Praise for the first edition: "An excellent textbook which is well planned, well written, and pitched at the correct level for psychology students. I would not hesitate to recommend Greene and d'Oliveira to all psychology students looking for an introductory text on statistical methodology." Bulletin of the British Psychological Society Learning to Use Statistical Tests in Psychology third edition has been updated throughout. It continues to be a key text in helping students to understand and conduct statistical tests in psychology without panic! It takes students from the most basic elements of statistics teaching them: How psychologists plan experiments and statistical tests Which considerations must be made when planning experiments How to analyze and comprehend test results Like the previous editions, this book provides students with a step-by-step guide to the simplest non-parametric tests through to more complex analysis of variance designs. There are clear summaries in progress boxes and questions for the student to answer in order to be sure that they have understood what they have read. The new edition is divided into four discrete sections and within this structure each test covered is illustrated through a chapter of its own. The sections cover: The principles of psychological research and psychological statistics Statistical tests for experiments with two or three conditions Statistical tests based on ANOVA (Analysis of Variance) conditions as well as tests for multiple comparisons between individual conditions Statistical tests to analyze relationships between variables Presented in a student-friendly textbook format, Learning to Use Psychological Tests in Psychology enables readers to select and use the most appropriate statistical tests to evaluate the significance of data obtained from psychological experiments. An errata sheet detailing the Decision Chart which is referred to can be downloaded by clicking here

The second edition of this text is an introduction to the use of statistical tests in psychology experiments: statistics without panic. Presented in a new textbook format, its key objective is to enable students to select appropriate statistical tests to evaluate the significance of data obtained from psychological experiments. Improvements ion the organization of chapters emphasize even more clearly the principle of introducing complex experimental designs on a need to know basis, leaving more space for an extended interpretation of analysis of variance. In an important development for the second edition, students are introduced to modern statistical packages as a useful tool for calculations, the emphasis being on understanding and interpretation.

"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

Taking a non-technical approach, 'Understanding and Using Statistics in Psychology' encourages the reader to understand why a particular test is being used and what the results mean in the context of a psychological study, focusing on meaning and understanding rather than mindless numerical calculations.

This new introductory statistics text from Dawn M. McBride, best-selling author of The Process of Research in Psychology, covers the background and process of statistical analysis, along with how to use essential tools for working with data from the field. Research studies are included throughout from both the perspective of a student conducting their own research study and of someone encountering research in their daily life. McBride helps readers gain the knowledge they need to become better consumers of research and statistics used in everyday decision-making and connects the process of research design with the tools employed in statistical analysis. Instructors and students alike will appreciate the extra opportunities for practice with the accompanying Lab Manual for Statistical Analysis, also written by McBride and her frequent collaborator, J. Cooper Cutting.

A unique textbook introducing and demonstrating the use of R in psychology. Statistics for Psychology Using R comprehensively covers standard statistical methods along with advanced topics such as multivariate techniques, factor analysis, and multiple regression widely used in the field of psychology and other social sciences. Its innovative structure and pedagogical approach coupled with numerous worked-out examples and self-assessment tests make it a user-friendly and easy-to-understand companion for students and scholars with limited background in statistics. The standout feature of this textbook is that it demonstrates the application of R—a free, flexible, and dynamically changing software for statistical computing and data analysis, which is becoming increasingly popular across social and behavioral sciences.

Providing easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Randall E. Schumacker’s comprehensive book describes in detail the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Learning Statistics Using R covers data input from vectors, arrays, matrices and data frames, as well as the input of data sets from SPSS, SAS, STATA and other software packages. Schumacker’s text provides the freedom to effectively calculate, manipulate, and graphically display data, using R, on different computer operating systems without the expense of commercial software. Learning Statistics Using R places statistics within the framework of conducting research, where statistical research hypotheses can be directly addressed. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers′ understanding of statistics through statistical computing and modeling.