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.

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.

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

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. "

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

Originally published in 1987, this book presents papers from the First Conference of European Clinical Psychologists, held at the University of Kent Canterbury in July of that year. It shows some of the most exciting and recent developments in research and innovations in professional practice from many European countries with an overall theme of the WHO strategy of Health for all by the year 2000. The whole range of clinical psychology is covered, including: cognitive therapy, clinical psychology and WHO strategy, the mental health of ethnic minority groups, health psychology, care in the community, and many other topics. The book is likely to be of interest for anyone concerned with the recent history and policies in clinical psychology."

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.

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.

Research Methods, Statistics, and Applications by Kathrynn A. Adams and Eva K. McGuire is designed to give students the experience of being a researcher by combining the interrelated concepts of research methods and statistics to better explain how the research process incorporates both elements. Employing a conversational tone throughout, coupled with an emphasis on decision-making, this best-selling text will spark students' interest in conducting research and improve their ability to critically analyze research in their daily lives. The Third Edition includes a new chapter on measurement to better highlight its critical importance, updates for the 7th edition of the Publication Manual of the American Psychological Association, new examples related to social justice, additional sections on qualitative research methods, and more thorough integration of research ethics information and tips throughout each chapter.

Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods. Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author's website.

The normal distribution is widely known and used by scientists and engineers. However, there are many cases when the normal distribution is not appropriate, due to the data being skewed. Rather than leaving you to search through journal articles, advanced theoretical monographs, or introductory texts for alternative distributions, the Handbook of Exponential and Related Distributions for Engineers and Scientists provides a concise, carefully selected presentation of the properties and principles of selected distributions that are most useful for application in the sciences and engineering. The book begins with all the basic mathematical and statistical background necessary to select the correct distribution to model real-world data sets. This includes inference, decision theory, and computational aspects including the popular Bootstrap method. The authors then examine four skewed distributions in detail: exponential, gamma, Weibull, and extreme value. For each one, they discuss general properties and applicability to example data sets, theoretical characterization, estimation of parameters and related inferences, and goodness of fit tests. The final chapter deals with system reliability for series and parallel systems. Presenting methods based on statistical simulations and numerical computations, the Handbook of Exponential and Related Distributions for Engineers and Scientists supplies hands-on tools for applied researchers in need of practical tools for data analysis.

Emerging from a qualitative research study on the rehabilitation experiences of adult male probationers with mental health illness, this book describes the treatment and rehabilitation experiences of these individuals and contextualizes their experiences within the landscape of mental health treatment in the United States. Often underserved in outpatient community support programs, probationers with mental health illness (PMIs) face stigma and obstacles in seeking mental health treatment and rehabilitation. Examining the lived experiences of both PMIs and their probation officers, this book offers insights into the study of stigma as it relates to probationers and the work of probation officers in furthering treatment and rehabilitation options for PMIs.

How Can You Improve Your Learning Capabilites? How Can You Enhance Your Potential for Change and Personal Growth? Most of us accept that education does not meet the needs of learners today, or their employers. This mismatch is a key reason why a high level of demotivated youth, as well as workers and managers remain unable to develop themselves. They have been other-organised and are unprepared for the world of work and the challenges of life. First published in 1991, this title offers a radical approach to human learning and personal change. Based on the reflective procedures of Learning Conversations, it enables a deep exploration of the learning process and allows individuals, teams and even whole organisations to create dynamic learning cultures capable of adaptive, constructive and continuing growth. Available again after some years this book is as relevant, if not of greater value, in our ever-changing society than when originally published.

Incorporating a hands-on pedagogical approach, Nonparametric Statistics for Social and Behavioral Sciences presents the concepts, principles, and methods used in performing many nonparametric procedures. It also demonstrates practical applications of the most common nonparametric procedures using IBM's SPSS software. This text is the only current nonparametric book written specifically for students in the behavioral and social sciences. Emphasizing sound research designs, appropriate statistical analyses, and accurate interpretations of results, the text: Explains a conceptual framework for each statistical procedure Presents examples of relevant research problems, associated research questions, and hypotheses that precede each procedure Details SPSS paths for conducting various analyses Discusses the interpretations of statistical results and conclusions of the research With minimal coverage of formulas, the book takes a nonmathematical approach to nonparametric data analysis procedures and shows students how they are used in research contexts. Each chapter includes examples, exercises, and SPSS screen shots illustrating steps of the statistical procedures and resulting output.

In this collection, international contributors come together to discuss how qualitative and quantitative methods can be used in psychotherapy research. The book considers the advantages and disadvantages of each approach, and recognises how each method can enhance our understanding of psychotherapy. Divided into two parts, the book begins with an examination of quantitative research and discusses how we can transfer observations into numbers and statistical findings. Chapters on quantitative methods cover the development of new findings and the improvement of existing findings, identifying and analysing change, and using meta-analysis. The second half of the book comprises chapters considering how qualitative and mixed methods can be used in psychotherapy research. Chapters on qualitative and mixed methods identify various ways to strengthen the trustworthiness of qualitative findings via rigorous data collection and analysis techniques. Adapted from a special issue of Psychotherapy Research, this volume will be key reading for researchers, academics, and professionals who want a greater understanding of how a particular area of research methods can be used in psychotherapy.

This is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.This book will help readers do just that. Sample computer code in R, SPSS, and SAS at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal. Organized according to various techniques, chapters 1 - 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 - 6 cover common statistical procedures -- analysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 - 11 review the new statistical procedures -- multi-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (HLM) and/or multilevel modeling and SEM taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the book's unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.

Interpreting Statistics for Beginners teaches readers to correctly read and interpret results of basic statistical procedures as they are presented in scientific literature, and to understand what they can and cannot infer from such results.

The first of its kind, this book explains key elements of scientific paradigms and philosophical concepts that the use of statistics is based on and introduces readers to basic statistical concepts, descriptive statistics and basic elements and procedures of inferential statistics. Explanations are accompanied with detailed examples from scientific publications to demonstrate how the procedures are used and correctly interpreted. Additionally, Interpreting Statistics for Beginners shows readers how to recognize pseudoscientific claims that use statistics or statements not based on the presented data, which is an important skill for every professional relying on statistics in their work.

Written in an easy-to-read style and focusing on explaining concepts behind statistical calculations, the book is most helpful for readers with no previous training in statistics, and also those wishing to bridge the conceptual gap between doing the statistical calculations and interpreting the results.

Albert Einstein said we can't solve problems by using the same kind of thinking we used when we created them. If we don't have the kinds of health and human services or even the kinds of lives, communities and organisations we want, then we need to think differently. Yoland Wadsworth offers an inspired insight and radically new proposition: that the act of our 'inquiring', of researching and evaluating together, is the way by which every living organism and all collective human life goes about continuously achieving the conditions for life. Building in Research and Evaluation explores this new approach for bringing about both wanted change and stability. By inquiring around 'whole cycles' of acting, observing, questioning, feeling, reflecting, thinking, planning and acting again, Yoland identifies how new life might be brought to what we do.

This book reviews the latest techniques in exploratory data mining (EDM) for the analysis of data in the social and behavioral sciences to help researchers assess the predictive value of different combinations of variables in large data sets. Methodological findings and conceptual models that explain reliable EDM techniques for predicting and understanding various risk mechanisms are integrated throughout. Numerous examples illustrate the use of these techniques in practice. Contributors provide insight through hands-on experiences with their own use of EDM techniques in various settings. Readers are also introduced to the most popular EDM software programs. A related website at http://mephisto.unige.ch/pub/edm-book-supplement/offers color versions of the book's figures, a supplemental paper to chapter 3, and R commands for some chapters. The results of EDM analyses can be perilous - they are often taken as predictions with little regard for cross-validating the results. This carelessness can be catastrophic in terms of money lost or patients misdiagnosed. This book addresses these concerns and advocates for the development of checks and balances for EDM analyses. Both the promises and the perils of EDM are addressed. Editors McArdle and Ritschard taught the "Exploratory Data Mining" Advanced Training Institute of the American Psychological Association (APA). All contributors are top researchers from the US and Europe. Organized into two parts--methodology and applications, the techniques covered include decision, regression, and SEM tree models, growth mixture modeling, and time based categorical sequential analysis. Some of the applications of EDM (and the corresponding data) explored include: selection to college based on risky prior academic profiles the decline of cognitive abilities in older persons global perceptions of stress in adulthood predicting mortality from demographics and cognitive abilities risk factors during pregnancy and the impact on neonatal development Intended as a reference for researchers, methodologists, and advanced students in the social and behavioral sciences including psychology, sociology, business, econometrics, and medicine, interested in learning to apply the latest exploratory data mining techniques. Prerequisites include a basic class in statistics.

This is the first book of its kind to include the personal accounts of people who have survived injury to the brain, along with professional therapists' reports of their progress through rehabilitation. The paintings and stories of survivors combine with experts' discussions of the theory and practice of brain injury rehabilitation to illustrate the ups and downs that survivors encounter in their journey from pre-injury status to insult and post-injury rehabilitation. Wilson, Winegardner and Ashworth's focus on the survivors' perspective shows how rehabilitation is an interactive process between people with brain injury, health care staff, and others, and gives the survivors the chance to tell their own stories of life before their injury, the nature of the insult, their early treatment, and subsequent rehabilitation. Presenting practical approaches to help survivors of brain injury achieve functionally relevant and meaningful goals, Life After Brain Injury: Survivors' Stories will help all those working in rehabilitation understand the principles involved in holistic brain injury rehabilitation and how these principles, combined with theory and models, translate into clinical practice. This book will be of great interest to anyone who wishes to extend their knowledge of the latest theories and practices involved in making life more manageable for people who have suffered damage to the brain. Life After Brain Injury: Survivors' Stories will also be essential for clinical psychologists, neuropsychologists, and anybody dealing with acquired brain injury whether they be a survivor of a brain injury themselves, a relative, a friend or a carer.

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.

Features: Provides an overview of methods for assessing the reliability of generating data Expands a statistic proposed by the author, already widely used in the social sciences Includes many easy to follow numerical examples to illustrate the measures Written to be useful to beginning and advanced researchers from many disciplines, notably linguistics, sociology, psychometric and educational research, and medical science.

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.