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    Square root of AVE (Average Variance Extracted) can be used to verify _______ validity of a psychological questionnaire.
    Question

    Square root of AVE (Average Variance Extracted) can be used to verify _______ validity of a psychological questionnaire.

    A.

    Criterion-related

    B.

    Convergent

    C.

    Discriminant

    D.

    Predictive

    Correct option is C

    The square root of AVE (Average Variance Extracted) is used to assess discriminant validity in structural equation modeling and confirmatory factor analysis. The Fornell-Larcker criterion states that the square root of AVE for each construct should be greater than its correlations with other constructs. This ensures that each construct shares more variance with its own indicators than with other constructs, confirming discriminant validity.

    Information Booster: Key Points about AVE and Discriminant Validity

    • Fornell-Larcker Criterion: The primary method using square root of AVE to establish discriminant validity between constructs
    • Variance Comparison: AVE measures how much variance a construct captures from its indicators relative to measurement error
    • Threshold Value: AVE should exceed 0.5, meaning the construct explains more than half the variance in its indicators
    • Discriminant vs. Convergent: While AVE assesses convergent validity directly, its square root is used for discriminant validity testing
    • SEM Applications: Critical in structural equation modeling for validating measurement models before testing structural relationships
    • Construct Distinctiveness: Ensures that constructs in a model are sufficiently different from each other

    Additional Knowledge

    1. Criterion-related: This validity is assessed by correlating the measure with external criteria, not using AVE.
    2. Convergent: While AVE can assess convergent validity directly (AVE > 0.5), the square root of AVE is specifically used for discriminant validity.
    4. Predictive: This is a type of criterion-related validity that examines how well a measure predicts future outcomes.

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