DescriptionPolicy learning is a multilevel notion. Individual learning is a psychological mechanism through which policymakers change their beliefs about policies. At aggregate levels (group, network, coalition, organization, etc.), policy learning can be viewed as a sum of individual learning processes or as a specific concept that accounts for collective dynamics. Few policy learning research has been multilevel in approach. Most existing studies focus on aggregate-level determinants (e.g., organizational characteristics) of collective learning (e.g., organizational learning). Multilevel regression analysis allows to aggregate individual-level measures of policy learning systematically. Weighted multilevel regression analysis allows to account for specific conceptualizations of collective learning in which some individual policymakers are given more weight than the others (e.g., the most influential ones). Finally, weighting procedures allow to compensate the scarcity of data among certain categories of individual policymakers, in a survey. By clarifying the measurement and the relations between individual learning and collective learning, weighted multilevel analysis helps to reduce conceptual stretching and to improve theories. As this method provides tools to assess the respective influence of individual-level and aggregate-level determinants of policy learning, the added value of this method is not only theoretical but also practical. The paper illustrates the application of weighted multilevel analysis in a study of policy learning among 335 policymakers involved in the European liberalization process of two Belgian network industries: the rail sector and the electricity sector. This method is valuable for the analysis of any multilevel aspect of the policy process.
|Location||University of Colorado Denver|