A comparison of latent growth models for constructs measured by multiple indicators

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dc.contributor.advisorStapleton, Laura M.en
dc.creatorLeite, Walter Lanaen
dc.date.accessioned2008-08-28T22:08:33Zen
dc.date.accessioned2017-05-11T22:16:35Z
dc.date.available2008-08-28T22:08:33Zen
dc.date.available2017-05-11T22:16:35Z
dc.date.issued2005en
dc.descriptiontexten
dc.description.abstractLatent growth modeling (LGM) of composites of multiple items (for example, means or sums of items) has been frequently used to analyze the growth of latent constructs. However, composites are only equivalent to latent constructs if the items’ factor loadings are equal to one and there is no measurement error (Bollen & Lennox, 1991). In this study, the adequacy of using univariate LGM to model composites of multiple items, as well three other alternative methods were evaluated through a Monte Carlo simulation study. The four methods evaluated in this study were the univariate LGM, the univariate LGM with fixed error variances, the univariate LGM with the correction for attenuation, and the curve-of-factors model (McArdle, 1988; Tisak and Meredith, 1990). This simulation study manipulated the number of items per construct, the number of measurement times, the sample size, the reliability of the composites, the invariance of item parameters, and whether the items were essentially tau-equivalent or essentially congeneric. One thousand datasets were simulated for each of the conditions. The results indicate that using univariate LGM with composites of multiple items only produces unbiased parameter estimates and standard errors if the items are essentially tau-equivalent. The univariate LGM with fixed error variances performed identically to the univariate LGM. The univariate LGM with the correction for attenuation produced unbiased parameter estimates when the items were essentially tauequivalent, but produced negatively biased estimates of standard errors. The curve-of-factors model was found to be the most appropriate method to analyze the growth of latent constructs measured by multiple items. The curve-of-factors model was able to provide unbiased parameter estimates and standard errors under all conditions evaluated in this study. However, with sample sizes of 100 or 200, a large percentage of chi-square statistics were positively biased and the fit indices indicated inadequate model fit. This study’s recommendation is that the curve-of-factors model should be preferred to analyze the growth of latent variables measured by multiple items, but the use of sample sizes larger than 200 is strongly recommended to help ensure that adequate fit statistics and fit indices are obtained for appropriate models.
dc.description.departmentEducational Psychologyen
dc.format.mediumelectronicen
dc.identifierb59937051en
dc.identifier.oclc61409208en
dc.identifier.proqst3175266en
dc.identifier.urihttp://hdl.handle.net/2152/1609en
dc.language.isoengen
dc.rightsCopyright 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.en
dc.subject.lcshLatent structure analysisen
dc.subject.lcshLongitudinal methoden
dc.subject.lcshPsychometricsen
dc.titleA comparison of latent growth models for constructs measured by multiple indicatorsen
dc.type.genreThesisen

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