Using Monte Carlo Simulations to Determine Power and Sample Size for Planned Missing Designs

Autor/inn/en	Schoemann, Alexander M.; Miller, Patrick; Pornprasertmanit, Sunthud; Wu, Wei
Titel	Using Monte Carlo Simulations to Determine Power and Sample Size for Planned Missing Designs
Quelle	In: International Journal of Behavioral Development, 38 (2014) 5, S.471-479 (9 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0165-0254
DOI	10.1177/0165025413515169
Schlagwörter	Monte Carlo Methods; Simulation; Sample Size; Research Design; Data Collection; Computer Software; Psychological Studies; Data Analysis; Research Methodology; Correlation; Hypothesis Testing + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Simulation program; Simulationsprogramm; Forschungsdesign; Data capture; Datensammlung; Auswertung; Research method; Forschungsmethode; Korrelation; Hypothesenprüfung; Hypothesentest
Abstract	Planned missing data designs allow researchers to increase the amount and quality of data collected in a single study. Unfortunately, the effect of planned missing data designs on power is not straightforward. Under certain conditions using a planned missing design will increase power, whereas in other situations using a planned missing design will decrease power. Thus, when designing a study utilizing planned missing data researchers need to perform a power analysis. In this article, we describe methods for power analysis and sample size determination for planned missing data designs using Monte Carlo simulations. We also describe a new, more efficient method of Monte Carlo power analysis, software that can be used in these approaches, and several examples of popular planned missing data designs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10