The Supermatrix Technique: A Simple Framework for Hypothesis Testing with Missing Data

Autor/inn/en	Lang, Kyle M.; Little, Todd D.
Titel	The Supermatrix Technique: A Simple Framework for Hypothesis Testing with Missing Data
Quelle	In: International Journal of Behavioral Development, 38 (2014) 5, S.461-470 (10 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0165-0254
DOI	10.1177/0165025413514326
Schlagwörter	Hypothesis Testing; Data Analysis; Error of Measurement; Computation; Multivariate Analysis; Research Problems; Monte Carlo Methods; Measurement Techniques; Maximum Likelihood Statistics; Statistical Bias; Models; Accuracy + Suchen Sie Ihr Suchwort? Hypothesenprüfung; Hypothesentest; Auswertung; Messfehler; Multivariate Analyse; Forschungskritik; Monte-Carlo-Methode; Messtechnik; Analogiemodell
Abstract	We present a new paradigm that allows simplified testing of multiparameter hypotheses in the presence of incomplete data. The proposed technique is a straight-forward procedure that combines the benefits of two powerful data analytic tools: multiple imputation and nested-model ?2 difference testing. A Monte Carlo simulation study was conducted to assess the performance of the proposed technique. Full information maximum likelihood (FIML) and single regression imputation were included as comparison conditions against which the performance of the suggested technique was judged. The imputation-based conditions demonstrated much higher convergence rates than the FIML conditions. ??2 statistics derived from the proposed technique were more accurate than such statistics derived from both the FIML conditions and the regression imputation conditions. Limitations of the current work and suggestions for future directions are also addressed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10