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Informing Harmonization Decisions in Integrative Data Analysis: Exploring the Measurement Multiverse.

Veronica T Cole1, Andrea M Hussong2, Nisha C Gottfredson3

  • 1Department of Psychology, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, NC, 27109, USA. colev@wfu.edu.

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Summary
This summary is machine-generated.

Integrative data analysis (IDA) involves harmonizing data, where decisions impact results. Factor scores for delinquency were robust to varying harmonization choices, unlike measurement model parameters.

Keywords:
Data harmonizationLatent variable modelsMultiverse analysis

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Area of Science:

  • Psychometrics
  • Data Science
  • Behavioral Science

Background:

  • Integrative data analysis (IDA) combines multiple datasets, requiring careful data harmonization.
  • Decisions in logical and analytic harmonization can cumulatively impact study outcomes.
  • The influence of these harmonization decisions on psychometric models is not well understood.

Purpose of the Study:

  • To investigate the cumulative effects of harmonization decisions in IDA.
  • To assess the impact of varying harmonization strategies on psychometric model parameters and factor scores.
  • To examine the robustness of the relationship between alcohol use and delinquency estimates under different harmonization approaches.

Main Methods:

  • Conducted an IDA using three datasets (N=2245) on alcohol use and delinquency.
  • Employed moderated nonlinear factor analysis (MNLFA) for analytic harmonization and factor score generation.
  • Systematically varied logical and analytic harmonization decisions 72 times to assess cumulative influence.

Main Results:

  • MNLFA parameter estimates showed variability across different harmonization paths.
  • Estimates for factor scores and regression parameters linking delinquency to alcohol use were less affected by harmonization choices.
  • Subtle differences in harmonization decisions had a notable impact on measurement model parameters.

Conclusions:

  • Factor scores derived from MNLFA appear relatively robust to variations in data harmonization decisions.
  • Measurement model parameters are more sensitive to the specific choices made during data harmonization.
  • Researchers should be mindful of the cumulative impact of harmonization decisions in IDA studies.