"Describes recent developments and surveys important topics in the areas of multivariate analysis, design of experiments, and survey sampling. Features the work of nearly 50 international leaders."

Select the Optimal Model for Interpreting Multivariate Data Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering. The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.

* Explores the overlap/parallels between the work of clinicians and qualitative researchers. * Suggests how postmodern therapeutic approaches can be integrated into methods of data collection and analysis. * Examines a range of postmodern therapies, including collaborative language systems, narrative therapy, and solution-focused brief therapy. * Offers an innovative and unique way of enhancing the skills of the qualitative researcher, with an emphasis on reflective practice.

There is a recent surge in the use of randomized controlled trials (RCTs) within education globally, with disproportionate claims being made about what they show, 'what works', and what constitutes the best 'evidence'. Drawing on up-to-date scholarship from across the world, Taming Randomized Controlled Trials in Education critically addresses the increased use of RCTs in education, exploring their benefits, limits and cautions, and ultimately questioning the prominence given to them. While acknowledging that randomized controlled trials do have some place in education, the book nevertheless argues that this place should be limited. Drawing together all arguments for and against RCTs in a comprehensive and easily accessible single volume, the book also adds new perspectives and insights to the conversation; crucially, the book considers the limits of their usefulness and applicability in education, raising a range of largely unexplored concerns about their use. Chapters include discussions on: The impact of complexity theory and chaos theory. Design issues and sampling in randomized controlled trials. Learning from clinical trials. Data analysis in randomized controlled trials. Reporting, evaluating and generalizing from randomized controlled trials. Considering key issues in understanding and interrogating research evidence, this book is ideal reading for all students on Research Methods modules, as well as those interested in undertaking and reviewing research in the field of education.

* Explores the overlap/parallels between the work of clinicians and qualitative researchers. * Suggests how postmodern therapeutic approaches can be integrated into methods of data collection and analysis. * Examines a range of postmodern therapies, including collaborative language systems, narrative therapy, and solution-focused brief therapy. * Offers an innovative and unique way of enhancing the skills of the qualitative researcher, with an emphasis on reflective practice.

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting. The frameworks draw on recent advances in statistical methodology to provide new tools for tackling challenges such as disaggregation, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems. The methods unify estimation and forecasting, and yield detailed measures of uncertainty. The book assumes minimal knowledge of statistics, and no previous knowledge of demography. The authors have developed a set of R packages implementing the methods. Data and code for all applications in the book are available on www.bdef-book.com. "This book will be welcome for the scientific community of forecasters...as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future." ~Daniel Courgeau, Institut national d'etudes demographiques

Confidence Intervals for Proportions and Related Measures of Effect Size illustrates the use of effect size measures and corresponding confidence intervals as more informative alternatives to the most basic and widely used significance tests. The book provides you with a deep understanding of what happens when these statistical methods are applied in situations far removed from the familiar Gaussian case. Drawing on his extensive work as a statistician and professor at Cardiff University School of Medicine, the author brings together methods for calculating confidence intervals for proportions and several other important measures, including differences, ratios, and nonparametric effect size measures generalizing Mann-Whitney and Wilcoxon tests. He also explains three important approaches to obtaining intervals for related measures. Many examples illustrate the application of the methods in the health and social sciences. Requiring little computational skills, the book offers user-friendly Excel spreadsheets for download at www.crcpress.com, enabling you to easily apply the methods to your own empirical data.

Measures of Interobserver Agreement and Reliability, Second Edition covers important issues related to the design and analysis of reliability and agreement studies. It examines factors affecting the degree of measurement errors in reliability generalization studies and characteristics influencing the process of diagnosing each subject in a reliability study. The book also illustrates the importance of blinding and random selection of subjects. New to the Second Edition New chapter that describes various models for methods comparison studies New chapter on the analysis of reproducibility using the within-subjects coefficient of variation Emphasis on the definition of the subjects' and raters' population as well as sample size determination This edition continues to offer guidance on how to run sound reliability and agreement studies in clinical settings and other types of investigations. The author explores two ways of producing one pooled estimate of agreement from several centers: a fixed-effect approach and a random sample of centers using a simple meta-analytic approach. The text includes end-of-chapter exercises as well as downloadable resources of data sets and SAS code.

There is a recent surge in the use of randomized controlled trials (RCTs) within education globally, with disproportionate claims being made about what they show, 'what works', and what constitutes the best 'evidence'. Drawing on up-to-date scholarship from across the world, Taming Randomized Controlled Trials in Education critically addresses the increased use of RCTs in education, exploring their benefits, limits and cautions, and ultimately questioning the prominence given to them. While acknowledging that randomized controlled trials do have some place in education, the book nevertheless argues that this place should be limited. Drawing together all arguments for and against RCTs in a comprehensive and easily accessible single volume, the book also adds new perspectives and insights to the conversation; crucially, the book considers the limits of their usefulness and applicability in education, raising a range of largely unexplored concerns about their use. Chapters include discussions on: The impact of complexity theory and chaos theory. Design issues and sampling in randomized controlled trials. Learning from clinical trials. Data analysis in randomized controlled trials. Reporting, evaluating and generalizing from randomized controlled trials. Considering key issues in understanding and interrogating research evidence, this book is ideal reading for all students on Research Methods modules, as well as those interested in undertaking and reviewing research in the field of education.

This is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences.

Survey Development: A Theory-Driven Mixed Methods Approach provides both an overview of standard methods and tools for developing and validating surveys and a conceptual basis for survey development. It advocates logical reasoning that combines theory related to construct validity with theory regarding design, and theory regarding survey response, item review, and identification of misfitting responses. The book has 14 chapters which are divided into four parts. Part A includes six chapters that deal with theory and methodology. Part B has five chapters and it gets into the process of constructing the survey. Part C comprises two chapters devoted to assessing the quality or psychometric properties (reliability and validity) of survey responses. Finally, the one chapter in Part D is an attempt to present a synopsis of what was covered in the previous chapters in regard to developing a survey with the Theory-Driven-Mixed-Methods (TDMM) framework for developing survey and conducting survey research. This provides a full process for survey development intended to yield results that can support validity. A mixed methods approach integrates both qualitative and quantitative data outcomes. Including detailed online resources, this book is suitable for graduate students who use or are responsible for interpretation of survey research and survey data as well as survey methodologists and practitioners who use surveys in their field.

This book, originally published in 1970, concerns the new technique of computer simulation in psychology at the time. Computer programs described include models of learning, problem-solving, pattern recognition, the use of language, and personality. More general topics are discussed including the evaluation of such models, the relation of the field to cybernetics, and the problem posed by consciousness. Today it can be read and enjoyed in its historical context.

The number of innovative applications of randomization tests in various fields and recent developments in experimental design, significance testing, computing facilities, and randomization test algorithms have necessitated a new edition of Randomization Tests. Updated, reorganized, and revised, the text emphasizes the irrelevance and implausibility of the random sampling assumption for the typical experiment in three completely rewritten chapters. It also discusses factorial designs and interactions and combines repeated-measures and randomized block designs in one chapter. The authors focus more attention on the practicality of N-of-1 randomization tests and the availability of user-friendly software to perform them. In addition, they provide an overview of free and commercial computer programs for all of the tests presented in the book. Building on the previous editions that have served as standard textbooks for more than twenty-five years, Randomization Tests, Fourth Edition includes downloadable resources of up-to-date randomization test programs that facilitate application of the tests to experimental data. This CD-ROM enables students to work out problems that have been added to the chapters and helps professors teach the basics of randomization tests and devise tasks for assignments and examinations.

Multi-State Survival Models for Interval-Censored Data introduces methods to describe stochastic processes that consist of transitions between states over time. It is targeted at researchers in medical statistics, epidemiology, demography, and social statistics. One of the applications in the book is a three-state process for dementia and survival in the older population. This process is described by an illness-death model with a dementia-free state, a dementia state, and a dead state. Statistical modelling of a multi-state process can investigate potential associations between the risk of moving to the next state and variables such as age, gender, or education. A model can also be used to predict the multi-state process. The methods are for longitudinal data subject to interval censoring. Depending on the definition of a state, it is possible that the time of the transition into a state is not observed exactly. However, when longitudinal data are available the transition time may be known to lie in the time interval defined by two successive observations. Such an interval-censored observation scheme can be taken into account in the statistical inference. Multi-state modelling is an elegant combination of statistical inference and the theory of stochastic processes. Multi-State Survival Models for Interval-Censored Data shows that the statistical modelling is versatile and allows for a wide range of applications.

Develop a Deep Understanding of the Statistical Issues of APC Analysis Age-Period-Cohort Models: Approaches and Analyses with Aggregate Data presents an introduction to the problems and strategies for modeling age, period, and cohort (APC) effects for aggregate-level data. These strategies include constrained estimation, the use of age and/or period and/or cohort characteristics, estimable functions, variance decomposition, and a new technique called the s-constraint approach. See How Common Methods Are Related to Each Other After a general and wide-ranging introductory chapter, the book explains the identification problem from algebraic and geometric perspectives and discusses constrained regression. It then covers important strategies that provide information that does not directly depend on the constraints used to identify the APC model. The final chapter presents a specific empirical example showing that a combination of the approaches can make a compelling case for particular APC effects. Get Answers to Questions about the Relationships of Ages, Periods, and Cohorts to Important Substantive Variables This book incorporates several APC approaches into one resource, emphasizing both their geometry and algebra. This integrated presentation helps researchers effectively judge the strengths and weaknesses of the methods, which should lead to better future research and better interpretation of existing research.

This core textbook offers an introduction to the basics of statistics that is uniquely suited to behavioural science students. The book offers coverage anchored to real-world stories, a highly visual approach, helpful mathematical support, and useful step-by-step examples. The book focuses on emerging trends that are redefining contemporary behavioural statistics.This textbook helps you get to grips with a challenging subject in an enjoyable and engaging way. The book can also be purchased with the breakthrough online resource, LaunchPad, which offers innovative media content, curated and organised for easy assignability. LaunchPad's intuitive interface presents quizzing, flashcards, animations and much more to make learning actively engaging.

This breakthrough volume details the psychological and interpersonal skills needed to meet the practical challenges of building, developing, adapting, training, and managing multicultural global teams. Its self-regulation approach offers cognitive keys to understanding and embracing difference and its associated complexities for successful global collaborations and lasting results. From this foundation, the book moves on to the various roles of leadership in facilitating team process, from establishing trust to defusing conflicts, reducing biases, and using feedback effectively. This synthesis of research and practice effectively blends real-world experience and the science of global team leadership to address the complex issues facing modern organizations. Core skills covered by the book: Structuring successful global virtual teams. Developing cross-cultural competencies through global teams. Managing active faultlines and conflicts in global teams. Coaching global teams and global team leaders. Utilizing feedback effectively across cultures. Meeting the global need for leaders through Guided Mindfulness. Leading Global Teams is mind-opening reading for students, scholars, and practitioners in industrial and organizational psychology, organizational behavior, work psychology, and applied psychology programs looking for the most current research and best practices regarding its timely subject.

This book, specifically developed for students of psychology, covers a wide range of topics in statistics and research designs taught in psychology, in particular, and other disciplines like management, sociology, education, home science, and nutrition, in general, in most universities. It explains how to use Excel to analyze research data by elaborating statistical concepts. Each chapter contains sections like "Check you Computing skill" and "Check your Statistical Concepts" to enable students to assess their knowledge in a graded manner. The book addresses one of the major challenges in psychology research, viz., how to measure subjective phenomenon like attitude, desire, and preferences of an individual. Separate emphasis has been given to the measurement techniques which are essential tools to assess these subjective parameters in numerical form, required for statistical analysis to draw meaningful conclusions. The book is equally helpful to students of humanities, life sciences and other applied areas. Consisting of 14 chapters, the book covers all relevant topics of statistics and research designs which are important for students to plan and complete their research work.

Statistical Analysis of Contingency Tables is an invaluable tool for statistical inference in contingency tables. It covers effect size estimation, confidence intervals, and hypothesis tests for the binomial and the multinomial distributions, unpaired and paired 2x2 tables, rxc tables, ordered rx2 and 2xc tables, paired cxc tables, and stratified tables. For each type of table, key concepts are introduced, and a wide range of intervals and tests, including recent and unpublished methods and developments, are presented and evaluated. Topics such as diagnostic accuracy, inter-rater reliability, and missing data are also covered. The presentation is concise and easily accessible for readers with diverse professional backgrounds, with the mathematical details kept to a minimum. For more information, including a sample chapter and software, please visit the authors' website.

The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists.

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 volume provides an integrative review of the emerging and increasing use of network science techniques in cognitive psychology, first developed in mathematics, computer science, sociology, and physics. The first resource on network science for cognitive psychologists in a growing international market, Vitevitch and a team of expert contributors provide a comprehensive and accessible overview of this cutting-edge topic. This innovative guide draws on the three traditional pillars of cognitive psychological research-experimental, computational, and neuroscientific-and incorporates the latest findings from neuroimaging. The network perspective is applied to the fundamental domains of cognitive psychology including memory, language, problem-solving, and learning, as well as creativity and human intelligence, highlighting the insights to be gained through applying network science to a wide range of approaches and topics in cognitive psychology Network Science in Cognitive Psychology will be essential reading for all upper-level cognitive psychology students, psychological researchers interested in using network science in their work, and network scientists interested in investigating questions related to cognition. It will also be useful for early career researchers and students in methodology and related courses.

Although qualitative approaches to psychological research have a long history in the discipline, they have also been, and remain, marginalized from the canon of mainstream scientific psychology. At the current moment, however, there is growing recognition of the importance of qualitative methods and a movement toward a more inclusive and eclectic stance on psychological research. This volume reflects upon the historical and contemporary place of qualitative methods in psychology and considers future possibilities for further integration of these methods in the discipline. Scholars representing a wide-range of perspectives in qualitative and theoretical psychology reflect on the historical and contemporary positions of qualitative methods in psychology with an eye to the future of research and theory in the discipline. This book encourages a more critical and inclusive stance on research, recognizing both the limits and contributions that different methodological approaches can make to the project of psychological knowledge.

The Modern Kleinian Approach to Psychoanalytic Technique: Clinical Illustrations describes how today's practitioner typically treats a number of types of very disturbed and hard-to-reach patients who, while prone to intense acting out and early termination, are in great need of in-depth psychological reorganization. Many cases barely get off the ground due to levels of pathological conflict and destructive phantasy that make self/object connection extremely fragile. However, the modern Kleinian approach makes it possible to establish analytic contact within even the most chaotic situations and create a therapeutic experience that can be significant and meaningful. In doing so, there can be a healing process and the birth of new object relational experiences and interpersonal exchanges. Robert Waska details a more flexible method of practicing psychoanalysis, Analytic Contact, an approach that brings the healing possibilities of psychoanalysis to the more disturbed patients who tend to fill private practice offices. In addition, Analytic Contact enables the clinician to reach populations that are not usually considered easily treatable by the psychoanalytic method, including psychotic patients, couples who are seeking help with marital issues, and chronic borderline and narcissistic individuals.

Taking philosophical principles as a point of departure, this book provides essential distinctions for thinking through the history and systems of Western psychology. The book is concisely designed to help readers navigate through the length and complexity found in history of psychology textbooks. From Plato to beyond Post-Modernism, the author examines the choices and commitments made by theorists and practitioners of psychology and discusses the philosophical thinking from which they stem. What kind of science is psychology? Is structure, function, or methodology foremost in determining psychology's subject matter? Psychology, as the behaviorist views it, is not the same as the psychoanalyst's view of it, or the existentialist's, so how may contemporary psychology philosophically-sustain both pluralism and incommensurability? This book will be of great value to students and scholars of the history of psychology.