Measurement MANA 4328 Dr. Jeanne Michalski

Employment Tests  Employment Test  An objective and standardized measure of a sample of behavior that is used to gauge a person’s knowledge, skills, abilities, and other characteristics (KSAOs) in relation to other individuals.  Pre-employment testing has the potential for lawsuits.

Classification of Employment Tests  Cognitive Ability Tests  Aptitude tests Measures of a person’s capacity to learn or acquire skills.  Achievement tests Measures of what a person knows or can do right now.  Personality and Interest Inventories  “Big Five” personality factors: Extroversion, agreeableness, conscientiousness, neuroticism, openness to experience.

Classification of Employment Tests (cont’d)  Physical Ability Tests  Must be related to the essential functions of job.  Job Knowledge Tests  An achievement test that measures a person’s level of understanding about a particular job.  Work Sample Tests  Require the applicant to perform tasks that are actually a part of the work required on the job.

Reliability: Basic Concepts  Observed score = true score + error  Error is anything that impacts test scores that is not the characteristic being measured  Reliability measures error  Lower the error the better the measure  Things that can be observed are easier to measure than things that are inferred

Basic Concepts of Measurement 1. Variability and comparing test scores  Mean / Standard Deviation 2. Correlation coefficients 3. Standard Error of Measurement 4. The Normal Curve  Many people taking a test  Z scores and Percentiles

EEOC Uniform Guidelines Reliability – consistency of the measure If the same person takes the test again will he/she earn the same score? Potential contaminations:  Test takers physical or mental state  Environmental factors  Test forms  Multiple raters

Reliability Test Methods  Test – retest  Alternate or parallel form  Inter-rater  Internal consistency  Methods of calculating correlations between test items, administrations, or scoring.

Correlation  How strongly are two variables related?  Correlation coefficient (r)  Ranges from to 1.00  Shared variation = r 2 If two variables are correlated at r =.6 then they share.6 2 or 36% of the total variance.  Illustrated using scatter plots  Used to test consistency and accuracy of measure

Correlation Scatterplots Figure 5.3

Summary of Types of Reliability Compare scores within T1 Compare Scores across T1 and T2 Objective Measures (Test items) Internal Consistency or Alternate Form Test-retest Subjective Ratings Interrater – Compare different Raters Intrarater – Compare same Rater different times

Standard Error of Measure (SEM)  Estimate of the potential error for an individual test score  Uses variability AND reliability to establish a confidence interval around a score  95% Confidence Interval (CI) means if one person took the test 100 times, 95 of the scores will fall within the upper and lower bounds. SEM = SD * √ (1- reliability)  There is a 5% chance that scores observed outside the CI are due to chance, therefore the differences are “significant”.

Standard Error of Measure (SEM) SEM = SD * √ (1- reliability) Assume a mathematical ability test has a reliability of.9 and a standard deviation of 10: SEM = 10 * √ (1-.9) = 3.16 If an applicant scores a 50, the SEM is the degree to which the score would vary if she were retested on another day. Plus or minus 2 SEM gives you a ~95% confidence interval (3.16) = – 2(3.16) = 43.68

Standard Error of Measure  If an applicant scores 2 points above a passing score and the SEM is 3.16 – then there is a good chance of making a bad selection choice.  If two applicants score within 2 points of one another and the SEM is 3.16 then it is possible that the difference is due to chance.

Standard Error of Measure  The higher the reliability, the lower the SEM Std. Dev.rSEM

Confidence Intervals Jim -- 40Mary -- 50Jen SEM-2 SEM +2 SEM -2 SEM +2 SEM -2 SEM +2 SEM Do the applicants differ when SEM = 2? Do the applicants differ when SEM = 4?

Validity  Accuracy of the measure Are you measuring what you intend to measure? OR Does the test measure a characteristic related to job performance?  Types of test validity  Criterion – test predicts job performance  Predictive or Concurrent  Content – test representative of the job

Approaches to Validation  Content validity  The extent to which a selection instrument, such as a test, adequately samples the knowledge and skills needed to perform a particular job. Example: typing tests, driver’s license examinations, work sample  Construct validity  The extent to which a selection tool measures a theoretical construct or trait. Example: creative arts tests, honesty tests

Approaches to Validation  Criterion-related Validity  The extent to which a selection tool predicts, or significantly correlates with, important elements of work behavior. A high score indicates high job performance potential; a low score is predictive of low job performance.  Two types of Criterion-related validity  Concurrent Validity  Predictive Validity

Approaches to Validation  Concurrent Validity  The extent to which test scores (or other predictor information) match criterion data obtained at about the same time from current employees. High or low test scores for employees match their respective job performance.  Predictive Validity  The extent to which applicants’ test scores match criterion data obtained from those applicants/ employees after they have been on the job for some indefinite period. A high or low test score at hiring predicts high or low job performance at a point in time after hiring.

Tests of Criterion-Related Validity  Predictive validity “Future Employee or Follow-up Method” Test Applicants Performance of Hires Time mos.Time 2  Concurrent validity “Present Employee Method” Test Existing Employee AND Measure Performance Time 1

Types of Validity Job Duties KSA’s Selection Tests Job Performance Criterion-Related Content-Related

Reliability vs. Validity  Validity Coefficients  Reject below.11  Very useful above.21  Rarely exceed.40  Reliability Coefficients  Reject below.70  Very useful above.90  Rarely approaches 1.00 Why the difference?

More About Comparing Scores

The Normal Curve % 2% 16% 50% 84% 98% 99.9% Rounded Percentiles Z Scores Note: Not to Scale

Variability  How did an individual score compared to others?  How to compare scores across different tests? Test 1 Test 2 BobJimSueLinda Raw Score

Variability  How did an individual score compared to others?  How to compare scores across different tests? Test 1 Test 2 BobJimSueLinda Raw Score Mean48 46

Variability  How did an individual score compared to others?  How to compare scores across different tests? Test 1 Test 2 BobJimSueLinda Raw Score Mean48 46 Std. Dev2.5.80

Score – Mean Score – Mean Z Score = Std. Dev Std. Dev Z Score or “Standard” Score Test 1 Test 2 BobJimSueLinda Raw Score Mean48 46 Std. Dev Z score

The Normal Curve Note: Not to Scale Jim Bob Linda Sue

Z scores and Percentiles  Look up z scores on a “standard normal table”  Corresponds to proportion of area under normal curve  Linda has z score of 1.25  Standard normal table =.9265  Percentile score of 92.65%  Linda scored better than 92.65% of test takers Z score Percentile % % % % 15.9% % %

Proportion Under the Normal Curve Note: Not to Scale Jim Bob Linda Sue