## Abstract

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.

Original language | English (US) |
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Pages (from-to) | 1-19 |

Number of pages | 19 |

Journal | Structural Equation Modeling |

Volume | 11 |

Issue number | 1 |

DOIs | |

State | Published - 2004 |

## ASJC Scopus subject areas

- Decision Sciences(all)
- Modeling and Simulation
- Sociology and Political Science
- Economics, Econometrics and Finance(all)