On the Distribution of Job Performance: The Role of Measurement Characteristics in Observed Departures from Normality
质疑工作绩效分布高度偏斜的结论，提出7项测量标准，并通过多源数据证明，当测量方法符合这些标准时，正态分布拟合更优，偏离正态多为测量伪像。
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.