Correcting Model Fit Criteria for Small Sample Latent Growth Models with Incomplete Data

Autor/inn/en	McNeish, Daniel; Harring, Jeffrey R.
Titel	Correcting Model Fit Criteria for Small Sample Latent Growth Models with Incomplete Data
Quelle	In: Educational and Psychological Measurement, 77 (2017) 6, S.990-1018 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164416661824
Schlagwörter	Growth Models; Goodness of Fit; Error Correction; Sampling; Sample Size; Maximum Likelihood Statistics; Computation; Simulation; Evaluation Criteria; Evaluation Methods; Incidence; Statistical Data; Statistical Studies; Data Analysis + Suchen Sie Ihr Suchwort? Korrektur; Simulation program; Simulationsprogramm; Vorkommen; Auswertung
Abstract	To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor small sample performance of these global data-model fit criteria and three post hoc small sample corrections have been proposed and shown to perform well with complete data. However, these corrections use sample size in their computation--whose value is unclear when missing data are accommodated with full information maximum likelihood, as is common with LGMs. A simulation is provided to demonstrate the inadequacy of these small sample corrections in the near ubiquitous situation in growth modeling where data are incomplete. Then, a missing data correction for the small sample correction equations is proposed and shown through a simulation study to perform well in various conditions found in practice. An applied developmental psychology example is then provided to demonstrate how disregarding missing data in small sample correction equations can greatly affect assessment of global data-model fit. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01