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Sensitivity Analysis of Multiple Informant Models When Data are Not Missing at Random.

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Handling missing data in structural equation models (SEM) is crucial. This study explores advanced methods like saturated correlates models and multiple imputation to improve parameter estimates when data are not missing at random.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Quantitative Psychology

Background:

  • Missing data are prevalent in multi-informant studies, complicating the analysis of clustered individuals.
  • Traditional structural equation modeling (SEM) relies on assumptions of data missing completely at random (MCAR) or missing at random (MAR).
  • Violations of these assumptions can bias parameter estimates in SEM.

Purpose of the Study:

  • To evaluate the sensitivity of SEM parameter estimates to different missing data assumptions.
  • To compare the performance of standard SEM with advanced methods when data are not missing at random (NMAR).
  • To demonstrate the utility of saturated correlates models and multiple imputation for handling NMAR data in family studies.

Main Methods:

  • Estimation of structural equation models using raw data to accommodate incomplete datasets.
  • Implementation of a saturated correlates model incorporating correlates of missingness.
  • Application of multiple imputation techniques, potentially utilizing correlates of missing data.
  • Analysis of family data to assess the impact of missing data assumptions on parameter estimates.

Main Results:

  • Advanced methods (saturated correlates model, multiple imputation) can yield ignorable missing data problems under NMAR conditions.
  • These approaches offer advantages over standard SEM when data are not missing at random.
  • Parameter estimates can be sensitive to assumptions made about the missing data mechanism.

Conclusions:

  • Saturated correlates models and multiple imputation are valuable strategies for addressing NMAR data in SEM.
  • Researchers should carefully consider and test assumptions about missing data mechanisms.
  • These methods are implementable using standard SEM software, facilitating their adoption.