Human Resource Management Assignment 2. See Instructions and Build off of assignment 1
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HRM637PPTCh7.pptxStaffing Organizations
Chapter 7:
Measurement
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Measurement 1
Importance and Use of Measures
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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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Goals and Uses
Goals
Obtain a score for a person on a given attribute.
Differentiate individuals and make decisions.
Example
Applicants’ scores on an ability test: hiring.
Employees’ performance evaluation rating scores: promotion.
Applicants’ ratings of rewards in terms of their importance: how to reward good performance.
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Measurement 2
Key Concepts
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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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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
Percentiles
Percentage of people scoring below an individual in a distribution of scores.
Standard scores
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Scores, Central Tendency, and Variability
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Correlation Coefficient
Correlation coefficient
Value of r summarizes:
Strength of relationship between two sets of scores.
Direction of relationship.
Values can range from r = -1.0 to r = 1.0
Correlation between two variables does not imply causation between them
Access the text alternative for slide images.
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Calculating Correlation Coefficients
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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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Measurement 3
Quality of Measures
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Quality of Measures: Reliability 1
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.
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Summary of Types of Reliability
Table divided into three columns summarizes data for types of reliability. Column 1 notes Objective measure (test items), and Subjective measure (raters). The column headers from 2 to 3 are marked as: Compare scores within T1 and T2 and Compare scores between T1 and T2.
| Compare scores within T1 and T2 | Compare scores between T1 and T2 | |
| Objective measure (test items) | Internal consistency | Test-retest |
| Subjective measure (raters) | Interrater | Intrarater |
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Quality of Measures: Reliability 2
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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Sources of Contamination Error and Suggestions for Control
Table divided into three columns summarizes sources of contamination error and suggestions for control. The column headers are marked from left to right as: Source of contamination, example, and suggestion for control.
| Source of contamination | Example | Suggestion for Control |
| Content domain | Irrelevant material on the test | Define domain of test material to be covered |
| Standardization | Different time limits for same test | Have same time limits for everyone |
| Chance response tendencies | Guessing by test taker | Impossible to control in advance |
| Rater | Rater gives inflated ratings to people | Train rater in rating accuracy |
| Rating situation | Interviewees are asked different questions | Ask all interviewees the same questions |
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Calculation of Percent Agreement Among Raters
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Quality of Measures: Reliability 3
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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Accuracy of Measures
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Accuracy of Prediction: General Illustration
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Accuracy of Prediction: Selection Example
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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.
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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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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.
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Content Validation Study
Job Analysis: First-Level Supervisor—Maryland Department of Transportation
Seven performance dimensions and task statements:
Organizing work; assigning work; monitoring work; managing consequences; counseling, efficiency reviews, and discipline; setting an example; employee development
Fourteen KSAOs and definitions:
Organizing; analysis and decision making; planning; communication (oral and written); delegation; work habits; carefulness; interpersonal skill; job knowledge; organizational knowledge; toughness; integrity; development of others; listening
Predictor Measures: Five Assessment Methods
Multiple-choice in-basket exercise
(assume role of new supervisor and work through in-basket on desk)
Structured panel interview
(predetermined questions about past experiences relevant to the KSAOs)
Presentation exercise
(make presentation to a simulated work group about change in their work hours)
Writing sample
(prepare a written reprimand for a fictitious employee)
Training and experience evaluation exercise
(give examples of training and work achievements relevant to certain KSAOs)
Source: Adapted from M. A. Cooper, G. Kaufman, and W. Hughes, “Measuring Supervisory Potential,” IPMA News, December 1996, pp. 8–18.
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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
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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Hypothetical Validity Generalization Example
Table divided into 6 columns summarizes the hypothetical validity generalization example. The column headers are marked from left to right as: study, sample size n, validity r sub xy, reliability predictor (x) r sub xx, reliability criterion (y) r sub yy, and corrected validity r sub c.
| Study | Sample Size n | Validity rxy | Reliability Predictor (x) rxx | Reliability Criterion (y) ryy | Corrected Validity rc |
| Birch, 2013 | 454 | .41 | .94 | .94 | .44 |
| Cherry, 1990 | 120 | .19 | .66 | .76 | .27 |
| Elm, 1978 | 212 | .34 | .91 | .88 | .38 |
| Hickory, 2009 | 37 | -.21 | .96 | .90 | -.23 |
| Locust, 2000 | 92 | .12 | .52 | .70 | .20 |
| Maple, 1961 | 163 | .32 | .90 | .84 | .37 |
| Oak, 1948 | 34 | .09 | .63 | .18 | .27 |
| Palm, 2007 | 202 | .49 | .86 | .92 | .55 |
| Pine, 1984 | 278 | .27 | .80 | .82 | .33 |
| Walnut, 1971 | 199 | .18 | .72 | .71 | .25 |
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Measurement 4
Collection of Assessment Data
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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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Measurement 5
Legal Issues
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Legal Issues 1
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Legal Issues 2
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 in Staffing
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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