Advisor

Hutchinson, Susan R.

Committee Member

Gilliam, David

Committee Member

Rue, Lisa

Committee Member

Welsh, Marilyn

Department

Applied Statistics & Research Methods

Institution

University of Northern Colorado

Type of Resources

Text

Place of Publication

Greeley (Colo.)

Publisher

University of Northern Colorado

Date Created

12-1-2009

Genre

Thesis

Extent

165 pages

Digital Origin

Born digital

Description

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., x², NNFI, CFI, and RMSEA) estimated in correct LGC models. The CFI performed the best, followed by the NNFI, x², 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 x² 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 x², 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.

Notes

Dean's Citation for Excellence and Dean's Citation for Outstanding Dissertation

Degree type

PhD

Degree Name

Doctoral

Language

English

Local Identifiers

DeRoche_unco_0161N_10021

Rights Statement

Copyright is held by author.

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