TAPAS - Tailored Adaptive Personality Assessment System Performance Prediction Models 2

Question 1

In TAPAS performance prediction research, what does 'incremental validity' specifically refer to?

Accepted Answer

The degree to which TAPAS scores improve predictive accuracy beyond what is already explained by cognitive ability tests like the ASVAB

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The increase in TAPAS test reliability when more items are added to the adaptive pool

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The improvement in prediction accuracy achieved by adding moderator variables to the base model

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The gain in criterion-related validity observed when a prediction model is applied to a larger sample

Question 2

When researchers 'cross-validate' a TAPAS prediction model on a new sample, what phenomenon are they primarily trying to quantify?

Accepted Answer

Validity shrinkage — the reduction in predictive accuracy caused by capitalizing on chance relationships in the derivation sample

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Criterion contamination introduced by supervisors who know applicants' TAPAS scores

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The moderating effect of demographic variables on criterion correlations

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The degree to which adaptive item selection introduces measurement error across testing occasions

Question 3

How does 'criterion contamination' differ from 'criterion deficiency' in evaluating TAPAS performance prediction models?

Accepted Answer

Criterion contamination occurs when the criterion measure is influenced by knowledge of TAPAS scores, inflating apparent validity; criterion deficiency occurs when the criterion fails to capture all relevant performance dimensions

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Criterion contamination refers to missing performance facets, while criterion deficiency refers to irrelevant variance in the criterion

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Criterion contamination describes low inter-rater reliability, whereas criterion deficiency describes test–retest unreliability of TAPAS scores

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Criterion contamination applies only to objective performance metrics, while criterion deficiency applies only to supervisory ratings

Question 4

What is 'differential prediction' in the context of TAPAS, and why is it important for fair employment decisions?

Accepted Answer

Differential prediction examines whether the TAPAS prediction equation produces equally accurate and unbiased performance forecasts across demographic subgroups, such as gender or ethnicity

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Differential prediction refers to using separate TAPAS composites for each military occupational specialty rather than a single universal score

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Differential prediction describes how adaptive testing adjusts item difficulty based on prior responses to yield more precise trait estimates

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Differential prediction measures the extent to which predictor–criterion correlations change over different time horizons after hire

Question 5

In TAPAS prediction research, what is the purpose of examining a 'suppressor variable' within a prediction composite?

Accepted Answer

A suppressor variable increases the composite's validity by removing irrelevant variance from another predictor, even though the suppressor itself may correlate weakly with the criterion

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A suppressor variable is a demographic covariate removed from the model to prevent adverse impact in TAPAS-based selection

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A suppressor variable replaces the primary criterion measure when supervisor ratings are unavailable

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A suppressor variable identifies moderators that reduce TAPAS validity in specific occupational contexts

Question 6

What does 'validity generalization' mean when applied to TAPAS performance prediction models across military occupational specialties (MOS)?

Accepted Answer

Validity generalization is the empirical finding that situational specificity is largely an artifact of sampling error, and that TAPAS validity coefficients are more consistent across MOS contexts than classic study-by-study comparisons suggest

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Validity generalization refers to the process of updating TAPAS norms when the model is applied to a civilian workforce outside the military

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Validity generalization describes the expansion of the TAPAS item pool to cover additional personality dimensions not present in the original instrument

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Validity generalization is the statistical technique used to correct TAPAS criterion correlations for range restriction in applicant samples