Growth Mixture Modeling with Nonnormal Distributions: Implications for Data Transformation

Autor/inn/en	Nam, Yeji; Hong, Sehee
Titel	Growth Mixture Modeling with Nonnormal Distributions: Implications for Data Transformation
Quelle	In: Educational and Psychological Measurement, 81 (2021) 4, S.698-727 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Nam, Yeji)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164420976773
Schlagwörter	Probability; Models; Statistical Analysis; Statistical Distributions; Statistical Bias; Computation; Classification; Sample Size; Children; Surveys; Longitudinal Studies; Scores; Reading Achievement; Early Childhood Longitudinal Survey + Suchen Sie Ihr Suchwort? Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Analogiemodell; Statistische Analyse; Wahrscheinlichkeitsverteilung; Classification system; Klassifikation; Klassifikationssystem; Child; Kind; Kinder; Survey; Umfrage; Befragung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Leseleistung
Abstract	This study investigated the extent to which class-specific parameter estimates are biased by the within-class normality assumption in nonnormal growth mixture modeling (GMM). Monte Carlo simulations for nonnormal GMM were conducted to analyze and compare two strategies for obtaining unbiased parameter estimates: relaxing the within-class normality assumption and using data transformation on repeated measures. Based on unconditional GMM with two latent trajectories, data were generated under different sample sizes (300, 800, and 1500), skewness (0.7, 1.2, and 1.6) and kurtosis (2 and 4) of outcomes, numbers of time points (4 and 8), and class proportions (0.5:0.5 and 0.25:0.75). Of the four distributions, it was found that skew-t GMM had the highest accuracy in terms of parameter estimation. In GMM based on data transformations, the adjusted logarithmic method was more effective in obtaining unbiased parameter estimates than the use of van der Waerden quantile normal scores. Even though adjusted logarithmic transformation in nonnormal GMM reduced computation time, skew-t GMM produced much more accurate estimation and was more robust over a range of simulation conditions. This study is significant in that it considers different levels of kurtosis and class proportions, which has not been investigated in depth in previous studies. The present study is also meaningful in that investigated the applicability of data transformation to nonnormal GMM. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01