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Even very well-designed, well-executed research can result in missing responses at any rate, particularly in survey research. This Monte Carlo study investigated the effectiveness of the inclusive strategy with incomplete data, in a structural equation modeling framework with multiple imputation. Specifically, the study examined the influence of sample size, missing rates, various missingness mechanism combinations, and the inclusive strategy on convergence failure, bias, standard error, and confidence interval coverage of parameters, and model fit. The inclusive strategy, which includes additional variables in the imputation model, was found to improve parameter estimation in most cases, particularly with the convex type of missingness and the nonignorable cases caused by MAR(missing at random) and the restrictive strategy. Implications and future directions are discussed. SAS macro programs are attached.
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L'autore
Dr. Jin Eun Yoo is an associate professor at Korea National University of Education (major: educational measurement, evaluation, and statistics). She received her Ph. D. from Purdue University, and worked as a psychometrician with Pearson (location: Austin, TX). Her research interests include methodological issues in applied statistics.
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Multiple Imputation with Structural Equation Modeling
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LAP Lambert Academic Publishing 2013-08, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Multiple Imputation with Structural Equation Modeling: Using auxiliary variables when data are missing
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Multiple Imputation with Structural Equation Modeling Using auxiliary variables when data are missing
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Multiple Imputation with Structural Equation Modeling: Using auxiliary variables when data are missing
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Multiple Imputation with Structural Equation Modeling
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LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Even very well-designed, well-executed research can result in missing responses at any rate, particularly in survey research. This Monte Carlo study investigated the effectiveness of the inclusive strategy with incomplete data, in a structural equation modeling framework with multiple imputation. Specifically, the study examined the influence of sample size, missing rates, various missingness mechanism combinations, and the inclusive strategy on convergence failure, bias, standard error, and confidence interval coverage of parameters, and model fit. The inclusive strategy, which includes additional variables in the imputation model, was found to improve parameter estimation in most cases, particularly with the convex type of missingness and the nonignorable cases caused by MAR(missing at random) and the restrictive strategy. Implications and future directions are discussed. SAS macro programs are attached. 128 pp. Englisch. Codice articolo 9783659435829
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Multiple Imputation with Structural Equation Modeling : Using auxiliary variables when data are missing
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Even very well-designed, well-executed research can result in missing responses at any rate, particularly in survey research. This Monte Carlo study investigated the effectiveness of the inclusive strategy with incomplete data, in a structural equation modeling framework with multiple imputation. Specifically, the study examined the influence of sample size, missing rates, various missingness mechanism combinations, and the inclusive strategy on convergence failure, bias, standard error, and confidence interval coverage of parameters, and model fit. The inclusive strategy, which includes additional variables in the imputation model, was found to improve parameter estimation in most cases, particularly with the convex type of missingness and the nonignorable cases caused by MAR(missing at random) and the restrictive strategy. Implications and future directions are discussed. SAS macro programs are attached. Codice articolo 9783659435829
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Multiple Imputation with Structural Equation Modeling
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LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yoo Jin EunDr. Jin Eun Yoo is an associate professor at Korea National University of Education (major: educational measurement, evaluation, and statistics). She received her Ph. D. from Purdue University, and worked as a psychometric. Codice articolo 5156092
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Multiple Imputation with Structural Equation Modeling
Editore:
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Even very well-designed, well-executed research can result in missing responses at any rate, particularly in survey research. This Monte Carlo study investigated the effectiveness of the inclusive strategy with incomplete data, in a structural equation modeling framework with multiple imputation. Specifically, the study examined the influence of sample size, missing rates, various missingness mechanism combinations, and the inclusive strategy on convergence failure, bias, standard error, and confidence interval coverage of parameters, and model fit. The inclusive strategy, which includes additional variables in the imputation model, was found to improve parameter estimation in most cases, particularly with the convex type of missingness and the nonignorable cases caused by MAR(missing at random) and the restrictive strategy. Implications and future directions are discussed. SAS macro programs are attached.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 128 pp. Englisch. Codice articolo 9783659435829
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Multiple Imputation with Structural Equation Modeling | Using auxiliary variables when data are missing
Editore:
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659435821
ISBN 13: 9783659435829
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Taschenbuch
Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Multiple Imputation with Structural Equation Modeling | Using auxiliary variables when data are missing | Jin Eun Yoo | Taschenbuch | 128 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659435829 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. Codice articolo 105706576
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