Fitting Latent Growth Models with Small Sample Sizes and Non-Normal Missing Data

Autor/inn/en	Shi, Dexin; DiStefano, Christine; Zheng, Xiaying; Liu, Ren; Jiang, Zhehan
Titel	Fitting Latent Growth Models with Small Sample Sizes and Non-Normal Missing Data
Quelle	In: International Journal of Behavioral Development, 45 (2021) 2, S.179-192 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Shi, Dexin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0165-0254
DOI	10.1177/0165025420979365
Schlagwörter	Growth Models; Maximum Likelihood Statistics; Factor Analysis; Sample Size; Research Problems; Error of Measurement; Robustness (Statistics); Goodness of Fit + Suchen Sie Ihr Suchwort? Faktorenanalyse; Forschungskritik; Messfehler; Widerstandsfähigkeit
Abstract	This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML estimators, "MLR" was the optimal choice, as it was found to be robust to both non-normality and missing data while also yielding more accurate standard error estimates and growth parameter coverage. However, the choice "MLMV" produced the most accurate p values for the X[superscript 2] test statistic under conditions studied. Regarding the goodness of fit indices, as sample size decreased, all three fit indices studied (i.e., comparative fit index, root mean square error of approximation, and standardized root mean square residual) exhibited worse fit. When the sample size was very small (e.g., N < 60), the fit indices would imply that a proposed model fit poorly, when this might not be actually the case in the population. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01