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.

Reliable Measurement Matters

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One of the basic assumptions of quantitative research is that we are measuring the variables we think we are measuring with a high degree of reliability (the actual assumption is that of perfectly reliable measurement, which is not attainable). Our variables are often difficult to measure, making measurement error a concern, particularly in the social/behavioral and health sciences.

Despite impressive advancements in measurement in recent years (particularly the broad dissemination of structural equation modeling, Rasch measurement methodologies, and item response theory [IRT], to name but a few), the reliability of measurement remains an issue. In simple analyses in which there is only a single predictor, unreliable measurement causes relationships or effects to be underestimated (or attenuated), increasing the risk of Type ...

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