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Part 4
Staffing Activities: Selection

Chapter 7: Measurement

Chapter 8: External Selection I

Chapter 9: External Selection II

Chapter 10: Internal Selection

McGraw-Hill/Irwin

Copyright © 2012 by The McGraw-Hill Companies, Inc., All Rights Reserved.

Part 4
Staffing Activities: Selection

Chapter 7:

Measurement

 

Staffing Policies and Programs

Staffing System and Retention Management

Support Activities

Legal compliance

Planning

Job analysis

Core Staffing Activities

Recruitment: External, internal

Selection:
Measurement, external, internal

Employment:
Decision making, final match

Staffing Organizations Model

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Chapter Outline

  • Importance and Use of Measures
  • Key Concepts
  • Measurement
  • Scores
  • Correlation Between Scores
  • Quality of Measures
  • Reliability of Measures
  • Validity of Measures
  • Validation of Measures in Staffing
  • Validity Generalization
  • Staffing Metrics and Benchmarks
  • Collection of Assessment Data
  • Testing Procedures
  • Acquisition of Tests and Test Manuals
  • Professional Standards
  • Legal Issues
  • Determining Adverse Impact
  • Standardization
  • Best Practices

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Learning Objectives for This Chapter

  • Define measurement and understand its use and importance in staffing decisions
  • Understand the concept of reliability and review the different ways reliability of measures can be assessed
  • Define validity and consider the relationship between reliability and validity
  • Compare and contrast the two types of validation studies typically conducted
  • Consider how validity generalization affects and informs validation of measures in staffing
  • Review the primary ways assessment data can be collected

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Discussion Questions for This Chapter

  • Imagine and describe a staffing system for a job in which there are no measures used.
  • Describe how you might go about determining scores for applicants’ responses to (a) interview questions, (b) letters of recommendation, and (c) questions about previous work experience.
  • Give examples of when you would want the following for a written job knowledge test
  • a low coefficient alpha (e.g., α = .35)
  • a low test–retest reliability.
  • Assume you gave a general ability test, measuring both verbal and computational skills, to a group of applicants for a specific job. Also assume that because of severe hiring pressures, you hired all of the applicants, regardless of their test scores.
  • How would you investigate the criterion-related validity of the test?
  • How would you go about investigating the content validity of the test?
  • What information does a selection decision maker need to collect in making staffing decisions? What are the ways in which this information can be collected?

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Key Concepts

  • Measurement
  • the process of assigning numbers to objects to represent quantities of an attribute of the objects
  • Scores
  • the amount of the attribute being assessed
  • Correlation between scores
  • a statistical measure of the relation between the two sets of scores

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Importance and Use of Measures

  • Measures
  • Methods or techniques for describing and assessing attributes of objects
  • Examples
  • Tests of applicant KSAOs
  • Job performance ratings
    of employees
  • Applicants’ ratings of their
    preferences for various types
    of job rewards

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Importance and Use of Measures
(continued)

  • Summary of measurement process
  • (a) Choose an attribute of interest
  • (b) Develop operational definition of attribute
  • (c) Construct a measure of attribute as operationally
    defined
  • (d) Use measure to actually gauge attribute
  • Results of measurement process
  • Scores become indicators of attribute
  • Initial attribute and its operational definition are transformed into a numerical expression of attribute

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Measurement: Definition

  • Process of assigning numbers to objects to represent quantities of an attribute of the objects
  • Attribute/Construct – Knowledge of mechanical principles
  • Objects – Job applicants

Ex. 7.1 Use of Measures in Staffing

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Measurement: Standardization

  • Involves
  • Controlling influence of extraneous factors
    on scores generated by a measure and
  • Ensuring scores obtained reflect the attribute measured
  • Properties of a standardized measure
  • Content is identical for all objects measured
  • Administration of measure is identical for all objects
  • Rules for assigning numbers are clearly specified and agreed on in advance

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Measurement: Levels

  • Nominal
  • A given attribute is categorized and numbers are assigned to categories
  • No order or level implied among categories
  • Ordinal
  • Objects are rank-ordered according to how much of attribute they possess
  • Represents relative differences among objects
  • Interval
  • Objects are rank-ordered
  • Differences between adjacent points on measurement scale are equal in terms of attribute
  • Ratio
  • Similar to interval scales – equal differences between scale points for attribute being measured
  • Have a logical or absolute zero point

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Measurement: Differences in
Objective and Subjective Measures

  • Objective measures
  • Rules used to assign numbers to attribute are predetermined, communicated, and applied
    through a system
  • Subjective measures
  • Scoring system is more elusive, often involving a rater who assigns the numbers
  • Research shows these may not be strongly related, but purely objective measures can miss important parts of job performance

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Scores

  • Definition
  • Measures provide scores to represent
    amount of attribute being assessed
  • Scores are the numerical indicator of attribute
  • Central tendency and variability
  • Exh. 7.2: Central Tendency and Variability: Summary Statistics
  • Percentiles
  • Percentage of people scoring below an individual in a distribution of scores
  • Standard scores

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Discussion questions

  • Imagine and describe a staffing system for a job in which there are no measures used.
  • Describe how you might go about determining scores for applicants’ responses to (a) interview questions, (b) letters of recommendation, and (c) questions about previous work experience.

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Correlation Between Scores

  • Scatter diagrams
  • Used to plot the joint distribution of the two sets of scores
  • Exh. 7.3: Scatter Diagrams and Corresponding Correlations
  • Correlation coefficient
  • Value of r summarizes both
  • Strength of relationship between two sets of scores and
  • Direction of relationship
  • Values can range from r = -1.0 to r = 1.0
  • Interpretation – Correlation between two variables does not imply causation between them
  • Exh. 7.4: Calculation of Product-Movement Correlation Coefficient

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Exh. 7.3: Scatter Diagrams and
Corresponding Correlations

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Exh. 7.3: Scatter Diagrams and
Corresponding Correlations

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Exh. 7.3: Scatter Diagrams and
Corresponding Correlations

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Significance of the Correlation Coefficient

  • Practical significance
  • Refers to size of correlation coefficient
  • The greater the degree of common variation
    between two variables, the more one variable
    can be used to understand another variable
  • Statistical significance
  • Refers to likelihood a correlation exists in a population, based on knowledge of the actual value of r in a sample from that population
  • Significance level is expressed as p < value
  • Interpretation — If p < .05, there are fewer than 5 chances in 100 of concluding there is a relationship in the population when, in fact, there is not

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Quality of Measures

  • Reliability of measures
  • Validity of measures
  • Validity of measures in staffing
  • Validity generalization

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Quality of Measures: Reliability

  • Definition: Consistency of measurement of an attribute
  • A measure is reliable to the extent it provides a consistent set of scores to represent an attribute
  • Reliability of measurement is of concern
  • Both within a single time period and between time periods
  • For both objective and subjective measures
  • Exh. 7.6: Summary of Types of Reliability

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Ex. 7.6: Summary of Types of Reliability

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Quality of Measures: Reliability

  • Measurement error
  • Actual score = true score + error
  • Deficiency error
  • Failure to measure some aspect of attribute assessed
  • Contamination error
  • Occurrence of unwanted or undesirable influence on the measure and on individuals being measured

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Ex. 7.7 – Sources of Contamination Error and Suggestions for Control

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Quality of Measures: Reliability

  • Procedures to calculate reliability estimates
  • Coefficient alpha
  • Should be least .80 for a measure to have an acceptable degree of reliability
  • Interrater agreement
  • Minimum level of interrater agreement – 75% or higher
  • Test-Retest reliability
  • Concerned with stability of measurement
  • Level of r should range between r = .50 to r = .90
  • Intrarater agreement
  • For short time intervals between measures, a fairly high relationship is expected – r = .80 or 90%

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Quality of Measures: Reliability

  • Implications of reliability
  • Standard error of measurement
  • Since only one score is obtained from an applicant, the critical issue is how accurate the score is as an indicator of an applicant’s true level of knowledge
  • Relationship to validity
  • Reliability of a measure places an upper limit on the possible validity of a measure
  • A highly reliable measure is not necessarily valid
  • Reliability does not guarantee validity – it only makes it possible

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Quality of Measures: Validity

  • Definition: Degree to which a measure truly measures the attribute it is intended to measure
  • Accuracy of measurement
  • Exh. 7.9: Accuracy of Measurement
  • Accuracy of prediction
  • Exh. 7.10: Accuracy of Prediction

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Ex. 7.9: Accuracy of Measurement

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Discussion questions

  • Give examples of when you would want the following for a written job knowledge test
  • a low coefficient alpha (e.g., α = .35)
  • a low test–retest reliability.

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Exh. 7.12: Accuracy of Prediction

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Exh. 7.12: Accuracy of Prediction

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Validity of Measures in Staffing

  • Importance of validity to staffing process
  • Predictors must be accurate representations of KSAOs to be measured
  • Predictors must be accurate in predicting job success
  • Validity of predictors explored through validation studies
  • Two types of validation studies
  • Criterion-related validation
  • Content validation

Ex. 7.13: Criterion-Related Validation

Criterion Measures: measures of performance on tasks and task dimensions

Predictor Measure: it taps into one or more of the KSAOs identified in job analysis

Predictor–Criterion Scores: must be gathered from a sample of current employees or job applicants

Predictor–Criterion Relationship: the correlation must be calculated.

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Ex. 7.14: Concurrent and Predictive
Validation Designs

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Ex. 7.14: Concurrent and Predictive
Validation Designs

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Content Validation

  • Content validation involves
  • Demonstrating the questions/problems (predictor scores) are a representative sample of the kinds of situations occurring on the job
  • Criterion measures are not used
  • A judgment is made about the probable correlation between predictors and criterion measures
  • Used in two situations
  • When there are too few people to form a sample for criterion-related validation
  • When criterion measures are not available
  • Exh. 7.16: Content Validation

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Validity Generalization

  • Degree to which validity can be extended to other contexts
  • Contexts include different situations, samples of people and time periods
  • Situation-specific validity vs. validity generalization
  • Exh. 7.18: Hypothetical Validity Generalization Example
  • Distinction is important because
  • Validity generalization allows greater latitude than situation specificity
  • More convenient and less costly not to have to conduct a separate validation study for every situation

Exhibit 7.18 Hypothetical Validity Generalization Example

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Discussion questions

  • Assume you gave a general ability test, measuring both verbal and computational skills, to a group of applicants for a specific job. Also assume that because of severe hiring pressures, you hired all of the applicants, regardless of their test scores.
  • How would you investigate the criterion-related validity of the test?
  • How would you go about investigating the content validity of the test?
  • What information does a selection decision maker need to collect in making staffing decisions? What are the ways in which this information can be collected?

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Staffing Metrics and Benchmarks

  • Metrics
  • quantifiable measures that demonstrate the effectiveness (or ineffectiveness) of a particular practice or procedure
  • Staffing metrics
  • job analysis
  • validation
  • Measurement
  • Benchmarking as a means of developing metrics

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Collection of Assessment Data

  • Testing procedures
  • Paper and pencil measures
  • PC- and Web-based approaches
  • Applicant reactions
  • Acquisition of tests and test manuals
  • Paper and pencil measures
  • PC- and Web-based approaches
  • Professional standards

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Legal Issues

  • Disparate impact statistics
  • Applicant flow statistics
  • Applicant stock statistics
  • Standardization
  • Lack of consistency in treatment of applicants is
    a major factor contributing to discrimination
  • Example: Gathering different types of background information from protected vs. non-protected groups
  • Example: Different evaluations of information for protected vs. non-protected groups
  • Validation
  • If adverse impact exists, a company must either eliminate it or justify it exists for job-related reasons (validity evidence)

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Ethical Issues

  • Issue 1
  • Do individuals making staffing decisions have an ethical responsibility to know measurement issues? Why or why not?
  • Issue 2
  • Is it unethical for an employer to use a selection measure that has high empirical validity but lacks content validity? Explain.
 
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