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Chap007 measurement in_selection_editing

Chap007 measurement in_selection_editing






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    Chap007 measurement in_selection_editing Chap007 measurement in_selection_editing Presentation Transcript

    • 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 07: Measurement
    • Staffing Organizations Model 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 7- Organization Strategy HR and Staffing Strategy Organization Mission Goals and Objectives
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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 7-
    • 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
    • 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
    • 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 results
    • 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
    • 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
    • Exh. 7.3: Scatter Diagrams and Corresponding Correlations 7-
    • Exh. 7.3: Scatter Diagrams and Corresponding Correlations 7-
    • Exh. 7.3: Scatter Diagrams and Corresponding Correlations 7-
    • 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
    • Quality of Measures
      • Reliability of measures
      • Validity of measures
      • Validity of measures in staffing
      • Validity generalization
    • 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
    • Ex. 7.6: Summary of Types of Reliability 7-
    • Quality of Measures: Reliability
      • Measurement error
        • Actual score = true score + error
        • Deficiency error: Occurs when there is failure to measure some aspect of attribute assessed
        • Contamination error: Represents occurrence of unwanted or undesirable influence on the measure and on individuals being measured
    • Ex. 7.7 - Sources of Contamination Error and Suggestions for Control 7-
    • 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%
    • 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
    • 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
    • Ex. 7.9: Accuracy of Measurement 7-
    • 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.
    • Exh. 7.10: Accuracy of Prediction 7-
    • Exh. 7.10: Accuracy of Prediction 7-
    • 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.
    • Ex. 7.12: Concurrent and Predictive Validation Designs 7-
    • Ex. 7.12: Concurrent and Predictive Validation Designs 7-
    • 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
    • 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
    • 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
    • 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
    • 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)