### Abstract

A Monte Carlo simulation examined full information maximum-likelihood estimation (FIML) in structural equation models with nonnormal indicator variables. The impacts of 4 independent variables were examined (missing data algorithm, missing data rate, sample size, and distribution shape) on 4 outcome measures (parameter estimate bias, parameter estimate efficiency, standard error coverage, and model rejection rates). Across missing completely at random and missing at random patterns, FIML parameter estimates involved less bias and were generally more efficient than those of ad hoc missing data techniques. However, similar to complete-data maximum-likelihood estimation in structural equation modeling, standard errors were negatively biased and model rejection rates were inflated. Simulation results suggest that recently developed correctives for missing data (e.g., rescaled statistics and the bootstrap) can mitigate problems that stem from nonnormal data.

Original language | English (US) |
---|---|

Pages (from-to) | 352-370 |

Number of pages | 19 |

Journal | Psychological Methods |

Volume | 6 |

Issue number | 3 |

State | Published - Dec 2001 |

Externally published | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Psychology(all)

### Cite this

*Psychological Methods*,

*6*(3), 352-370.

**The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data.** / Enders, Craig K.

Research output: Contribution to journal › Article

*Psychological Methods*, vol. 6, no. 3, pp. 352-370.

}

TY - JOUR

T1 - The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data

AU - Enders, Craig K.

PY - 2001/12

Y1 - 2001/12

N2 - A Monte Carlo simulation examined full information maximum-likelihood estimation (FIML) in structural equation models with nonnormal indicator variables. The impacts of 4 independent variables were examined (missing data algorithm, missing data rate, sample size, and distribution shape) on 4 outcome measures (parameter estimate bias, parameter estimate efficiency, standard error coverage, and model rejection rates). Across missing completely at random and missing at random patterns, FIML parameter estimates involved less bias and were generally more efficient than those of ad hoc missing data techniques. However, similar to complete-data maximum-likelihood estimation in structural equation modeling, standard errors were negatively biased and model rejection rates were inflated. Simulation results suggest that recently developed correctives for missing data (e.g., rescaled statistics and the bootstrap) can mitigate problems that stem from nonnormal data.

AB - A Monte Carlo simulation examined full information maximum-likelihood estimation (FIML) in structural equation models with nonnormal indicator variables. The impacts of 4 independent variables were examined (missing data algorithm, missing data rate, sample size, and distribution shape) on 4 outcome measures (parameter estimate bias, parameter estimate efficiency, standard error coverage, and model rejection rates). Across missing completely at random and missing at random patterns, FIML parameter estimates involved less bias and were generally more efficient than those of ad hoc missing data techniques. However, similar to complete-data maximum-likelihood estimation in structural equation modeling, standard errors were negatively biased and model rejection rates were inflated. Simulation results suggest that recently developed correctives for missing data (e.g., rescaled statistics and the bootstrap) can mitigate problems that stem from nonnormal data.

UR - http://www.scopus.com/inward/record.url?scp=0035756118&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035756118&partnerID=8YFLogxK

M3 - Article

C2 - 11778677

AN - SCOPUS:0035756118

VL - 6

SP - 352

EP - 370

JO - Psychological Methods

JF - Psychological Methods

SN - 1082-989X

IS - 3

ER -