| Summary: | For empirical work on macro-micro-macro issues, it is indispensable to have statistical models reflecting in a plausible way the data structures and theoretical relations that are the basis of such issues. A basic family of models for macro-micro effects is provided by multilevel analysis. An important way of studying micro-macro transitions is by means of simulation.Network modeling provides another, particularly appropriate, approach to macro-micro-macro issues. Network models represent, in a more subtle way than multilevel models, that what can be observed at the macro level is the result of phenomena at the micro level, while these phenomena themselves are conditioned by the macro level. The use of appropriate statistical network models, with sufficient attention to model fit, can lead to important progress in our understanding of macro-micro-macro issues. This is more a matter of promises for the future than of past achievements, however. A major reason for the limitations in the current state of the art is that the feedback issues which are essential to how networks operate, are so difficult to express in statistical models in a manageable and plausible way. In models for single (i.e., one-moment) observations of a social network, the time dimension of the feedback process is beaten flat, which leads to grave difficulties both of interpretation and of mathematical modeling. Although modeling single observations of social networks still is necessary and useful, more insight may be gained from repeated (multi-moment) observations of social networks. If such a dynamic model would include not only the relations between actors but also changing actor attributes (e.g., attitudes, behavior, performance) and the larger pattern of social settings in which networks are embedded, it would capture a great part of what we regard as macro-micro-macro processes. Such an integrated model does not yet exist, but work is currently under way toward the construction of this type of models.
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