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High correlation between measures does not guarantee suitability for linking. A new statistical method reveals differences that can compromise data interchangeability in multivariable analyses.

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Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Classical test theory is often used to assess measure generalizability.
  • Existing literature commonly compares established subpopulations to evaluate data linkage.
  • Multivariable analyses require a nuanced understanding of data linkage generalizability.

Purpose of the Study:

  • To examine data linkage generalizability for multivariable analyses using classical test theory.
  • To introduce a structural equation modeling (SEM) based statistical method for evaluating data linkage suitability.
  • To assess linkage appropriateness beyond simple correlation, considering external variables.

Main Methods:

  • Development of an SEM-based statistical methodology.
  • Application of the method to PROMIS® Parent Proxy and Early Childhood Global Health measures.
  • Evaluation of linkage suitability for continuous and categorical external variables.

Main Results:

  • A high correlation (r = .829) between measures suggested general suitability.
  • Detailed analysis revealed significant differences in content and measurement structure.
  • These differences can compromise data interchangeability in specific use cases.

Conclusions:

  • Statistical quality of a linkage is insufficient on its own.
  • Users must evaluate the appropriateness of a linkage for specific research questions and multivariable analyses.
  • Consideration of content and structure is crucial for reliable data linkage.