### Abstract

Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software.

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

Pages (from-to) | 283-298 |

Number of pages | 16 |

Journal | Structural Equation Modeling |

Volume | 20 |

Issue number | 2 |

DOIs | |

State | Published - Apr 1 2013 |

### Fingerprint

### Keywords

- auxiliary variables
- missing data
- missing not at random
- multiple imputation
- multiple informant data

### ASJC Scopus subject areas

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

### Cite this

*Structural Equation Modeling*,

*20*(2), 283-298. https://doi.org/10.1080/10705511.2013.769393

**Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random.** / Blozis, Shelley A.; Ge, Xiaojia; Xu, Violet Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Leve, Leslie D.; Reiss, David.

Research output: Contribution to journal › Article

*Structural Equation Modeling*, vol. 20, no. 2, pp. 283-298. https://doi.org/10.1080/10705511.2013.769393

}

TY - JOUR

T1 - Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random

AU - Blozis, Shelley A.

AU - Ge, Xiaojia

AU - Xu, Violet Shu

AU - Natsuaki, Misaki N.

AU - Shaw, Daniel S.

AU - Neiderhiser, Jenae M.

AU - Scaramella, Laura V.

AU - Leve, Leslie D.

AU - Reiss, David

PY - 2013/4/1

Y1 - 2013/4/1

N2 - Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software.

AB - Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software.

KW - auxiliary variables

KW - missing data

KW - missing not at random

KW - multiple imputation

KW - multiple informant data

UR - http://www.scopus.com/inward/record.url?scp=84876270832&partnerID=8YFLogxK

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U2 - 10.1080/10705511.2013.769393

DO - 10.1080/10705511.2013.769393

M3 - Article

AN - SCOPUS:84876270832

VL - 20

SP - 283

EP - 298

JO - Structural Equation Modeling

JF - Structural Equation Modeling

SN - 1070-5511

IS - 2

ER -