Demonstrating causal relationships has been of particular importance in organizational stress research. Longitudinal studies are typically suggested to overcome problems of reversed causation and third variables (e.g., social desirability and negative affectivity). This article reviews the empirical longitudinal literature and discusses designs and statistical methods used in these studies. Forty-three longitudinal field reports on organizational stress were identified. Most of the investigations used a 2-wave panel design and a hierarchical multiple regression approach. Six studies with 3 and more waves were found. About 50% of the studies analyzed potential strain-stressor (reversed causation) relationships. In about 33% of the studies there was some evidence of reverse causation. The power of longitudinal studies to rule out third variable explanations was not realized in many studies. Procedures of how to analyze longitudinal data are suggested. Occupational stress research has been quickly developing during the last two decades. This has led to many excellent studies-many of them longitudinal-that produced a wide range of knowledge in this field. However, even though this development is welcomed, we argue that the power of longitudinal designs has not yet been fully realized. This is because designs and analyses of the stress investigations are far from optimal. Thus, the review of the literature in this article on longitudinal stress research from a methodological angle is supposed to show the pitfalls and difficulties in design and analysis strategies but should also point out good examples of longitudinal research. Obviously, we are not the only ones who present a critique of stress research (

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Although almost all literature on burnout implicitly assumes that burnout is primarily caused by stressful employee-customer interactions, only a few studies have addressed this empirically. A principal-components analysis of a newly developed instrument assessing various forms of customer-related social stressors (CSS) in 3 different service jobs (N = 591) revealed 4 themes of CSS: disproportionate customer expectations, customer verbal aggression, disliked customers, and ambiguous customer expectations. These 4 CSS predict burnout beyond a variety of control variables. Contrary to other predictors of burnout analyzed in previous studies, the amount of variance explained in exhaustion (14%) by the 4 CSS scales is not higher than for personal accomplishment (14%) and is considerably lower than for depersonalization (23%).

This article investigates the causes of mobbing (bullying) at work. Mobbing is defined as a severe form of social stressors at work. Unlike``normal'' social stressors, mobbing is a long lasting, escalated conflict with frequent harassing actions systematically aimed at a target person. It is argued that the organisation, the social system, a certain perpetrator and the victim have to be considered as potential causes of mobbing. Results of two samples of mobbing victims and a control group support this view. It is concluded that one-sided explanations on the causes of mobbing are likely to be inappropriate and that many cases are characterised by multi-causality ± a common finding in conflict research.

Abstract. In recent years, researchers in work and organizational psychology have increasingly become interested in short-term processes and everyday experiences of working individuals. Diaries provide the necessary means to examine these processes. Although diary studies have become more popular in recent years, researchers not familiar with this method still find it difficult to get access to the required knowledge. In this paper, we provide an introduction to this method of data collection. Using two diary study examples, we discuss methodological issues researchers face when planning a diary study, examine recent methodological developments, and give practical recommendations. Topics covered include different types of diary studies, the research questions to be examined, compliance and the issue of missing data, sample size, and issues of analyses.

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