Multi‐level models of stress and well‐being

Most behavioural science models of stress acknowledge that a complete understanding of the structure and process of stress invariably requires a consideration of individual, group and environmental/situational characteristics (Sulsky Hobfall's (1989) Conservation of Resources theory; French and colleagues' P-E fit model, (French, Caplan, job and organizational factors (e.g., work hours and schedules, role overload/underload/ambiguity, emotional labour, job insecurity, organizational climate); and even societal and national factors (e.g. cultural values; social, economic and environmental indicators). Yet, all too often, these variables are all measured at a single level, usually at the individual level. Thus, while we acknowledge that individual, group, organizational and even cultural variables are theoretically necessary to understand and predict stress outcomes, we rarely measure these variables at their appropriate level. The purpose of this editorial is to encourage researchers to take a multilevel approach to considering the relationships between occupational stress and well-being. This entails intentionally reflecting on the individual, job, organizational and macroeconomic antecedents of employee stress. Such a multilevel framework can be fruitful not only for understanding the stress-strain process, but also crucial in developing and conceptualizing stress interventions. Thus, a multilevel approach can enhance both our theoretical and empirical understanding of occupational stress as well as the practical implications stemming from our research. Using a multi-level modelling (MLM) approach means explicitly taking into account the often hierarchical nature of our data structure (e.g. individuals working in groups, students in classrooms, children in families). Such a hierarchical dataset has traditionally posed three problems within an ordinary least squares (OLS) regression approach: (1) the units of analysis problem; (2) the violation of independent observations assumption problem; and (3) the heterogeneity of slopes problem. Let us consider each in turn. As associate editor, I often review manuscripts where the researchers have (admirably) considered individual and contextual factors in their investigations of occupational stress. Yet, they often fail to realize that their subsequent analyses and interpretation of results fall prey to what is known as the ecological fallacy. The ecological fallacy occurs when a researcher makes inferences about individual-level processes based on aggregate data for a group. The reverse situation can also cause problems (e.g. where inappropriate inferences are made about group-level processes based on individual-level data). In either case, the process of aggregating or disaggregating data can lead to faulty inferences regarding theoretical processes of interest. Next, although OLS regression and MLM techniques both consider within- and between-group variability, OLS techniques fail to take into account the violation of the independent observations assumption that can occur with nested data (Raudenbush cohesion and stress among soldiers and units, Griffith, 2002; physical activity and stress among college students, Nguyen-Michel, Unger, Hamilton, classroom climate and student distress, Torsheim Raudenbush Snijders & Bosker, 1999).