De

No abstract

Self-regulation at work is conceived in terms of within-person processes that occur over time. These processes are proposed to occur within a hierarchical framework of negative feedback systems that operate at different levels of abstraction and with different time cycles. Negative feedback systems respond to discrepancies in a manner that reduces deviations from standards (i.e., goals). This is in contrast to positive feedback systems in which discrepancies are created, which can lead to instability. We organize our discussion around four hierarchical levels-self, achievement task, lower-level task action, and knowledge/working memory. We theorize that these levels are loosely connected by multiple constraints and that both automatic and more conscious processes are essential to self-regulation. Within- and cross-level affective and cognitive processes interact within this system to motivate goal-related behaviors while also accessing needed knowledge and protecting current intentions from interference. Complications common in the work setting (as well as other complex, real-life settings) such as the simultaneous pursuit of multiple goals, the importance of knowledge access and expertise, and team and multiperson processes are also discussed. Finally, we highlight the usefulness of newer research methodologies and data-analytic techniques for examining such hierarchical, dynamic, within-person processes.

For theoretical and empirical reasons, researchers may combine item-level responses into aggregate item parcels to use as indicators in a structural equation modeling context. Yet the effects of specific parceling strategies on parameter estimation and model fit are not known. In Study 1, different parceling combinations meaningfully affected parameter estimates and fit indicators in two organizational data sets. Based on the concept of external consistency, the authors proposed that combining items that shared an unmodeled secondary influence into the same parcel (shared uniqueness strategy) would enhance the accuracy of parameter estimates. This proposal was supported in Study 2, using simulated data generated from a known model. When the unmodeled secondary influence was related to indicators of only one latent construct, the shared uniqueness parceling strategy resulted in more accurate parameter estimates. When indicators of both target latent constructs were contaminated, bias was present but appropriately signaled by worsened fit statistics.

Statistical issues associated with multilevel data are becoming increasingly important to organizational researchers. This paper concentrates on the issue of assessing the factor structure of a construct at aggregate levels of analysis. Specifically, we describe a recently developed procedure for performing multilevel confirmatory factor analysis (MCFA) [Muthen, B.O. (1990). Mean and covariance structure analysis of hierarchical data. Paper presented at the Psychometric Society, Princeton, NJ; Muthen, B.O. (1994). Multilevel covariance structure analysis. Sociological Methods and Research, 22,, and provide an illustrative example of its application to leadership data reflecting both the organizational and societal level of analysis. Overall, the results of our illustrative analysis support the existence of a valid societal-level leadership construct, and show the potential of this multilevel confirmatory factor analysis procedure for leadership research and the field of I/O psychology in general.