Based on Neil J. Salkind’s bestselling text, Statistics for People Who (Think They) Hate Statistics, this adapted Excel 2016 version presents an often intimidating and difficult subject in a way that is clear, informative, and personable. Researchers and students uncomfortable with the analysis portion of their work will appreciate the book′s unhurried pace and thorough, friendly presentation. Opening with an introduction to Excel 2016, including functions and formulas, this edition shows students how to install the Excel Data Analysis Tools option to access a host of useful analytical techniques and then walks them through various statistical procedures, beginning with correlations and graphical representation of data and ending with inferential techniques and analysis of variance. New to the Fourth Edition: A new chapter 20 dealing with large data sets using Excel functions and pivot tables, and illustrating how certain databases and other categories of functions and formulas can help make the data in big data sets easier to work with and the results more understandable. New chapter-ending exercises are included and contain a variety of levels of application. Additional TechTalks have been added to help students master Excel 2016. A new, chapter-ending Real World Stats feature shows readers how statistics is applied in the everyday world. Basic maths instruction and practice exercises for those who need to brush up on their math skills are included in the appendix.

Now in its third edition, this title teaches an often intimidating and difficult subject in a way that is informative, personable, and clear.

Using his signature, conversational writing style and straightforward presentation, Neil J. Salkind’s best-selling Tests & Measurement for People Who (Think They) Hate Tests & Measurement guides readers through an overview of categories of tests, the design of tests, the use of tests, and some of the basic social, political, and legal issues that the process of testing involves. The Third Edition includes a new chapter on item response theory, new sections on neuropsychological testing, new cartoons, and additional end-of-chapter exercises. Free online resources accompany the text to make teaching easier and provide students with the practice tools they need to master the material.

The real-world examples and journal article citations presented within each chapter are very useful. I like having the references and the ability to share the full article with students. I find the use of humor throughout the book as a key component. Very serious topics (which can sometimes produce anxiety in students) are approached in a humorous manner that prevents students from becoming anxious as they start the material.

Publisher Description

Test Development and Validation by Gary Skaggs frameworks for test development and validation, and guidance for developing tests in straightforward language in one core text. Covering the changes in testing, technical development of tests and determining validity of tests, this book offers clear explanations within a real-world context.

"Measurement Error and Research Design is an ideal text for research methods courses across the social sciences, especially those in which a primer on measurement is needed. For the novice researcher, this book facilitates understanding of the basic principles required to design measures and methods for empirical research. For the experienced researcher, this book provides an in-depth analysis and discussion of the essence of measurement error and the procedures to minimize it. Most important, the book's unique approach bridges measurement and methodology through clear illustrations of the intangibles of scientific research."--BOOK JACKET.

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

In an increasingly data-driven world, it is more important than ever for students as well as professionals to better understand basic statistical concepts. 100 Questions (and Answers) About Statistics addresses the essential questions that students ask about statistics in a concise and accessible way. It is perfect for instructors, students, and practitioners as a supplement to more comprehensive materials, or as a desk reference with quick answers to the most frequently asked questions.