In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. The author returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

Curvilinear Interactions Between Independent Variables

Advance Organizer

To this point in the book, we have explored both curvilinear effects in simple models and relatively simple nonadditive effects (interactions, or moderator effects). In Chapter 9, we explored linear interactions, in which the linear effect of one variable is different, or modified, by another variable. However, there is no reason to expect that effects are always linear, and there is no reason to expect that curvilinear effects are identical across all groups or across the entire range of another variable. Thus, we have to accept the possibility that there are curvilinear effects that are moderated by, or contingent on, another independent variable (IV). I will refer to these effects as nonlinear, or curvilinear, interactions. This chapter will ...

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