Utility Analysis and BCG Formula

Utility Analysis

• Taylor-Russell tables and Naylor-Shine tables can be used for utility-related decisions, especially for employment tests and selection ratios.
• Taylor-Russell tables estimate the percentage of future employees who will be successful, based on three components: validity, base rate, and selection ratio.
• Limitations of Taylor-Russell tables include only being applicable to interval data and difficulty in determining "successful" and "unsuccessful" employees.

Naylor-Shine Tables

• Naylor-Shine tables calculate the difference between the means of selected and unselected groups to derive an index of the test's added value to established procedures.

Brogden-Cronbach-Gleser Formula

• The formula estimates the dollar amount of utility gain resulting from the use of a particular selection instrument under specified conditions.
• Utility gain refers to the estimated benefit (monetary or otherwise) of using a particular test or selection method.

Cut Scores

• Norm-referenced cut scores are set with reference to the performance of a group, dividing data into two or more classifications.
• Fixed cut scores, also referred to as absolute cut scores, are set with reference to a judgment concerning a minimum level of proficiency required.
• Multiple cut scores are used to categorize test-takers with reference to one predictor.

Multistage Selection Process

• Each stage has a cut score designed to ensure applicants possess a minimum level of a specific attribute or skill.
• Multiple hurdles are part of a multistage decision-making process where achieving a particular cut score on one test is necessary to advance to the next stage.

Compensatory Model of Selection

• High scores on one attribute can compensate for low scores on another attribute in this model.

Decision Theory and Test Utility

• Base rate, hit rate, miss rate, false positive, and false negative are important concepts in decision theory and test utility.
• Base rate is the proportion of true cases in a population, and the hit rate is the proportion of true cases detected by a test.
• False positives and false negatives can occur, and the goal is to achieve a high hit rate and minimize false positives/negatives.

Proportion of Correct Decisions

• The proportion of correct decisions with a test is calculated by adding correct rejections and correct acceptances and dividing by the total employees.
• Baseline correct decisions are calculated by dividing successful employees by total employees.