Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on Glenn Geher's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the core principles that provide the foundation for modern-day statistics. With step-by-step guidance on the nuts and bolts of computing these statistics, the book includes detailed tutorials how to use state-of-the-art software, SPSS, to compute the basic statistics employed in modern academic and applied research. Across 13 succinct chapters, this text presents statistics using a conceptual approach along with information on the relevance of the different tools in different contexts and summaries of current research examples.

Students should find this book easy useful and engaging in its presentation while instructors should find it detailed, comprehensive, accessible, and helpful in complementing a basic course in statistics.

Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.

Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods.

Visit http: //www.ats.ucla.edu/stat/examples/alda.htm for:

� Downloadable data sets

� Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more

� Additional material for data analysis

The introduction to statistics that psychology students can't afford to be without

Understanding statistics is a requirement for obtaining and making the most of a degree in psychology, a fact of life that often takes first year psychology students by surprise. Filled with jargon-free explanations and real-life examples, Psychology Statistics For Dummies makes the often-confusing world of statistics a lot less baffling, and provides you with the step-by-step instructions necessary for carrying out data analysis.

Psychology Statistics For Dummies:

• Serves as an easily accessible supplement to doorstop-sized psychology textbooks
• Provides psychology students with psychology-specific statistics instruction
• Includes clear explanations and instruction on performing statistical analysis
• Teaches students how to analyze their data with SPSS, the most widely used statistical packages among students

Master the art of APA-style writing with this newly updated and accessible resource

The newly and thoroughly revised Third Edition of Effective Writing in Psychology: Papers, Posters, and Presentations offers compelling and comprehensive guidance to readers who want to create powerful and persuasive prose in a rigorous, scientific, and APA-compliant framework. Distinguished academics and authors Bernard and Agatha Beins walk readers through the foundational and advanced topics they must grasp to generate convincing and credible APA-stye writing.

The book combines an accessible and approachable guide to effective writing with the most current best practices from the 7th edition of the American Psychological Association's publication manual. New writers and experienced authors alike will benefit from Effective Writing in Psychology's descriptions of the most frequently used and important aspects of APA-style writing.

The authors minimize their use of technical jargon and include explanations of how to create effective posters, deliver high-quality oral presentations, and publish electronically. The book also includes:

• An up-to-date presentation of ethical, inclusive writing and proper use of modern pronouns
• Step-by-step guidance on the use of APA formatting in scholarly papers
• Explanations of how to create effective posters for poster sessions
• Descriptions of how to organize convincing and credible oral presentations that leave listeners and conference attendees impressed and edified
• The basics of creating and formatting electronic documents for publication on the web

Effective Writing in Psychology: Papers, Posters, and Presentations is an invaluable resource for psychology and social, and behavioral science students at any level. It also belongs on the bookshelves of practicing psychology professionals, researchers, and academics who would like to brush up on their technical writing abilities.

IBM SPSS for Introductory Statistics is designed to help students learn how to analyze and interpret research. In easy-to-understand language, the authors show readers how to choose the appropriate statistic based on the design, and to interpret outputs appropriately. There is such a wide variety of options and statistics in SPSS, that knowing which ones to use and how to interpret the outputs can be difficult. This book assists students with these challenges.

Comprehensive and user-friendly, the book prepares readers for each step in the research process: design, entering and checking data, testing assumptions, assessing reliability and validity, computing descriptive and inferential parametric and nonparametric statistics, and writing about results. Dialog windows and SPSS syntax, along with the output, are provided. Several realistic data sets, available online, are used to solve the chapter problems. This new edition includes updated screenshots and instructions for IBM SPSS 25, as well as updated pedagogy, such as callout boxes for each chapter indicating crucial elements of APA style and referencing outputs.

IBM SPSS for Introductory Statistics is an invaluable supplemental (or lab text) book for students. In addition, this book and its companion, IBM SPSS for Intermediate Statistics, are useful as guides/reminders to faculty and professionals regarding the specific steps to take to use SPSS and/or how to use and interpret parts of SPSS with which they are unfamiliar.

The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website (www.appliedmissingdata.com) includes data sets and analysis examples from the book, up-to-date software information, and other resources.

New to This Edition
*Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables.
*New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics.
*Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples.
*Most of the book is entirely new.

An essential introduction to statistics for students of psychology and the social sciences

Statistical thinking is increasingly essential to understanding our complex world and making informed decisions based on uncertain data. This incisive undergraduate textbook introduces students to the main ideas of statistics in a way that focuses on deep comprehension rather than rote application or mathematical immersion. The presentation of statistical concepts is thoroughly modern, sharing cutting-edge ideas from the fields of machine learning and data science that help students effectively use statistical methods to ask questions about data. Statistical Thinking provides the tools to describe complex patterns that emerge from data and to make accurate predictions and decisions based on data.

• Introduces statistics from a uniquely modern standpoint, helping students to use the basic ideas of statistics to analyze real data
• Presents a model of statistics that ties together a broad range of statistical techniques that can be used to answer many different kinds of questions
• Explains how to use statistics to generate reproducible findings and avoid common mistakes in statistical practice
• Includes a wealth of examples using real-world data
• Accompanied by computer code in R and in Python--freely available online--that enables students to see how each example is generated and to code their own analyses

At last, a comprehensive guide that meets psychologists and other social scientists' growing need to master the tools of statistical prediction.

Prediction Statistics for Psychological Assessment, by R. Karl Hanson, is the first book to teach students and practitioners the nuts and bolts of prediction statistics, while illustrating the utility of prediction and prediction tools in applied psychological practice.

This valuable resource uses real-world examples, helpful explanations and practice exercises to support the use of prediction tools in psychological assessment.

Actuarial risk assessment evaluators need to know how prediction tools work, how to evaluate them, and how to interpret their results in applied assessments.

Written in a clear and accessible manner, this user-friendly book helps readers understand how to evaluate and interpret different kinds of prediction tools, appreciate the numeric information used in risk communication, and utilize prediction tools to inform evidence-based decision making.

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. An explicit aim of the book is to integrate the transformational and the latent variable approach, two diverse but complementary traditions dealing with the analysis of categorical data. This is the first introductory text to cover models and methods for discrete dependent variables, cross-classifications, and longitudinal data in a rigorous, yet accessible, manner in a single volume.The second edition of this book includes new material on multilevel models for categorical data. Several chapters have undergone extensive revisions and extensions to include new applications and examples. Highlights of the 2nd edition include a detailed discussion of classical and Bayesian estimation techniques for hierarchical/multilevel models, extensive coverage of discrete-time hazard models and Cox regression models, and methods for evaluating and accommodating departures from model assumptions. The accompanying website contains programming scripts to replicate each example using various statistical packages, which has proven to be an invaluable resource for instructors, students, and researchers. This book presents the essential methods and models that form the core of contemporary social statistics. The book covers a remarkable range of models that have applications in sociology, demography, psychometrics, econometrics, political science, biostatistics, and other fields. It will be especially useful as a graduate textbook for students in advanced social statistics courses and as a reference book for applied researchers. Companion website also available, at https: //webspace.utexas.edu/dpowers/www/

This practical introduction to second-order and growth mixture models using Mplus introduces simple and complex techniques through incremental steps.

The authors extend latent growth curves to second-order growth curve and mixture models and then combine the two using normal and non-normal (e.g., categorical) data. To maximize understanding, each model is presented with basic structural equations, figures with associated syntax that highlight what the statistics mean, Mplus applications, and an interpretation of results. Examples from a variety of disciplines demonstrate the use of the models and exercises allow readers to test their understanding of the techniques. A comprehensive introduction to confirmatory factor analysis, latent growth curve modeling, and growth mixture modeling is provided so the book can be used by readers of various skill levels. The book's datasets are available on the web.

New to this edition:

* Two new chapters providing a stepwise introduction and practical guide to the application of second-order growth curves and mixture models with categorical outcomes using the Mplus program. Complete with exercises, answer keys, and downloadable data files.

* Updated illustrative examples using Mplus 8.0 include conceptual figures, Mplus program syntax, and an interpretation of results to show readers how to carry out the analyses with actual data.

This text is ideal for use in graduate courses or workshops on advanced structural equation, multilevel, longitudinal or latent variable modeling, latent growth curve and mixture modeling, factor analysis, multivariate statistics, or advanced quantitative techniques (methods) across the social and behavioral sciences.

The Student Study Guide With IBM(R) SPSS(R) Workbook for Statistics for the Behavioral Sciences, Third Edition includes a review of chapter learning objectives, chapter outlines and key terms, essential statistical formulas, special tips and insights for students, and chapter summaries. To help students practice skills, the guide offers word searches and crossword puzzles for each chapter, extensive practice quizzes linked to chapter learning objectives, and SPSS in Focus exercises which complement those in the core text.

Gregory J. Privitera's Student Study Guide With IBM^(R) SPSS^(R) Workbook for Essential Statistics for the Behavioral Sciences, Second Edition includes a review of chapter learning objectives, chapter outlines and key terms, essential statistical formulas, special tips and insights for students, and chapter summaries. To help students practice skills, the guide offers word searches and crossword puzzles for each chapter, extensive practice quizzes linked to chapter learning objectives, and SPSS in Focus exercises which complement those in Privitera's core text, Essential Statistics for the Behavioral Sciences, Second Edition.

This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis.

Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes.

The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis.

An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.

Free textbook and practice problems at https: //statisticstextbook.com. Revised edition March 2024. This book teaches students and researchers the math and application (statistical tests) of Statistics using simple arithmetic and only five simple formulas. All parametric statistical tests, from the most simple to very complex, are covered with examples. Few introductory Statistics textbooks cover this wide range. Few Statistics books cover statistical design. This is necessary in experimental sciences. This book explains with words the logic of applied statistics and provides many step-by-step examples. The author of this book is a retired professor of Medicine with a life-long teaching and research experience.

TABLE OF CONTENTS
(425 pages)
PART 1 THEORY CONCEPTS STATISTICAL TESTS
What is Statistics
Descriptive Statistics
Inferential Statistics
Non-parametric Statistics
Parametric Statistics
The median
The mode
The mean
Variance
The standard normal curve
The normal-distribution table - how to use
Standard error of the mean
The t-test
The t-distribution table
Paired t-test
2 x 2 factorial Analysis of Variance ANOVA
2 x 3 factorial Analysis of Variance ANOVA
ANOVA mixed split plot complex design
ANOVA repeated measures
List of summary tables of ANOVA designs
Formulas for df in ANOVA

PART 2 LECTURE AND LAB
Lecture 1 Numbers Quantities Measurement
Lecture 2 Goddess Normal Curve
Lecture 3 Variance and standard deviation
Lecture 4 The uses of the normal distribution
Lecture 5 The t-test
Lecture 6 Analysis of variance One-way ANOVA
Lecture 7 Analysis of Variance Factorial Designs Two-Way ANOVA
Lecture 8 Repeated measures, correlated groups
Lecture 9 Complex, mixed, split-plot designs

PART 3.STATISTICAL DESIGN
Identify the design-case 1
Identify the design-case 2
Identify the design-case 3
Identify the design-case 4
Identify the design-case 5
Identify the design-case 6
Identify the design-case 7
Identify the design-case 8
Identify the design-case 9
Identify the design-case 10
Identify the design-case 11
Identify the design-case 12
Identify the design-case 13

PART 4. PRACTICE EXERCISES
The mean: practice exercises and solutions
Variance: practice exercises and solution
The problem
The solution
The sum of squares: practice exercises and solutions
The problem
The solution
Standard deviation practice exercises and solutions
The problem
The solution
ANOVA mixed split plot- practice example
Statistical design practice question 2

APPENDIX
Five simple formulas for all parametric statistics
Statistical Tables
The Normal Distribution Table
The t-distribution Table
The F Distribution Table
Key to the design questions of Part 3
FAQ Frequently Asked Questions
The standard normal curve formula

Epilogue

Nonlinear Contingency Analysis is a guide to treating clinically complex behavior problems such as delusions and hallucinations. It's also a framework for treating behavior problems, one that explores solutions based on the creation of new or alternative consequential contingencies rather than the elimination or deceleration of old or problematic thoughts, feelings, or behaviors.

Chapters present strategies, analytical tools, and interventions that clinicians can use in session to think about clients' problems using decision theory, experimental analysis of behavior, and clinical research and practice.

By treating thoughts and emotions not as causes of behavior but as indicators of the environmental conditions that are responsible for them, patients can use that knowledge to make changes that not only result in changes in behavior, but in the thoughts and feelings themselves.

The new edition of this innovative book, R Data Analysis without Programming, prepares the readers to quickly analyze data and interpret statistical results using R. Professor Gerbing has developed lessR, a ground-breaking method in alleviating the challenges of R programming. The lessR extends R, removing the need for programming. This edition expands upon the first edition's introduction to R through lessR, which enables the readers to learn how to organize data for analysis, read the data into R, and generate output without performing numerous functions and programming exercises first. With lessR, readers can select the necessary procedure and change the relevant variables with simple function calls. The text reviews and explains basic statistical procedures with the lessR enhancements added to the standard R environment. Using lessR, data analysis with R becomes immediately accessible to the novice user and easier to use for the experienced user.

Highlights along with content new to this edition include:

• Explanation and Interpretation of all data analysis techniques; much more than a computer manual, this book shows the reader how to explain and interpret the results.
• Introduces the concepts and commands reviewed in each chapter.
• Clear, relaxed writing style more effectively communicates the underlying concepts than more stilted academic writing.
• Extensive margin notes highlight, define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number for the initial introduction.
• Scenarios that highlight the use of a specific analysis followed by the corresponding R/lessR input, output, and an interpretation of the results.

Numerous examples of output from psychology, business, education, and other social sciences, that demonstrate the analysis and how to interpret results.

• Two data sets are analyzed multiple times in the book, provide continuity throughout.
• Comprehensive: A wide range of data analysis techniques are presented throughout the book.
• Integration with machine learning as regression analysis is presented from both the traditional perspective and from the modern machine learning perspective.
• End of chapter problems help readers test their understanding of the concepts.
• A website at www.lessRstats.com that features the data sets referenced in both standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, R/lessR videos to help readers better understand the program, and more.

This book is ideal for graduate and undergraduate courses in statistics beyond the introductory course, research methods, and/or any data analysis course, taught in departments of psychology, business, education, and other social and health sciences; this book is also appreciated by researchers doing data analysis. Prerequisites include basic statistical knowledge, though the concepts are explained from the beginning in the book. Previous knowledge of R is not assumed.

Statistics for Psychology is an interactive, highly visual, and accessible guide to the statistical practices you will encounter as a psychology student. Its software-agnostic approach keeps the focus on the core principles, rather than getting bogged down in complicated formulae and jargon. This book is based on the authors′ BPS commended programme. It focusses on providing the strong foundational understanding you'll need to use statistics confidently and creatively rather than the software-specific way in which statistics is often taught.

This edition includes:

• new content throughout on being a responsible researcher,

• a new chapter to support you in presenting your research to a critical audience,

• carefully designed graphics to explain statistical principles,

• your turn exercises to test your understanding of each chapter.

This book is the ideal guide for students approaching statistics and research methods in psychology for the first time.

Roger Watt is Emeritus Professor of Psychology at the University of Stirling.
Elizabeth Collins is a researcher with a PhD in Psychology.

A student-friendly, highly visual guide to really understanding the big picture and principles behind the many different statistical practices in Psychology.

Statistical Power Analysis explains the key concepts in statistical power analysis and illustrates their application in both tests of traditional null hypotheses (that treatments or interventions have no effect in the population) and in tests of the minimum-effect hypotheses (that the population effects of treatments or interventions are so small that they can be safely treated as unimportant). It provides readers with the tools to understand and perform power analyses for virtually all the statistical methods used in the social and behavioral sciences.

Brett Myors and Kevin Murphy apply the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. This book starts with a review of the key concepts that underly statistical power. It goes on to show how to perform and interpret power analyses, and the ways to use them to diagnose and plan research. We discuss the uses of power analysis in correlation and regression, in the analysis of experimental data, and in multilevel studies. This edition includes new material and new power software. The programs used for power analysis in this book have been re-written in R, a language that is widely used and freely available. The authors include R codes for all programs, and we have also provided a web-based app that allows users who are not comfortable with R to perform a wide range of analyses using any computer or device that provides access to the web.

Statistical Power Analysis helps readers design studies, diagnose existing studies, and understand why hypothesis tests come out the way they do. The fifth edition includes updates to all chapters to accommodate the most current scholarship, as well as recalculations of all examples. This book is intended for graduate students and faculty in the behavioral and social sciences; researchers in other fields will find the concepts and methods laid out here valuable and applicable to studies in many domains.

Longitudinal Structural Equation Modeling is a comprehensive resource that reviews structural equation modeling (SEM) strategies for longitudinal data to help readers determine which modeling options are available for which hypotheses.

This accessibly written book explores a range of models, from basic to sophisticated, including the statistical and conceptual underpinnings that are the building blocks of the analyses. By exploring connections between models, it demonstrates how SEM is related to other longitudinal data techniques and shows when to choose one analysis over another. Newsom emphasizes concepts and practical guidance for applied research rather than focusing on mathematical proofs, and new terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues and each chapter also includes examples of each model type, descriptions of model extensions, comment sections that provide practical guidance, and recommended readings.

Expanded with new and updated material, this edition includes many recent developments, a new chapter on growth mixture modeling, and new examples. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this new edition will continue to appeal to researchers in these fields.