chapter 6 - selection (14 mc, 2 sa) Flashcards
selection and selection ratio
Selection
The process of choosing from among the individuals who have relevant qualifications in the applicant pool to fill existing or projected job openings.
Success of selection decisions is dependent on successful recruitment efforts.
Selection ratio = # of positions / # of qualified applicants
reliability vs. validity
**Reliability:
The degree to which interviews, tests, and other selection procedures yield consistent data
Reliability takes into account the effect of error in measurement
We always expect some degree of error when it comes to measuring psychological characteristics
e.g., intelligence v. height
Reliability – consistency of measurement
1. Can a test be reliable and not valid?
2. Can a test be valid but not reliable?
**Validity:
Degree to which the inferences we make from a test score are appropriate
e.g., an applicant gives you a wimpy handshake
Should you infer that the person is timid and shy?
Is this an appropriate inference? NO
Validity depends on the reliability of the test (rxx)
Maximum possible validity of a test = square root of its reliability estimate
So if the reliability (rxx) of a test is .81…
…what is the maximum validity (rxy) you could obtain from the scores on that test?
Max rxy = √.81 = .90
sources of error in measurements
Environmental factors
Examples: Room temperature, lighting, noise
Personal factors
Examples: Anxiety, mood, fatigue, illness
Interviewer/recruiter behavior
Example: If smiling during an interview with one applicant then not with another
Test item difficulty level
What difficulty level is most reliable?
Item response format
MC v. T/F – which is more reliable?
Length of the test
Longer or shorter test – which is more reliable?
interpreting reliability coefficents
Reliability coefficient (rxx)
x = selection test or assessment
y = the thing we are trying to predict (usually job performance)
r = correlation
So, rxx = the correlation of the test “X” with itself (the test itself)
rxy = correlating the test with something else
Y can be job performance etc.
Shows % of score that is thought to be due to true differences on the attribute being measured
rxx = 0 – no reliability (100% error measurement)
rxx = 1.0 – perfect reliability (0% error measurement)
rxx = .90 – 90% of the true variance between individual’s scores are due to true differences on the attribute being measured (10% due to error)
How high is high enough? Depends…
rxx = .80 or higher is one rule of thumb
types of reliability estimates: TEST-RETEST
Test-retest = How consistent are scores on a test over time?
Give different groups of people the same test at different times then we correlate them
Test-retest reliability – estimates the degree to which individuals’ test scores tend to vary over time on the test
Time 1————————Time 2
Test X Test X
Two person-related factors that effect test-retest:
1) Memory – when the test taker simply recalls how they answered they responded to the question at Time 1 and answers the same way at Time 2
Will memory inflate or deflate reliability of a test?
2) Learning – Learning means that the test taker has changed (e.g., learned new information) between Times 1 and 2 and therefore answers the questions differently
Will learning inflate or deflate the reliability of a test?
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types of reliability estimates: PARALLEL FORMS
Parallel Forms = How consistent are scores across different forms of a test?
Form a and form b which are different questions but should report the same thing
Parallel forms - Examines the consistency of measurement of an attribute across forms
Similar to test-retest but controls the effect of memory by using 2 different versions of a test and seeing how well the scores correlate:
Time 1————————Time 2
Form A Form B
Can administer both forms at same time (Time 1 – Form A and B)
Example tests with multiple forms? SAT, ACT, GMAT, etc…
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types of reliability estimates: INTERNAL CONSISTENCY
Internal Consistency = How consistent are the items on a test in measuring a construct?
One test at one time and run analyses to see how you responded to each item
Internal consistency – Measures whether all items are acting in a consistent manner? (i.e., do item responses hang together as you would predict?)
Most commonly used reliability estimate in HR research
Two types of internal consistency estimates: IMAGE IN NOTES
1. Split half reliability estimate
2. Cronbach’s alpha
**1. Split half reliability estimate
Administer one test, one time, then divide the items into 2 halves (odd, even) and correlate the two sides’ scores
**2. Cronbach’s alpha
Administer one test, one time, then divide the items into every possible split half to calculate the average reliability across all ways to split the items in half (computer programs do this calculation for you)
Multiple split halves are going to give a better estimate than calculating reliability off of one split half
types of reliability estimates: INTER-RATER
Inter-rater = How consistent are scores across raters?
Multiple raters rate the same people (ask how raters evaluate someone)
Inter-rater - Measures the degree to which multiple raters give similar ratings to applicants
Are some raters biased? Too Lenient? Too strict?
Need multiple raters so we can know how reliable any one rater is
Inter-rater reliability estimate assesses the degree of objectivity in ratings
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3 ways to validate a test (validation approaches)
- Criterion-related validity – do test scores predict future job performance?
- Content validity – does the test adequately measure KSAs needed on the job?
- Construct validity – does the test adequately measure a particular theoretical construct?
4 ways to see if a test is RELIABLE
Test-retest = How consistent are scores on a test over time?
Parallel Forms = How consistent are scores across different forms of a test?
Internal Consistency = How consistent are the items on a test relative to one another in measuring the construct of interest?
Inter-rater = How consistent are scores across raters?
Criterion-related Validity
The extent to which a selection test (x) predicts, or significantly correlates with, important elements of work behavior (y).
directionality of relationship
If a test has criterion-related validity, a high test score indicates high job performance potential; a low test score is predictive of low job performance.OR
A high test score could indicate low job performance; a low test score could indicate high job performance
What we are looking for is a relationship b/t the two
Two options for determining the criterion-related validity of a selection test:
**1. Concurrent validation - Use current employees as sample
**2. Predictive validation - Use applicant pool as sample
Directionality (or sign) of the correlation (look at it based on number. Doesn’t matter if its positive or negative)
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Criterion-related validity - concurrent validation
Concurrent validation: examining the extent to which test scores correlate with job performance data obtained from current employees
Steps:
1. Have employees take the test
2. Collect most recent job performance ratings on these employees
3. Correlate the two measures to obtain validity estimate
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Advantage
Can be done quickly because you can collect all the pieces of data can be collected simultaneously (can send out a link)
Disadvantages
Motivation to take the test
Job tenure
Sample may not generalize demographically to our applicant pool - Do our employees look like our applicants – they might not – (US v. Georgia Power)
Criterion-related validity - predictive validation
Predictive validation: examination of 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. (x variable is who is taking the test)
Steps:
1. Administer the test to your applicants (as part of the selection process but don’t use the scores for hiring purposes)
2. File the test scores away
3. Collect job performance measures on the applicants you ended up hiring (6 months to 1 yr later)
4. Correlate the test scores with the job performance measures
Advantages
High motivation to try hard if you think it helps with your application as a candidate for a job
No sample generalizability problem (applicant pool is the sample)
Equal job tenure
Disadvantages
Time interval required between test and job performance data collection
Amount of time needed to get a large enough sample to be statistically (N = 300)
validity coefficient
rxy can range between -1.0 to +1.0
rxy = 0 = no validity
rxy = 1.0 = perfectly positively correlated
rxy = -1.0 = perfectly negatively correlated
How high is high enough? Depends on the type of test you are looking at…
Unlike reliability coefficients, there is no minimum accepted rule of thumb for validity coefficients
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reliability coefficient vs validity coefficient
- reliability coefficent (rxx)
x = selection test or assessment
y = the thing we are trying to predict (usually job performance)
r = correlation
So, rxx = the correlation of the test “X” with itself (the test itself)
rxy = correlating the test with something else
Y can be job performance etc.
Shows % of score that is thought to be due to true differences on the attribute being measured
rxx = 0 – no reliability (100% error measurement)
rxx = 1.0 – perfect reliability (0% error measurement)
rxx = .90 – 90% of the true variance between individual’s scores are due to true differences on the attribute being measured (10% due to error)
How high is high enough? Depends…
rxx = .80 or higher is one rule of thumb - validity coefficient (rxy)
rxy can range between -1.0 to +1.0
rxy = 0 = no validity
rxy = 1.0 = perfectly positively correlated
rxy = -1.0 = perfectly negatively correlated
How high is high enough? Depends on the type of test you are looking at…
Unlike reliability coefficients, there is no minimum accepted rule of thumb for validity coefficients