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Using Parallel Splits with Self-Report and Other Measures to Enhance Precision in Generalizability Theory Analyses
Walter P Vispoel1, Guanlan Xu1, Wei S Schneider1
1Department of Psychological and Quantitative Foundations, University of Iowa.
Journal of Personality Assessment
|July 29, 2021
Summary
Generalizability theory (G-theory) reliability can be improved by using parallel splits as the unit of analysis. This method enhances score consistency and reduces measurement error in psychological assessments.
Area of Science:
- Psychometrics
- Psychological Measurement
Background:
- Generalizability theory (G-theory) offers reliability indices accounting for multiple error sources.
- G-theory indices are often conservative, reflecting random error over classical parallelism.
Purpose of the Study:
- To demonstrate the integration of parallel splits into G-theory designs for improved reliability.
- To address the conservative nature of traditional G-theory indices.
Main Methods:
- Utilized parallel splits as the unit of analysis within extended G-theory designs.
- Applied data from the Big Five Inventory-2 (BFI-2) for analysis.
- Provided R code for creating parallel splits and analyzing G-theory designs.
Main Results:
- Properly designed parallel splits approximated classical parallelism.
- The use of parallel splits improved overall score consistency.
- Key components of measurement error were effectively reduced.
Conclusions:
- Parallel splits offer a viable method to enhance reliability in G-theory.
- G-theory variance components aid in evaluating split quality and optimizing measurement.
- The study provides practical tools for applying these techniques to various measures.
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