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This paper discusses two basic issues related to the assessment of the goodness-of-fit of sociological models to behavioral data: How simplifying assumptions contained in models should be evaluated and whether analysis of data should be carried out at the individual group or aggregate level. The discussion is conducted in the context of the fit of a model of participation rates by Fisek, Berger, and Norman (1991) to data collected by Smith-Lovin, Skvoretz, and Hudson (1986) in a Bales-type situation. The conclusions are that simplifying assumptions should be evaluated from the point of view of robustness of model to violations of such assumptions, and that the assessment of the adequacy of minimal actor models should be in terms of aggregated data. The fit of the model and data under consideration is assessed to be adequate. [ABSTRACT FROM AUTHOR]

The estimated effect of a regressor on an outcome is inconsistent when that regressor is determined simultaneously with that outcome. Instrumental variables estimation is a means of obtaining consistent parameter estimates in this situation. The best-known form of instrumental variables is two-stage least squares; unfortunately, this procedure cannot be simply extended to non-linear models such as logistic regression. Instrumental variables estimation, however, is still possible, and using the Generalized Method of Moments, this paper is the first to produce instrumental variables estimates for logistic regression. Obtaining these estimates is easy using widely available software. An illustrative example is provided. This methodology should be useful to social scientists familiar with 2SLS and logistic regression. [ABSTRACT FROM AUTHOR]