In this fully updated edition of Using Basic Statistics in the Behavioral and Social Sciences, Annabel Ness Evans presents introductory statistics in a practical, conceptual, and humorous way, reducing the anxiety that many students experience in introductory courses. Avoiding complex notation and derivations, the book focuses on helping readers develop an understanding of the underlying logic of statistics, rather than rote memorization. Focus on Research boxes engage students with realistic applications of statistics, and end-of-chapter exercises ensure student comprehension. This exciting new edition includes a greater number of realistic and engaging global examples within the social and behavioral sciences, making it ideal for use within many departments or in interdisciplinary settings.

Ideal for introductory statistics courses at both the undergraduate and graduate levels, Basic Statistics for the Behavioral and Social Sciences Using R is specifically designed to make adoption simple in a variety of disciplines. The text includes topics typically covered in introductory textbooks: probability, descriptive statistics, visualization, comparisons of means, tests of association, correlations, OLS regression, and power analysis. However, it also transcends other books at this level by covering topics such as bootstrapping and an introduction to R, for those who are novices to this powerful tool. In a straightforward and easy-to-understand format, the authors provide readers with a plethora of freely available and robust resources and examples that are applicable to a wide variety of behavioral and social science disciplines, including social work, psychology, and physical and occupational therapy. The book is a must-read for all professors and students endeavoring to learn basic statistics.

Fundamental Statistics for the Social and Behavioral Sciences, Second Edition, places statistics within the research process, illustrating how they are used to answer questions and test ideas. Students learn not only how to calculate statistics, but also how to interpret and communicate the results of statistical analyses in light of a study’s research hypothesis. Featuring accessible writing and well-integrated research examples, the book gives students a greater understanding of how research studies are conceived, conducted, and communicated. The Second Edition includes a new chapter on regression; covers how collected data can be organized, presented and summarized; the process of conducting statistical analyses to test research questions, hypotheses, and issues/controversies; and examines statistical procedures used in research situations that vary in the number of independent variables in the study. Every chapter includes learning checks, such as review questions and summary boxes, to reinforce the content students just learned, and exercises at the end of every chapter help assess their knowledge. Also new to the Second Edition -- animated video tutorials!

In addition to learning how to apply classic statistical methods, students need to understand when these methods perform well, and when and why they can be highly unsatisfactory. Modern Statistics for the Social and Behavioral Sciences illustrates how to use R to apply both standard and modern methods to correct known problems with classic techniques. Numerous illustrations provide a conceptual basis for understanding why practical problems with classic methods were missed for so many years, and why modern techniques have practical value. Designed for a two-semester, introductory course for graduate students in the social sciences, this text introduces three major advances in the field: Early studies seemed to suggest that normality can be assumed with relatively small sample sizes due to the central limit theorem. However, crucial issues were missed. Vastly improved methods are now available for dealing with non-normality. The impact of outliers and heavy-tailed distributions on power and our ability to obtain an accurate assessment of how groups differ and variables are related is a practical concern when using standard techniques, regardless of how large the sample size might be. Methods for dealing with this insight are described. The deleterious effects of heteroscedasticity on conventional ANOVA and regression methods are much more serious than once thought. Effective techniques for dealing heteroscedasticity are described and illustrated. Requiring no prior training in statistics, Modern Statistics for the Social and Behavioral Sciences provides a graduate-level introduction to basic, routinely used statistical techniques relevant to the social and behavioral sciences. It describes and illustrates methods developed during the last half century that deal with known problems associated with classic techniques. Espousing the view that no single method is always best, it imparts a general understanding of the relative merits of various techniques so that the choice of method can be made in an informed manner.

Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis, decision-making for performing parametric or nonparametric analysis, and practical significance determination. In addition, the author details how data analysis is used to reveal the underlying patterns and relationships between the variables and connects those trends to the data’s contextual situation. Filling the gap in quantitative data analysis literature, this book teaches the methods and thought processes behind data analysis, rather than how to perform the study itself or how to perform individual statistical tests. With a clear and conversational style, readers are provided with a better understanding of the overall structure and methodology behind performing a data analysis as well as the needed techniques to make informed, meaningful decisions during data analysis. The book features numerous data analysis examples in order to emphasize the decision and thought processes that are best followed, and self-contained sections throughout separate the statistical data analysis from the detailed discussion of the concepts allowing readers to reference a specific section of the book for immediate solutions to problems and/or applications. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences also features coverage of the following: • The overall methodology and research mind-set for how to approach quantitative data analysis and how to use statistics tests as part of research data analysis • A comprehensive understanding of the data, its connection to a research situation, and the most appropriate statistical tests for the data • Numerous data analysis problems and worked-out examples to illustrate the decision and thought processes that reveal underlying patterns and trends • Detailed examples of the main concepts to aid readers in gaining the needed skills to perform a full analysis of research problems • A conversational tone to effectively introduce readers to the basics of how to perform data analysis as well as make meaningful decisions during data analysis Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research methods. Michael J. Albers, Ph.D., is Professor in the Department of English at East Carolina University. His research interests include information design with a focus on answering real-world questions, the presentation of complex information, and human–information interaction. Dr. Albers received his Ph.D. in Technical Communication and Rhetoric from Texas Tech University.

An updated edition of a classic text on applying statistical analyses to the social sciences, with reviews, new chapters, an expanded set of post-hoc analyses, and information on computing in Excel and SPSS Now in its second edition,Statistical Applications for the Behavioral and Social Sciences has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas. The authors cover descriptive statistics and z scores, the theoretical underpinnings of inferential statistics, z and t tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory. This important resource: Contains information regarding the use of statistical software packages; both Excel and SPSS Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the social sciences Puts renewed emphasis on presentation of data and findings using the APA format Includes supplementary material consisting of a set of "kick-start" quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, Statistical Applications for the Behavioral and Social Sciences, Second Edition continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences.

Designed to make wildflower identification as easy as possible for the walker or rambler, this guide covers over 250 species with colour photographs of each. The flowers are categorized in eight sections: seashore and coastal; fresh water; heaths and moors; marshes, fens and bogs; cultivated, arable and waste land; grassland and meadows; gardens, paths and walls; and woodland and hedgerows. Each habitat section has a set of introductory photographs for easy identification and larger photographs alongside essential information which includes the botanical name, month of flowering and particular characteristics of the species.

Using and Interpreting Statistics in the Social, Behavioral, and Health Sciences is designed to be paired with any undergraduate introduction to research methods text used by students in a variety of disciplines. It introduces students to statistics at the conceptual level—examining the meaning of statistics, and why researchers use a particular statistical technique, rather than computational skills. Focusing on descriptive statistics, and some more advanced topics such as tests of significance, measures of association, and regression analysis, this brief, inexpensive text is the perfect companion to help students who have not yet taken an introductory statistics course or are confused by the statistics used in the articles they are reading.

Nolan and Heinzen’s engaging introduction to statistics has captivated students with its easy readability and vivid examples drawn from everyday life. The mathematics of statistical reasoning are made accessible with careful explanations and a helpful three-tier approach to working through exercises: Clarifying the Concepts, Calculating the Statistics, and Applying the Concepts. New pedagogy, end-of-chapter material, and the groundbreaking learning space StatsPortal give students even more tools to help them master statistics than ever before.

Focusing on principles and techniques that are appropriate for the introductory level, this text provides a conceptual, intuitive approach to applied statistics that replaces proofs and derivations with examples and illustrations. The focus throughout is on the usefulness of statistics and content is organized according to statistical applications, not statistical assumptions.