Richard G. Lomax – författare

Statistical Concepts—A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results. In this new edition, Hahs-Vaughn and Lomax discuss sensitivity, specificity, false positive and false negative errors. Coverage of effect sizes has been expanded upon and more organizational features (to summarize key concepts) have been included. A final chapter on mediation and moderation has been added for a more complete presentation of regression models. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R.This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.

Statistical Concepts—A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results. In this new edition, Hahs-Vaughn and Lomax discuss sensitivity, specificity, false positive and false negative errors. Coverage of effect sizes has been expanded upon and more organizational features (to summarize key concepts) have been included. A final chapter on mediation and moderation has been added for a more complete presentation of regression models. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R.This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.

The fifth edition of An Introduction to Statistical Concepts is designed to help students truly understand statistical concepts, the situations in which they can be used, and how to apply them to data. Written without assuming any prior knowledge of statistics, it is designed for students to become capable researchers and intelligent consumers and producers of statistics.Hahs-Vaughn and Lomax discuss both the most popular and lesser-known procedures and models while also exploring nonparametric procedures used when standard assumptions are violated. They provide in-depth coverage of testing assumptions and highlight several online tools for computing statistics, such as effect sizes and their confidence intervals and power.This new edition also includes:Fully updated content, additional references, and end-of-chapter problems to enhance student learning;Discussions of bootstrapping, open science, microdata, data visualization, and more;Expanded coverage of simple effects, normality tests, effect sizes, and applications of SPSS and R; andSupplemental digital resources, including self-testing elements for students and a test bank and PowerPoint slides for instructors.With crystal-clear explanations and inclusion of only the most crucial equations, this book is an invaluable resource for students taking statistic courses in any number of social science and behavioral disciplines—from education, business, communication, exercise science, psychology, sociology and more.

Now in its second edition, Statistical Concepts—A First Course presents the first 11 chapters from the fifth edition of An Introduction to Statistical Concepts. Designed for first and lower-level statistics courses, this book communicates a conceptual, intuitive understanding of statistics that does not assume extensive or recent training in mathematics and only requires a rudimentary knowledge of algebra.The book describes the most basic statistical concepts and is designed to help readers truly understand statistical concepts, in what situations they can be applied, and how to apply them to data. Topics covered include ways of representing data graphically, statistical measures that describe a set of data, the normal distribution and other types of standard scores, and an introduction to probability and sampling, as well as various inferential tests, including those involving tests of means (e.g., t tests), proportions, variances, and correlations. This new edition includes fully updated content, expanded coverage of concepts and applications in SPSS and R, and new discussions of open science and data visualization. Additionally, supplemental digital resources are available to support instructors and students alike.Providing accessible and comprehensive coverage of topics suitable for an undergraduate or graduate course in statistics, this book is an invaluable resource for students undertaking an introductory course in statistics in any number of social science and behavioral science disciplines.

Now in its second edition, Statistical Concepts—A First Course presents the first 11 chapters from the fifth edition of An Introduction to Statistical Concepts. Designed for first and lower-level statistics courses, this book communicates a conceptual, intuitive understanding of statistics that does not assume extensive or recent training in mathematics and only requires a rudimentary knowledge of algebra.The book describes the most basic statistical concepts and is designed to help readers truly understand statistical concepts, in what situations they can be applied, and how to apply them to data. Topics covered include ways of representing data graphically, statistical measures that describe a set of data, the normal distribution and other types of standard scores, and an introduction to probability and sampling, as well as various inferential tests, including those involving tests of means (e.g., t tests), proportions, variances, and correlations. This new edition includes fully updated content, expanded coverage of concepts and applications in SPSS and R, and new discussions of open science and data visualization. Additionally, supplemental digital resources are available to support instructors and students alike.Providing accessible and comprehensive coverage of topics suitable for an undergraduate or graduate course in statistics, this book is an invaluable resource for students undertaking an introductory course in statistics in any number of social science and behavioral science disciplines.

Now in its second edition, Statistical Concepts—A Second Course presents the last 10 chapters from the fifth edition of An Introduction to Statistical Concepts. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results.The book describes intermediate statistical concepts including analysis of variance models, analysis of covariance, and a complete presentation of several regression models (simple, multiple, logistic, and multinomial) along with a chapter on mediation and moderation. It includes expanded coverage of effect sizes and more organizational features to summarize key concepts. This new edition includes fully updated content, expanded coverage of concepts and applications in SPSS and R, and new discussions of multinomial logistic regression. Additionally, supplemental digital resources are available to support instructors and students alike.This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.

Now in its second edition, Statistical Concepts—A Second Course presents the last 10 chapters from the fifth edition of An Introduction to Statistical Concepts. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results.The book describes intermediate statistical concepts including analysis of variance models, analysis of covariance, and a complete presentation of several regression models (simple, multiple, logistic, and multinomial) along with a chapter on mediation and moderation. It includes expanded coverage of effect sizes and more organizational features to summarize key concepts. This new edition includes fully updated content, expanded coverage of concepts and applications in SPSS and R, and new discussions of multinomial logistic regression. Additionally, supplemental digital resources are available to support instructors and students alike.This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.

This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics. Instructors select the topics that are most appropriate for their course. Its conceptual approach helps students more easily understand the concepts and interpret SPSS and research results. Key concepts are simply stated and occasionally reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts. Only crucial equations are included.In addition to updating throughout, the new edition features:New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award.A new chapter on logistic regression models for today's more complex methodologies.More on computing confidence intervals and conducting power analyses using G*Power.Many more SPSS screenshots to assist with understanding how to navigate SPSS and annotated SPSS output to assist in the interpretation of results.Extended sections on how to write-up statistical results in APA format.New learning tools including chapter-opening vignettes, outlines, and a list of key concepts, many more examples, tables, and figures, boxes, and chapter summaries.More tables of assumptions and the effects of their violation including how to test them in SPSS.33% new conceptual, computational, and all new interpretative problems.A website that features PowerPoint slides, answers to the even-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets that can be used in SPSS and other packages, and more.Each chapter begins with an outline, a list of key concepts, and a vignette related to those concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides instructions for how to run SPSS, including annotated output, and tips to develop an APA style write-up. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. 'Stop and Think' boxes provide helpful tips for better understanding the concepts. Each chapter includes computational, conceptual, and interpretive problems. The data sets used in the examples and problems are provided on the web. Answers to the odd-numbered problems are given in the book.The first five chapters review descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution, and probability and sampling. The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance and regression models. Topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple and logistic regression models are also reviewed.Intended for one- or two-semester courses in statistics taught in education and/or the behavioral sciences at the graduate and/or advanced undergraduate level, knowledge of statistics is not a prerequisite. A rudimentary knowledge of algebra is required.