State-trace analysis meets personality measurement: Why the Big Five tests are not based on five latent dimensions and how to fix them
Johannes Titz
TL;DR
This paper shows how a mathematical method called state-trace analysis can improve personality tests by checking if items truly measure a single trait.
Contribution
The paper introduces state-trace analysis as a novel and rigorous tool to evaluate unidimensionality in personality measurement.
Findings
Many items in the five-factor model violate the monotonicity criterion required for unidimensionality.
Significant revisions to items are needed to maintain the five-factor model's validity.
State-trace analysis offers a practical and generalizable approach for improving personality tests.
Abstract
Unidimensionality is a fundamental yet often overlooked prerequisite for measurement. In the context of psychological measurement, the central question is whether a set of items can be logically reduced to a single latent factor. This study advocates for the application of state-trace analysis, an underutilized method from mathematical psychology, as a decisive tool to address this question. State-trace analysis provides a simple, general, and rigorous criterion for unidimensionality: monotonicity between item pairs. Identifying items within a factor that violate this criterion is straightforward, offering a practical approach to evaluating unidimensionality. This paper demonstrates the utility of state-trace analysis through exemplary applications within the framework of the five-factor model, analyzing data from the International Personality Item Pool-NEO-120 (N = 618, 000) and the…
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Taxonomy
TopicsMental Health Research Topics · Advanced Statistical Modeling Techniques · Cognitive Abilities and Testing