Đang tải... (xem toàn văn)
2 Learning Objectives After studying this chapter, you should be able to: §Describe the purpose of recruiting. §Explain what recruitment “spillover effects” are. §Understand what makes a recruiter more or less effective. §Describe the various strategies used to attract applicants. §Describe how recruiting guides and the EEOC’s best recruiting practices promote recruiting consistency and quality.
15/03/2021 COPYRIG HT © 2015 PEARSON EDU CATION , IN C 1-1 Chapter 8 – Measurement COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-2 15/03/2021 Learning Objectives After studying this chapter, you should be able to: •Describe why measurement and assessment are important to staffing •Describe patterns in data •Understand correlation and regression and explain how each is used •Define both practical and statistical significance, and explain why they are important •Define reliability and validity and explain how they affect the evaluation of a measure •Explain why standardization and objectivity are important in measurement COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-3 Why Is Proper Measurement Important? Effective measurement and data analytics can result in a competitive edge Improperly assessing and measuring candidate characteristics can lead to: § Systematically hiring the wrong people § Offending and losing good candidates § Exposing your company to legal action There are many legal issues involved with candidate assessment and measurement COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-4 15/03/2021 What Is Measurement? Measurement is the process of assigning numbers according to some rule or convention to aspects of people, jobs, job success, or aspects of the staffing system The measures enable improvement of the staffing system by identifying patterns useful for understanding and predicting relevant processes and outcomes The measures relevant to staffing are those that assess: § The characteristics of the job, which enables the creation of job requirements and job rewards matrices § Aspects of the staffing system such as the number of days a job posting is run, where it is run, and the recruiting message § The characteristics of job candidates such as ability or personality ◦ Staffing outcomes, such as performance or turnover COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-5 What Is Data? The numerical outcomes of measurement are data There are 2 types of data: § § Predictive data is information about measures used to make projections about outcomes Criterion data is information about important outcomes of the staffing process o o Traditionally, this data includes measurement of employee job success, which is the organization’s unique definition of success and performance in the job and in the firm Criterion data should also include all outcome data that is relevant to the evaluation of the effectiveness of the staffing system against its goals This may include measures of job success, time-to-hire, promotion rates, and tenure rates as well as job and company engagement, fit with company values, and willingness to help other employees COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-6 15/03/2021 Types of Measurements §Nominal: numbers are assigned to discrete labels or categories (e.g., race, gender, college major) §Ordinal: attributes are ranked in ascending or descending order (e.g., ranking from best to worst performance) §Interval: zero point is arbitrary but distance between scores has meaning (e.g., intelligence or interview scores) ĐRatio: distance between scores has meaning and there is a true zero point (e.g., salary, typing speed) COPYRIG HT â 2015 PEARSON EDU CATION , IN C 8-7 Describing Data Scoring: The process of assigning numerical values during measurement Raw scores: the unadjusted scores on a measure § Criterion-referenced measures: measures in which the scores have meaning in and of themselves § Norm-referenced measures: measures in which the scores have meaning only in comparison to the scores of other respondents Normal curve: a symmetrical, bell-shaped curve representing the distribution of a characteristic COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-8 https://www.renaissance.com/2018/07/11/blog-criterion-referenced-tests-norm-referenced-tests/ 15/03/2021 The Normal Curve 8-9 COPYRIG HT © 2015 PEARSON EDU CATION , IN C Describing the Normal Curve Percentile score: a raw score that has been converted into an expression of the percentage of people whose score falls at or below that score Central tendency: describes the midpoint or center of data § Mean: the average of the scores § Median: the middle score , or the point below which 50 percent of the scores fall § Mode: the most commonly observed score (bimodal = two modes) Variability: describes the spread of the data around the midpoint § Range: the difference between the highest & lowest observed score § Outlier: score much higher or lower than most of the scores in a distribution § Variance: a mathematical measure of spread based on squared deviations of scores from the mean § Standard deviation: positive square root of the variance; conceptually similar to the average distance from the mean of a set of scores COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-10 15/03/2021 Standard Scores Standard scores: Converted raw scores that indicate where a person’s score lies in comparison to a referent group § A common standard score is the z score § A z score indicates how many units of standard deviations the individual’s score is above or below the mean of the referent group A z score is negative when the target individual’s raw score is below the referent group’s mean, and positive when the target individual’s raw score is above the referent group’s mean COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-11 Converting Raw Scores to Standard Scores Mean StdDev 18.25 3.00 Mean 78.25 StdDev 7.46 zscore = (Individual’s raw score – Referent group mean) / Referent group standard deviation) Meaningfully combining the raw scores would be difficult Combining the z scores is easy and results in a single number reflecting how each candidate did on both of the assessments relative to the other candidates COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-12 15/03/2021 Shifting the Normal Applicant Talent Curve §When making selection decisions, it is often assumed that in the applicant pool, the distribution of applicant fit with the job reflects the normal curve A large burden is then placed on the selection system to accurately identify which candidates are in the far right tail of the normal curve §However, many of the most desirable people for the position are likely to be actively and happily employed elsewhere and are semi-passive job seekers at best In this case, the distribution of applicant fit with the job might resemble the A distribution shown on the next slide 8-13 COPYRIG HT © 2015 PEARSON EDU CATION , IN C Shifting the Applicant Talent Curve COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-14 15/03/2021 Shifting the Normal Applicant Talent Curve §If done strategically, sourcing and recruiting can discourage poor fits from applying and increase the number of high quality passive and semi-passive candidates who apply §This shifts the curve to reflect a distribution like that shown by the B distribution §The B distribution clearly reduces the burden on the selection system to identify quality candidates and significantly increases the likelihood of identifying a high-quality candidate COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-15 Correlation Coefficient Correlation coefficient, also called “Pearson’s r” or the “bivariate correlation,” is a single number that ranges from -1 to +1 that reflects the direction (positive or negative) and magnitude (strength) of the relationship between two variables § A value of r = 0 indicates that values of one measure are unrelated to values of the other measure § A value of r = +1 means that there is a perfectly linear, positive relationship between the two measures; as values of one measure increase, values of the other measure increase exactly the same amount in standard deviations § A value of r = -1 means that there is a perfectly negative or inverse relationship between the two measures; as values of one measure increase, values of the other variable decrease exactly the same amount in standard deviations COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-16 15/03/2021 Graphing Correlations Scatter plot: graphical illustration of the relationship between two variables § Each point on the chart corresponds to how a person scored on a measure and how he or she performed on the job COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-17 Scatter Plot of r = -.43 Would this test be useful in making hiring decisions? COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-18 15/03/2021 Scatter Plot of a Curvilinear Relationship (r = 04) COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-19 Diagrams for Correlations COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-20 10 15/03/2021 Reliability and Validity COPYRIG HT © 2015 PEARSON EDU CATION , IN C 8-39 What Is Validation? Validation is the cumulative and ongoing process of establishing the job relatedness of a measure There are three types of validation processes: § Content-related validation: Demonstrating that the content of a measure assesses important job-related behaviors § Construct-related validation: Demonstrating that a measure assesses the construct, or characteristic, it claims to measure Đ Criterion-related validation: Demonstrating that there is a statistical relationship between scores from a measure and the criterion, usually some aspect of job success COPYRIG HT â 2015 PEARSON EDU CATION , IN C 8-40 20