© 2013 by Nelson Education1 Foundations of Recruitment and Selection I: Reliability and Validity

© 2013 by Nelson Education 1

Chapter 2Foundations of Recruitment and Selection

I: Reliability and Validity


Chapter Learning Outcomes

After reading this chapter you should:◦ Understand the basic components that make up

a traditional personnel selection model

◦ Have a good understanding of the concepts of reliability and validity

◦ Recognize the importance and necessity of establishing the reliability and validity of measures used in personnel selection


Chapter Learning Outcomes (continued)

◦ Identify common strategies that are used to provide evidence on the reliability and validity of measures used in personnel selection

◦ Appreciate the requirement for measures used in personnel selection to evaluate applicants fairly and in an unbiased fashion


The Recruitment and Selection Process

An employer’s goal is to hire an applicant who possesses the knowledge, skills, abilities, or other attributes (KSAOs) required to perform the job


Recruitment and Selection Today 2.1 Selection Process for Winnipeg Police

Constables: Minimum Qualifications◦ Age 18 years or older◦ Education (Manitoba Grade 12 or equivalency–

e.g. GED) Foreign credentials require assessment by

Manitoba Labour and Immigration prior to application

Valid Full Class 5 driver’s licence with no more than four demerits on Driver’s abstract as assessed by the Winnipeg Police Service


Recruitment and Selection Today 2.1(continued)

◦ Canadian citizen or landed immigrant◦ No involvement in any criminal activity within

the last two years (including illegal drugs)◦ No criminal record for which a pardon has not

been granted


Recruitment and Selection Today 2.1(continued)

◦ Must meet current Winnipeg Police Service vision standards

◦ Successful completion of job-related physical test (Winnipeg Police Service—Physical Abilities Test: WPS-PAT) within 4 minutes and 15 seconds



Human Resources Management: Science versus Practice in Selection


A Selection Model


Construct: an idea or concept constructed or invoked to explain relationships between observations

Construct


Reliability: the degree to which observed scores are free from random measurement errors; an indication of the stability or dependability of a set of measurements over repeated applications of the measurement procedure

Building a Foundation


True score: the average score that an individual would earn on an infinite number of administrations of the same test or parallel versions of the same test

Error score (or measurement error): the hypothetical difference between an observed score and a true score

Interpreting Reliability Coefficients


Factors Affecting Reliability

Temporary Individual Characteristics Lack of Standardization Chance


Methods of Estimating Reliability Test and Retest Alternate Forms Internal Consistency Inter-Rater Reliability Choosing an Index of Reliability


Validity: the legitimacy or correctness of the inferences that are drawn from a set of measurements or other specified procedures; the degree to which accumulated evidence and theory support specific interpretations of test scores in the context of the test’s proposed use

Validity


Construct, Content, Criterion-related Validity

Construct and content validity: validation strategies that provide evidence based on test content

Criterion validity: related validity provides evidence based on relationships to other variables



Predictive and Concurrent Evidence for Test Criterion Relationships Predictive evidence: obtained through

research designs that establish a correlation between predictor scores obtained before an applicant is hired and criteria obtained at a later time, usually after an applicant is employed


Concurrent evidence: obtained through research designs that establish a correlation between predictor and criteria scores from information that is collected at approximately the same time from a specific group of workers

Predictive and Concurrent Evidence for Test Criterion Relationships (continued)


Validity generalization: the application of validity evidence, obtained through meta-analysis of data obtained from many situations, to other situations that are similar to hose on which the meta-analysis is based

Validity Generalization


Factors Affecting Validity Coefficients

Range Restriction Measurement Error Sampling Error


1. Discuss why it is better to base a selection system on science than a ‘gut feeling’?

2. Does an organization have an obligation to make the enterprise as profitable as possible on behalf of its owners, or does it have an obligation to meet the objectives of society by providing equal employment opportunities for members of different population groups?

Class Activity


Bias: systematic errors in measurement, or inferences made from those measurements, that are related to different identifiable group membership characteristic such as age, sex, or race

Bias and Fairness



Fairness: the value judgments people make about the decisions or outcomes that are based on measurements◦ Principle that every test taker should be

assessed in an equitable manner

Fairness


Different Views of Fairness:◦ Fairness as lack of bias◦ Fairness as equitable treatment in the testing

process◦ Fairness as opportunity to learn ◦ Fairness as equality in outcomes of testing◦ Fairness in selection and prediction

Recruitment and Selection Today 2.2


Be familiar with measurement, reliability, and validity issues

The reliability and validity of the information used as part of personnel selection procedures must be established empirically

Summary


1. We presented a summary of the Meiorin case at the outset of this chapter. Can you think of procedures or changes that the consultants could have done that would have allowed the test to meet the objections of the Supreme Court?

2. Can an invalid selection test be reliable? Can an unreliable selection best be valid?

Discussion Questions

Documents

© 2013 by Nelson Education1 Foundations of Recruitment and Selection I: Reliability and Validity