Performance Prediction Models 6 Flashcards

In TAPAS performance prediction research, what does 'incremental validity' measure?

Answer: The degree to which TAPAS scores improve prediction of job performance beyond what cognitive ability tests alone can predict

Incremental validity specifically quantifies how much predictive accuracy a new predictor — in this case TAPAS personality dimensions — adds over and above existing predictors such as cognitive ability measures. It is expressed as the change in R² when TAPAS is entered into a regression model after cognitive scores.

Why is cross-validation a critical step when developing a TAPAS performance prediction model?

Answer: It verifies that the prediction weights derived from one sample generalize to an independent sample and are not simply artifacts of capitalization on chance

Cross-validation guards against overfitting: regression weights optimized in a development sample often inflate apparent validity because they exploit random sampling error. Applying those weights to a hold-out sample yields a shrunken, more realistic estimate of operational predictive accuracy.

How does 'range restriction' typically distort the observed validity coefficient of a TAPAS prediction model?

Answer: It attenuates the coefficient because the selected workforce sample has less variability on TAPAS dimensions than the full applicant pool

When only hired applicants — who already passed initial screening — are used to estimate validity, their restricted variance on both predictor and criterion compresses the observed correlation below its true population value. Statistical corrections for direct or indirect range restriction are applied to recover an unbiased estimate.

What is 'predictive bias' in the context of TAPAS performance prediction, and how is it typically tested?

Answer: Bias exists when the regression line relating TAPAS scores to performance criteria has a different slope or intercept for different subgroups; it is tested via moderated multiple regression

Predictive bias (differential prediction) is formally evaluated by testing whether subgroup membership moderates the TAPAS score–criterion relationship. A significant interaction term (slope bias) or a significant group main effect with equal slopes (intercept bias) in moderated multiple regression indicates that the model predicts differentially across groups.

In a TAPAS validity study, what does a 'shrunken R²' (adjusted R²) communicate to researchers?

Answer: The proportion of criterion variance explained by TAPAS after penalizing for the number of predictors, yielding a less optimistic but more generalizable estimate

Adjusted R² corrects the positive bias in ordinary R² that arises because adding any predictor — even a random one — always increases R². The adjustment penalizes for each additional parameter estimated, producing a conservative estimate that better reflects expected predictive accuracy in new samples.

What is the purpose of conducting a 'utility analysis' after establishing the validity of a TAPAS performance prediction model?

Answer: To translate the statistical validity coefficient into practical economic value — estimating the dollar benefit to an organization from using TAPAS for selection

Utility analysis (e.g., the Brogden-Cronbach-Gleser model) converts a validity coefficient into a monetary return on investment by incorporating selection ratio, base rate of success, standard deviation of job performance in dollar terms, and the number of hires. This helps decision-makers weigh the operational value of adopting TAPAS against its implementation costs.