Utility Flashcards
The practical value of testing to improve efficiency
A. Utility
B. Psychometric Soundness
C. Costs
D. Benefits
A. Utility
The higher the criterion-related validity of test scores, the higher the utility of the test
A. Utility
B. Psychometric Soundness
C. Costs
D. Benefits
B. Psychometric Soundness
One of the most basic elements of utility analysis
A. Utility
B. Psychometric Soundness
C. Costs
D. Benefits
C. Costs
Weighed against the costs of administering, scoring, and interpreting the test
A. Utility
B. Psychometric Soundness
C. Costs
D. Benefits
D. Benefits
An assumption is made that high scores on one attribute can “balance out” low scores on another attribute
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
A. Compensatory Model of Selection
The likelihood that a test taker will score within some interval of scores on a criterion measure
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
B. Expectancy Data
You must be able to create a set of norms where your score will fall under
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
B. Expectancy Data
Provide an estimate of the percentage of employees hired by the use of a particular test who will be successful at their jobs
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
C. Taylor-Russell Tables
Help obtain the difference between the means of the selected and unselected groups to derive an index of what the test (or some other tool of assessment) is adding to already established procedures
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
D. Naylor-Shine Tables
The validity coefficient comes from concurrent validation procedures.
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
D. Naylor-Shine Tables
Many other variables may play a role in selection decisions, including applicants’ minority status, general physical or mental health, or drug use.
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
D. Naylor-Shine Tables
Used to calculate the dollar/peso amount of a utility gain resulting from the use of a particular selection instrument under specified conditions
A. Compensatory Model of Selection
B. Expectancy Data
C. Taylor-Russell Tables
D. Naylor-Shine Tables
E. Brogden-Cronbach-Gleser Formula
E. Brogden-Cronbach-Gleser Formula
Some utility models are based on the assumption that there will be a ready supply of viable applicants from which to choose and fill positions.
A. The Pool of Job Applicants
B. The Complexity of the Job
C. Cut-Off Score
A. The Pool of Job Applicants
The same kind of utility models are used for a variety of positions, yet the more complex the job, the bigger the difference in people who perform well or poorly
A. The Pool of Job Applicants
B. The Complexity of the Job
C. Cut-Off Score
B. The Complexity of the Job
Reference point derived as a result of a judgment
A. The Pool of Job Applicants
B. The Complexity of the Job
C. Cut-Off Score
C. Cut-Off Score
Used to divide a set of data into two or more classifications as basis for some actions to be taken or some inferences to be made
A. The Pool of Job Applicants
B. The Complexity of the Job
C. Cut-Off Score
C. Cut-Off Score
Reference point that is set based on norm-related considerations rather than on the relationship of test scores to a criterion
A. Relative cut score
B. Fixed cut score
C. Multiple cut score
D. Multiple Hurdle
A. Relative cut score