Abstract
This chapter deals with making comparisons between fixed groups in the framework of generalized linear models. First, we briefly introduce generalized linear models, the most common type of regression models. Next, we discuss a simple system for analyzing group differences in regression. We primarily focus on two types of comparisons—analyzing differences in the parameter vectors of the linear predictor and differences in the underlying distributions for the groups in the model. To illustrate such comparative methods for group differences, we perform analyses using real-world data. Theoretically, group differences in regression estimates can be viewed as an example of conditional causality. Practically, testing group differences in regression may see many useful applications in social science research.
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Liao, T.F. (2013). Group Differences in Generalized Linear Models. In: Morgan, S. (eds) Handbook of Causal Analysis for Social Research. Handbooks of Sociology and Social Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6094-3_9
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DOI: https://doi.org/10.1007/978-94-007-6094-3_9
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