Statistical Methods in Human Resource Research: Dearth or Diversity? Evidence from Published Literature. (September 2020)

• Record Type: Journal Article
• Authors: Mutua, Mbithi
• Is Part Of: Asia-Pacific journal of management research and innovation. Volume 16:Number 3(2020)
• Page Start: 238
• Page End: 247
• Publication Date: 2020-09
• Subjects: HRM statistics -- Data Science -- HR Research -- E HRM; Industrial management -- India -- Periodicals; Industrial management -- Periodicals; Commerce -- Periodicals; Business enterprises -- India -- Periodicals; India -- Commerce -- Periodicals; 330.0954
• DOI: 10.1177/2319510X211005665
• Languages: English
• ISSNs: 2319-510X

Abstract

This article attempts to find out if there is breadth in application of quantitative techniques in published literature within the field of human resource management (HRM). In addition, it investigates the holistic use of specific categories of statistics, and if there are categories that are neglected. The study utilises a combination of research questions and hypotheses. The broad categories of statistics that this study focussed on include descriptive, data science statistics, exploratory graphical, advanced statistics such as structural equation modelling, Bayesian statistics and inferential statistics. It goes further to study application of machine learning statistics in HRM research. Using archival methodology, the article utilises a sample of 120 journal papers to answer formulated research questions and hypotheses. Descriptive statistics, exploratory graphical analysis and inferential statistics are used in the analysis. The findings indicate that there are neglected statistics in HRM research. Overall, most statistical categories are underutilised. HRM journal editors, researchers and practitioners must stock HRM methodological toolbox.