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Applying the Latent State-Trait Analysis to Decompose State, Trait, and Error Components of the Self-Esteem Implicit Association Test

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In the literature, self-report scales of Self-Esteem (SE) often showed a higher test-retest correlation and a lower situational variability compared to implicit measures. Moreover, several studies showed a close to… Click to show full abstract

In the literature, self-report scales of Self-Esteem (SE) often showed a higher test-retest correlation and a lower situational variability compared to implicit measures. Moreover, several studies showed a close to zero implicit-explicit correlation. Applying a latent state-trait (LST) model on a sample of 95 participants (80 females, mean age: 22.49 ± 6.77 years) assessed at five measurement occasions, the present study aims at decomposing latent trait, latent state residual, and measurement error of the SE Implicit Association Test (SE-IAT). Moreover, in order to compare implicit and explicit variance components, a multi-construct LST was analyzed across two occasions, including both the SE-IAT and the Rosenberg Self-Esteem Scale (RSES). Results revealed that: (1) the amounts of state and trait variance in the SE-IAT were rather similar; (2) explicit SE showed a higher consistency, a lower occasion-specificity, and a lower proportion of error variance than SE-IAT; (3) latent traits of explicit and implicit SE showed a positive and significant correlation of moderate size. Theoretical implications for the implicit measurement of self-esteem were discussed.

Keywords: state; trait; latent state; test; state trait; self esteem

Journal Title: European Journal of Psychological Assessment
Year Published: 2019

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