* It is a straightforward, conversational introduction to statistics that delivers exactly what its title promises. * Each chapter begins with a brief overview of a statistic that describes what the statistic does and when to use it, followed by a detailed step-by-step explanation of how the statistic works and exactly what information it provides. * Chapters also include an example of the statistic (or statistics) in use in real-world research, "Worked Examples," "Writing It Up" sections that demonstrate how to write about each statistic, "Wrapping Up and Looking Forward" sections, and practice work problems. * A new chapter on person-centered analyses, including cluster analysis and latent class analysis (LCA) has been added (Chapter 16). * Person-centered analysis is an important alternative to the more commonly used variable-centered analyses (e.g., t tests, ANOVA, regression) and is gaining popularity in social-science research. * The chapter on non-parametric statistics (Chapter 14) was enhanced significantly with in-depth descriptions of Mann-Whitney U, Kruskall-Wallace, and Wilcoxon Signed-Rank analyses. * These non-parametric statistics are important alternatives to statistics that rely on normally distributed data. * This new edition also includes more information about the assumptions of various statistics, including a detailed explanation of the assumptions and consequences of violating the assumptions of regression (Chapter 13). * There is more information provided about the importance of the normal distribution in statistics (Chapters 4 and 7). * Each of the last nine chapters includes an example from the real world of research that employs the statistic, or statistics, covered in the chapter. * Altogether, these improvements provide important foundational information about how inferential statistics work and additional statistical tools that are commonly used by researchers in the social sciences. * The text works as a standalone or as a supplement and covers a range of statistical concepts from descriptive statistics to factor analysis and person-centered analyses.

This book covers statistical consequences of breaches of research integrity such as fabrication and falsification of data, and researcher glitches summarized as questionable research practices. It is unique in that it discusses how unwarranted data manipulation harms research results and that questionable research practices are often caused by researchers' inadequate mastery of the statistical methods and procedures they use for their data analysis. The author's solution to prevent problems concerning the trustworthiness of research results, no matter how they originated, is to publish data in publicly available repositories and encourage researchers not trained as statisticians not to overestimate their statistical skills and resort to professional support from statisticians or methodologists. The author discusses some of his experiences concerning mutual trust, fear of repercussions, and the bystander effect as conditions limiting revelation of colleagues' possible integrity breaches. He explains why people are unable to mimic real data and why data fabrication using statistical models stills falls short of credibility. Confirmatory and exploratory research and the usefulness of preregistration, and the counter-intuitive nature of statistics are discussed. The author questions the usefulness of statistical advice concerning frequentist hypothesis testing, Bayes-factor use, alternative statistics education, and reduction of situational disturbances like performance pressure, as stand-alone means to reduce questionable research practices when researchers lack experience with statistics.

The editors of this volume suggest that there are missing elements in the conceptualization upon which standard test theory is based. Those elements are models for just how people know what they know and do what they can do, and the ways in which they increase these capacities. Different models are useful for different purposes; therefore, broader or alternative student models may be appropriate. The chapters in this volume consider a variety of directions in which standard test theory might be extended. Topics covered include: the role of test theory in light of recent work in cognitive and educational psychology, test design, student modeling, test analysis, and the integration of assessment and instruction.

Gathering scholars from different disciplines, this book is the first on how to study emotions using sociological, historical, linguistic, anthropological, psychological, cultural, and mixed approaches. Bringing together the emerging lines of inquiry, it lays foundations for an overdue methodological debate. The volume offers entrancing short essays, richly illustrated with examples and anecdotes, that provide basic knowledge about how to pursue emotions in texts, interviews, observations, spoken language, visuals, historical documents, and surveys. The contributors are respectful of those being researched and are mindful of the effects of their own feelings on the conclusions. The book thus touches upon the ethics of research in vivid first person accounts. Methods are notoriously difficult to teach-this collection fills the gap between dry methods books and students' need to know more about the actual research practice.

This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many 'random effects'. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write-up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to 'translate' their skills with more traditional models to a Bayesian framework, will benefit greatly from the lessons in this text.

Interest in the field of neurodevelopmental disorders has grown exponentially in recent years across a range of disciplines, including psychology, psychiatry, education and neuroscience. The research itself has become more sophisticated, using multidisciplinary methods to probe interdisciplinary questions. Neurodevelopmental Disorders: Research Challenges and Solutions provides a thorough overview of the key issues involved in researching neurodevelopmental disorders. The volume includes 14 chapters, arranged over three sections. Chapters in the first section address general research challenges for the study of neurodevelopmental disorders. The second section draws upon specific disorders (such as Williams syndrome, Autism Spectrum Disorders, Down Syndrome, Fragile X Syndrome, ADHD, and Language Disorders) to consider the syndrome-specific issues or challenges that may be crucial to advancing our understanding of aspects of cognition and behavior associated with them. The final section considers how research evidence may be translated into practice to begin making an impact upon the lives of individuals who have neurodevelopmental disorders and their families. Each chapter in the book also includes 'practical tips' for either conducting research with individuals who have neurodevelopmental disorders or considering wider practical issues. The book will be indispensable reading for advanced students, researchers, and practitioners in the fields of developmental psychology, developmental psychopathology, special needs education, neuropsychology, and neurodevelopmental disorders.

This book addresses issues related with researching sensitive topics in social work, focusing on marginalized, vulnerable and hard to reach people. It covers the definition, characteristics, challenges and opportunities of sensitive research, its philosophical roots and methodological debates, and the skills and values that are required along with the ethical, political and legal issues involved in conducting social work research. This book will cover innovative research methods appropriate for research on sensitive topics involving vulnerable people. It shines light on how to use traditional research methods sensitively, and how to generate data while minimizing the harm that can potentially be caused to research participants and researchers.

CODING MANUAL INFORMATION IS AVAILABLE FROM THE CHAPTER AUTHORS, AND THEIR E-MAIL ADDRESSES CAN BE FOUND ON PAGE XV OF THE BOOK. Family studies is an area that has enjoyed the benefits of conceptual and methodological advances in recent years including the widespread adoption of observational research techniques. The selection of an appropriate coding system is critical to achieving a better understanding of the complex family processes related to normative and pathological development. This book presents 14 examples of family observational coding systems, chosen for the wide range of constructs and phenomena they capture. Each system is described in detail, and excerpts from the coding manual are presented (links to the full coding manuals are available to purchasers of the book at LEA's Web site, www.erlbaum.com). Each chapter follows a consistent outline, so that the different coding systems can be more easily compared to one another. They include the theoretical underpinnings of the measure, its reliability and validity, the coding process, strategies for coder training, and examples of studies in which it has been used. This volume will prove invaluable to students and researchers in family studies, clinicians, and other practitioners who need to interpret data from family observations.

This book focuses on the practical issues and approaches to handling longitudinal and multilevel data. All data sets and the corresponding command files are available via the Web. The working examples are available in the four major SEM packages--LISREL, EQS, MX, and AMOS--and two Multi-level packages--HLM and MLn. All equations and figural conventions are standardized across each contribution. The material is accessible to practicing researchers and students. Users can compare and contrast various analytic approaches to longitudinal and multiple-group data including SEM, Multi-level, LTA, and standard GLM techniques. Ideal for graduate students and practicing researchers in social and behavioral sciences.

Comprehensive and comprehensible, this classic text covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and applying its use. This includes the theory as well as the empirical evaluations. The overall goal is to show readers how to use factor analysis in their substantive research by highlighting when the differences in mathematical procedures have a major impact on the substantive conclusions, when the differences are not relevant, and when factor analysis might not be the best procedure to use. Although the original version was written years ago, the book maintains its relevance today by providing readers with a thorough understanding of the basic mathematical models so they can easily apply these models to their own research. Readers are presented with a very complete picture of the "inner workings" of these methods. The new Introduction highlights the remarkably few changes that the author would make if he were writing the book today. An ideal text for courses on factor analysis or as a supplement for multivariate analysis, structural equation modeling, or advanced quantitative techniques taught in psychology, education, and other social and behavioral sciences, researchers who use these techniques also appreciate this book's thorough review of the basic models. Prerequisites include a graduate level course on statistics and a basic understanding of algebra. Sections with an asterisk can be skipped entirely if preferred.

The political and legislative changes which took place in South Africa during the 1990s, with the dissolution of apartheid, created a unique set of social conditions. As official policies of segregation were abolished, people of both black and white racial groups began to experience new forms of social contact and intimacy. By examining these emerging processes of intergroup contact in South Africa, and evaluating related evidence from the US, Racial Encounter offers a social psychological account of desegregation. It begins with a critical analysis of the traditional theories and research models used to understand desegregation: the contact hypothesis and race attitude theory. It then analyzes every day discourse about desegregation in South Africa, showing how discourse shapes individuals' conception and management of their changing relationships and acts as a site of ideological resistance to social change. The connection between place, identity and re-creation of racial boundaries emerge as a central theme of this analysis. This book will be of interest to social psychologists, students of intergroup relations and all those interested in post-apartheid South Africa.

Comprehensive and comprehensible, this classic text covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and applying its use. This includes the theory as well as the empirical evaluations. The overall goal is to show readers how to use factor analysis in their substantive research by highlighting when the differences in mathematical procedures have a major impact on the substantive conclusions, when the differences are not relevant, and when factor analysis might not be the best procedure to use. Although the original version was written years ago, the book maintains its relevance today by providing readers with a thorough understanding of the basic mathematical models so they can easily apply these models to their own research. Readers are presented with a very complete picture of the "inner workings" of these methods. The new Introduction highlights the remarkably few changes that the author would make if he were writing the book today. An ideal text for courses on factor analysis or as a supplement for multivariate analysis, structural equation modeling, or advanced quantitative techniques taught in psychology, education, and other social and behavioral sciences, researchers who use these techniques also appreciate this book's thorough review of the basic models. Prerequisites include a graduate level course on statistics and a basic understanding of algebra. Sections with an asterisk can be skipped entirely if preferred.

This controversial book argues that 20th century psychotherapy has been fundamentally characterized by serious disagreement on views of human nature, treatment rationales and goals. Focusing on the differences rather than the commonalities in therapy, eight eminent practitioners demonstrate the diversities in therapies and why, for the most part, it is not possible to tolerate or integrate with other approaches. `The book awakened me to understanding more about how a core belief or orientation can result in polarised attitudes towards the person. At the same time, in some cases, there is fundamental common ground which could potentially lead to genuine integration' - [ac]Eisteach, The Journal of the Irish Association for Counselling and Therapy `Eight distinguished practitioners address twelve different questions, aimed at identifying the distinctive qualities of their own approach and demonstrating how it has been arrived at. The result is a book that will allow both experienced practitioners and trainees to become familiar with and compare the current thinking of these well-known people... the very passion of these opposing and sometimes exclusive convictions may be the well-spring for the efficacy and achievements of these eminent practitioners and trainers' - Self & Society Each therapist highlights the distinctive properties of his or her orientation, and discusses questions such as: why and how they came to found, adapt or choose the approach they currently practise; what criticisms of the approach they consider to be valid; which approaches they consider to be ineffective, misleading or dangerous, and, conversely, more promising or effective; why their approach is more effective or comprehensive, and why it may be more suited to certain clients or client problems; and how they account for research which suggests that no one approach seems more effective than any other.

Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. "

Written for non-mathematically-oriented readers, this text features:

• "
• A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data
• An emphasis on how the terminology, interpretation, and estimation" "of familiar general linear models relates to those of more complex models for longitudinal data
• Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers understanding of the overall model-building process
• Sample results sections for each example to provide useful templates for published reports
• Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and M"plus" at www.PilesOfVariance.com to help readers apply the models to their own data

The book opens with the building blocks of longitudinal analysis general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces "unconditional "longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. "Conditional "longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data.

Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text."

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.

Written by one of the masters of the foundation of measurement, Louis Narens' new book thoroughly examines the basis for the measurement-theoretic concept of meaningfulness and presents a new theory about the role of numbers and invariance in science. The book associates with each portion of mathematical science a subject matter that the portion of science is intended to investigate or describe. It considers those quantitative or empirical assertions and relationships that belong to the subject matter to be meaningful (for that portion of science) and those that do not belong to be meaningless. The first two chapters of the Theories of Meaningfulness introduce meaningfulness concepts, their place in the history of science, and some of their traditional applications. The idea that meaningfulness will have different, but interrelated uses is then introduced. To provide formal descriptions of these, the author employs a powerful framework that incorporates pure mathematics, provides for qualitative objects and relations, and addresses the relationships between qualitative objects and pure mathematics. The framework is then applied to produce axiomatic theories of meaningfulness, including generalizations and a new foundation for the famous Erlanger Program of mathematics. The meaningfulness concept is further specialized with the introduction of intrinsicness, which deals with meaningful concepts and relations that are lawful and qualitativeness, which is concerned with qualitative concepts. The concept of empiricalness is then introduced to distinguish it from meaningfulness and qualitativeness. The failure to distinguish empiricalness from meaningfulness and qualitativeness has produced much confusion in the foundations of science literature and has generated many pseudo-controversies. This book suggests that many of these disappear when empiricalness is intersected with the other concepts to produce "meaningful and empirical relations," "empirical laws," and "qualitative and empirical concepts." A primary goal of this book is to show that the new theories of meaningfulness and intrinsicness developed in this book are not only descriptive but are also potent. Asserting that they do more than codify already existing concepts the book: *works out logical relationships between meaningfulness concepts that were previously unrecognized; *clarifies certain well-known and important debates by providing rich languages with new concepts and technical results (theorems) that yield insights into the debated issues and positions taken on them; and *provides new techniques and results in substantive scientific areas of inquiry. This book is about the role of mathematics in science. It will be useful to those concerned with the foundations of science in their respective fields. Various substantive examples from the behavioral sciences are presented.

Crafting Scholarship helps readers improve their writing and publishing success in academia. Framed within the context of the editorial and peer review process, the book explores writing, editing, and reviewing in academic publishing. As such it provides unique coverage of how successful writers work, how they manage criticism, and more. Examples from successful scholars provide helpful tips in writing articles, grants, books, book chapters, and reviews. Each chapter features tools that facilitate learning including "Best Practices" and "Writer s Resource" boxes to help maximize success, discussion questions and case studies to stimulate critical thinking, and recommended readings to encourage self exploration. A Facebook page provides an opportunity for readers to post writing updates and for instructors to share materials.

"

Highlights include: "

-Insights on working with journal boards, reviewers, and contributors drawn from the author s 30 years of experience in editing journal articles and writing books.

-Describes writing quantitative and qualitative reports, theory and literature reviews, books and chapters, grants, and book reviews.

-Identifies common problems academics face in writing and publishing along with practical solutions.

-Explores best practices in writing peer reviews, responding to reviewers and editors, and how to calculate and interpret acceptance rates and impact factors.

-Addresses how to write each section of a journal article and select keywords that facilitate digital search engines to help potential readers find an article.

-Includes examples of published work and tips on writing research syntheses using meta-analytic techniques or narrative analyses.

-Examines the practices of successful writers, the pros and cons of collaborations, what publishers look for, and managing criticism.

-Reviews pertinent empirical literature on the core topics of writing, reviewing, and editing.

Intended for graduate or advanced undergraduate courses in professional development, writing in an academic field, or research methods taught in psychology, education, human development and family studies, sociology, communication, and other social sciences, this practical guide also appeals to those interested in pursuing an academic career and new and seasoned researchers. "

Want to master research methods in psychology? Look no further! Written with new students in mind, this book will help you understand and apply research methods to every part of your degree. It will help you develop your critical and analytical skills by making features of key issues such as ethics and cultural context. It aims to entertain with interesting facts, and will enhance your understanding with examples of real studies. Other features include: Skoolkid errors Ig Nobel Prize Whoa there! and are all designed to make the study of methods enjoyable and easy.

Crafting Scholarship helps readers improve their writing and publishing success in academia. Framed within the context of the editorial and peer review process, the book explores writing, editing, and reviewing in academic publishing. As such it provides unique coverage of how successful writers work, how they manage criticism, and more. Examples from successful scholars provide helpful tips in writing articles, grants, books, book chapters, and reviews. Each chapter features tools that facilitate learning including "Best Practices" and "Writer s Resource" boxes to help maximize success, discussion questions and case studies to stimulate critical thinking, and recommended readings to encourage self exploration. A Facebook page provides an opportunity for readers to post writing updates and for instructors to share materials.

"

Highlights include: "

-Insights on working with journal boards, reviewers, and contributors drawn from the author s 30 years of experience in editing journal articles and writing books.

-Describes writing quantitative and qualitative reports, theory and literature reviews, books and chapters, grants, and book reviews.

-Identifies common problems academics face in writing and publishing along with practical solutions.

-Explores best practices in writing peer reviews, responding to reviewers and editors, and how to calculate and interpret acceptance rates and impact factors.

-Addresses how to write each section of a journal article and select keywords that facilitate digital search engines to help potential readers find an article.

-Includes examples of published work and tips on writing research syntheses using meta-analytic techniques or narrative analyses.

-Examines the practices of successful writers, the pros and cons of collaborations, what publishers look for, and managing criticism.

-Reviews pertinent empirical literature on the core topics of writing, reviewing, and editing.

Intended for graduate or advanced undergraduate courses in professional development, writing in an academic field, or research methods taught in psychology, education, human development and family studies, sociology, communication, and other social sciences, this practical guide also appeals to those interested in pursuing an academic career and new and seasoned researchers. "

First Published in 2000. Routledge is an imprint of Taylor & Francis, an informa company.

First Published in 1985. Routledge is an imprint of Taylor & Francis, an informa company.

In this volume academic and researchers from across disciplines including education, psychology and health studies come together to discuss personal, political and professional narratives of struggle, resilience and hope. Contributors draw from a rich body of auto-biographical research examining the role of narrative and how it can be constructed, considering the roles of significant others, inspirational, educational and fiction characters, as well as myth and legend, to compose a life story. The book discusses how personal narrative, often neglected in social and psychological enquiry, can be a valuable resource across a range of settings. Reference is made to the evolving role of narrative in studies of educational process, in health and other forms of professional practice, and in psychotherapy. The book also includes examples that go beyond words, demonstrating other ways of representing lives through research, using art, fiction and poetry. Rather than providing a 'how to' guide, this book illustrates the range of work possible using narrative.Each chapter outlines a specific study, describes the context, explains the methodology and illuminates important findings with examples from the research. Further implication for research and practice are also discussed making this book important reading for postgraduate and doctoral students, those teaching research methods, as well as academics and researchers developing biographical and auto/biographical narrative research. The text will appeal to those from a range of disciplines including education, health and social care, counselling and psychotherapy.

Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure people's preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well as those prepared by the authors. It focuses on core SP methods, including contingent valuation (CV), discrete choice experiments (DCEs), and best-worst scaling (BWS). Several example data sets illustrate empirical applications of each method with R. Examples of CV draw on data from well-known environmental valuation studies, such as the Exxon Valdez oil spill in Alaska. To explain DCEs, the authors use synthetic data sets related to food marketing and environmental valuation. The examples illustrating BWS address valuing agro-environmental and food issues. All the example data sets and code are available on the authors' website, CRAN, and R-Forge, allowing readers to easily reproduce working examples. Although the examples focus on agricultural and environmental economics, they provide beginners with a good foundation to apply SP methods in other fields. Statisticians, empirical researchers, and advanced students can use the book to conduct applied research of SP methods in economics and market research. The book is also suitable as a primary text or supplemental reading in an introductory-level, hands-on course.

A Practical Guide to Teaching Research Methods in Education brings together more than 60 faculty experts. The contributors share detailed lesson plans about selected research concepts or skills in education and related disciplines, as well as discussions of the intellectual preparation needed to effectively teach the lesson. Grounded in the wisdom of practice from exemplary and award-winning faculty from diverse institution types, career stages, and demographic backgrounds, this book draws on both the practical and cognitive elements of teaching educational (and related) research to students in higher education today. The book is divided into eight sections, covering the following key elements within education (and related) research: problems and research questions, literature reviews and theoretical frameworks, research design, quantitative methods, qualitative methods, mixed methods, findings and discussions, and special topics, such as student identity development, community and policy engaged research, and research dissemination. Within each section, individual chapters specifically focus on skills and perspectives needed to navigate the complexities of educational research. The concluding chapter reflects on how teachers of research also need to be learners of research, as faculty continuously strive for mastery, identity, and creativity in how they guide our next generation of knowledge producers through the research process. Undergraduate and graduate professors of education (and related) research courses, dissertation chairs/committee members, faculty development staff members, and graduate students would all benefit from the lessons and expert commentary contained in this book.

Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher.Mathematical derivations are placed in optional appendices for those interested in this detailed coverage. Highlights include: Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project but some chapters can be omitted entirely if preferred.Step by step examples of each test help readers see how the material is applied in a variety of disciplines. Although the book can be used with any program, examples of how to use the tests in SPSS & EXCEL foster conceptual understanding. Exploring the concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding. Problems in each chapter help readers test their understanding of the material. Emphasizes selecting tests that maximize power to help readers avoid marginally significant results. Website featuring datasets for the book's examples and problems, and for the instructor Power Points, author's course syllabus, and answers to the even numbered problems. Chapters 1-3 cover basic concepts in probability, especially the binomial formula followed by two chapters that address the analysis of contingency tables. Chapters 6-8 address nonparametric tests involving at least one ordinal variable, including testing for nonparametric interaction effects, a topic omitted from other texts. The book then turns to situations that involve one metric variable.Chapter 9 reviews concepts that are foundational to CDA, including linear regression and generalized linear models. Chapters 10-11 cover logistic, ordinal, and Poisson regression. Chapters 12 and 13 review loglinear models and the General Estimating Equations (GEE) methodology for measuring outcomes from multiple time points. For a deeper understanding of how various CDA techniques work, chapter 14 covers estimation methods, such as Newton-Raphson and Fisher scoring. The book concludes with a summary of factors that need to be considered when choosing the best statistical technique. Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t-tests and ANOVA.