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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Latent variable models for harmonization of test scores: A case study on memory.

Edwin R van den Heuvel1, Lauren E Griffith2, Nazmul Sohel2

  • 1Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|October 5, 2019
PubMed
Summary

This study introduces harmonizing test scores from different studies using latent variable models. It ensures data comparability across diverse measurement instruments and methods for robust scientific pooling.

Keywords:
agreementcontent equivalencefactorial invarianceharmonizationindividual participants datalatent variable modelsmeasurement reliabilitymeta-analysis

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Data Science

Background:

  • Combining data across studies is crucial for robust scientific findings.
  • Inconsistent measurement methods and instruments pose challenges to data pooling.
  • Harmonization is needed to ensure equivalent information across studies with varying methodologies.

Observation:

  • This paper presents a framework for harmonizing test scores from observational studies.
  • Key concepts include calibration, invariance, and exchangeability within latent variable models.
  • Harmonization differs from measurement invariance and generalizes test equating.

Findings:

  • Harmonization requires specific conditions for transforming test scores between different instruments.
  • Observed test scores connecting all studies are essential for validating harmonization assumptions.
  • The framework is illustrated using memory test scores from three Canadian studies.

Implications:

  • This work provides a rigorous approach to data harmonization in multi-study research.
  • It enables more reliable meta-analyses and data pooling by ensuring cross-study comparability.
  • The proposed concepts advance the methodology for integrating heterogeneous data sources in scientific research.