A Unified Approach to Measurement Error and Missing Data: Details and Extensions

Autor/inn/en	Blackwell, Matthew; Honaker, James; King, Gary
Titel	A Unified Approach to Measurement Error and Missing Data: Details and Extensions
Quelle	In: Sociological Methods & Research, 46 (2017) 3, S.342-369 (28 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124115589052
Schlagwörter	Error of Measurement; Correlation; Simulation; Bayesian Statistics; Statistical Inference; Probability; Predictive Measurement; Computation; Social Sciences; Multivariate Analysis; Research Problems; Monte Carlo Methods; Equations (Mathematics); Models; Data Analysis; Maximum Likelihood Statistics + Suchen Sie Ihr Suchwort? Messfehler; Korrelation; Simulation program; Simulationsprogramm; Inferential statistics; Schließende Statistik; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Multivariate Analyse; Forschungskritik; Monte-Carlo-Methode; Equations; Mathematics; Gleichungslehre; Analogiemodell; Auswertung
Abstract	We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model specifications and estimation procedures, and analyses to assess the approach's robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research. [For the companion article, "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," see EJ1148091.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01