Leadership: A Crucial Ingredient in Unstable Times

Social work is experiencing an unprecedented degree of institutional instability, particularly in the advanced industrial nations which, to varying degrees and via differing paths, have abandoned the Keynesian Welfare State. It has been replaced with a fundamentally different workfare regime in which operates on quite different assumptions – all of which pose fundamental challenges to social work. The degree of change is such that it can be understood as institutional change. The profession needs a number of strategies in response the contemporary de-stabilization. Drawing on theoretical and empirical literature about institutional change we show why it is that professional leadership is crucial in the current environment. The paper reviews what in currently know about leadership, both in general and in relation to social work. Referring to the notion of institutional entrepreneurs and on the role played by other non-social work professional associations in situations of change, we articulate what role leadership can play. We conclude with recommendations about how leadership could be promoted, particularly by the professional associations.

How large are actor and partner effects of personality on relationship satisfaction? The importance of controlling for shared method variance

Previous research suggests that the personality of a relationship partner predicts not only the individual’s own satisfaction with the relationship but also the partner’s satisfaction. Based on the actor–partner interdependence model, the present research tested whether actor and partner effects of personality are biased when the same method (e.g., self-report) is used for the assessment of personality and relationship satisfaction and, consequently, shared method variance is not controlled for. Data came from 186 couples, of whom both partners provided self- and partner reports on the Big Five personality traits. Depending on the research design, actor effects were larger than partner effects (when using only self-reports), smaller than partner effects (when using only partner reports), or of about the same size as partner effects (when using self- and partner reports). The findings attest to the importance of controlling for shared method variance in dyadic data analysis.