Sub-Sampling at the Researcher's Peril: New Insights Into Sampling Strategy to Avoid Invalid Findings

Many researchers currently make scientific claims about a general population that differs in material dimensions from the subsample utilized in the analysis, without fully describing their sample characteristics. It is essential to fully disclose relevant facets of the sample, to enable future stakeholders to make appropriate adjustments: we argue that all publications are valuable independently of the sampling strategy, however; their usefulness will dramatically increase when the authors include all conceivable sample characteristics. By employing a Big-Data set of over 3,300,000 workers (including 300,000 foreign workers) over 10 years, we illustrate how focusing on narrow subsets of a target group can lead to very different conclusions. We address methodological and ethical challenges for the HRM research field providing recommendations on how to avoid the possibility of flawed validity results and how to make the study more relevant, impactful and ethically robust. For practitioners, we highlight how managers can draw learning from academic studies by appreciating differences in subgroups' outcomes that incorporate “context,” which eventually can inform strategic management and managerial decisions.