, Welsh, Marilyn|
|School:||University of Northern Colorado|
|Department:||Applied Statistics & Research Methods|
|School Location:||United States -- Colorado|
|Source:||DAI-B 71/04, Dissertation Abstracts International|
|Subjects:||Statistics, Quantitative psychology|
|Keywords:||Fit indexes, Fit statistics, Latent growth curve modeling, Longitudinal modeling, Structural equation modeling|
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