Statistics Explained is an accessible introduction to statistical concepts and ideas. It makes few assumptions about the reader's statistical knowledge, carefully explaining each step of the analysis and the logic behind it. The book: provides a clear explanation of statistical analysis and the key statistical tests employed in analysing research data gives accessible explanations of how and why statistical tests are used includes a wide range of practical, easy-to-understand worked examples. Building on the international success of earlier editions, this fully updated revision includes developments in statistical analysis, with new sections explaining concepts such as bootstrapping and structural equation modelling. A new chapter - 'Samples and Statistical Inference' - explains how data can be analysed in detail to examine its suitability for certain statistical tests. The friendly and straightforward style of the text makes it accessible to all those new to statistics, as well as more experienced students requiring a concise guide. It is suitable for students and new researchers in disciplines including Psychology, Education, Sociology, Sports Science, Nursing, Communication, and Media and Business Studies. Presented in full colour and with an updated, reader-friendly layout, this new edition also comes with a companion website featuring supplementary resources for students. Unobtrusive cross-referencing makes it the ideal companion to Perry R. Hinton's SPSS Explained, also published by Routledge. Perry R. Hinton has many years of experience in teaching statistics to students from a wide range of disciplines and his understanding of the problems students face forms the basis of this book.

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.

This book focuses on how statistical reasoning works and on training programs that can exploit people's natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.

This new monograph presents Dr. Luce's current understanding of the behavioral properties people exhibit (or should exhibit) when they make selections among alternatives and how these properties lead to numerical representations of those preferences. It summarizes, and places in historical perspective, the research Dr. Luce has done on utility theory for over 10 years. Included are axiomatic theoretical formulations, experiments designed to test individual assumptions, and analyses of the fit to bodies of data of numerical representations derived from the theory.

Volume I is the first of two volumes that document the three components of the CHILDES Project. It is divided into two parts which provide an introduction to the use of computational tools for studying language learning. The first part is the CHAT manual, which describes the conventions and principles of CHAT transcription and recommends specific methods for data collection and digitization. The second part is the CLAN manual, which describes the uses of the editor, sonic CHAT, and the various analytic commands. The book will be useful for both novice and experienced users of the CHILDES tools, as well as instructors and students working with transcripts of child language. Volume II describes in detail all of the corpora included in the CHILDES database. The conversational interactions in the corpora come from monolingual children and their caregivers and siblings, as well as bilingual children, older school-aged children, adult second-language learners, children with various types of language disabilities, and aphasic recovering from language loss. The database includes transcripts in 26 different languages.The CD-ROM that accompanies these volumes includes the transcript files described in Volume II. It runs on both Windows and Macintosh platforms. For more information or updates to the files, visit the CHILDES website at http: //childes.psy.cmu.ed

Teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology. Has been thoroughly revised to incorporate recent research and applications. Includes a new chapter on nonprobability samples, and more than 200 new examples and exercises have been added.

This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable. It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a "user-friendly" style such that even the "novice" data analyst can easily apply the techniques. Each chapter features: a minimum discussion of mathematical detail; an empirical example applying the technique; and a discussion of the software related to that technique. Content highlights include analysis of mixed, multi-level, structural equation, and categorical data models. It is ideal for researchers, professionals, and students working with repeated measures data from the social and behavioral sciences, business, or biological sciences.

Single Case Research in Schools addresses and examines the variety of cutting-edge issues in single case research (SCR) in educational settings. Featuring simple and practical techniques for aggregating data for evidence-based practices, the book delves into methods of selecting behaviors of interest and measuring them reliably. The latter part of Single Case Research in Schools is devoted to a step-by-step model of using SCR to evaluate practices in schools. This includes considerations such as measurement, date collection, length of phases, design consideratoins, calculating effect size and reliability of measures.

This comprehensive volume explores the set of theoretical, methodological, ethical and analytical issues that shape the ways in which visual qualitative research is conducted in psychology. Using visual data such as film making, social media analyses, photography and model making, the book uniquely uses visual qualitative methods to broaden our understanding of experience and subjectivity. In recent years, visual research has seen a growing emphasis on the importance of culture in experience-based qualitative methods. Featuring contributors from diverse research backgrounds including narrative psychology, personal construct theory and psychoanalysis, the book examines the potential for visual methods in psychology. In each chapter of the book, the contributors explore and address how a visual approach has contributed to existing social and psychological theory in their line of research. The book provides up-to-date insights into combining methods to create new multi-modal methodologies, and analyses these with psychology-specific questions in mind. It covers topics such as sexuality, identity, group processes, child development, forensic psychology, race and gender, and would be the ideal companion for those studying or undertaking research in disciplines like psychology, sociology and gender studies.

Neil J. Salkind's best-selling Statistics for People Who (Think They) Hate Statistics has been helping ease student anxiety around an often intimidating subject since it first published in 2000. Now the bestselling SPSS and Excel versions are joined by a first edition of the text for use with the R software. New co-author Leslie A. Shaw carries forward Neil's signature humorous, personable, and informative approach. The text guides students through various statistical procedures, beginning with descriptive statistics, correlation, and graphical representation of data, and ending with inferential techniques and analysis of variance. Features and benefits: * Lots of support for getting started with R: Included are two introductory chapters on R and on R Studio, plus an appendix on other R packages and resource sites. * Step-by-step demonstrations of each statistical procedure in R: The authors show how to import the dataset, enter the syntax to run the test, and understand the output. * Additional resources make it easy to transition to this text, and to R: Code and datasets are available on an accompanying website, which also includes screencast R tutorial videos for students, and PowerPoint slides and additional test questions for instructors.

Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author's many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author's website.

Categorical data are quantified as either nominal variables--distinguishing different groups, for example, based on socio-economic status, education, and political persuasion--or ordinal variables--distinguishing levels of interest, such as the preferred politician or the preferred type of punishment for committing burglary. This new book is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets. This volume concentrates on latent class analysis and item response theory. These methods use latent variables to explain the relationships among observed categorical variables. Latent class analysis yields the classification of a group of respondents according to their pattern of scores on the categorical variables. This provides insight into the mechanisms producing the data and allows the estimation of factor structures and regression models conditional on the latent class structure. Item response theory leads to the identification of one or more ordinal or interval scales. In psychological and educational testing these scales are used for individual measurement of abilities and personality traits. The focus of this volume is applied. After a method is explained, the potential of the method for analyzing categorical data is illustrated by means of a real data example to show how it can be used effectively for solving a real data problem. These methods are accessible to researchers not trained explicitly in applied statistics. This volume appeals to researchers and advanced students in the social and behavioral sciences, including social, developmental, organizational, clinical and health psychologists, sociologists, educational and marketing researchers, and political scientists. In addition, it is of interest to those who collect data on categorical variables and are faced with the problem of how to analyze such variables--among themselves or in relation to metric variables.

*Major revision of a work that gives researchers the tools to select the right theory for their evaluation task. *Clearly lays out the goals of each theory, how it translates into practice, and the conditions under which it is best applicable. *Third edition has an increased focus on diversity/equity and is organized around theories rather than individual theorists. *Top editors and contributors are highly regarded, experienced evaluators.

The study of intuition and its relation to thoughtful reasoning is a burgeoning research topic in psychology and beyond. While the area has the potential to radically transform our conception of the mind and decision making, the procedures used for establishing empirical conclusions have often been vaguely formulated and obscure. This book fills a gap in the field by providing a range of methods for exploring intuition experimentally and thereby enhancing the collection of new data. The book begins by summarizing current challenges in the study of intuition and gives a new foundation for intuition research. Going beyond classical dual-process models, a new scheme is introduced to classify the different types of processes usually collected under the label of intuition. These new classifications range from learning approaches to complex cue integration models. The book then goes on to describe the wide variety of behavioural methods available to investigate these processes, including information search tracing, think aloud protocols, maximum likelihood methods, eye-tracking, and physiological and non-physiological measures of affective responses. It also discusses paradigms to investigate implicit associations and causal intuitions, video-based approaches to expert research, methods to induce specific decision modes as well as questionnaires to assess individual preferences for intuition or deliberation. By uniquely providing the basis for exploring intuition by introducing the different methods and their applications in a step-by-step manner, this text is an invaluable reference for individual research projects. It is also very useful as a course book for advanced decision making courses, and could inspire experimental explorations of intuition in psychology, behavioural economics, empirical legal studies and clinical decision making.

Psychologists are under increasing pressure to demonstrate the ecological validity of their assessment procedures--to show that the recommendations concluding their evaluations are relevant to urgent concerns in the legal and social policy arenas, such as predicting dangerousness, awarding compensation, and choosing a custodial parent. How much damage does a referred patient have? Who or what "caused" the damage? What impact will it have on his or her future life, work, and family? And what can be done to remediate the damage? The purpose of this book is to provide sound objective methods for answering these questions. It integrates the knowledge of experienced practitioners who offer state-of-the-art summaries of the best current approaches to evaluating difficult cases with that of basic theorists who describe emerging methods in both predictive and inferential statistics, such as Bayesian networks, that have proven their value in other scientific fields. Arguably, the enterprise of psychological assessment is so interdependent with that of data analysis that attempts to make inferences without consideration of statistical implications is malpractice. Prediction in Forensic and Neuropsychology: Sound Statistical Practices clarifies the process of hypothesis testing and helps to push the clinical interpretation of psychological data into the 21st century. It constitutes a vital resource for all the stakeholders in the assessment process--practitioners, researchers, attorneys, and policymakers.

This book highlights the effects of the COVID-19 pandemic on the mental health needs of children and adolescents in order to shed light on future practice and reform needed to better deal with the aftermath of such devastating events. The book identifies the conditions during any public health crisis that heighten the mental health needs of children and adolescents and suggests the reforms of mental health services needed to better meet the needs of children and youths during and following pandemics and other public health crises. Importance is placed not only on addressing the effects of COVID-19 but on anticipating and preparing for other public health disruptions to the lives of those who have not reached adulthood. Although mental health services in all settings are considered, special attention is given to the role of schools in providing for the mental health of children and adolescents and preparing for the mental health implications of future public health disruptions. The book will be of equal use to both students and researchers in the fields of mental health, well-being, and education as well as teachers, educational psychologists, social workers, and practitioners working in schools and communities to address students' mental health needs. It will help readers better understand how and why COVID-19 was a negative influence on students' mental health, and unpack how best to deal with the aftermath of the pandemic.

The motivation for this volume in the History and Theory of Psychology series is to look across sub-disciplines within psychology and highlight instances where researchers transcended the tendency to think about methodology along traditional lines. Contributors have located examples of researchers who built upon existing ideas to create methods true to their interests and theoretical convictions.

Emerging Methods in Psychology shows how a discipline creates new methods and carves out possibilities that not only generate data, but also advance knowledge of human psychological functioning. It concentrates on showcasing the possibilities that exist when the researcher focuses on the relationship between theory, method, and data.

The question of what kind of expertise is required is a key issue. This is particularly the case in psychology where the tradition of standardizing methods over the last century has served to stabilize research questions. Knowledge creation is deeply affective and ambiguous rather than the secure accumulation of data by a socially legitimized procedure. This innovative volume moves beyond psychology as social engineering into new varieties of social knowledge.

*Showcases science-based, theoretically informed interventions to foster healthier relationships. *Reviews applications in parent–child, couple, and individual therapy, as well as extensions to teacher–student, doctor–patient, and other dyads. *Concise, coherent, forward-thinking roadmap of established and emerging attachment-based practices. *From leaders in the field with decades of experience. *Together with the related volume, Attachment Theory Expanded, it provides a state-of-the-science resource and graduate-level teaching tool.

Mastering the Semi-Structured Interview and Beyond offers an in-depth and captivating step-by-step guide to the use of semi-structured interviews in qualitative research. By tracing the life of an actual research project-an exploration of a school district's effort over 40 years to address racial equality-as a consistent example threaded across the volume, Anne Galletta shows in concrete terms how readers can approach the planning and execution of their own new research endeavor, and illuminates unexpected real-life challenges they may confront and how to address them. The volume offers a close look at the inductive nature of qualitative research, the use of researcher reflexivity, and the systematic and iterative steps involved in data collection, analysis, and interpretation. It offers guidance on how to develop an interview protocol, including the arrangement of questions and ways to evoke analytically rich data. Particularly useful for those who may be familiar with qualitative research but have not yet conducted a qualitative study, Mastering the Semi-Structured Interview and Beyond will serve both undergraduate and graduate students as well as more advanced scholars seeking to incorporate this key methodological approach into their repertoire.

Now in its fourth edition, Behavioral Research and Analysis: An Introduction to Statistics within the Context of Experimental Design presents an overview of statistical methods within the context of experimental design. It covers fundamental topics such as data collection, data analysis, interpretation of results, and communication of findings. New in the Fourth Edition: Extensive improvements based on suggestions from those using this book in the classroom Statistical procedures that have been developed and validated since the previous edition Each chapter in the body now contains relevant key words, chapter summaries, key word definitions, and end of chapter exercises (with answers) Revisions to include recent changes in the APA Style Manual When looking for a book for their own use, the authors found none that were totally suitable. They found books that either reviewed the basics of behavioral research and experimental design but provided only cursory coverage of statistical methods or they provided coverage of statistical methods with very little coverage of the research context within which these methods are used. No single resource provided coverage of methodology, statistics, and communication skills. In a classic example of necessity being the mother of invention, the authors created their own. This text is ideal for a single course that reviews research methods, essential statistics through multi-factor analysis of variance, and thesis (or major project) preparation without discussion of derivation of equations, probability theory, or mathematic proofs. It focuses on essential information for getting a research project completed without prerequisite math or statistics training. It has been revised many times to help students at a variety of academic levels (exceptional high school students, undergraduate honors students, masters students, doctoral students, and post-doctoral fellows) across varied academic disciplines (e.g., human factors and ergonomics, behavioral and social sciences, natural sciences, engineering, exercise and sport sciences, business and management, industrial hygiene and safety science, health and medical sciences, and more). Illustrating how to plan, prepare, conduct, and analyze an experimental or research report, the book emphasizes explaining statistical procedures and interpreting obtained results without discussing the derivation of equations or history of the method. Destined to spend more time on your desk than on the shelf, the book will become the single resource you reach for again and again when conducting scientific research and reporting it to the scientific community. Illustrates how to plan, conduct, analyze, and prepare an experimental or research report Includes new statistical procedures that have been developed and validated since the previous edition Incorporates SAS in the exercises at the end of each chapter Takes into account the changes in the APA guidebook Provides new examples in exercise and sport science, public health, gerontology, and biomedical areas Solutions manual available upon qualifying course adoption

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors' highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

Highlighting the progress made by researchers in using Web-based surveys for data collection, this timely volume summarizes the experiences of leading behavioral and social scientists from Europe and the US who collected data using the Internet. Some chapters present theory, methodology, design, and implementation, while others focus on best practice examples and/or issues such as data quality and understanding paradata. A number of contributors applied innovative Web-based research methods to the LISS panel of CentERdata collected from over 5,000 Dutch households. Their findings are presented in the book. Some of the data is available on the book website. The book addresses practical issues such as data quality, how to reach difficult target groups, how to design a survey to maximize response, and ethical issues that need to be considered. Innovative applications such as the use of biomarkers and eye-tracking techniques are also explored. Part 1 provides an overview of Internet survey research including its methodologies, strengths, challenges, and best practices. Innovative ways to minimize sources of error are provided along with a review of mixed-mode designs, how to design a scientifically sound longitudinal panel and avoid sampling problems, and address ethical requirements in Web surveys. Part 2 focuses on advanced applications including the impact of visual design on the interpretability of survey questions, the impact survey usability has on respondents' answers, design features that increase interaction, and how Internet surveys can be effectively used to study sensitive issues. Part 3 addresses data quality, sample selection, measurement and non-response error, and new applications for collecting online data. The issue of underrepresentation of certain groups in Internet research and the measures most effective at reducing it are also addressed. The book concludes with a discussion of the importance of paradata and the Web data collection process in general, followed by chapters with innovative experiments using eye-tracking techniques and biomarker data. This practical book appeals to practitioners from market survey research institutes and researchers in disciplines such as psychology, education, sociology, political science, health studies, marketing, economics, and business who use the Internet for data collection, but is also an ideal supplement for graduate and/or upper level undergraduate courses on (Internet) research methods and/or data collection taught in these fields.

This latest edition has been fully updated to accommodate the needs of users of SPSS Releases 17, 18 and 19 while still being applicable to users of SPSS Releases 15 and 16. As with previous editions, Alan Bryman and Duncan Cramer continue to offer a comprehensive and user-friendly introduction to the widely used IBM SPSS Statistics. The simple, non-technical approach to quantitative data analysis enables the reader to quickly become familiar with SPSS and with the tests available to them. No previous experience of statistics or computing is required as this book provides a step-by-step guide to statistical techniques, including: Non-parametric tests Correlation Simple and multiple regression Analysis of variance and covariance Factor analysis. This book comes equipped with a comprehensive range of exercises for further practice, and it covers key issues such as sampling, statistical inference, conceptualization and measurement and selection of appropriate tests. The authors have also included a helpful glossary of key terms. The data sets used in Quantitative Data Analysis with IBM SPSS 17, 18 and 19 are available online at http://www.routledgetextbooks.com/textbooks/_author/bryman-9780415579193/; in addition, a set of multiple-choice questions and a chapter-by-chapter PowerPoint lecture course are available free of charge to lecturers who adopt the book.

The Student Study Guide With SPSS Workbook for Essential Statistics for the Behavioral Sciences 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 book.

Good Science is an account of psychological research emphasizing the moral foundations of inquiry. This volume brings together existing disciplinary critiques of scientism, objectivism, and instrumentalism, and then discusses how these contribute to institutionalized privilege and to less morally responsive research practices. The author draws on historical, critical, feminist, and science studies traditions to provide an alternative account of psychological science and to highlight the irreducibly moral foundations of everyday scientific practice. This work outlines a theoretical framework for thinking about and practicing psychology in ways that center moral responsibility, collective commitment, and justice. The book then applies this framework, describing psychological research practices in terms of the their moral dilemmas. Also included are materials meant to aid in methods instruction and mentoring.