TY - JOUR

T1 - Using an EM covariance matrix to estimate structural equation models with missing data

T2 - Choosing an adjusted sample size to improve the accuracy of inferences

AU - Enders, Craig K.

AU - Peugh, James L.

N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2004

Y1 - 2004

N2 - Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no single value of N is applicable to the EM covariance matrix, and this may compromise inferences gained from the model fit statistic and parameter standard errors. The purpose of this study was to identify a value of N that provides accurate inferences when using EM. If all confirmatory factor analysis model indicators have MD, results suggest that the minimum N per covariance term yields honest Type 1 error rates. If MD are restricted to a subset of indicators, the minimum N pet variance works well. With respect to standard errors, the harmonic mean N per variance term produces honest confidence interval coverage rates.

AB - Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no single value of N is applicable to the EM covariance matrix, and this may compromise inferences gained from the model fit statistic and parameter standard errors. The purpose of this study was to identify a value of N that provides accurate inferences when using EM. If all confirmatory factor analysis model indicators have MD, results suggest that the minimum N per covariance term yields honest Type 1 error rates. If MD are restricted to a subset of indicators, the minimum N pet variance works well. With respect to standard errors, the harmonic mean N per variance term produces honest confidence interval coverage rates.

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U2 - 10.1207/S15328007SEM1101_1

DO - 10.1207/S15328007SEM1101_1

M3 - Article

AN - SCOPUS:2642541763

VL - 11

SP - 1

EP - 19

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 1

ER -