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Prediction in HRM research - A gap between rhetoric and reality
[journal article]
Abstract
There are broadly two dimensions on which researchers can evaluate their statistical models: explanatory power and predictive power. Using data on job satisfaction in ageing workforces, we empirically highlight the importance of distinguishing between these two dimensions clearly by showing that a m... view more
There are broadly two dimensions on which researchers can evaluate their statistical models: explanatory power and predictive power. Using data on job satisfaction in ageing workforces, we empirically highlight the importance of distinguishing between these two dimensions clearly by showing that a model with a certain degree of explanatory power can produce vastly different levels of predictive power and vice versa - in the same and different contexts. In a further step, we review all the papers published in three top-tier human resource management journals between 2014 and 2018 to show that researchers generally confuse explanation and prediction. Specifically, while almost all authors rely solely on explanatory power assessments (i.e., assessing whether the coefficients are significant and in the hypothesised direction), they also derive practical recommendations, which inherently result from a predictive scenario. Based on our results, we provide HRM researchers recommendations on how to improve the rigour of their explanatory studies.... view less
Keywords
explanation; prognosis; relevance; statistical analysis; work satisfaction; job satisfaction
Classification
Human Resources Management
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
explanation; explanatory power; generalisability; prediction; predictive power; relevance; ZA6770: International Social Survey Programme: Work Orientations IV - ISSP 2015
Document language
English
Publication Year
2022
Page/Pages
p. 485-513
Journal
Human Resource Management Journal, 32 (2022) 2
DOI
https://doi.org/10.1111/1748-8583.12400
ISSN
1748-8583
Status
Published Version; peer reviewed
Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0