This text investigates the efficacy of model comparison as a primary framework for statistical inference and data analysis. The authors, Carey S. Ryan, Gary H. McClelland, and Charles M. Judd, provide a comprehensive pedagogical approach designed to move students beyond rote memorization of formulas. By emphasizing the conceptual underpinnings of statistical models, the book argues that comparing competing models allows researchers to better understand the variance and predictive power inherent in their data sets.

Experts in the field of behavioral and social sciences frequently cite this text as a foundational resource for graduate-level statistics courses. Readers often note the academic density of the prose, which requires a solid grasp of introductory algebra to fully comprehend the presented models.