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4.3 Personnel Selection Summary – Get yourself a wonderful grade CA$15.01   Add to cart
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4.3 Personnel Selection Summary – Get yourself a wonderful grade

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  • Course
  • Institution
  • Erasmus Universiteit Rotterdam (EUR)

This is a wonderful guide to ace your exam in Personnel Selection at Erasmus University. Study: Work and Organisational Psychology (Arbeids- en Organisatiepsychologie) The exam guide contains: (1) General tips, (2) Overview of Key Concepts and short summary of the literature, (4) Overview o...

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Document information

  • November 17, 2023
  • 137
  • 2021/2022
  • Summary

Subjects

  • personnel selection
  • assessment
  • marise born
  • erasmus
  • work and organisational psychology
  • arbeids en organisatiepsychologie

Written for

  • Erasmus Universiteit Rotterdam (EUR)
  • Psychologie
  • Unknown

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By: Anoukdeg • 1 year ago

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By: aya12 • 11 months ago

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Content preview

Master in Psycholog
Erasmus University Rotterda
Course Instructor: Marise Born

Literature Overview & Lecture Notes




Pe r s o n n e l S e l e c t i o n

Exam Guide




Contents of this document
• Sec on 1. General ps (page 2)
• Sec on 2. Overview of Key Concepts (and thereby short summary of the literature) (page 3)
• Sec on 3. Overview of all formulas (page 29)
• Sec on 4. Overview of all models (page 34)
• Sec on 5. Full summary of the literature (page 50)




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, Pe r s o n n e l S e l e c t i o n




•


General Tip
Recommenda ons
• if you want to study to obtain an 6-8 grade: read sec on 1 un l sec on 4
• if you want to study to obtain > 8 grade: read the ar cles themselves, and then read Sec on 5 (of this
document) to repeat & process the knowledge again.

Theme 2

Explana on on using Bartram’s Tables for Adverse Impact – input informa on
• di erent tables for propor on majority group in total popula on (99%, 98% … 50%)
• Z di erence: di erences in standard devia on of the scores of minority and majority group
• selec on ra o: (number of vacancies) / (total number of applicants) > you can read this horizontally (per
Z-di erence) or ver cally ( nd a selec on ra o that most closely resembles the organisa on)
• adverse impact: has an asterisk* if it is below 0.80




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, Overview of Key Concept

Theme 1 Overvie
21st century skills

Cook, 2016, CH3
• Rapid change implies less need for task-speci c skills and more need for general abili es, to adapt, solve
problems, de ne one’s own direc on, and work in teams

Three trends – lead to reduc on of organisa onal rou nes & prac ces & increase in non rou ne and
interac ve tasks
• skill-based technological change
• job polarisa on
• o shoring

Two 21st century skills
• complex problem solving
• collabora ve problem solving
- both emphasise the importance of adap ng to new situa ons and problems for which no rou ne
solu on is readily available
- characteris c features of complex and collabora ve problems can be systema cally varied in assessment,
building on the construct in focus and following theore cally de ned and empirically validated
dimensions

Complex problem solving
• addresses individuals’ transversal skill in successfully dealing with dynamic decision-making: handling
complex, dynamic and intransparent situa ons (i.e., those without a readily apparent solu on), requiring
the ac ve acquisi on and applica on of knowledge in various domains
• characteris cs of problems targeted in CPS are:
• complexity of the problem structure (i.e., a mul tude of inter-related elements)
• the dynamics of the system (i.e., changes due to me or to interac ng with the problem)
• interconnectedness of elements (i.e., a change in one part of the system has repercussions in other
parts)
• mul ple goals requiring simultaneous considera on, and the intransparency of the problem situa on
requiring ac ve inves ga on

Collabora ve problem solving:
• addresses problem solving in group se ngs
• regards the assessment of similar skills as complex problem solving, but then in interac ve se ngs
• requires to share the understanding and e ort to reach a solu on, to pool knowledge, skills and e orts

Psychological prediction vs Machine Learning

Liem et al., 2018


please do not share without permission PSN Exam Guide



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, Psychology Machine Learning

1 focus is on understanding data data is used to verify that a robust model has been
trained

2 essential is: human-interpretable ML expert mostly is interested in understanding &
meaning of x and y and link to theory improving the learning procedure; the origin of x and y
are irrelevant in machine learning research

3 linear regression models are commonly large exibility in choosing f(x) model - focus is on
chosen for f (x), and not typically identifying the model that obtains most accurate
contrasted with alternative models predictions (often not linear model)

4 little attention is paid to criterion validity, more attention is paid to criterion validity, even though
considering the alignment of y with what certain types of measures tend to dominate
is supposed to be measured

5 uses exploratory factor analysis to uses unsupervised learning techniques to examine the
examine relationships among data relations (in terms of the original input dimensions)



Correc on for a enua on
• this problem relates to the unreliability of your scales (eg when you don't get the same scales when
assessing answers in an interview)
• a enua on: the predic ve validity weakens because of unreliability
• Marise shows a formula that can be used to reach the predic ve validity coe cient that corrects
unreliable predictor and criterion
• Marise: many people say that we should not use this formula, because only in paradise, scales are
completely reliable. But in everyday life, things are unreliable




please do not share without permission PSN Exam Guide



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