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.

Modeling in Large, Complex Samples: The Importance of Using Appropriate Weights and Design Effect Compensation

Modeling in Large, Complex Samples: The Importance of Using Appropriate Weights and Design Effect Compensation

Advance Organizer

Large, governmental or international data sets (of the type we have used in many of the chapters in this book) are important resources for researchers across many disciplines and sciences. They present researchers with the opportunity to examine trends and hypotheses within nationally (or internationally) representative data sets that are difficult to acquire without the resources of a large research institution or governmental agency.

However, there are challenges to using these types of data sets. For example, individual researchers must take the data as given—in other words, we have no control over the types of questions asked, how they are asked, to whom they are asked, and when they ...

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