多样性(政治)
多样性(控制论)
概念化
清晰
范畴变量
构造(python库)
领域(数学)
计算机科学
数据科学
管理科学
人工智能
心理学
社会学
机器学习
数学
工程类
化学
程序设计语言
纯数学
生物化学
人类学
作者
Torsten Biemann,Kim De Meulenaere
出处
期刊:Proceedings - Academy of Management
[Academy of Management]
日期:2021-08-01
卷期号:2021 (1): 13003-13003
标识
DOI:10.5465/ambpp.2021.13003abstract
摘要
Research on workplace diversity is proliferating. Inspired by Harrison and Klein’s (2007) conceptualization of diversity into three types of diversity—separation, disparity, and variety—and their associated framework of measures, diversity scholars have paid increasing attention to the measurement of their diversity construct. In this paper, we argue that the widely used measurement framework of Harrison and Klein suffers from important limitations— that is, the separation measures assume non-linear effects, the disparity measures do not take into account left-skewed attribute distributions, and the proposed variety measures can be used for categorical variables only. To solve these issues, we take a distance-based approach to the three types of diversity and provide a unifying, easy-to-use framework of measures, including measures for diversity constructs for which no adequate measures were available yet. Using simulated data of work units, we show how our proposed measures behave and how they relate to the measures proposed by Harrison and Klein. This manuscript concludes with an accessible decision tree, easily guiding diversity scholars in when and how to use the different diversity measures. Our hope is that our framework contributes to Harrison and Klein’s (2007) mission to improve the concept clarity, measurement, and empirical testing in the field of workplace diversity.
科研通智能强力驱动
Strongly Powered by AbleSci AI