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.

A Brief Introduction to Hierarchical Linear Modeling

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No Child Left Behind (NCLB) was an initiative within the United States to attempt to improve the educational system—particularly to ensure that lower-performing students are brought up to standards that are considered at least minimally acceptable. Although there is great controversy over whether NCLB was a good idea or a poor one, and whether it was helpful or harmful, one policy was to evaluate whether schools were being successful (and by proxy, whether teachers were being successful) by evaluating student achievement. This has long been a central question, and central conflict, within the culture and community of researchers—how best to measure the effects of schools and teachers on students (and vice versa). It makes sense that ...

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