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 volume presents the first wide-ranging critical review of validity generalization (VG)--a method that has dominated the field since the publication of Schmidt and Hunter's (1977) paper "Development of a General Solution to the Problem of Validity Generalization." This paper and the work that followed had a profound impact on the science and practice of applied psychology. The research suggests that fundamental relationships among tests and criteria, and the constructs they represent are simpler and more regular than they appear. Looking at the history of the VG model and its impact on personnel psychology, top scholars and leading researchers of the field review the accomplishments of the model, as well as the continuing controversies. Several chapters significantly extend the maximum likelihood estimation with existing models for meta analysis and VG. Reviewing 25 years of progress in the field, this volume shows how the model can be extended and applied to new problems and domains. This book will be important to researchers and graduate students in the areas of industrial organizational psychology and statistics.

Built around a problem solving theme, this book extends the intermediate and advanced student's expertise to more challenging situations that involve applying statistical methods to real-world problems. Data relevant to these problems are collected and analyzed to provide useful answers. Building on its central problem-solving theme, a large number of data sets arising from real problems are contained in the text and in the exercises provided at the end of each chapter. Answers, or hints to providing answers, are provided in an appendix. Concentrating largely on the established SPSS and the newer S-Plus statistical packages, the author provides a short, end-of-chapter section entitled Computer Hints that helps the student undertake the analyses reported in the chapter using these statistical packages.

The Generic Qualitative Approach to a Dissertation in the Social Sciences: A Step by Step Guide is a practical guide for the graduate students and faculty planning and executing a generic qualitative dissertation in the social sciences. Generic qualitative research is a methodology that seeks to understand human experience by taking a qualitative stance and using qualitative procedures. Based on Sandra Kostere and Kim Kostere's experiences of serving on dissertation committees, this book aims to demystify both the nuances and the procedures of qualitative research, with the aim of empowering students to conduct meaningful dissertation research and present findings that are rigorous, credible, and trustworthy. It examines the fundamental principles and assumptions underlying the generic qualitative method, then covers each stage of the research process including creation of research questions, interviews, and then offers three ways of analyzing the data gathered and presenting the results. With examples of the generic qualitative method in practice to show students how to conduct their research confidently, and chapters designed to walk the researcher through each step of the dissertation process, this book is specifically tailored for the accessible generic method, and will be useful for graduate students and faculty developing dissertations in Psychology, Education, Nursing and the social sciences.

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.

Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors' collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions. The book makes two essential contributions to quantitative studies of time-related change. Through the introduction of the GLMM framework, it shows how innovative estimation methods and new model specifications can be used to tackle the "model identification problem" that has hampered the development and empirical application of APC analysis. The book also addresses the major criticism against APC analysis by explaining the use of new models within the GLMM framework to uncover mechanisms underlying age patterns and temporal trends. Encompassing both methodological expositions and empirical studies, this book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. It compares new and existing models and methods and provides useful guidelines on how to conduct APC analysis. For empirical illustrations, the text incorporates examples from a variety of disciplines, such as sociology, demography, and epidemiology. Along with details on empirical analyses, software and programs to estimate the models are available on the book's web page.

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.

Notwithstanding the mythical demise of "introspection," self-observation has always been an integral aspect of the social sciences. In the century following the "behavioral revolution," psychology has seen a reduction not so much in the frequency as in the rigor with which self-observation is practiced. A great deal of self-observation has been renamed or obscured (as, for example, "self-report"), but this has served only to defer and impoverish important theoretical and technical work.

This volume, which contributes to the development of a rigorous theory of self-observation, is organized around three general objectives: to re-animate a discourse on self-observation through a historical analysis of various self-observation traditions; to outline and begin to address some of the unique theoretical challenges of self-observation; and to elaborate some of the technical and practical details necessary for realizing a program of research dedicated to self-observation.

In the first section of the book, three historians of psychology trace the evolution of self-observation. In the second, three scholars who are currently working in contemporary traditions of self-observation discuss the basic theoretical and practical challenges involved in conducting self-observation research. In the final two sections of the book, scholars from the phenomenological and narrative traditions trace the history, theory, and practice of self-observation in their respective traditions. Self-Observation in the Social Sciences continues the fine tradition set by Transaction's History and Theory of Psychology series edited by Jaan Valsiner. It is of interest to psychologists and to those who study methodology within the social sciences.

This new edited volume features contributions from many of the leading scientists in probability and statistics from the latter part of the 20th century. It is the only book to assemble the views of these leading scientists--the pioneers in their respective fields. Stochastic Musings features contributions by: *Sir David Cox on statistics and econometrics; *C.R. Rao, M.B. Rao, and D.N. Shanbhag on convex sets of multivariate distributions and their extreme points; *Bradley Efron on the future of statistics; *David Freedman on regression association and causation; *Vic Barnett on sample ordering for effective statistical inference with particular reference to environmental issues; *David Bartholomew on a unified statistical approach to some measurement problems in the social sciences; *Joe Gani on scanning a lattice for a particular pattern; *Leslie Kish on new paradigms for probability sampling (his last paper); *Samuel Kotz and Norman L. Johnson on limit distributions of uncorrelated but dependent distributions on the unit square; *Samuel Kotz and Saralees Nadarajah on some new elliptical distributions; *Jef Teugels on the life span of a renewal; *Wolfgang Urfer and Katharina Emrich on maximum likelihood estimates of genetic effects; and **Vladimir M. Zolotarev on convergence rate estimates in functional limit theorems. The volume also includes the following contributions by faculty members of the Department of Statistics, Athens University of Economics and Business: *J. Panaretos, E. Xekalaki, and S. Psarakis on a predictive model evaluation and selection approach--the correlated gamma ratio distribution; *J. Panaretos and Z. Tsourti on extreme value index estimators and smoothing alternatives; *E. Xekalaki and D. Karlis on mixtures everywhere; and * Ir. Moustaki on latent variable models with covariates. Stochastic Musings will appeal to researchers, professionals, and students interested in the history and development of statistics and probability as well as in related areas, such as physics, biometry, economics, and mathematics. Academic and professional statisticians will benefit from the book's coverage of the latest developments in the field, as well as reflections on the future directions of the discipline.

* Aims to revive the field study method and demonstrate the importance of studying the behaviour of subjects in real-life, rather than laboratory conditions while complying with the current methodological and ethical standards * Examines the advantages and limitations of the field study method, whilst offering practical guidance on how it can be used in experiments now and in the future * Suitable for graduate and undergraduate students taking courses in methodology, and researchers looking to use field study methods in their research

A Journey into Open Science and Research Transparency in Psychology introduces the open science movement from psychology through a narrative that integrates song lyrics, national parks, and concerns about diversity, social justice, and sustainability. Along the way, readers receive practical guidance on how to plan and share their research, matching the ideals of scientific transparency. This book considers all the fundamental topics related to the open science movement, including: (a) causes of and responses to the Replication Crisis, (b) crowdsourcing and meta-science research, (c) preregistration, (d) statistical approaches, (e) questionable research practices, (f) research and publication ethics, (g) connections to career topics, (h) finding open science resources, (i) how open science initiatives promote diverse, just, and sustainable outcomes, and (j) the path moving forward. Each topic is introduced using terminology and language aimed at intermediate-level college students who have completed research methods courses. But the book invites all readers to reconsider their research approach and join the Scientific Revolution 2.0. Each chapter describes the associated content and includes exercises intended to help readers plan, conduct, and share their research. This short book is intended as a supplemental text for research methods courses or just a fun and informative exploration of the fundamental topics associated with the Replication Crisis in psychology and the resulting movement to increase scientific transparency in methods.

Originally published in 1986, the impetus for this volume developed from a conference organized by Barbara Snell Dohrenwend and the editors on behalf of the Society for Life History Research in Psychopathology, the Society of the Study of Social Biology, and the Center for Studies of Mental Health of Aging at the National Institute of Mental Health. The theme of the conference was life span research on the prediction of psychopathology, and the goal was to bring together outstanding researchers who were engaged in longitudinal investigations at the time and whose work, collectively, covered the entire life-span, from infancy to old age. The papers that were presented at the conference were updated, so that the chapters that follow represented current, state-of-the-art considerations in some of the best ongoing studies concerned with the prediction of psychopathology at that time.

- The application of situational crime prevention theory to homicide by people with a serious mental disorder make this book a novel resource. - Research is reviewed with a focus on implications for prevention.

Design and Analysis in Educational Research Using jamovi is an integrated approach to learning about research design alongside statistical analysis concepts. Strunk and Mwavita maintain a focus on applied educational research throughout the text, with practical tips and advice on how to do high-quality quantitative research. Based on their successful SPSS version of the book, the authors focus on using jamovi in this version due to its accessibility as open source software, and ease of use. The book teaches research design (including epistemology, research ethics, forming research questions, quantitative design, sampling methodologies, and design assumptions) and introductory statistical concepts (including descriptive statistics, probability theory, sampling distributions), basic statistical tests (like z and t), and ANOVA designs, including more advanced designs like the factorial ANOVA and mixed ANOVA. This textbook is tailor-made for first-level doctoral courses in research design and analysis. It will also be of interest to graduate students in education and educational research. The book includes Support Material with downloadable data sets, and new case study material from the authors for teaching on race, racism, and Black Lives Matter, available at www.routledge.com/9780367723088.

Foregrounding the voices of women who have survived experiences of domestic sex trafficking in the US, this text implements qualitative research methodologies to illustrate how experiences of complex trauma have impact on women's identities, sexuality, relationships, and re-integration into communities. Building on theoretical understandings of complex trauma and posttraumatic growth, this volume centers insights from in-depth interviews and photovoice methodology to document survivors' experience of sex trafficking and recovery. Outlining the nature of support and services available, the text identifies recommendations for effective recovery and in doing so, emphasizes women's capacity for post-traumatic growth. Relationship development, therapeutic and peer-support are highlighted as primary sources of healing. Ultimately, the text affirms the need for trauma-informed, ecological, and relational perspectives in the care of survivors. This text will benefit researchers, academics, and educators with an interest in trauma studies, clinical social work, and those working in mental health research more broadly. The text will also support further discussion and reflection around mental health services and support systems, adult trauma counselling, and mental health policy.

Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods in Psychology is the complete tool box to deliver the most valid and generalizable answers to today's complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.

This research volume serves as a comprehensive resource for psychophysiological research on media responses. It addresses the theoretical underpinnings, methodological techniques, and most recent research in this area. It goes beyond current volumes by placing the research techniques within a context of communication processes and effects as a field, and demonstrating how the real-time measurement of physiological responses enhances and complements more traditional measures of psychological effects from media.

This volume introduces readers to the theoretical assumptions of psychophysiology as well as the operational details of collecting psychophysiological data. In addition to discussing specific measures, it includes brief reviews of recent experiments that have used psychophysiological measures to study how the brain processes media. It will serve as a valuable reference for media researchers utilizing these methodologies, or for other researchers needing to understand the theories, history, and methods of psychophysiological research.

Modeled after Barbara Byrne's other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using Mplus Version 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author "walks" the reader through all steps involved in testing the SEM model including:

• an explanation of the issues addressed
• illustrated and annotated testing of the hypothesized and post hoc models
• explanation and interpretation of all Mplus input and output files
• important caveats pertinent to the SEM application under study
• a description of the data and reference upon which the model was based
• the corresponding data and syntax files available at http: //www.psypress.com/sem-with-mplus/datasets .

The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models.

Intended for researchers, practitioners, and students who use SEM and Mplus, this book is an ideal resource for graduate level courses on SEM taught in psychology, education, business, and other social and health sciences and/or as a supplement for courses on applied statistics, multivariate statistics, intermediate or advanced statistics, and/or research design. Appropriate for those with limited exposure to SEM or Mplus, a prerequisite of basic statistics through regression analysis is recommended.

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

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

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

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

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

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

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

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

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.

Reviewing the use of natural light by architects in the era of electricity, this book aims to show that natural light not only remains a potential source of order in architecture, but that natural lighting strategies impose a usefully creative discipline on design.

Considering an approach to environmental context that sees light as a critical aspect of place, this book explores current attitudes to natural light by offering a series of in-depth studies of recent projects and the particular lighting issues they have addressed. It gives a more nuanced appraisal of these lighting strategies by setting them within their broader topographic, climatic and cultural contexts.

In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, you'll discover how to characterize complex multivariate data in: Studies involving genetic databases Patterns in the progression of diseases and disabilities Combinations of topics covered by text documents Political ideology or electorate voting patterns Heterogeneous relationships in networks, and much more The handbook spans more than 20 years of the editors' and contributors' statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.

This comprehensive Handbook is the first to provide a practical, interdisciplinary review of ethical issues as they relate to quantitative methodology including how to present evidence for reliability and validity, what comprises an adequate tested population, and what constitutes scientific knowledge for eliminating biases. The book uses an ethical framework that emphasizes the human cost of quantitative decision making to help researchers understand the specific implications of their choices. The order of the Handbook chapters parallels the chronology of the research process: determining the research design and data collection; data analysis; and communicating findings. Each chapter: Explores the ethics of a particular topic Identifies prevailing methodological issues Reviews strategies and approaches for handling such issues and their ethical implications Provides one or more case examples Outlines plausible approaches to the issue including best-practice solutions. Part 1 presents ethical frameworks that cross-cut design, analysis, and modeling in the behavioral sciences. Part 2 focuses on ideas for disseminating ethical training in statistics courses. Part 3 considers the ethical aspects of selecting measurement instruments and sample size planning and explores issues related to high stakes testing, the defensibility of experimental vs. quasi-experimental research designs, and ethics in program evaluation. Decision points that shape a researchers' approach to data analysis are examined in Part 4 - when and why analysts need to account for how the sample was selected, how to evaluate tradeoffs of hypothesis-testing vs. estimation, and how to handle missing data. Ethical issues that arise when using techniques such as factor analysis or multilevel modeling and when making causal inferences are also explored. The book concludes with ethical aspects of reporting meta-analyses, of cross-disciplinary statistical reform, and of the publication process. This Handbook appeals to researchers and practitioners in psychology, human development, family studies, health, education, sociology, social work, political science, and business/marketing. This book is also a valuable supplement for quantitative methods courses required of all graduate students in these fields.

Capstone projects have long been used in other disciplines but are now being seen within psychology courses. This textbook is a one-stop-shop for anyone looking to undertake a capstone project or other final project. In a friendly and collaborative style, this book guides the student through everything they need to know to ensure a successful capstone or other final project.