Chap007 measurement in_selection_editing

Part 4Staffing Activities: Selection

Chapter 7: MeasurementChapter 8: External Selection IChapter 9: External Selection IIChapter 10: Internal Selection

McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Part 4Staffing Activities: Selection

Chapter 07:

Measurement

Organization StrategyOrganization Strategy HR and Staffing StrategyHR and Staffing Strategy

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

OrganizationMission

Goals and Objectives

Staffing Organizations Model

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7-4

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 Disparate Impact

Statistics Standardization

and Validation

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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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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 scoresa 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 typesof 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 objectsAttribute/Construct - Knowledge of

mechanical principlesObjects - 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 inObjective and Subjective Measures

Objective measuresRules used to assign numbers to attribute

are predetermined, communicated, and appliedthrough a system

Subjective measuresScoring system is more elusive, often

involving a rater who assigns the numbersResearch results

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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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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 andCorresponding 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 variablecan 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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Measurement errorActual score = true score + errorDeficiency error: Occurs when there is

failureto measure some aspect of attribute assessed

Contamination error: Represents 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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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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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 measurementExh. 7.9: Accuracy of Measurement

Accuracy of predictionExh. 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 testa low coefficient alpha (e.g., α = .35)a low test–retest reliability.

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Exh. 7.10: Accuracy of Prediction

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Exh. 7.10: 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. 7.11: 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. 7-33

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Ex. 7.12: Concurrent and PredictiveValidation Designs

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Ex. 7.12: Concurrent and PredictiveValidation 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. 7.14: 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. 7.16: 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

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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 proceduresPaper and pencil measuresPC- and Web-based approaches

Applicant reactionsAcquisition of tests and test manuals

Paper and pencil measuresPC- 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)

Technology

Chap007 measurement in_selection_editing