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On the Distribution of Job Performance: The Role of Measurement Characteristics in Observed Departures from Normality.

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Title: On the Distribution of Job Performance: The Role of Measurement Characteristics in Observed Departures from Normality.
Authors: Beck, James W., Beatty, Adam S., Sackett, Paul R.
Source: Personnel Psychology. Autumn2014, Vol. 67 Issue 3, p531-566. 36p. 14 Graphs.
Subjects: Job performance, Employee reviews, Performance standards, Human behavior, Performance
Abstract: In a recent article, O'Boyle and Aguinis () argued that job performance is not distributed normally but instead is nonnormal and highly skewed. However, we believe the extreme departures from normality observed by these authors may have been due to characteristics of performance measures used. To address this issue, we identify 7 measurement criteria that we argue must be present for inferences to be made about the distribution of job performance. Specifically, performance measures must: (a) reflect behavior, (b) include an aggregation of multiple behaviors, (c) include the full range of performers, (d) include the full range of performance, (e) be time bounded, (f) focus on comparable jobs, and (g) not be distorted by motivational forces. Next, we present data from a wide range of sources-including the workplace, laboratory, athletics, and computer simulations-that illustrate settings in which failing to meet one or more of these criteria led to a highly skewed distribution providing a better fit to the data than a normal distribution. However, measurement approaches that better align with the 7 criteria listed above resulted in a normal distribution providing a better fit. We conclude that large departures from normality are in many cases an artifact of measurement. [ABSTRACT FROM AUTHOR]
Copyright of Personnel Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: <searchLink fieldCode="JN" term="%22Personnel+Psychology%22">Personnel Psychology</searchLink>. Autumn2014, Vol. 67 Issue 3, p531-566. 36p. 14 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Job+performance%22">Job performance</searchLink><br /><searchLink fieldCode="DE" term="%22Employee+reviews%22">Employee reviews</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+standards%22">Performance standards</searchLink><br /><searchLink fieldCode="DE" term="%22Human+behavior%22">Human behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Performance%22">Performance</searchLink>
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  Data: In a recent article, O'Boyle and Aguinis () argued that job performance is not distributed normally but instead is nonnormal and highly skewed. However, we believe the extreme departures from normality observed by these authors may have been due to characteristics of performance measures used. To address this issue, we identify 7 measurement criteria that we argue must be present for inferences to be made about the distribution of job performance. Specifically, performance measures must: (a) reflect behavior, (b) include an aggregation of multiple behaviors, (c) include the full range of performers, (d) include the full range of performance, (e) be time bounded, (f) focus on comparable jobs, and (g) not be distorted by motivational forces. Next, we present data from a wide range of sources-including the workplace, laboratory, athletics, and computer simulations-that illustrate settings in which failing to meet one or more of these criteria led to a highly skewed distribution providing a better fit to the data than a normal distribution. However, measurement approaches that better align with the 7 criteria listed above resulted in a normal distribution providing a better fit. We conclude that large departures from normality are in many cases an artifact of measurement. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Personnel Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Job performance
        Type: general
      – SubjectFull: Employee reviews
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      – SubjectFull: Performance standards
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              Text: Autumn2014
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