When Generic Functions Use Dynamic Values

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Commonalities and contradictions in HRM and performance research

This is an overview of what the authors believe to be every empirical research article into the linkages between HRM and performance published in pre-eminent international refereed journals between 1994 and 2003. The analysis covers the design of the study, including the primary level of analysis and the identity of the respondents; the dominant theoretical framework(s) informing the article; how HRM is conceived and operationalised; how performance is conceived and operationalised; and which control and/or contingency variables are incorporated. Finally, the article examines how each study depicts the so-called 'black box' stage between HRM and performance. It reports wide disparities in the treatment of these components, but also some welcome commonalities and indicative trends that point towards a gradual convergence on how future research into this complex relationship might usefully be conducted. The findings are compared with previous reviews of the literature. The analysis should illuminate the ongoing debate about the linkages between HRM and performance, and prove valuable for future research designs

Random and systematic errors in case-control studies calculating the injury risk of driving under the influence of psychoactive substances

<p>Between 2006 and 2010, six population based case-control studies were conducted as part of the European research-project DRUID (DRiving Under the Influence of Drugs, alcohol and medicines). The aim of these case-control studies was to calculate odds ratios indicating the relative risk of serious injury in car crashes. The calculated odds ratios in these studies showed large variations, despite the use of uniform guidelines for the study designs. The main objective of the present article is to provide insight into the presence of random and systematic errors in the six DRUID case-control studies. Relevant information was gathered from the DRUID-reports for eleven indicators for errors. The results showed that differences between the odds ratios in the DRUID case-control studies may indeed be (partially) explained by random and systematic errors. Selection bias and errors due to small sample sizes and cell counts were the most frequently observed errors in the six DRUID case-control studies. Therefore, it is recommended that epidemiological studies that assess the risk of psychoactive substances in traffic pay specific attention to avoid these potential sources of random and systematic errors. The list of indicators that was identified in this study is useful both as guidance for systematic reviews and meta-analyses and for future epidemiological studies in the field of driving under the influence to minimize sources of errors already at the start of the study. (C) 2013 Elsevier Ltd. All rights reserved.</p>