FInal Flashcards
(139 cards)
If a validity coefficient has a reasonable p value, chances are good that it would yield a similar validity coefficient if the same predictor were used with different sets of job applicants.
P Value
Refers to relationship between predictor and criterion scores, assessed using a correlation
Validity
extent to which predictor adds value to prediction of job success
Practical Significance
direction of the relationship between predictor and criterion
Sign
refers to size; can range from 0 to 1.00
Magnitude
where a coefficient of
0 =
is least desirable
where a coefficient of
1.00 =
is most desirable
Validities above .15
are of moderate usefulness
Validities above .30
are of high usefulness
assessed by probability or p values- another factor used to interpret the validity coefficient
Statistical significance
Reasonable level of significance is p
p< . 05
A significance level of p<0.05
means that there are fewer than 5 chances in 100 of concluding there is a relationship in the population of job applicants, when in fact, there is not (KNOW THIS)’
stated as a probability and indicates a given predictor’s chances of yielding similar validity coefficients with different sets of applicants
concerns whether the selection measure appears valid to the applicant; potentially important to selection decision making in general, and choice of selection methods in particular if it affects applicant behavior
Face validity
refers to the extent that it provides a consistent set of scores to represent an attribute.
Reliability- Think test-retest reliability. If you run the same test over and over will you get the same results.
A predictor may also may also discriminate by screening out a disproportionate number of minorities and women. Any employment practice that has a discriminatory effect on a protected group.
Adverse Impact
represent a significant way of estimating the economic gains that may be anticipated with the use of a new (and valid) predictor.- the economic gain derived from using a predictor versus random selection- dollar value is most difficult to estimate
Economic gain formula-
refers to the bottom-line or monetary impact of a predictor on the organization.
Focuses on the monetary impact of using a predictor
Requires a wide range of information on current employees, validity, number of applicants, cost of testing, etc.
Economic Gain
The greater the economic gain the more useful the predictor
simplifies process of determining scores- IS the final assessment score; inconsistent
Single Predictor
Multiple Predictors
Compensatory model Clinincal Prediction model Unit weighing Rational weighing Multiple Regression Method Choosing among weighing schemes Multiples hurdles approach Combined Model
scores on one predictor are simply added to scores on another predictor to yield a total score. This means that high scores on one predictor can compensate for low scores on another. For example, if an employer is using an interview and grade point average (GPA) to select a person, an applicant with a low GPA who does well in the interview may still get the job.
Compensatory model
Managers use their expert judgment to arrive at a total score for each applicant. That final score may or may not be a simple addition of the three predictor scores shown in the exhibit.
Clinical prediction method
Each predictor is weighted the same at a value of 1.00 - predictor scores are simply added together to get a total score
Unit Weighing
each predictor receives a differential rather than equal weighting; managers and other SME’s establish the weights for each predictor according to the degree to which each is believed to predict job success
Rational Weighing
similar to rational weighting in that the predictors receive different weights. however, the weights are established on the basis of statistical procedures rather than on judgments by managers or other SMEs. The statistical weights are developed from (1) the correlation of each predictor with the criterion 2) the correlations among the predictors. As a result, weights provide optimal weights in the sense that they will yield the highest total validity.
KNOW THIS:will be more precise than unit weighting if there is a small number of predictors, low correlations between predictors, and a large sample.
Multiple regression method-