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Samenvatting

Summary problem 7.2

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Summary for block 1.7 at Erasmus university (). I'm enrolled in international psychology, however the sources and study materials are the same in both psychology courses. Hence, these summaries may also be useful for Dutch students. The summaries are based on at least 2 of the required reading materials. For this course my final grade was a 9.0. Therefore, I hope they will be of assistance in preparation for your exams.

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Geüpload op
9 juli 2019
Aantal pagina's
7
Geschreven in
2018/2019
Type
Samenvatting

Voorbeeld van de inhoud

Summary #2 Selective selection

Health/wellbeing of an organization depends largely on the steady flow of new people. This
flow of new people happens in steps.
1. Planning
- Fill vacancies (of those who left)
- Fill new positions
A comparison of demand/supply is important in choosing the selection approach or the
training approach. (e.g.; shift in assembly liners  technicians)
2. Recruitment
- Advertising
- Employee referral
- Employment agencies choice the depends on the ease with which
- School recruiters organizations can recruit applicants
- Walk-ins
- The web
The higher-level vacancy/the more undersupply = more effort to recruit the right person

Organization needs to know what it’s looking for in terms of
 Knowledge, Skill, Ability and other characteristics (KSAO’s)

Directed recruitment is possible
3. Selection
+ applicants = + selective = + chance of a good employee

Purpose of selection:
 hire those who are likely to be successful on the job  better organizational performance

2 important elements in selection:
- Criterion
= definition of a good employee; involves performance and many other aspects
- Predictor
= anything assessed in job applicants that relates to the criterion (e.g.; measures
of KSAO’s)

Predictor needs to relate to criterion
 validation study (quantify predictor/criterion  statistical test  correlation coefficient)

5 steps in a validation study
2 types of validation studies/criterion-related validity
- Concurrent

, = predictor and criterion are collected at the same time in current employees. The test scores
are correlated with recent performance
No time delay/less time consuming
Not having to hire people with ‘bad’ scores
Different population than actual applicants
These people may already have acquired skills on the job
- Predictive
= predictor measured before criterion. All in the sample are hired. After time has passed,
criteria are assessed.
Vigorous
Practical issues (money, time)
Hard to ignore predictor data during selection

Other forms of validity
- Incremental validity
= how much adding another predictor contributes to the predictive power of
selection
- Face validity
= when something looks right/ logical link between test & job performance. 
positive impact on acceptability  otherwise applicant may take legal action
- Content validity
= no validity studies, validity is based on a logical basis
- Construct validity
= involves relationships between predictors and characteristics that are not directly
observable (psychological characteristics).
 measured by comparing new test to well-established pre-existing test
Adverse impact
= the impact of a selection method on a protected class. The adverse impact can be found by
comparing the selection ratio of such a protected class to a comparison (usually white males).
80% rule/four fifths rule
 there is adverse impact if the ratio of the protected class is less than 80% than the
comparison. (divide minority by majority)
Direct VS indirect discrimination
= specifically, purposefully VS. unintended discrimination in the process (by setting a rule
that is applies to everyone, but negatively affects a specific subgroup)

How to use the predictor in selection procedures
- The multiple hurdle approach
= For each predictor a passing score has to be met  hurdle is passed
Each hurdle some applicants are eliminated (hurdles are increasingly expensive =
the company spends most money on those with high potential)
 There is no room for compensation


- Regression approach
= use an equation to combine all predictors to provide a numerical forecast of the
criterion
Low scores on some predictors can be compensated for  overcome by combining the 2
approaches.

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