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.

中文翻译：

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人力资源管理研究中的预测——修辞与现实之间的差距

研究人员可以大致从两个维度评估他们的统计模型：解释能力和预测能力。使用老龄化劳动力的工作满意度数据，我们通过表明具有一定解释力的模型可以产生截然不同的预测能力水平（反之亦然）——在相同的和不同的上下文。进一步，我们回顾了 2014 年至 2018 年间在三个顶级人力资源管理期刊上发表的所有论文，以表明研究人员通常混淆了解释和预测。具体来说，虽然几乎所有作者都仅依赖解释力评估（即评估系数是否显着并在假设的方向上），他们还得出实用的建议，这些建议本质上来自预测场景。根据我们的结果，我们为 HRM 研究人员提供了有关如何提高解释性研究严谨性的建议。