Bücher von Jason W. Osborne

This book explores the fun things researchers can do with logistic regression, explicates and simplifies the confounding complexities of understanding what logistic regression is, and provides evidence-based guidance as to what the best practices in performing logistic regression are.

This book provides a clear, step-by-step process of examining and cleaning data in order to decrease error rates and increase both the power and replicability of results.

Best Practices in Quantitative Methods follows the tradition of 'handbooks' in that it calls on the top researchers in the field to share with us what they know. In this case, the focus of the chapters is on best practices for the vast field of quantitative methods. The volume provides readers with the most effective, evidence-based ways to use and analyze quantitative methods and quantitative data across the social and behavioral sciences and education .The text is divided into three main sections: Basics of Best Practices, in which a comprehensive review of basic statistic and methodological practices is covered, including core statistical methods and critical data analysis issues such as power, effect sizes, and assumptions; Advanced Best Practices, leading with logistic regression, and moving through IRT, Rasch Measurement, HLM, Meta-Analysis, and the inimitable area of Sampling; and The Implications of Best Practices, including a discussion of the ethical implications of quantitative analysis. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-rangning examples along with any empirical evidence to show why certain techniques are better. The book encourages best practices in three very distinct ways: 1) Some chapters will describe important implicit knowledge to readers. For example, one of the most common data transformations is the square root transformation. Statistics and quantitative methods are filled with examples of these seemingly mundane aspects of research life that makes a substantial difference. Chapters in this book gather the important details, make them accessible to readers, and demonstrate why it is important to pay attention to these details. 2) Other chapters compare and contrast analytic techniques to give readers information they need to decide the best way to analyze particular data. For example, exploratory factor analysis has up to eight extraction methods, several rotation options, multiple ways to decide how many factors you have, and it is often the case that the options are not clearly described or discussed. Some of the chapters will examine instances where there are multiple options for doing things, and make recommendations as to what the ôbestö choice (or choices, as what is best often depends on the circumstances) are. 3) Finally, there are always new procedures being developed and disseminated. Many times (not all) newer procedures represent improvements over old procedures. Some chapters will present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use.This book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource to go to for practical and sound advice from leading experts in quantitative methods.