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Factor score estimation in multimethod measurement designs with planned missing data.

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Multimethod measurement designs with planned missing data (MMM-PMD) can yield biased individual factor scores. While group-level analyses may still be valid, individual scores from MMM-PMD should not inform consequential decisions.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Research Design

Background:

  • Multimethod measurement designs with planned missing data (MMM-PMD) integrate cost-effective proxy measures with expensive gold-standard methods.
  • These designs aim to enhance research efficiency while maintaining data quality.

Purpose of the Study:

  • To evaluate the accuracy of factor scores and the trustworthiness of confidence/credible intervals for gold-standard methods in MMM-PMD.
  • To assess the performance of different statistical estimators under planned missing data conditions.

Main Methods:

  • A comprehensive simulation study was conducted.
  • Investigated estimators like Bartlett-FIML, regression-FIML, and fully Bayesian approaches.
  • Analyzed factor score accuracy and interval coverage for complete and missing data cases.

Main Results:

  • Bartlett-FIML, regression-FIML, and Bayesian estimators performed similarly for complete data.
  • Estimated factor scores were biased for subjects with planned missing data, particularly with Bartlett-FIML.
  • Confidence/credible intervals were trustworthy for complete cases but variable for missing data.

Conclusions:

  • Individual factor scores from MMM-PMD designs are unreliable for consequential decisions due to bias in planned missing data.
  • Factor scores may still be valuable for group-level analyses, such as propensity score matching or factor score regression.
  • Employing multiple, highly correlated but distinct proxy methods maximizes factor score accuracy and interval reliability.