### Abstract

Recent missing data studies have argued in favor of an "inclusive analytic strategy" that incorporates auxiliary variables into the estimation routine, and Graham (2003) outlined methods for incorporating auxiliary variables into structural equation analyses. In practice, the auxiliary variables often have missing values, so it is reasonable to ask whether the inclusion of such variables will improve the estimation of model parameters. Simulation results indicated that the proportion of missing data and the missing data mechanism of the auxiliary variables had little impact on bias. Even when an auxiliary variable was missing not at random, bias was relegated to the auxiliary variable portion of the model, and did not propagate into the model of substantive interest. The study results suggest that the inclusion of an auxiliary variable is beneficial, even if the auxiliary variable has a substantial proportion of missing data.

Original language | English (US) |
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Pages (from-to) | 434-448 |

Number of pages | 15 |

Journal | Structural Equation Modeling |

Volume | 15 |

Issue number | 3 |

DOIs | |

State | Published - Jul 2008 |

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### ASJC Scopus subject areas

- Psychology(all)
- Sociology and Political Science
- Education
- Political Science and International Relations
- Economics, Econometrics and Finance(all)

### Cite this

**A note on the use of missing auxiliary variables in full information maximum likelihood-based structural equation models.** / Enders, Craig K.

Research output: Contribution to journal › Article

*Structural Equation Modeling*, vol. 15, no. 3, pp. 434-448. https://doi.org/10.1080/10705510802154307

}

TY - JOUR

T1 - A note on the use of missing auxiliary variables in full information maximum likelihood-based structural equation models

AU - Enders, Craig K.

PY - 2008/7

Y1 - 2008/7

N2 - Recent missing data studies have argued in favor of an "inclusive analytic strategy" that incorporates auxiliary variables into the estimation routine, and Graham (2003) outlined methods for incorporating auxiliary variables into structural equation analyses. In practice, the auxiliary variables often have missing values, so it is reasonable to ask whether the inclusion of such variables will improve the estimation of model parameters. Simulation results indicated that the proportion of missing data and the missing data mechanism of the auxiliary variables had little impact on bias. Even when an auxiliary variable was missing not at random, bias was relegated to the auxiliary variable portion of the model, and did not propagate into the model of substantive interest. The study results suggest that the inclusion of an auxiliary variable is beneficial, even if the auxiliary variable has a substantial proportion of missing data.

AB - Recent missing data studies have argued in favor of an "inclusive analytic strategy" that incorporates auxiliary variables into the estimation routine, and Graham (2003) outlined methods for incorporating auxiliary variables into structural equation analyses. In practice, the auxiliary variables often have missing values, so it is reasonable to ask whether the inclusion of such variables will improve the estimation of model parameters. Simulation results indicated that the proportion of missing data and the missing data mechanism of the auxiliary variables had little impact on bias. Even when an auxiliary variable was missing not at random, bias was relegated to the auxiliary variable portion of the model, and did not propagate into the model of substantive interest. The study results suggest that the inclusion of an auxiliary variable is beneficial, even if the auxiliary variable has a substantial proportion of missing data.

UR - http://www.scopus.com/inward/record.url?scp=47949104235&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=47949104235&partnerID=8YFLogxK

U2 - 10.1080/10705510802154307

DO - 10.1080/10705510802154307

M3 - Article

VL - 15

SP - 434

EP - 448

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 3

ER -