A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires
Abstract
This article reviews the causal implications of latent variable and psychometric network models for the validation of personality trait questionnaires. These models imply different data generating mechanisms that have important consequences for the validity and validation of questionnaires. From this review, we formalize a framework for assessing the evidence for the validity of questionnaires from the psychometric network perspective. We focus specifically on the structural phase of validation, where items are assessed for redundancy, dimensionality, and internal structure. In this discussion, we underline the importance of identifying unique personality components (i.e. an item or set of items that share a unique common cause) and representing the breadth of each trait's domain in personality networks. After, we argue that psychometric network models have measures that are statistically equivalent to factor models but we suggest that their substantive interpretations differ. Finally, we provide a novel measure of structural consistency, which provides complementary information to internal consistency measures. We close with future directions for how external validation can be executed using psychometric network models. © 2020 European Association of Personality Psychology
Citing Literature
Number of times cited according to CrossRef: 2
- Paul J Silvia, Alexander P Christensen, Looking up at the curious personality: individual differences in curiosity and openness to experience, Current Opinion in Behavioral Sciences, 10.1016/j.cobeha.2020.05.013, 35, (1-6), (2020).
- Leo Alexander, Evan Mulfinger, Frederick L. Oswald, Using Big Data and Machine Learning in Personality Measurement: Opportunities and Challenges, European Journal of Personality, 10.1002/per.2305, (2020).




