Psychological methodology Books
Macmillan Learning Research Methods
Book Synopsis
£63.64
Taylor & Francis Functional Analytic Psychotherapy
Book SynopsisFollowing in the steps of the first edition, Functional Analytic Psychotherapy: Distinctive Features, 2nd Edition, provides a history, context, and building blocks for a behavior therapist to incorporate Functional Analytic Psychotherapy (FAP) into their work.This new volume updates material based upon research that has occurred since the first edition, as well as philosophical and theoretical shifts in behavior therapy, such as an emphasis on FAP as a process-based therapy. Each FAP principle is presented in terms of its intended purpose and is clearly linked to the underlying theory, providing clinicians with a straightforward guide for when and how to apply each technique. Practical tips have been added to aid in case conceptualization and the integration of a FAP framework into other process-based, behavioral conceptualizations. The added breadth and depth also emphasize FAPâs unique role in meeting the needs of diverse and marginalized people and applying FAP acro
£19.99
Taylor & Francis An Introduction to Multilevel Modeling Techniques
Book SynopsisMultilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; <Trade Review"Developing a basic modeling strategy that researchers can follow to investigate multilevel data structures can be challenging. Heck and Thomas have once again presented a must-have reference book to get the job done. This edition’s use of four different software packages and additional easy-to-follow illustrative examples enhance what was already a superb resource for both students and researchers." – George A. Marcoulides, University of California, Santa Barbara, USA Table of ContentsPreface 1. Introduction 2. Getting Started with Multilevel Analysis 3. Multilevel Regression Models 4. Extending the Two-Level Regression Model 5. Methods for Examining Individual and Organizational Change 6. Multilevel Models with Categorical Variables 7. Multilevel Structural Equation Variables 8. Multilevel Latent Growth and Mixture Models 9. Data Consideration in Examining Multilevel Models
£54.99
Taylor & Francis Ltd The Essence of Multivariate Thinking
Book SynopsisFocusing on the underlying themes that run through most multivariate methods, in this fully updated 3rd edition of The Essence of Multivariate Thinking Dr. Harlow shares the similarities and differences among multiple multivariate methods to help ease the understanding of the basic concepts. The book continues to highlight the main themes that run through just about every quantitative method, describing the statistical features in clear language. Analyzed examples are presented in 12 of the 15 chapters, showing when and how to use relevant multivariate methods, and how to interpret the findings both from an overarching macro- and more specific micro-level approach that includes focus on statistical tests, effect sizes and confidence intervals. This revised 3rd edition offers thoroughly revised and updated chapters to bring them in line with current information in the field, the addition of R code for all examples, continued SAS and SPSS code for seven chapters, two new chapteTrade Review"Harlow breaks down concepts in simple terms and draws insightful comparisons across different statistical techniques. The text is an excellent resource for all who conduct statistical analyses, from undergraduates to professionals, and everywhere in between."A. Nayena Blankson, Full Professor of Psychology, Spelman College, USA"Once again, Harlow writes with authority and great clarity. There’s discussion of estimation, replication, and reproducibility, and all through there’s R code and guidance. This shrewdly revised new edition is a wonderfully future-oriented guide to the multivariate world."Geoff Cumming, Professor Emeritus, La Trobe University, Melbourne, Australia "The Essence of Multivariate Thinking provides a gentle introduction to an expansive toolkit of methods with a through line focused on aptly applying them to research questions. Dr. Harlow seamlessly ties together several modeling frameworks (e.g., multiple regression, MANOVA, discriminant function analysis, logistic regression, SEM, and latent growth modeling) by emphasizing commonalities in their underlying assumptions, statistical tests, and effect size interpretations. All this was done with a clear and accessible writing style accompanied by a unifying data example using R, SAS, and SPSS."Jolynn Pek, Associate Professor of Quantitative Psychology, Ohio State University, USA "In this third edition of her landmark text on multivariate methods, readers are furnished with ways to think about a variety of multivariate techniques from start to finish: preliminary considerations, testing assumptions, conducting analyses, and writing up and interpreting the results. They will learn how the various multivariate methods are related, and when each should be used. Readers can pattern their own analyses after those used as examples in the book. Her example analyses are easy to follow and emulate.Every chapter ends by summarizing a topic around a set of core themes to help researchers understand and select appropriate multivariate methods. Professor Harlow is known for her didactic style and ability to communicate complex topics in clear, approachable language. In this third edition she adds new material on multi-sample SEM and latent growth curve models. The provision of R code for all analyses is a welcome addition. Readers will emerge knowing about a variety of multivariate methods—how they are related and distinct, when to use them, how to conduct them, and how to communicate the results to readers. Harlow has a knack for explaining complicated methods in clear, approachable language. In this revised third edition of her landmark text, she provides readers with all the tools they need to become expert users and consumers of multivariate techniques."Kristopher J. Preacher, Lois Autrey Betts Chair in Education & Human Development, Vanderbilt University, USA "Lisa Harlow's third edition of The Essence of Multivariate Thinking is the perfect textbook for introductory graduate statistics or a multivariate statistics course. Each chapter provides readers with the foundational knowledge and relevant software code to start implementing the analyses in their own work right away. Harlow's accessible chapters strike the right balance of unique information and related themes." Alyssa Counsell, Assistant Professor in Quantitative Psychology, Toronto Metropolitan University, Ontario, CanadaTable of ContentsI. OVERVIEWChapter 1: Introduction and Multivariate ThemesChapter 2: Background ThemesII. INTERMEDIATE MULTIVARIATE METHODS WITH ONE CONTINUOUS OUTCOMEChapter 3: Multiple RegressionChapter 4: Analysis of CovarianceIII. MULTIVARIATE GROUP METHODS WITH CATEGORICAL VARIABLE(S)Chapter 5. Multivariate Analysis of VarianceChapter 6: Discriminant Function AnalysisChapter 7: Logistic RegressionIV. MULTIVARIATE DIMENSIONAL METHODS WITH CONTINUOUS VARIABLESChapter 8: Principal Components and Factor AnalysisV: STRUCTURAL EQUATION MODELINGChapter 9: Structural Equation ModelingChapter 10: Path AnalysisChapter 11: Confirmatory Factor AnalysisChapter 12: Latent Variable ModelingChapter 13: Multiple Sample AnalysisChapter 14: Latent Growth ModelingVI: SUMMARYChapter 15: Integration of Multivariate Methods
£51.99
Taylor & Francis Interpreting Basic Statistics
Book SynopsisInterpreting Basic Statistics gives students valuable practice in interpreting statistical reporting as it actually appears in peer-reviewed journals. Features of the ninth edition: Covers a broad array of basic statistical concepts, including topics drawn from the New Statistics Up-to-date journal excerpts reflecting contemporary styles in statistical reporting Strong emphasis on data visualization Ancillary materials include data sets with almost two hours of accompanying tutorial videos, which will help students and instructors apply lessons from the book to real-life scenarios About this book Each of the 63 exercises in the book contain three central components: 1) an introduction to a statistical concept, 2) a brief excerpt from a published research article that uses the statistical concept, and 3) a set of questions (with answers) that guides students into deeper learning about the concept. The questioTrade ReviewThe 9th edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USAThe ninth edition of this workbook is an engaging and invaluable tool for teaching students how to interpret statistics as they encounter them in articles written within the psychological, social, and health sciences. By choosing article excerpts that are sure to interest undergraduate readers, the authors may entice those many students who say they fear numbers into taking their first halting steps toward understanding. By providing clear and concise descriptions of key concepts and posing astute questions, the workbook demystifies the scientific enterprise and explains its importance for comprehending the social world. And by starting with the simplest ideas and gradually, step by step, moving toward a more complex understanding, the authors gently lead students on a learning journey that is sure to be deeply informative – and maybe even fun! -- Dan P. McAdams, the Henry Wade Rogers Professor of Psychology, Northwestern University, USA"This introduction to reading and understanding statistics is very basic and easy to understand, but at the same time it is scientifically oriented, contemporary in outlook and forward looking in methodology. It points students in exactly the right direction, emphasizing meaningful interpretation of scientific results over recitation of cookbook formulas. Students will come away with the tools they need for comprehending graphical analysis, effect size, and statistical power." -- Eric Turkheimer, PhD, Hugh Scott Hamilton Professor, Department of Psychology, University of Virginia, USATable of Contents1. Basic Descriptions of the Data: Measurement and Frequency 2. Describing the Data 3. Displaying Data: Visualizing What is There 4. Finding Relationships: Association and Prediction 5. Group Differences with Normal Distributions 6. Nonparametric Tests for Group Differences 7. Test Construction
£171.00
CRC Press The Reliability of Generating Data
Book SynopsisAll data are the result of human actions whether by experimentations, observations, or declarations. As such, the presumption of knowing what data are about is subject to imperfections that can affect the validity of research efforts. With calls for data-based research comes the need to assure the reliability of generated data. The reliability of converting texts into analyzable data has become a burning issue in several areas. However, this issue has been met by only a few limited, and sometimes misleading measures of the extent to which data can be trusted as surrogates of the phenomena of analytical interests. The statistic proposed by the author â Krippendorffâs Alpha â is widely used in the social sciences, not only where human judgements are involved but also where measurements are compared. The Reliability of Generating Data expands on the authorâs seminal work in content analysis and develops methods for assessing the reliability of the kind Table of ContentsHow I became interested in reliability issues. 1. On the epistemology of reliable data. 2. Simplest kinds: The replicability of categorizing predefined units. 3. Some properties of the Alpha. 4. Alpha compared with primarily nominal agreement measures. 5. Metric differences between single-valued units.6. The quadrilogy for single-valued predefined units and big data. 7. Multi-valued coding of predefined units.8. Partitioning continua and coding relevant segments. 9. Preserving the coherency of identified segments in continua. 10. Distinctions drawn within continua. 11. Text mining and information retrieval. 12. Diagnostic devices and remedial actions. 13. Some special applications. 14. Statistical considerations. 15. Reliability standards. 16. Toward a general calculus of differences and agreements. Appendix. References
£105.00
Taylor & Francis Categories in Social Interaction
Book SynopsisThis book investigates the situated (re)production of categories, from the most mundane and unremarkable to those most strongly associated with power and privilege. By examining the reciprocal relationships between categorial phenomena and the basic structures and practices of social interaction, the book provides a new framework for integrating conversation analysis and membership categorization analysis.Across its ten chapters, the book describes a conversation analytic approach to studying categories and categorization, charts the development and history of membership categorization analysis, and addresses core methodological challenges and practices associated with using this approach. After mapping out the new framework developed in the book, each chapter describes intersections between categorial phenomena and the domains that comprise the infrastructure of social interaction. The book concludes by exploring applications, interventions, and impacts of understanding cate
£39.99
Routledge Statistical Concepts
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£59.39
Cambridge University Press Dynamic Testing The Nature and Measurement of
Book SynopsisThe goal of this book is to present and evaluate the concept of dynamic testing. Unlike 'static' tests such as the SAT or IQ tests, dynamic testing emphasizes learning potential rather than past learning accomplishments. The book opens with a theoretical framework of abilities as forms of developing expertise. It then continues with an introduction to dynamic testing and then a capsule history of dynamic testing. The book also reviews the approaches of Feuerstein and Budoff and other diverse approaches to dynamic testing. The Drs Sternberg and Grigorenko present their own three-prong approach to dynamic testing along with two case studies using dynamic testing in their own research. The authors conclude that dynamic testing has enormous potential which has not yet been tapped.Trade Review"...a valuable book for school psychologists interested in understanding more about dynamic testing of abilities and the progress in the area in the last two decades. It is a thorough, readable, and thoughtful summary of the area." School Psychology Quarterly"...written by two of the world's leading authorities on he assessment of human abilities and intelligence...extremely important." APA Review of Books"Strongly grounded in theory, this book affords an in-depth look at the foundation, current research, and application of dynamic testing. Cognitive psychologists, educators interested in measurement and evaluation issues, and individuals involved in special education will find this a comprehensive and useful volume. Highly recommended." ChoiceTable of ContentsPart I. Theoretical Framework: 1. Abilities as forms of developing expertise; Part II. The Nature of Dynamic Testing: 2. An introduction to dynamic testing; 3. A capsule history of dynamic testing; Part III. Leading Modern Approaches to Dynamic Testing: 4. The approach of Feuerstein; 5. The approach of Budoff; 6. Diverse approaches to dynamic testing; 7. A three-prong approach to dynamic testing; Part IV. Two Case Studies using Dynamic Testing: 8. Using dynamic testing to reveal hidden potential; 9. Combining instruction with assessment: measuring foreign-language learning ability; 10. Measurement aspects of dynamic testing: quantifying change; Epilogue.
£45.60
Taylor & Francis Inc Methodological Issues in Aging Research
Book SynopsisMethodological Issues in Aging Research is the first volume in the Notre Dame Series on Quantitative Methodology. This new series provides practical training on the latest quantitative methods used in social and behavioral research. Each volume features contributions from leading experts in state-of-the-art techniques applicable to a selected substantive topic.The first series volume provides researchers with innovative techniques for the collection and analyses of data focusing on aging and lifespan development. The book addresses such techniques as structural equation modeling, latent class analysis, hierarchical linear growth curve modeling, dynamical systems analysis, multivariate Rasch models, survival analysis, multilevel modeling, and quantitative genetic methods. These new techniques provide: better estimates of the direct effect of environmental or treatment effects and the dynamic pattern of genetic and environmental influences on adult development <Trade Review "The illustrative chapters of current methods for studying age change in Bergeman and Boker's 'Methodological Issues in Aging Research' exemplify the quantum leap that developmental methods have taken in the last 40 years.... one can appreciate the welcome benefits and scientific advances these new methods bring with them."—PsycCRITIQUES "The illustrative chapters of current methods for studying age change in Bergeman and Boker's 'Methodological Issues in Aging Research' exemplify the quantum leap that developmental methods have taken in the last 40 years.... one can appreciate the welcome benefits and scientific advances these new methods bring with them."—PsycCRITIQUES Table of ContentsContents: C.S. Bergeman, S.M. Boker, Preface. J.R. Nesselroade, Quantitative Modeling in Adult Development and Aging: Reflections and Projections. C.S. Bergeman, K.A. Wallace, The Theory-Methods Interface. J.J. McArdle, F. Hamagami, Longitudinal Tests of Dynamic Hypotheses on Intellectual Abilities Measured Over Sixty Years. P.J. Curran, D.J. Bauer, M.T. Willoughby, Testing and Probing Interactions in Hierarchical Linear Growth Models. C. Johnson, S.W. Raudenbush, A Repeated Measures, Multilevel Rasch Model With Application to Self-Reported Criminal Behavior. C. Schuster, Latent-Class Analysis Approaches to Determining the Reliability of Nominal Classifications: A Comparison Between the Response-Error and the Target-Type Approach. S.M. Boker, T.L. Bisconti, Dynamical Systems Modeling in Aging Research. M.J. Wenger, C. Schuster, L.E. Petersen, R.C. Petersen, Applying Proportional Hazards Models to Response Time Data. M.C. Neale, S.M. Boker, C.S. Bergeman, H.H. Maes, The Utility of Genetically Informative Data in the Study of Development.
£47.99
Duke University Press Materiality
Book SynopsisA collection of essays rethinking the current uses of material culture study in anthropology, including engagements with art, science, and technology.Trade Review“A milestone collection. Of all of the recent works on material culture available, this is the one that exposes the complete range of perspectives and theoretical strategies that the most noted scholars are trying out and the interdisciplinary connections and alliances that are shaping the field.”—George Marcus, Rice University“There have been many recent stabs at the idea of materiality. With both authority and intellectual generosity, these anthropologists and their colleagues take us beyond ‘things’ and ‘objects’ to ask about concrete presences, qualities, surfaces, and the formation of phenomena. A magisterial and highly original collection.”—Marilyn Strathern, University of Cambridge“This is first-class scholarship: lively, consequential, engaging, informed, and lucid. Daniel Miller and his colleagues explore—with imagination, ethnographic insight, and remarkable clarity—a range of related issues central to current debates within and beyond cultural anthropology.”—Donald Brenneis, University of California, Santa CruzTable of ContentsMateriality: An Introduction / Daniel Miller 1 Objects in the Mirror Appear Closer Than They Are / Lynn Meskell 51 A Materialist Approach to Materiality / Michael Rowlands 72 Some Properties of Art and Culture: Ontologies of the Image and Economies of Exchange / Fred Myers 88 Sticky Subjects and Sticky Objects: The Substance of African Christian Healing / Matthew Engelke 118 Does Money Matter? Abstraction and Substitution in Alternative Financial Forms / Bill Maurer 140 The Materiality of Finance Theory / Hirokazu Miyazaki 165 Signs Are Not the Garb of Meaning: On the Social Analysis of Material Things / Webb Keane 182 Materiality and Cognition: The Changing Face of Things / Susanne Kuchler 206 Beyond Meditation: Three New Material Registers and Their Consequences / Nigel Thrift 231 Things Happen: Or, From Which Moment Does That Object Come? / Christopher Pinney 256 Contributors 273 Index 277
£25.19
Saint Philip Street Press Theoretical and Practical Advances in
Book Synopsis
£44.06
Saint Philip Street Press Theoretical and Practical Advances in
Book Synopsis
£47.66
Taylor & Francis Ltd The Generic Qualitative Approach to a Dissertation in the Social Sciences
Book SynopsisThe 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 presentiTable of Contents1. Beginning the Journey 2. The Research Question 3. Research Plan 4. Preparing for the Interview 5. Other Qualitative Data Collection Methods 6. Data Analysis 7. Presentation of the Data 8. Completing the Journey
£21.68
Taylor & Francis Ltd Social Media Analytics in Predicting Consumer
Book SynopsisInformation is very important for businesses. Businesses that use information correctly are successful while those that don't, decline. Social media is an important source of data. This data brings us to social media analytics. Surveys are no longer the only way to hear the voice of consumers. With the data obtained from social media platforms, businesses can devise marketing strategies. It provides a better understanding consumer behavior. As consumers are at the center of all business activities, it is unrealistic to succeed without understanding consumption patterns. Social media analytics is useful, especially for marketers. Marketers can evaluate the data to make strategic marketing plans. Social media analytics and consumer behavior are two important issues that need to be addressed together. The book differs in that it handles social media analytics from a different perspective. It is planned that social media analytics will be discussed in detail in terms of consumer Table of ContentsThe Concept of Social Media. Social Media Marketing. Formulating a Social Media Strategy . Introduction to Social Media Analytics. Social Media Analytics in Consumer Behavior. Social Media Actions Analytics. Measuring Web Site Performance with Web Analytics. Mobile Analytics. Ethics and Social Media Analytics.
£135.00
CRC Press Introduction to Regression Methods for Public
Book Synopsis
£89.99
Taylor & Francis Advanced Methods in Family Therapy Research
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£42.99
Taylor & Francis Regression Basics
Book SynopsisUsing an accessible, nontechnical approach, the third edition of Regression Basics introduces readers to the fundamentals of statistical regression. Accessible to anyone with an introductory statistics background, the book draws on engaging examples using real-world data and software programs SPSS , Stata , and R to illustrate the key concepts of the least squares regression methodology.The book emphasizes the intuition of regression methodology and provides a hands-on approach, as well as helpful end-of-chapter summaries and questions to consolidate learning. This new edition has been substantially revised and enhanced, with features including the following: Fully updated to show procedures in R, SPSS , and Stata Color images and substantially revised visual presentation A suite of online resources including data sets, software instructions, and PowerPoint slides for instructors New and updated examples throughout Expanded material to help students overcome math anxiety Expanded material on multicollinearity, heteroskedasticity, and robust standard errors This well-paced book is ideal for advanced undergraduate and graduate students focusing on quantitative methods, research design, and statistical regression in the social and behavioral sciences, political science, and economics.
£48.99
Taylor & Francis Multivariate Statistics and Machine Learning
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£145.00
Taylor & Francis Ltd Machine Learning Toolbox for Social Scientists
Book SynopsisMachine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targTable of Contents1. How We Define Machine Learning 2. Preliminaries Part 1. Formal Look at Prediction 3. Bias-Variance Tradeoff 4. Overfitting Part 2. Nonparametric Estimations 5. Parametric Estimations 6. Nonparametric Estimations - Basics 7. Smoothing 8. Nonparametric Classifier - kNN Part 3. Self-learning 9. Hyperparameter Tuning 10. Tuning in Classification 11. Classification Example Part 4. Tree-based Models 12. CART 13. Ensemble Learning 14. Ensemble Applications Part 5. SVM & Neural Networks 15. Support Vector Machines 16. Artificial Neural Networks Part 6. Penalized Regressions 17. Ridge 18. Lasso 19. Adaptive Lasso 20. Sparsity Part 7. Time Series Forecasting 21. ARIMA models 22. Grid Search for Arima 23. Time Series Embedding 24. Random Forest with Times Series 25. Recurrent Neural Networks Part 8. Dimension Reduction Methods 26. Eigenvectors and eigenvalues 27. Singular Value Decomposition 28. Rank r approximations 29. Moore-Penrose Inverse 30. Principle Component Analysis 31. Factor Analysis Part 9. Network Analysis 32. Fundamentals 33. Regularized Covariance Matrix Part 10. R Labs 34. R Lab 1 Basics 35. R Lab 2 Basics II 36. Simulations in R 37. Algorithmic Optimization 38. Imbalanced Data
£69.29
Taylor & Francis Pedagogy and Practice in Autoethnography
Book SynopsisAutoethnography Pedagogy and Practice supports and generates new insights into how autoethnography can be taught, supervised and practised by sharing the experiences and reflections of researchers from a wide range of fields and disciplines.An international cast of leading researchers provide practical examples of how autoethnography can be successfully introduced into health and human sciences curricula, showcasing examples of the power of autoethnography within and beyond academia. By privileging contributorsâ experiences within their own field of study as students, teachers, supervisors and researchers, this book explores how autoethnography can be introduced, nurtured and sustained in challenging academic environments. Each chapter considers three interrelated areas: Disciplinary Contexts, which examines autoethnographyâs impact across different fields; Relationships, which considers how to successfully manage relational and care dynamics from undergraduate through professor levels; and Ethics, which addresses the many ethical considerations that can arise across a wide range of contexts.Autoethnography Pedagogy and Practice is a book that encourages readers to engage in autoethnographic practice to create innovative, dialogical and collaborative texts that push the boundaries of polyvocality and diversity within their own disciplines. It will be of interest to researchers in Psychology, Medicine, Pharmacology, Allied Health, Nursing, Mental health, Sport and Exercise Science, Coaching, Sociology, Psychotherapy, Theatre Studies and Communication Studies.
£59.84
Taylor & Francis Understanding and Teaching Bronfenbrenners Bioecological Theory
Clarifying misinterpretations of Bronfenbrenner's bioecological theory and offering a fresh perspective, this insightful book provides practical guidance for scholars on effectively teaching Bronfenbrennerâs theory at both undergraduate and graduate levels, as well as applying it in research and practice. The book traces the evolution of Bronfenbrennerâs theory of human development, from its original ecological framework of the 1970s to the fully developed bioecological theory and the Process-Person-Context-Time (PPCT) model. Key concepts such as macrosystemic influences are clarified, and innovative adaptations like the inverse proximal process and neo-ecological theory are explored, addressing how virtual and digital contexts shape human development. The book offers adaptable strategies for applying Bronfenbrennerâs theory across a range of disciplines, demonstrating its versatility in undergraduate and graduate courses as well as in research. It includes practical t
£48.99
Taylor & Francis Heuristic Enquiries
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£36.99
Taylor & Francis Chatbot Therapy
Book Synopsis
£39.99
Taylor & Francis Statistics as Principled Argument
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£145.00
SAGE Publications Inc Applied Statistics I International Student
Book SynopsisApplied Statistics I:Basic Bivariate Techniques has been created from the first half of Rebecca M. Warner's popular Applied Statistics: From Bivariate Through Multivariate Techniques. The author's contemporary approach differs from some of the well-worn texts in the market, and reflects current thinking in the field. It spends less time on statistical significance testing, and moves in the direction of the new statistics by focusing more on confidence intervals and effect size. Instructors of upper undergraduate or beginning graduate level courses will find that the greater focus on basic concepts such as partition of variance and effect size is more useful to students, particularly as preparation for more advanced courses. Spending less time on statistical significance testing allows for more time to be devoted to more interesting and useful statistics that students will see in journal articles (such as correlation and regression). This introductory statisticTable of Contents1. Evaluating Numeric Information Introduction Guidelines for Numeracy Source Credibility Message Content Evaluating Generalizability Making Causal Claims Quality Control Mechanisms in Science Biases of Information Consumers Ethical Issues in Data Collection and Analysis Lying with Graphs and Statistics Degrees of Belief Summary 2. Basic Research Concepts Introduction Types of Variables Independent and Dependent Variables Typical Research Questions Conditions for Causal Inference Experimental Research Design Non-experimental Research Design Quasi- Experimental Designs Other Issues in Design and Analysis Choice of Statistical Analysis (Preview) Populations and Samples: Ideal Versus Actual Situations Common Problems in Interpretation of Results Appendix 2 A: More About Levels of Measurement Appendix 2 B: Justification for Use of Likert and Other Rating Scales as Quantitative Variables (In Some Situations) 3. Frequency Distribution Tables Introduction Use of Frequency Tables for Data Screening Frequency Tables for Categorical Variables Elements of Frequency Tables Using SPSS to Obtain a Frequency Table Mode, Impossible Score Values, and Missing Values Reporting Data Screening for Categorical Variables Frequency Tables for Quantitative Variables Frequency Tables for Categorical Versus Quantitative Variables Reporting Data Screening for Quantitative Variables What We Hope to See in Frequency Tables for Categorical Variables What We Hope to See in Frequency Tables for Quantitative Variables Summary Appendix 3 A: Getting Started in IBM SPSS ® version 25 Appendix 3 B: Missing Values in Frequency Tables Appendix 3 C: Dividing Scores into Groups or Bins 4. Descriptive Statistics Introduction Questions about Quantitative Variables Notation Sample Median Sample Mean (M) An Important Characteristic of M: Sum of Deviations from M = 0 Disadvantage of M: It is Not Robust Against Influence of Extreme Scores Behavior of Mean, Median and Mode in Common Real-World Situations Choosing Among Mean, Median, and Mode Using SPSS to Obtain Descriptive Statistics for a Quantitative Variable Minimum, Maximum, and Range: Variation among Scores The Sample Variance s2 Sample Standard Deviation (s or SD) How a Standard Deviation Describes Variation Among Scores in a Frequency Table Why Is There Variance? Reports of Descriptive Statistics in Journal Articles Additional Issues in Reporting Descriptive Statistics Summary Appendix 4 A Order of Arithmetic Operations Appendix 4 B Rounding 5. Graphs: Bar Charts, Histograms, and Box Plots Introduction Pie Charts for Categorical Variables Bar Charts for Frequencies of Categorical Variables Good Practice for Construction of Bar Charts Deceptive Bar Graphs Histograms for Quantitative Variables Obtaining a Histogram Using SPSS Describing and Sketching Bell-Shaped Distributions Good Practices in Setting up Histograms Box Plot (Box and Whiskers Plot) Telling Stories About Distributions Uses of Graphs in Actual Research Data Screening: Separate Bar Charts or Histograms for Groups Use of Bar Charts to Represent Group Means Other Examples Summary 6. The Normal Distribution and z Scores Introduction Locations of Individual Scores in Normal Distributions Standardized or “z” Scores Converting z Scores Back into Original Units of X Understanding Values of z Qualitative Description of Normal Distribution Shape More Precise Description of Normal Distribution Shape Reading Tables of Areas for the Standard Normal Distribution Dividing the Normal Distribution Into Three Regions: Lower Tail, Middle, Upper Tail Outliers Relative to a Normal Distribution Summary of First Part of Chapter Why We Assess Distribution Shape Departure from Normality: Skewness Another Departure from Normality: Kurtosis Overall Normality Practical Recommendations Reporting Information About Distribution Shape, Missing Values, Outliers, and Descriptive Statistics for Quantitative Variables Summary Appendix 6 A: The Mathematics of the Normal Distribution Appendix 6 B: How to Select and Remove Outliers in SPSS Appendix 6 C: Quantitative Assessments of Departure from Normality Appendix 6 D: Why Are Some Real-World Variables Approximately Normally Distributed? 7. Sampling Error and Confidence Intervals Descriptive Versus Inferential Uses of Statistics Notations for Samples Versus Populations Sampling Error and the Sampling Distribution for Values of M Prediction Error Sample Versus Population (Revisited) The Central Limit Theorem: Characteristics of the Sampling Distribution of M Factors that Influence Population Standard Error Effect of N on Value of the Population Standard Error Describing the Location of a Single Outcome for M Relative to a Population Sampling Distribution (Setting Up a z Ratio) What We Do When ?? Is Unknown The Family of t Distributions Tables for t Distributions Using Sampling Error to Set Up a Confidence Interval How to Interpret a Confidence Interval Empirical Example: Confidence Interval for Body Temperature Other Applications for CIs Error Bars in Graphs of Group Means Summary 8. The One-Sample t test: Introduction to Statistical Significance Tests Introduction Significance Tests as Yes/No Questions About Proposed Values of Population Means Stating a Null Hypothesis Selecting an Alternative Hypothesis The One-Sample t Test Choosing an Alpha (?) Level Specifying Reject Regions Based on ?, Halt and df Questions for the One-Sample t Test Assumptions for the Use of the One-Sample t Test Rules for the Use of NHST First Example: Mean Driving Speed (Nondirectional Test) SPSS Analysis: One Sample t Test for Mean Driving Speed “Exact” p Values Reporting Results for a Two-tailed One-Sample t Test The Driving Speed Data Reconsidered Using a One-Tailed Test Reporting Results for a One-tailed One-Sample t Test: Advantages/ Disadvantages of One Tailed Tests Traditional NHST Versus New Statistics Recommendations Things You Should Not Say About p Values Summary 9. Issues in Significance Tests: Effect Size, Statistical Power, and Decision Errors Beyond p Values Cohen’s d: An Effect Size Index Factors that Affect the Size of t Ratios Statistical Significance Versus Practical Importance Statistical Power Type I and Type II Decision Errors Meanings of “Error” Use of NHST in Exploratory Versus Confirmatory Research Inflated Risk of Type I Error From Multiple Tests Interpretation of Null Outcomes Interpretation of Null Outcomes Interpretation of Statistically Significant Outcomes Understanding Past Research Planning Future Research Guidelines for Reporting Results What You Cannot Say Summary Appendix 9 A Further Explanation of Statistical Power 10. Bivariate Pearson Correlation Research Situations Where Pearson r Is Used Correlation and Causal Inference How Sign and Magnitude of r Describe an X, Y Relationship Setting Up Scatter Plots With Examples of Perfect Linearity Most Associations Are Not Perfect Different Situations In Which r = 0 Assumptions for Use of Pearson r Preliminary Data Screening for Pearson r Effect of Extreme Bivariate Outliers Research Example Data Screening for Research Example Computation of Pearson r How Computation for Correlation Is Related to Pattern of Data Points in the Scatter Plot Testing the Hypothesis That ?0 = 0 Reporting Many Correlations and Inflated Risk of Type I Error Obtaining CIs for Correlations Pearson’s r and r2 as Effect-Size Indexes and Partition of Variance Statistical Power and Sample Size for Correlation Studies Interpretation of Outcomes for Pearson’s r SPSS Example Results Sections for One and Several Pearson r Values Reasons to Be Skeptical of Correlations Summary Appendix 10 A: Nonparametric Alternatives to Pearson r Appendix 10 B: Setting Up a 95% CI for Pearson r Appendix 10 C: Testing Significance of Differences Between Correlations Appendix 10 D: Factors That Artifactually Influence the Magnitude of Pearson’s r Appendix 10 E: Analysis of Non Linear Relationships 11. Bivariate Regression Research Situations Where Bivariate Regression is Used New Information Provided by Regression Regression Equations and Lines Two Versions of Regression Equations Steps in Regression Analysis Preliminary Data Screening Formulas for Bivariate Regression Coefficients Statistical Significance Tests for Bivariate Regression Confidence Intervals for Regression Coefficients Effect Size and Statistical Power Empirical Example Using SPSS: Salary Data SPSS Output: Salary Data Plotting the Regression Line: Salary Data Results Section: Salary Data Using Regression Equation to Predict Score for Individual: Joe’s Hr Data Partition of SS in Bivariate Regression: Joe’s Hr Data Issues in Planning a Bivariate Regression Study Plotting Residuals Standard Error of the Estimate, sy.x Summary Appendix 11 A OLS Derivation of Equation for Regression Coefficients Appendix 11 B Fully Worked Example for SS values: Joe’s HR Data 12. The Independent Samples t Test Research Situations Where the Independent Samples t Test is Used Hypothetical Research Example Assumptions for Use of the Independent Samples t Test Preliminary Data Screening: Evaluating Violations of Assumptions and Getting to Know Your Data Computation of Independent Samples t Test Statistical Significance of Independent Samples t Test Confidence Interval Around (M1 – M2) SPSS Commands for Independent Samples t Test SPSS Output for Independent Samples t Test Effect-Size Indexes for t Factors that Influence the Size of t Results Section Graphing Results: Means and CIs Decisions About Sample Size for the Independent Samples t Test Issues in Designing a Study Summary Appendix 12 A: A Nonparametric Alternative to the Independent Samples t Test 13. One-Way Between-S Analysis of Variance Research Situations Where Between-S One-Way ANOVA is Used Questions in One-Way Between S ANOVA Hypothetical Research Example Assumptions and Data Screening for One-Way ANOVA Computations for One-Way Between-S ANOVA Patterns of Scores and Magnitudes of SSbetween and SSwithin Confidence Intervals (CIs) For Group Means Effect Sizes for One-Way Between-S ANOVA Statistical Power Analysis for One-Way Between-S ANOVA Planned Contrasts Post Hoc or “Protected” Tests One Way Between S ANOVA Procedure in SPSS Output from SPSS for One Way Between S ANOVA Reporting Results from One Way Between S ANOVA Issues in Planning a Study Summary Appendix A ANOVA Model and Division of Scores Into Components Appendix B Expected Value of F When H0 is True Appendix C Comparison of ANOVA to t Test Appendix D Nonparametric Alternative to One Way Between S ANOVA 14. Paired Samples t-Test Independent Versus Paired Samples Designs Between-S and Within-S or Paired Groups Designs Types of Paired Samples Hypothetical Study: Effects of Stress on Heart Rate Review: Data Organization for Independent Samples New: Data Organization for Paired Samples A First Look at Repeated Measures Data Calculation of Difference (d) Scores Null Hypothesis for Paired Samples t Test Assumptions for Paired Samples t Test Formulas for Paired Samples t Test SPSS Paired Samples t Test Procedure Comparison of Results For Independent Samples t and Paired Samples t Tests Effect Size and Power Some Design Problems in Repeated Measures Designs Results for Paired Samples t-Test: Stress and HR Further Evaluation of Assumptions for Larger Dataset Summary Appendix A Nonparametric Alternative to Paired Samples t: Wilcoxon Signed Rank Test 15. One Way Repeated Measures ANOVA Introduction Null Hypothesis for Repeated Measures ANOVA Preliminary Assessment of Repeated Measures Data Computations for One-Way Repeated Measures ANOVA Use of SPSS Reliability Procedure for One Way Repeated Measures ANOVA Partition of SS in Between-S Versus Within-S ANOVA Assumptions for Repeated Measures ANOVA Choices of Contrasts in GLM Repeated Measures SPSS GLM Procedure for Repeated Measures ANOVA Output for GLM Repeated Measures ANOVA Paired Samples t Tests as Follow Up Results Effect Size Statistical Power Counterbalancing in Repeated Measures Studies More Complex Designs Summary Appendix 15 A Test for Person by Treatment Interaction 16. Factorial Analysis of Variance (Between – S) Research Situations Where Factorial Design Is Used Questions in Factorial ANOVA Null Hypotheses in Factorial ANOVA Screening for Violations of Assumptions Hypothetical Research Situation Computations for Between-S Factorial ANOVA Computation of SS, df, and MS in Two Way Factorial Effect Size Estimates for Factorial ANOVA Statistical Power Follow-Up Tests Factorial ANOVA Using the SPSS GLM Procedure SPSS Output Results Design Decisions and Magnitudes of SS Terms Summary Appendix 16 A: Unequal Cell ns in Factorial ANOVA Appendix 16 B: Weighted Versus Unweighted Means Appendix 16 C: Model for Factorial ANOVA Appendix 16 D: Fixed Versus Random Factors 17. Chi Square Analysis of Contingency Tables Evaluating Association Between Two Categorical Variables First Example: Contingency Tables for Titanic Data What is Contingency? Conditional and Unconditional Probabilities Null Hypothesis for Contingency Table Analysis Second Empirical Example: Dog Ownership Data Preliminary Examination of Dog Ownership Data Expected Cell Frequencies If H0 True Computation of Chi Squared Significance Test Evaluation of Statistical Significance of ?2. Effect Sizes for Chi Squared Chi Squared Example Using SPSS Output from Crosstabs Procedure Reporting Results Assumptions and Data Screening For Contingency Tables Other Measures of Association for Contingency Tables Summary Appendix 17 A: Margin of Error For Percentages in Surveys Appendix 17 B: Contingency Tables With Repeated Measures: McNemar Test Appendix 17 C: Fisher Exact Test Appendix 17 D: How Marginal Distributions for X and Y Constrain Maximum Value of ?? Appendix 17 E: Other Uses of ?2 18. Selection of Bivariate Analyses and Review of Key Concepts Selecting Appropriate Bivariate Analyses Types of Independent and Dependent Variables (Categorical Versus Quantitative) Parametric Versus Nonparametric Analyses Comparisons of Means or Medians Across Groups (Categorical IV and Quantitative DV) Problems with Selective Reporting of Evidence and Analyses Limitations of Statistical Significance Tests and p Values Statistical Versus Practical Significance Generalizability Issues Causal Inference Results Sections Beyond Bivariate Analyses: Adding Variables Some Multivariable or Multivariate Analyses Degrees of Belief
£105.62
Taylor & Francis Ltd Writing with Clarity and Style
Book SynopsisWriting with Clarity and Style, 2nd Edition, will help you to improve your writing dramatically. The book shows you how to use dozens of classical rhetorical devices to bring power, clarity, and effectiveness to your writing. You will also learn about writing styles, authorial personas, and sentence syntax as tools to make your writing interesting and persuasive. If you want to improve the appeal and persuasion of your speeches, this is also the book for you. From strategic techniques for keeping your readers engaged as you change focus, down to the choice of just the right words and phrases for maximum impact, this book will help you develop a flexible, adaptable style for all the audiences you need to address.Each chapter now includes these sections: Style Check, discussing many elements of style, including some enhanced and revised sections Define Your Terms, asking students to uTable of ContentsIntroduction Index of Tables Chapter 1: Balance Parallelism Chiasmus Antithesis Chapter 2: Emphasis I Climax Asyndeton Polysyndeton Sentential Adverb Chapter 3: Emphasis II Irony Understatement Litotes Hyperbole Chapter 4: Transition Metabasis Procatalepsis Hypophora Chapter 5: Clarity Distinctio Exemplum Amplification Metanoia Chapter 6: Syntax I Zeugma Diazeugma Prozeugma Mesozeugma Hypozeugma Syllepsis Chapter 7: Syntax II Hyperbaton Anastrophe Appositive Parenthesis Chapter 8: Figurative Language I Simile Analogy Metaphor Catachresis Chapter 9: Figurative Language II Metonymy Synecdoche Personification Chapter 10: Figurative Language III Allusion Eponym Apostrophe Transferred Epithet Chapter 11: Restatement I Anaphora Epistrophe Simploce Chapter 12: Restatement II Anadiplosis Conduplicatio Epanalepsis Chapter 13: Restatement III Diacope Epizeuxis Antimetabole Scesis Onomaton Chapter 14: Sound Alliteration Onomatopoeia Assonance Consonance Chapter 15: Drama Rhetorical Question Aporia Apophasis Anacoluthon Chapter 16: Word Play Oxymoron Pun Anthimeria Appendix A: Blog Posting Appendix B: Business Email Appendix C: Counsellor’s Report About a Client Appendix D: Graduate School Application Essay Appendix E: Short Story Appendix F: Winston Churchill—A Speaker’s Rhetoric Index
£36.99
Taylor & Francis Ltd Practical Multivariate Analysis
Book SynopsisThis is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computiTrade Review"This book is an excellent resource for students and researchers of all levels. I have used earlier editions repeatedly in data-analysis courses for advanced undergraduates and graduate students in applied fields. The level of mathematical presentation is well matched to such settings. Not only are there excellent examples from biostatistics and public health, but there are also some very good business financial examples. The new chapter on Data Visualization in the new, sixth edition will be especially useful. Overall, the book is exceptionally well written and readable."- Stanley Sclove, University of Illinois at Chicago "Editions of Practical Multivariate Analysis have been the mainstay of my graduate-level service course in applied data-analysis since 1985. It remains an extraordinary book -- packed with excellent examples, clear explanation and fine advice -- and has my highest possible recommendation. Among many reasons it remains so extraordinary, are three signaled directly in its title: it is practical rather than theoretical, analytic rather than technical, and it embodies a broader-than-usual conception of utilitarian multivariate methods. Practical Multivariate Analysis connects readily to its audience’s reality. It uses concrete research questions and real data to motivate its content, illustrated by exemplary analyses using R, SAS, SPSS and STATA. It models how complex findings can be made comprehensible to a broader community. It reaches beyond the typical spectrum of multivariate methods. It begins sensibly, discussing how multivariate data can be explored and displayed before complex analysis. Then come chapters on useful extensions to multiple regression analysis. While not usually considered “multivariate,” these latter methods connect an incoming audience to earlier acquired skills and extend them. Then follow the core chapters on “standard” multivariate methods, including canonical correlation, discriminant, principal-components, factor and cluster analyses. All are clearly presented, and then extended by excellent chapters on logistic regression, survival and log-linear analyses, and multilevel modeling, techniques that have proven useful and ubiquitous throughout social-science research.In my view, Practical Multivariate Analysis is an excellent roadmap for conducting such analyses, and a fine model for ensuring that their complex findings can be communicated successfully to others."- John B. Willett, Charles William Eliot Research Professor, Harvard University Graduate School of Education "The Practical Multivariate Analysis is a fun statistical modeling book to read. I enjoyed the rich insights the book has provided, which can only be accumulated through years of experience with the complexity in real data. It covers a large collection of statistical methods and models with a clear focus on application. Always discussing a model or method along with data examples, the book helps readers focus on important perspectives in applying the model, from choice of appropriate methods to interpretation of the results, while it still manages to maintain thetechnique details at a minimal level. Readers with different backgrounds can all benefit from this book. It is valuable for researchers who are interested in analyzing their data with classical statistical models and interpreting the results. It is a good reading for new graduates in statistics who have not had a lot of experience with real data as the book provides many importance guidance in handling real data as well as watch-out advices. It can be used by applied data scientists and serve as a resourceful reference book for experienced consultants."- Xia Wang, University of Cincinnati "The monograph belongs to the series Texts in Statistical Science and presents the sixth upgraded edition of the popular manual. It was first issued in 1984, and from that time won recognition as one of the best textbooks on the applied statistical modeling and analysis...Most of chapters of the first part of the textbook contain such subsections as “Introduction” or “Definition,” “Discussion” or “Examples,” “Summary” and “Problems”...This structure makes the book very reader-friendly written, helping to students and researchers in various fields to understand what for a statistical tool can serve, how to apply it, and to interpret computer outputs. There is not much of mathematical and statistical derivation, neither modern statistical techniques, but plenty of examples oriented to the easy “know-how” practical implementations of the classical multivariate methods."- Stan Lipovetsky, Technometrics, Vol 62"The authors wrote the sixth edition of this book for biomedical scientists, behavioural scientists, and academic researchers, who wish to perform and understand the results of multivariate statistical analyses. The book also describes when to ask for help from a statistical expert on multivariate analysis...The sixth edition has been updated with, in particular, a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses, and a new section on generalized estimating equations. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians, since it provides a comprehensive, practical, and accessible introduction to multivariate analysis whilst keeping mathematical details to a minimum...The book is an excellent roadmap for multivariate analysis and a fine model for ensuring that complex findings can be successfully communicated in a paper."- Luca Bertolaccini, ISCB News, July 2020 "This book is an excellent resource for students and researchers of all levels. I have used earlier editions repeatedly in data-analysis courses for advanced undergraduates and graduate students in applied fields. The level of mathematical presentation is well matched to such settings. Not only are there excellent examples from biostatistics and public health, but there are also some very good business financial examples. The new chapter on Data Visualization in the new, sixth edition will be especially useful. Overall, the book is exceptionally well written and readable."- Stanley Sclove, University of Illinois at Chicago "Editions of Practical Multivariate Analysis have been the mainstay of my graduate-level service course in applied data-analysis since 1985. It remains an extraordinary book -- packed with excellent examples, clear explanation and fine advice -- and has my highest possible recommendation. Among many reasons it remains so extraordinary, are three signaled directly in its title: it is practical rather than theoretical, analytic rather than technical, and it embodies a broader-than-usual conception of utilitarian multivariate methods. Practical Multivariate Analysis connects readily to its audience’s reality. It uses concrete research questions and real data to motivate its content, illustrated by exemplary analyses using R, SAS, SPSS and STATA. It models how complex findings can be made comprehensible to a broader community. It reaches beyond the typical spectrum of multivariate methods. It begins sensibly, discussing how multivariate data can be explored and displayed before complex analysis. Then come chapters on useful extensions to multiple regression analysis. While not usually considered “multivariate,” these latter methods connect an incoming audience to earlier acquired skills and extend them. Then follow the core chapters on “standard” multivariate methods, including canonical correlation, discriminant, principal-components, factor and cluster analyses. All are clearly presented, and then extended by excellent chapters on logistic regression, survival and log-linear analyses, and multilevel modeling, techniques that have proven useful and ubiquitous throughout social-science research.In my view, Practical Multivariate Analysis is an excellent roadmap for conducting such analyses, and a fine model for ensuring that their complex findings can be communicated successfully to others."- John B. Willett, Charles William Eliot Research Professor, Harvard University Graduate School of Education "The Practical Multivariate Analysis is a fun statistical modeling book to read. I enjoyed the richinsights the book has provided, which can only be accumulated through years of experience withthe complexity in real data. It covers a large collection of statistical methods and models with aclear focus on application. Always discussing a model or method along with data examples, thebook helps readers focus on important perspectives in applying the model, from choice ofappropriate methods to interpretation of the results, while it still manages to maintain thetechnique details at a minimal level.Readers with different backgrounds can all benefit from this book. It is valuable for researcherswho are interested in analyzing their data with classical statistical models and interpreting theresults. It is a good reading for new graduates in statistics who have not had a lot of experiencewith real data as the book provides many importance guidance in handling real data as well aswatch-out advices. It can be used by applied data scientists and serve as a resourceful referencebook for experienced consultants."- Xia Wang, University of Cincinnati "The monograph belongs to the series Texts in Statistical Science and presents the sixth upgraded edition of the popular manual. It was first issued in 1984, and from that time won recognition as one of the best textbooks on the applied statistical modeling and analysis...Most of chapters of the first part of the textbook contain such subsections as “Introduction” or “Definition,” “Discussion” or “Examples,” “Summary” and “Problems”...This structure makes the book very reader-friendly written, helping to students and researchers in various fields to understand what for a statistical tool can serve, how to apply it, and to interpret computer outputs. There is not much of mathematical and statistical derivation, neither modern statistical techniques, but plenty of examples oriented to the easy “know-how” practical implementations of the classical multivariate methods."- Stan Lipovetsky, Technometrics, Vol 62 Table of ContentsPart I: Preparation for Analysis. What is Multivariate Analysis? Characterizing Data for Analysis. Preparing for Data Analysis. Data Visualization. Data Screening and Transformations. Data Visualization. Selecting Appropriate Analyses. Part II: Regression Analysis. Simple Regression and Correlation. Multiple Regression and Correlation. Variable Selection in Regression. Special Regression Topics. Discriminant analysis. Logistic Regression. Regression Analysis with Survival Data. Principal Components Analysis. Factor Analysis. Cluster Analysis. Log-Linear Analysis. Correlated Outcomes Regression.
£82.64
Guilford Publications Psychometric Methods
Book SynopsisGrounded in current knowledge and professional practice, this book provides up-to-date coverage of psychometric theory, methods, and interpretation of results. Essential topics include measurement and statistical concepts, scaling models, test design and development, reliability, validity, factor analysis, item response theory, and generalizability theory. Also addressed are norming and test equating, topics not typically covered in traditional psychometrics texts. Examples drawn from a dataset on intelligence testing are used throughout the book, elucidating the assumptions underlying particular methods and providing SPSS (or alternative) syntax for conducting analyses. The companion website presents datasets for all examples as well as PowerPoint slides of figures and key concepts. Pedagogical features include equation boxes with explanations of statistical notation, and end-of-chapter glossaries. The Appendix offers extensions of the topical chapters with example source code fromTrade Review“I like the book and it meshes well with what I plan to do in my course. I particularly like the generalizability theory, norms and test equating, scaling, and validation process chapters. It is very easy reading--I am planning to use the book next spring."--R. J. de Ayala, PhD, Chair of Educational Psychology, University of Nebraska–Lincoln "I encourage all psychologists and educators to read this marvelous book. I learned a lot from reading it. The key terms are very useful, as are the chapter summaries. Readers of all levels will find material relevant to them, including SPSS code and GfGc datasets on intelligence that will be quite useful in trying out the ideas. I give this book my highest recommendation and think it will be a great classroom text."--John J. McArdle, PhD, Department of Psychology, University of Southern California "This book is both comprehensive and accessible, laying the foundation for all the requisite skills needed to be both a successful consumer and producer of psychometrics. Scholars who are unfamiliar with measurement could easily teach themselves from this text, becoming quite proficient at psychometrics. There is excellent integration of quantitative statistics throughout, so that readers will be able not only to understand the psychometric concepts, but also to apply their knowledge. This is a useful text for a graduate-level Psychometric Methods or Measurement class."--Debbie L. Hahs-Vaughn, PhD, Department of Educational and Human Sciences, University of Central Florida "An encyclopedia of psychometric issues--a real 'must have' for anyone teaching Tests and Measurement or Research Methods, or directing student research projects. The book's high level of detail makes it invaluable for any professional who works with, creates, or analyzes psychometric material. The use of intelligence testing data throughout the chapters helps bring cohesiveness."--John Wallace, PhD, Department of Psychological Science, Ball State University "With vast expertise in psychometric instrument development, statistical applications, and research, Price has produced a theoretically informed, practical volume. Professionals in health-related fields will find this book extremely valuable for guidance in the development of rigorous instruments, such as patient-reported outcome measures. Featuring examples using a range of software, this text is ideal for graduate courses on measurement in schools of medicine, public health, nursing, or health professions."--Byron J. Gajewski, PhD, Department of Biostatistics, University of Kansas Medical Center-This book is suitable for emerging assessment professionals and practitioners who are interested in learning psychometrics but with little knowledge in statistics. It provides not only a theoretical foundation to the topics but also worked examples to highlight their practical applications. The syntax, output, and interpretations based on software programs like SPSS and BILOG-MG will help readers to bridge theory and methods with hands-on examples. This book would be a convenient toolbox for applied researchers who would like to conduct psychometric analyses, and it would also serve as a handbook for graduate students who study measurement and psychometrics.--Psychometrika, 03/29/2019ƒƒThis excellent book explores the basic concepts of psychometric knowledge and practice….This would be a great addition to the libraries of graduate students and researchers.--Doody's Review Service, 03/17/2017Table of Contents1. Introduction 1.1 Psychological Measurement and Tests 1.2 Tests and Samples of Behavior 1.3 Types of Tests 1.4 Origin of Psychometrics 1.5 Definition of Measurement 1.6 Measuring Behavior 1.7 Psychometrics and Its Importance to Research and Practice 1.8 Organization of This Book Key Terms and Definitions 2. Measurement and Statistical Concepts 2.1 Introduction 2.2 Numbers and Measurement 2.3 Properties of Measurement in Relation to Numbers 2.4 Levels of Measurement 2.5 Contemporary View on the Levels of Measurement and Scaling 2.6 Statistical Foundations for Psychometrics 2.7 Variables, Frequency Distributions, and Scores 2.8 Summation or Sigma Notation 2.9 Shape, Central Tendency, and Variability of Score Distributions 2.10 Correlation, Covariance, and Regression 2.11 Summary Key Terms and Definitions 3. Criterion, Content, and Construct Validity 3.1 Introduction 3.2 Criterion Validity 3.3 Essential Elements of a High-Quality Criterion 3.4 Statistical Estimation of Criterion Validity 3.5 Correction for Attenuation 3.6 Limitations to Using the Correction for Attenuation 3.7 Estimating Criterion Validity with Multiple Predictors: Partial Correlation 3.8 Estimating Criterion Validity with Multiple Predictors: Higher-Order Partial Correlation 3.9 Coefficient of Multiple Determination and Multiple Correlation 3.10 Estimating Criterion Validity with More Than One Predictor: Multiple Linear Regression 3.11 Regression Analysis for Estimating Criterion Validity: Development of the Regression Equation 3.12 Unstandardized Regression Equation for Multiple Regression 3.13 Testing the Regression Equation for Significance 3.14 Partial Regression Slopes 3.15 Standardized Regression Equation 3.16 Predictive Accuracy of a Regression Analysis 3.17 Predictor Subset Selection in Regression 3.18 Summary Key Terms and Definitions 4. Statistical Aspects of the Validation Process 4.1 Techniques for Classification and Selection 4.2 Discriminant Analysis 4.3 Multiple-Group Discriminant Analysis 4.4 Logistic Regression 4.5 Logistic Multiple Discriminant Analysis: Multinomial Logistic Regression 4.6 Model Fit in Logistic Regression 4.7 Content Validity 4.8 Limitations of the Content Validity Model 4.9 Construct Validity 4.10 Establishing Evidence of Construct Validity 4.11 Correlational Evidence of Construct Validity 4.12 Group Differentiation Studies of Construct Validity 4.13 Factor Analysis and Construct Validity 4.14 Multitrait–Multimethod Studies 4.15 Generalizability Theory and Construct Validity 4.16 Summary and Conclusions Key Terms and Definitions 5. Scaling 5.1 Introduction 5.2 A Brief History of Scaling 5.3 Psychophysical versus Psychological Scaling 5.4 Why Scaling Models Are Important 5.5 Types of Scaling Models 5.6 Stimulus-Centered Scaling 5.7 Thurstone’s Law of Comparative Judgment 5.8 Response-Centered Scaling 5.9 Scaling Models Involving Order 5.10 Guttman Scaling 5.11 The Unfolding Technique 5.12 Subject-Centered Scaling 5.13 Data Organization and Missing Data 5.14 Incomplete and Missing Data 5.15 Summary and Conclusions Key Terms and Definitions 6. Test Development 6.1 Introduction 6.2 Guidelines for Test and Instrument Development 6.3 Item Analysis 6.4 Item Difficulty 6.5 Item Discrimination 6.6 Point–Biserial Correlation 6.7 Biserial Correlation 6.8 Phi Coefficient 6.9 Tetrachoric Correlation 6.10 Item Reliability and Validity 6.11 Standard Setting 6.12 Standard-Setting Approaches 6.13 The Nedelsky Method 6.14 The Ebel Method 6.15 The Angoff Method and Modifications 6.16 The Bookmark Method 6.17 Summary and Conclusions Key Terms and Definitions 7. Reliability 7.1 Introduction 7.2 Conceptual Overview 7.3 The True Score Model 7.4 Probability Theory, True Score Model, and Random Variables 7.5 Properties and Assumptions of the True Score Model 7.6 True Score Equivalence, Essential True Score Equivalence, and Congeneric Tests 7.7 Relationship between Observed and True Scores 7.8 The Reliability Index and Its Relationship to the Reliability Coefficient 7.9 Summarizing the Ways to Conceptualize Reliability 7.10 Reliability of a Composite 7.11 Coefficient of Reliability: Methods of Estimation Based on Two Occasions 7.12 Methods Based on a Single Testing Occasion 7.13 Estimating Coefficient Alpha: Computer Programs and Example Data 7.14 Reliability of Composite Scores Based on Coefficient Alpha 7.15 Reliability Estimation Using the Analysis of Variance Method 7.16 Reliability of Difference Scores 7.17 Application of the Reliability of Difference Scores 7.18 Errors of Measurement and Confidence Intervals 7.19 Standard Error of Measurement 7.20 Standard Error of Prediction 7.21 Summarizing and Reporting Reliability Information 7.22 Summary and Conclusions Key Terms and Definitions 8. Generalizability Theory 8.1 Introduction 8.2 Purpose of Generalizability Theory 8.3 Facets of Measurement and Universe Scores 8.4 How Generalizability Theory Extends Classical Test Theory 8.5 Generalizability Theory and Analysis of Variance 8.6 General Steps in Conducting a Generalizability Theory Analysis 8.7 Statistical Model for Generalizability Theory 8.8 Design 1: Single-Facet Person by Item Analysis 8.9 Proportion of Variance for the p x i Design 8.10 Generalizability Coefficient and CTT Reliability 8.11 Design 2: Single-Facet Crossed Design with Multiple Raters 8.12 Design 3: Single-Facet Design with the Same Raters on Multiple Occasions 8.13 Design 4: Single-Facet Nested Design with Multiple Raters 8.14 Design 5: Single-Facet Design Multiple Raters Rating on Two Occasions 8.15 Standard Errors of Measurement: Designs 1–5 8.16 Two-Facet Designs 8.17 Summary and Conclusions Key Terms and Definitions 9. Factor Analysis 9.1 Introduction 9.2 Brief History 9.3 Applied Example with GfGc Data 9.4 Estimating Factors and Factor Loadings 9.5 Factor Rotation 9.6 Correlated Factors and Simple Structure 9.7 The Factor Analysis Model, Communality, and Uniqueness 9.8 Components, Eigenvalues, and Eigenvectors 9.9 Distinction between Principal Components Analysis and Factor Analysis 9.10 Confirmatory Factor Analysis 9.11 Confirmatory Factor Analysis and Structural Equation Modeling 9.12 Conducting Factor Analysis: Common Errors to Avoid 9.13 Summary and Conclusions Key Terms and Definitions 10. Item Response Theory 10.1 Introduction 10.2 How IRT Differs from CTT 10.3 Introduction to IRT 10.4 Strong True Sco
£75.99
Guilford Publications Handbook of Mindfulness First Edition
Book SynopsisAn authoritative handbook, this volume offers both a comprehensive review of the current science of mindfulness and a guide to its ongoing evolution. Leading scholars explore mindfulness in the context of contemporary psychological theories of attention, perceptual processing, motivation, and behavior, as well as within a rich cross-disciplinary dialogue with the contemplative traditions. After surveying basic research from neurobiological, cognitive, emotional/affective, and interpersonal perspectives, the book delves into applications of mindfulness practice in healthy and clinical populations, reviewing a growing evidence base. Examined are interventions for behavioral and emotion dysregulation disorders, depression, anxiety, and addictions, and for physical health conditions.Trade Review"Although psychologists were 2,500 years late to recognize the benefits of mindfulness, behavioral scientists and practitioners have made immense strides in understanding this important process. Drawing from work across the domains of psychology, this volume examines the psychological processes that underlie mindfulness, comprehensively reviews basic research, and describes mindfulness-based interventions for specific populations. The book makes an exceptional contribution as it summarizes the current state of knowledge, offers ideas for future research, and paves the way toward even more effective interventions."--Mark R. Leary, PhD, Department of Psychology and Neuroscience, Duke University "With the exponential growth of mindfulness science over the past 30 years, this book offers a timely and inclusive snapshot of how we now define mindfulness and how it might work, the ways it integrates with current psychological theory, and how it is being applied to help improve well-being and reduce suffering. For clinicians and researchers newly interested in mindfulness, this book will provide a thorough review. For those with more experience in this area, too, the volume will most certainly provide refreshing insights and perspectives."--Mark A. Lau, PhD, Vancouver CBT Center and Department of Psychiatry, University of British Columbia, Canada "Finally, the most comprehensive work on mindfulness! This handbook sums up the current state of the research and clinical applications and offers insightful discussions of multiple aspects of mindfulness. The chapters are written in a clear and interesting fashion by highly respected experts. This is useful reading for advanced students in psychology and cognitive sciences as well as health care professionals, and will be 'unputdownable' for anyone interested in learning more about mindfulness. I wholeheartedly recommend this excellent book."--Britta Hölzel, PhD, Harvard Medical School/Massachusetts General Hospital and Department of Neuroradiology, Technical University of Munich, Germany "The mindfulness literature is growing so fast that keeping up can seem impossible. This volume helps tremendously. Ancient Buddhist teachings and their relationship to contemporary Western scientific approaches are thoughtfully discussed. Theoretical viewpoints, measurement issues, and basic research findings on the brain, cognition, and emotion are covered in depth, as are mindfulness-based interventions for healthy individuals and those with mental and physical health problems. This is an outstanding volume from a distinguished group of contributors."--Ruth A. Baer, PhD, Department of Psychology, University of Kentucky "A welcome and needed addition to the burgeoning literature of mindfulness. Readers get a solid grasp of the historical roots and current applications of mindfulness and are introduced to psychological models--both well established and more recent--that provide a context for understanding the effects of contemplative practices on body, brain, and mind. Various levels of analysis are provided, including neurobiological, cognitive, affective and psychological perspectives. The emphasis on empirical research and reasoned argument will make this volume an invaluable text for graduate-level courses."--Tony Toneatto, PhD, Department of Psychiatry, University of Toronto, Canada -This is one of the most thorough books on mindfulness to date that I am aware of, with relevant and essential input of well-respected experts. It is a valued contribution, which summarizes not only the current state of knowledge, but provides some useful ideas for the next steps in mindfulness research.--Private Practice, 7/1/2016ƒƒ[The] Handbook of Mindfulness: Theory, Research, and Practice is an easy read that is tailored to those who are novices in exploring this timely topic. It would be most helpful to researchers who wish to have a compact volume that summarizes all aspects of the state of the art of meditation for mental health professionals….Because of the breadth examined, it would be an excellent choice for a textbook….Supervising clinicians who wish to quickly access common terms, themes, and clinical applications related to meditation as well as overviews of common mindfulness-based therapies would be satisfied. Content would be readable for trainees from the practicum to postdoctoral level of these well written and relatively brief 'meta-analyses' of sorts.--PsycCRITIQUES, 9/28/2015ƒƒThis book appears to be a major feat in the industry…seldom does one see something this complete, detailed, well balanced by the editors, informative, solid, and reliable in not pandering to the spiritual, but rather in answering the questions it set out to address: the nature of the theory, research, and practice of mindful interventions. It belongs on your shelf.--Metapsychology Online Reviews, 8/18/2015ƒƒImpressive….Highly recommended. Graduate students, researchers, professionals.--Choice Reviews, 8/1/2015Table of Contents1. Introduction: The Evolution of Mindfulness Science, Kirk Warren Brown, J. David Creswell, & Richard M. RyanI. Historical and Conceptual Overview of Mindfulness 2. Buddhist Conceptualizations of Mindfulness, Rupert Gethin 3. Developing Attention and Decreasing Affective Bias: Toward a Cross-Cultural Cognitive Science of Mindfulness, Jake H. Davis & Evan Thompson 4. Reconceptualizing Mindfulness: The Psychological Principles of Attending in Mindfulness Practice and Their Role in Well-Being, James CarmodyII. Mindfulness in the Context of Contemporary Psychological Theory 5. Mindfulness in the Context of the Attention System, Yi Yuan Tang & Michael I. Posner 6. Mindfulness in the Context of Processing Mode Theory, Edward R.Watkins 7. Being Aware and Functioning Fully: Mindfulness and Interest-Taking within Self-Determination Theory, Edward L. Deci, Richard M. Ryan, Patricia P. Schultz, & Christopher P. Niemiec 8. Mindfulness in Contextual Cognitive-Behavioral Models, Thomas G. Szabo, Douglas M. Long, Matthieu Villatte & Steven C. HayesIII. Basic Science of Mindfulness 9. From Conceptualization to Operationalization of Mindfulness, Jordan T. Quaglia, Kirk Warren Brown, Emily K. Lindsay, J. David Creswell, & Robert J. Goodman 10. The Neurobiology of Mindfulness Meditation, Fadel Zeidan 11. Cognitive Benefits of Mindfulness Meditation, Marieke K. van Vugt 12. Emotional Benefits of Mindfulness, Joanna J. Arch & Lauren N. Landy 13. The Science of Presence: A Central Mediator of the Interpersonal Benefits of Mindfulness, Suzanne C. Parker, Benjamin W. Nelson, Elissa S. Epel, Daniel J. Siegel 14. Did the Buddha Have a Self?: No-Self, Self, and Mindfulness in Buddhist Thought and Western Psychologies, Richard M. Ryan & C. Scott RigbyIV. Mindfulness Interventions for Healthy Populations 15. Mindfulness-Based Stress Reduction for Healthy Stressed Adults, Shauna L. Shapiro & Hooria Jazaieri 16. Mindfulness Training for Children and Adolescents: A State-of-the-Science Review, David S. Black 17. Mindfulness Training to Enhance Positive Functioning, Kirk Warren BrownV. Mindfulness Interventions for Clinical Populations 18. Mindfulness Interventions for Emotional Dysregulation Disorders: From Self-Control to Self-Regulation, Thomas R. Lynch, Sophie A. Lazarus, & Jennifer S. Cheavens 19. Mindfulness-Based Cognitive Therapy for Chronic Depression, Julie Anne Irving, Norman A. S.Farb, & Zindel V. Segal 20. Mindfulness in the Treatment of Anxiety, Sarah A. Hayes-Skelton & Lauren P. Wadsworth 21. A Mindfulness-Based Approach to Addiction, Sarah Bowen, Cassandra Vieten, Katie Witkiewitz, & Haley Douglas 22. Mindfulness-Based Interventions for Physical Conditions: A Selective Review, Linda E. Carlson 23. Biological Pathways Linking Mindfulness with Health, J. David Creswell
£43.69
Guilford Publications Attachment
Book SynopsisThe ongoing growth of attachment research has given rise to new perspectives on classic theoretical questions as well as fruitful new debates. This unique book identifies nine central questions facing the field and invites leading authorities to address them in 46 succinct chapters. Multiple perspectives are presented on what constitutes an attachment relationship, the best ways to measure attachment security, how internal working models operate, the importance of early attachment relationships for later behavior, challenges in cross-cultural research, how attachment-based interventions work, and more. The concluding chapter by the editors delineates points of convergence and divergence among the contributions and distills important implications for future theory and research.Trade Review"This book will prove richly rewarding to those already deeply steeped in attachment theory, research, clinical intervention, and even public policy, as well as those new to the subject. It could serve as a primary text for a graduate psychology class. The book comprises brief chapters by developmental, social, biological, and clinical psychologists who both embrace and critique attachment theory and research. It provides deep insight into such fundamental issues as conceptualization and measurement of attachment security across the life course, determinants and consequences of variation in security/insecurity and attachment state of mind, and underlying neurobiology. Classical and cutting-edge research is masterfully reported and evaluated in an effort to move the field in an interdisciplinary lifespan direction. This volume is an intellectual feast--enjoy the meal!"--Jay Belsky, PhD, Robert M. and Natalie Reid Dorn Professor, Program in Human Development, University of California, Davis "This is a volume of extraordinary importance for our knowledge about attachment relationships in human development, and for the application of that knowledge in systems across the lifespan and across societies. It could not be more timely as an incisive update on attachment research, which over the past decade has expanded and has been increasingly extended into neuroscience and education. Many fields will no doubt benefit from the rich insights provided by these chapters. I have no doubt that this landmark volume will be a standard reference for years to come."--Robert C. Pianta, PhD, Novartis US Foundation Professor of Education and Dean, School of Education and Human Development, University of Virginia "This is one of the more important books on attachment theory of the last few decades. Thompson, Simpson, and Berlin have brought together a who’s who of attachment scholars to confront nine fundamental issues. Several innovations make this a standout volume--among them, the mix of senior and emerging scholars, which leads to fresh perspectives on crucial questions, and the focused, concise chapter format. This book will serve to stimulate ideas in those familiar with the field and will be an excellent text for graduate courses on research and theory in developmental psychology."--Megan R. Gunnar, PhD, Regents Professor, Institute of Child Development, University of Minnesota "Attachment theory has grown continuously, with increasing relevance for theoretical, clinical, research, and public policy domains. Thompson, Simpson, and Berlin, together with their excellent contributors, have produced a volume of immeasurable significance. Chapters assess where the field of attachment currently stands and consider perspectives for the future. I highly recommend this comprehensive work to educators, researchers, and clinicians interested in early development."--Joy D. Osofsky, PhD, Paul J. Ramsay Endowed Chair of Psychiatry and Barbara Lemann Professor of Child Welfare, Louisiana State University Health Sciences Center "As one who has been reading and teaching and conducting research on attachment for 30-plus years, I cannot overstate this book's timeliness or value. This is exactly the book that the attachment field needs right now! And how wonderful that this theory, which has been extraordinarily generative for more than 50 years, still offers so many important questions to be explored. It was such a good idea to ask both seasoned and emerging scholars to collaborate in tackling the most fundamental questions. The editors certainly got the questions right--and the responses are penetrating and thought provoking. Every chapter is tightly focused and concise, making the book an ideal text for courses on attachment and related topics at both the graduate and advanced undergraduate levels. I am continually asked by friends and colleagues outside this area what they should read about attachment. This is the book I will be recommending!"--Cindy Hazan, PhD, Department of Psychology, Cornell University-Table of Contents1. Attachment Theory in the Twenty-First Century: Introduction to the Volume, Ross A. Thompson, Jeffry A. Simpson, & Lisa J. Berlin - TOPIC 1: Defining Attachment and Attachment Security 2. Attachment as a Relationship Construct, L. Alan Sroufe 3. What Kinds of Relationships Count as Attachment Relationships?, R. Pasco Fearon & Carlo Schuengel 4. Attachment to Child Care Providers, Lieselotte Ahnert 5. Defining Attachment Relationships and Attachment Security from a Personality–Social Perspective on Adult Attachment, Phillip R. Shaver & Mario Mikulincer 6. The Nature and Developmental Origins of Attachment Security in Adulthood, Deborah Jacobvitz & Nancy Hazen 7. Casting a Wider Net: Parents, Pair Bonds, and Other Attachment Partners in Adulthood, Ashleigh I. Aviles & Debra Zeifman - TOPIC 2: Measuring the Security of Attachment 8. Categorical Assessments of Attachment: On the Ontological Relevance of Group Membership, Howard Steele & Miriam Steele 9. Categorical or Dimensional Measures of Attachment?: Insights from Factor Analytic and Taxometric Research, K. Lee Raby, R. Chris Fraley, & Glenn I. Roisman 10. Representational Measures of Attachment: A Secure Base Script Perspective, Theodore Waters 11. Measuring the Security of Attachment in Adults: Narrative Assessments and Self-Report Questionnaires, Judith A. Crowell 12. Priming Approaches, Omri Gillath & Ting Ai - TOPIC 3: The Nature and Function of Internal Working Models 13. In the Service of Protection from Threat: Attachment and Internal Working Models, Jude Cassidy 14. From Internal Working Models to Script-Like Attachment Representations, Harriet S. Waters, Theodore E. A. Waters, & Everett Waters 15. Parental Insightfulness and Parent–Child Emotion Dialogues: Shaping Children's Internal Working Models, David Oppenheim & Nina Koren-Karie 16. Internal Working Models as Developing Representations, Ross A. Thompson 17. A Functional Account of Multiple Internal Working Models: Flexibility in Ranking, Structure and Content across Contexts and Time, Yuthika U. Girme & Nickola C. Overall - TOPIC 4: Stability and Change in the Security of Attachment 18. The Consistency of Attachment Security across Time and Relationships, R. Chris Fraley & Keely A. Dugan 19. Stability and Change in Attachment Security, Cathryn Booth-LaForce & Glenn I. Roisman 20. Beyond Stability: Toward Understanding the Development of Attachment beyond Childhood, Joseph Allen 21. Stability and Change in Adult Romantic Relationship Attachment Styles, Ramona L. Paetzold, W. Steven Rholes, & Tiffany George 22. Change in Adult Attachment Insecurity from an Interdependence Theory Perspective, Ximena B. Arriaga & Madoka Kumashiro - TOPIC 5: The Continuing Influence of Early Attachment 23. The Legacy of Early Attachments: Past, Present, Future, Glenn I. Roisman & Ashley M. Groh 24. Attachment Security and Disorganization: Etched on the Brain?, Marinus H. van IJzendoorn, Anne Tharner, & Marian J. Bakermans-Kranenburg 25. Early Attachment and Later Physical Health, Katie B. Ehrlich & Jude Cassidy 26. The Continuing Influence of Early Attachment Orientations Viewed from a Personality–Social Perspective on Adult Attachment, Mario Mikulincer & Philip R. Shaver 27. Early Attachment from the Perspective of Life History Theory, Ohad Szepsenwol & Jeffry A. Simpson - TOPIC 6: Culture and Attachment 28. Attachment Theory: Fact or Fancy?, Heidi Keller 29. Pluralities and Commonalities in Children's Relationships: Care of Efe Forager Infants as a Case Study, Gilda Morelli & Linxi Lu 30. Attachment Theory's Universality Claims: Asking Different Questions, Judi Mesman 31. Attachment in the Context of Human Adaptation, James Chisholm - TOPIC 7: Separation and Loss 32. Losing a Parent in Early Childhood: The Impact of Disrupted Attachment, Ann Chu & Alicia F. Lieberman 33. Attachment, Loss, and Grief Viewed from a Personality–Social Perspective on Adult Attachment, Philip R. Shaver & Mario Mikulincer 34. The Psychological and Biological Correlates of Separation and Loss, David A. Sbarra & Antina Manvelian 35. Breaking the Marital Ties That Bind: Divorce from a Spousal Attachment Figure, Brooke C. Feeney & Joan K. Moin 36. Normal and Pathological Mourning: Attachment Processes in the Development of Prolonged Grief, Fiona Maccallum - TOPIC 8: Attachment-Based interventions 37. Attachment-Based Interventions to Promote Secure Attachment in Children, Marian Bakermans-Kranenburg & Mirjam Oosterman 38. Mechanisms of Attachment-Based Intervention Effects on Child Outcomes, Mary Dozier & Kristin Bernard 39. Attachment-Based Intervention Processes in Disordered Parent–Child Relationships, Sheree L. Toth, Michelle E. Alto, & Jennifer Warmingham 40. Therapeutic Mechanisms in Attachment-Informed Psychotherapy with Adults, Alessandro Talia & Jeremy Holmes 41. Attachment Principles as a Guide to Therapeutic Change: The Example of Emotionally Focused Therapy, Susan M. Johnson - TOPIC 9: Attachment, Systems, and Services 42. Attachment and Child Care, Margaret Tresch Owen & Cynthia A. Frosch 43. Attachment and Early Childhood Education Systems in the United States, Bridget K. Hamre & Amanda P. Williford 44. An Attachment Theory Approach to Parental Separation, Divorce, and Child Custody, Michael E. Lamb 45. Attachment and Child Protective Systems, Jody Todd Manly, Anna Smith, Sheree L. Toth, & Dante Cicchetti 46. Attachment and Foster Care, Charles H. Zeanah & Mary Dozier 47. Attachment and Early Home Visiting: Toward a More Perfect Union, Lisa J. Berlin, Allison West, & Brenda Jones Harden 48. Concluding Commentary: Assembling the Puzzle--Interlocking Pieces, Missing Pieces, and the Emerging Picture, Ross A. Thompson, Lisa J. Berlin, & Jeffry A. Simpson Author Index Subject Index
£47.49
Guilford Publications ReInvention
Book SynopsisFrom Patricia Leavy, a leader in arts-based research, this is the first comprehensive guide to what social fiction is and how to write it. In an engaging, personal tone, Leavy explores the unique contribution that creative writing--such as novels, series, and short stories--can make to addressing qualitative research questions. In-depth discussions of narrative models (such as the three-act structure) and elements (such as plot, metaphor, dialogue) are accompanied by excerpts from Leavy's published fiction, reflections on the writing process, and technical suggestions. The book offers evaluation criteria for social fiction as well as practical publishing advice. Instructive features include tip bubbles with additional writing hints, end-of-chapter Skill-Building and Rethink Your Research exercises, and an appendix with suggested readings.Trade Review"Everything you need to know to write social fiction is in this book. One of the best-written methods books I have ever read, it is accessible, clear, and detailed. The examples demonstrate the fiction concepts, and the pedagogical elements provide valuable advice and questions to think about in order to get a writing project started."--Sandra L. Faulkner, PhD, School of Media and Communication, Bowling Green State University "Leavy does a wonderful job of introducing social fiction. The flow and style are confident and easy. There are humorous sections that keep the reader engaged in a way that is often missing in dry academic research methods texts.”—Kenya E. Wolff, PhD, Department of Teacher Education, University of Mississippi "This book is excellent at explaining both the theory behind fiction as method and the practical 'how-tos' of doing it well. The exemplars from Leavy's own novels are instructive. Leavy's reflections on how she writes—how fiction is structured, what's going on behind the scenes—are so helpful. This book will be especially valuable for students coming from disciplines where fiction is not normally used, but where it could serve to make research accessible to a broader audience. I will use it in my own class."—Jessica Smartt Gullion, PhD, Department of Sociology, Texas Woman’s University "This text would be great as either a supplemental text or central text for any course exploring truth and fiction. It is especially powerful for thinking about truth in inquiry. This book resonates well with our work through the Feminist Research Collective."--Barbara Dennis, PhD, Department of Counseling and Educational Psychology, Indiana University-Table of Contents1. Writing as Inquiry 2. Historical and Contemporary Context for Social Fiction 3. The Method: How to Write Social Fiction 4. Traditional Three-Act Structures 5. Sequels: More on Traditional Three-Act Structures 6. Series and Open Form Structures 7. Alternative Structures 8. Short Stories 9. Practical Advice for Publishing and Evaluating Social Fiction Appendix. Recommended Reading References Index
£29.99
Guilford Publications The Unconscious
Book SynopsisWeaving together state-of-the-art research, theory, and clinical insights, this book provides a new understanding of the unconscious and its centrality in human functioning. The authors review heuristics, implicit memory, implicit learning, attribution theory, implicit motivation, automaticity, affective versus cognitive salience, embodied cognition, and clinical theories of unconscious functioning. They integrate this work with cognitive neuroscience views of the mind to create an empirically supported model of the unconscious. Arguing that widely used psychotherapies--including both psychodynamic and cognitive approaches--have not kept pace with current science, the book identifies promising directions for clinical practice.Winner--American Board and Academy of Psychoanalysis Book Prize (Theory)Trade Review“A 'must read' for anyone who wants to know how our minds and brains really work when we’re not looking. No one other than Weinberger could have pulled together this important work--no one else has spent the last 35 years working with unconscious processes both in the laboratory and the clinic, without overvaluing or devaluing either setting. Weinberger and Stoycheva synthesize the best scientific and clinical thinking about the range of unconscious processes that control our thoughts, feelings, motivation, and behavior. The authors glide effortlessly from philosophical thought on the nature of consciousness, to complex experiments in cognitive neuroscience, to provocative ideas from contemporary psychoanalysis."--Drew Westen, PhD, Department of Psychology and Department of Psychiatry and Behavioral Sciences, Emory University "What an accomplishment! Just as the scientific community approaches consensus about the ubiquity and power of unconscious processes, Weinberger and Stoycheva provide a brilliant, generous, guided tour of that landscape. The authors explore and integrate knowledge about implicit mental processes that has emerged from different intellectual traditions. This readable book belongs not only in undergraduate and graduate courses in psychology and psychiatry, but also in the curricula of all postgraduate clinical training programs."--Nancy McWilliams, PhD, ABPP, Graduate School of Applied and Professional Psychology, Rutgers, The State University of New Jersey "This impressive, authoritative book beautifully integrates the profound insights of Freudian psychology with cutting-edge research. If you have wondered about the powers and pitfalls of the human mind that lie outside of conscious, deliberate thinking, or if you are merely curious about the deep processes that produce the remarkable capacity of human thinking, this book is the place to start. It offers a thorough overview of scholarly work on the unconscious, but it goes beyond that to offer a new synthesis. This is a terrific resource for researchers, practitioners, students, and anyone else interested in the mysterious depths of the human mind."--Roy F. Baumeister, PhD, Department of Psychology, University of Queensland, Australia "Weinberger is one of the world's leading experts on the unconscious mind. He has teamed with Stoycheva to produce a contemporary review that manages to be both sweeping in its scope and illuminating in its depth. Especially valuable is their integration of psychodynamic theory and other clinical models of unconscious thought and motivation with modern cognitive psychology research on implicit memory and implicit learning. This makes the book an ideal text for cognitive science courses on conscious and unconscious human information processing, providing a more complete and historical treatment than do most contemporary texts. Advanced clinical psychology courses, as well, would profit from the coverage of therapeutic approaches and the authors' appreciation that much can be learned about the human mind and human nature from the therapy setting, in addition to rigorous laboratory research. Because the authors worked hard to describe and cover the most recent research and theoretical developments, the coverage is fresh and up to date, ensuring this book will be a trusted and useful text and resource for decades to come."--John A. Bargh, PhD, James Rowland Angell Professor of Psychology, Yale University "Weinberger and Stoycheva offer a remarkably thorough and scholarly review of the evidence on unconscious processes. Courses in academic research on affect and cognition are now a required element for PhD programs in clinical psychology, but many such courses are only peripherally relevant to clinical work. This book masterfully integrates the academic and the clinical in a way that suits the needs of both beginning graduate students and sophisticated clinicians and researchers. It is a major achievement--I learned a great deal from reading it."--Paul L. Wachtel, PhD, Distinguished Professor, Doctoral Program in Clinical Psychology, City College and the Graduate Center of the City University of New York-Table of Contents1. Introduction I. Early History of the Unconscious 2. Philosophical Precursors 3. Dynamic Psychiatry and Early Academic Psychology 4. Psychoanalysis II. Empirical Approaches to the Unconscious 5. The Beginnings of Experimental Work on Unconscious Processes 6. Unconscious Processes Move from Outcast to Mainstream 7. Empirical Tests of Unconscious Phenomena: The Effects of Subliminal Exposure 8. Attention Models Bring the Unconscious to the Mainstream 9. Unconscious Processes: From Mainstream to Central Tenet III. The Unconscious Rediscovered 10. The Normative Unconscious 11. Implicit Memory 12. Implicit Learning 13. Implicit Motivation 14. Automaticity 15. Attribution Theory 16. Affective Primacy 17. From Metaphor to Embodied Cognition IV. Computational Neuroscience and the Unconscious 18. Computational Models of the Mind 19. Massive Modularity 20. Parallel Distributed Processing 21. From Exaptation to Neural Reuse 22. A Model of the Unconscious: Theory and Implications for Psychotherapy Glossary References Index
£32.99
Guilford Publications The Theory and Practice of Item Response Theory
Book SynopsisNoted for addressing both the hows and whys of item response theory (IRT), this text has been revised and updated with the latest techniques (multilevel models, mixed models, and more) and software packages. Simple to more complex models are covered in consistently formatted chapters that build sequentially. The book takes the reader from model development through the fit analysis and interpretation phases that would be performed in practice. To facilitate understanding, common data sets are used across chapters, with the examples worked through for increasingly complex models. Exemplary model applications include free (BIGSTEPS, NOHARM, Facets, R packages) and commercial (BILOG-MG, flexMIRT, SAS, WINMIRA, SPSS, SYSTAT) software packages. The companion website provides data files and online-only appendices. New to This Edition *Chapter on multilevel models. *New material on loglinear models, mixed models, the linear logistic trait model, and fit statistics.Trade Review"The second edition of the IRT 'bible' is now even more accessible and useful for psychometricians and educational measurement specialists who are new to IRT or want to upgrade their knowledge of the field. It expands on the first edition in meaningful ways, primarily with regard to the implementation of IRT. Virtually every chapter has been expanded with examples of IRT analyses using R, SAS, and/or flexMIRT. A very helpful new chapter covers multilevel IRT models, and new appendices cover the LLTM and mixture Rasch models and discuss contemporary model fit indices and other recent topics. I have been using the first edition in my advanced measurement class since it was first published and it has been well received by my advanced undergraduates and graduate students; the new material in the second edition makes the book even better. In addition, this book will be very informative to measurement specialists--especially those who are not mathematically sophisticated--so that they can produce instruments that benefit from the enhanced measurement power of IRT.”--David J. Weiss, PhD, Department of Psychology, University of Minnesota "This is the most comprehensive and accessible text on IRT. De Ayala does a remarkable job of clearly describing fundamental IRT concepts, basic models, and even advanced models. The text's explanations do not heavily rely on equations; instead, de Ayala takes a conceptual approach and often utilizes graphs to illustrate key ideas. The second edition is up to date on the most frequently applied models and estimation procedures. It includes applied examples using popular IRT software, including R. I highly recommend this book for graduate-level courses focusing on measurement, psychometrics, and IRT, and as a guide for researchers using IRT."--Ojmarrh Mitchell, PhD, School of Criminology and Criminal Justice, Arizona State University "I love this book, and find it quite readable. What sets this text apart are its extensive exposition of technical details related to models and estimation and its detailed explanations of concepts. For example, I had never seen an author decompose the partial credit model and show piece-by-piece computation of the probabilities, which de Ayala does very well. This text is a great contribution to the field of IRT that will be invaluable for both class and personal use."--Karen M. Schmidt, PhD, Department of Psychology, University of Virginia "An excellent treatment of IRT that combines a clear exposition of theoretical concepts with applied examples that are relevant and useful."--Larry R. Price, PhD, Director of Methodology, Measurement, and Statistical Analysis, Texas State University-A must read for practitioners who use item response theory to calibrate test data. It also would serve as a tremendous resource for measurement researchers who daily navigate the circuitous paths of various IRT estimation software programs to analyze and understand their assessment data....Each of the 12 chapters is packed with annotated examples of how to use IRT estimation software and the subsequent output....The author does an excellent job of supplementing explanations of various models with calibration examples and output of multiple data sets using several different IRT calibration software programs including BILOG, MULTILOG, BIGSTEPS, and NOHARM....The book is more practitioner-oriented and applied than previous classic books that provide foundational understanding of IRT models and applications....Would be an excellent text for a graduate level IRT class in which the goal of the course would be to review dichotomous, polytomous, and multidimensional IRT models an how to estimate parameters in the various models using a variety of commercially available software....I would encourage all testing practitioners who work with various IRT models, as well as graduate students who plan to go into the measurement field, to seriously consider this book. It is an excellent resource….I applaud Dr. de Ayala for all the time and effort he has put into this book. He has clearly done the measurement field a great service. (on the first edition)--Journal of Educational Measurement, 12/21/2010ƒƒThe main strength of the text is in the descriptions and elaborations of the common IRT models....De Ayala also covers fundamental relationships that exist between models, such as the relationships between the parameters of the nominal response model and the partial credit model. In addition, the chapters contain practical advice for sample sizes commonly used with each model and how to interpret the parameters. De Ayala also presents results as statistical indices and graphics for various examples across different contexts, which allows readers the ability to see how the models work from several different perspectives....Does a good job of introducing common estimation strategies employed in IRT software packages. Especially helpful are the illustrations de Ayala includes with the code from IRT software packages. (on the first edition)--Psychometrika, 12/01/2010Table of ContentsSymbols and Acronyms 1. Introduction to Measurement - Measurement - Some Measurement Issues - Item Response Theory - Classical Test Theory - Latent Class Analysis - Summary 2. The One-Parameter Model - Conceptual Development of the Rasch Model - The One-Parameter Model - The One-Parameter Logistic Model and the Rasch Model - Assumptions Underlying the Model - An Empirical Data Set: The Mathematics Data Set - Conceptually Estimating an Individual’s Location - Some Pragmatic Characteristics of Maximum Likelihood Estimates - The Standard Error of Estimate and Information - An Instrument’s Estimation Capacity - Summary 3. Joint Maximum Likelihood Parameter Estimation - Joint Maximum Likelihood Estimation - Indeterminacy of Parameter Estimates - How Large a Calibration Sample? - Example: Application of the Rasch Model to the Mathematics Data, JMLE, BIGSTEPS - Example: Application of the Rasch Model to the Mathematics Data, JMLE, mixRasch - Validity Evidence - Summary 4. Marginal Maximum Likelihood Parameter Estimation - Marginal Maximum Likelihood Estimation - Estimating an Individual’s Location: Expected A Posteriori - Example: Application of the Rasch Model to the Mathematics Data, MMLE, BILOG-MG - Metric Transformation and the Total Characteristic Function - Example: Application of the Rasch Model to the Mathematics Data, MMLE, mirt - Summary 5. The Two-Parameter Model - Conceptual Development of the Two-Parameter Model - Information for the Two-Parameter Model - Conceptual Parameter Estimation for the 2PL Model - How Large a Calibration Sample? - Metric Transformation, 2PL Model - Example: Application of the 2PL Model to the Mathematics Data, MMLE, BILOG-MG - Fit Assessment: An Alternative Approach for Assessing Invariance - Example: Application of the 2PL Model to the Mathematics Data, MMLE, mirt - Information and Relative Efficiency - Summary 6. The Three-Parameter Model - Conceptual Development of the Three-Parameter Model - Additional Comments about the Pseudo-Guessing Parameter, Xⱼ - Conceptual Parameter Estimation for the 3PL Model - How Large a Calibration Sample? - Assessing Conditional Independence - Example: Application of the 3PL Model to the Mathematics Data, MMLE, BILOG-MG - Fit Assessment: Conditional Independence Assessment - Fit Assessment: Model Comparison - Example: Application of the 3PL Model to the Mathematics Data, MMLE, mirt - Assessing Person Fit: Appropriateness Measurement - Information for the Three-Parameter Model - Metric Transformation, 3PL Model - Handling Missing Responses - Issues to Consider in Selecting among the 1PL, 2PL, and 3PL Models - Summary 7. Rasch Models for Ordered Polytomous Data - Conceptual Development of the Partial Credit Model - Conceptual Parameter Estimation of the PC Model - Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE, flexMIRT - Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE, mirt - The Rating Scale Model - Conceptual Parameter Estimation of the RS Model - Example: Application of the RS Model to an Attitudes Towards Condoms Scale, JMLE, BIGSTEPS - Example: Application of the PC Model to an Attitudes Towards Condoms Scale, JMLE, mixRasch - How Large a Calibration Sample? - Information for the PC and RS Models - Metric Transformation, PC and RS Models - Summary 8. Non-Rasch Models for Ordered Polytomous Data - The Generalized Partial Credit Model - Example: Application of the GPC Model to a Reasoning Ability Instrument, MMLE, flexMIRT - Example: Application of the GPC Model to a Reasoning Ability Instrument, MMLE, mirt - Conceptual Development of the Graded Response Model - How Large a Calibration Sample? - Information for Graded Data - Metric Transformation, GPC and GR Models - Example: Application of the GR Model to an Attitudes Towards Condoms Scale, MMLE, flexMIRT - Example: Application of the GR Model to an Attitudes Towards Condoms Scale, MMLE, mirt - Conceptual Development of the Continuous Response Model - Summary 9. Models for Nominal Polytomous Data - Conceptual Development of the Nominal Response Model - Information for the NR Model - Metric Transformation, NR Model - Conceptual Development of the Multiple-Choice Model - How Large a Calibration Sample? - Example: Application of the NR Model to a General Science Test, MMLE, mirt - Summary 10. Models for Multidimensional Data - Conceptual Development of a Multidimensional IRT Model - Multidimensional Item Location and Discrimination - Item Vectors and Vector Graphs - The Multidimensional Three-Parameter Logistic Model - Assumptions of the MIRT Model - Estimation of the M2PL Model - Information for the M2PL Model - Indeterminacy in MIRT - Metric Transformation, M2PL Model - Example: Calibration of interpersonal engagement instrument, M2PL Model, sirt.noharam - Obtaining Person Location Estimates - Example: Calibration of interpersonal engagement instrument, M2PL Model, mirt - Example: Calibration of interpersonal engagement instrument, M2PL Model, flexMIRT - Summary 11. Linking and Equating - Equating Defined - Equating: Data Collection Phase - Equating: Transformation Phase - Example: Application of the Total Characteristic Function Equating Method, EQUATE - Example: Application of the Total Characteristic Function Equating Method, SNSequate - Example: Fixed-item and Concurrent Calibration Equating - Summary 12. Differential Item Functioning - Differential Item Functioning and Item Bias - Mantel–Haenszel Chi-Square - The TSW Likelihood Ratio Test - Logistic Regression - Example: DIF Analysis of vocabulary test, SAS CMH - Example: DIF Analysis of vocabulary test, mantelhaen.test and difR - Example: DIF Analysis of vocabulary test, SAS proc logistic - Example: DIF Analysis of vocabulary test, glm and difR - Summary 13. Multilevel IRT Models - Multilevel IRT–Two Levels - Example: Equivalence of the Rasch model and its Multilevel Model Parameterization, proc glimmix - Example: Rasch model estimation, lme4 - Person-Level Predictors for Items - Example: Person-Level Predictors for Items–DIF Analysis, proc glimmix - Example: Person-Level Predictors for Items–DIF Analysis, lme4 - Person-Level Predictors for Respondents - Example: Person-Level Predictors for Respondents–Nutrition Literacy, proc glimmix - Example: Person-Level Predictors for Respondents, lme4 - Item-Level Predictors for Items - Example: Item-Level Predictors for Items - Nutrition Literacy, proc glimmix - Example: Item-Level Predictors
£67.44
Guilford Publications Bayesian Statistics for the Social Sciences
Book SynopsisThe second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www.guilford.coTrade Review"This very practical book is well suited to social science students because of the examples used (large-scale surveys) and the coverage of methods that social scientists often need (latent variables, variable selection, and dealing with missing data). The book also covers some topics readers may not know they need--Bayesian model averaging and workflow, for example. Illustrations use RStan, perhaps the most flexible of programs for Bayesian modeling. Full integration of RStan input and output is provided in the text."--David Rindskopf, PhD, Distinguished Professor of Educational Psychology and Psychology, The Graduate Center, The City University of New York "Kaplan's book is the perfect follow-up for those whose curiosity has been piqued about Bayesian statistics. The many code examples will give users a head start for applying Bayes' theorem to their data. I highly appreciate that the author uses open-source software for all models. The topics are introduced with a rich amount of background information, some equations (but never too many), detailed explanations, and code examples. Empirical results are used to illustrate each topic."--Rens van de Schoot, PhD, Department of Methodology and Statistics, Utrecht University, Netherlands "An excellent resource for researchers at the graduate level or above with an interest in Bayesian statistics. Readers are skillfully guided through the process of statistical reasoning from a Bayesian perspective. This book is practical and minimally technical while also introducing readers to interesting historical and philosophical issues. What makes the book especially helpful is Kaplan’s careful balance of breadth and depth of coverage of key topics. In this timely second edition, important recent advances in Bayesian statistics are distilled and disseminated for researchers in the social sciences."--Sierra A. Bainter, PhD, Department of Psychology, University of Miami "This book has all the essential components to help readers, especially quantitative researchers in social sciences, understand and conduct Bayesian modeling. The second edition includes new material on recent Markov chain Monte Carlo (MCMC) methods, such as Hamiltonian MC, in addition to a range of other updates."--Insu Paek, PhD, Senior Scientist, Human Resources Research Organization "I recommend this book for providing a careful overview of the Bayesian framework, at a level accessible to a wide audience, with examples, code, and key references. Kaplan does a great job of covering so many different aspects of Bayesian modeling in a coherent way and presenting a number of substantive methods for analyzing complex data. I liked the comparisons and analogies to the frequentist approach."--Irini Moustaki, PhD, Department of Statistics, London School of Economics and Political Science, United Kingdom-A valuable read for researchers, practitioners, teachers, and graduate students in the field of social sciences….Extremely accessible and incredibly delightful….The wide breadth of topics covered, along with the author's clear and engaging style of writing and inclusion of numerous examples, should provide an adequate foundation for any psychologist wishing to take a leap into Bayesian thinking. Furthermore, the technical details and analytic aspects provided in all chapters should equip readers with enough knowledge to embark on Bayesian analysis with their own research data. (on the first edition)--Psychometrika, 03/01/2017Table of ContentsI. Foundations 1. Probability Concepts and Bayes' Theorem 1.1 Relevant Probability Axioms 1.1.1 The Kolmogorov Axioms of Probability 1.1.2 The Rényi Axioms of Probability 1.2 Frequentist Probability 1.3 Epistemic Probability 1.3.1 Coherence and the Dutch Book 1.3.2 Calibrating Epistemic Probability Assessment 1.4 Bayes' Theorem 1.4.1 The Monty Hall Problem 1.5 Summary 2. Statistical Elements of Bayes' Theorem 2.1 Bayes' Theorem Revisited 2.2. Hierarchical Models and Pooling 2.3 The Assumption of Exchangeability 2.4 The Prior Distribution 2.4.1 Non-informative Priors 2.4.2 Jeffreys' Prior 2.4.3 Weakly Informative Priors 2.4.4 Informative Priors 2.4.5 An Aside: Cromwell's Rule 2.5 Likelihood 2.5.1 The Law of Likelihood 2.6 The Posterior Distribution 2.7 The Bayesian Central Limit Theorem and Bayesian Shrinkage 2.8 Summary 3. Common Probability Distributions and Their Priors 3.1 The Gaussian Distribution 3.1.1 Mean Unknown, Variance Known: The Gaussian Prior 3.1.2 The Uniform Distribution as a Non-informative Prior 3.1.3 Mean Known, Variance Unknown: The Inverse-Gamma Prior 3.1.4 Mean Known, Variance Unknown: The Half-Cauchy Prior 3.1.5 Jeffreys' Prior for the Gaussian Distribution 3.2 The Poisson Distribution 3.2.1 The Gamma Prior 3.2.2 Jeffreys' Prior for the Poisson Distribution 3.3 The Binomial Distribution 3.3.1 The Beta Prior 3.3.2 Jeffreys' Prior for the Binomial Distribution 3.4 The Multinomial Distribution 3.4.1 The Dirichlet Prior 3.4.2 Jeffreys' Prior for the Multinomial Distribution 3.5 The Inverse-Wishart Distribution 3.6 The LKJ Prior for Correlation Matrices 3.7 Summary 4. Obtaining and Summarizing the Posterior Distribution 4.1 Basic Ideas of Markov Chain Monte Carlo Sampling 4.2 The Random Walk Metropolis–Hastings Algorithm 4.3 The Gibbs Sampler 4.4 Hamiltonian Monte Carlo 4.4.1 No-U-Turn (NUTS) Sampler 4.5 Convergence Diagnostics 4.5.1 Trace Plots 4.5.2 Posterior Density Plots 4.5.3 Auto-Correction Plots 4.5.4 Effective Sample Size 4.5.5 Potential Scale Reduction Factor 4.5.6 Possible Error Messages When Using HMC/NUTS 4.6 Summarizing the Posterior Distribution 4.6.1 Point Estimates of the Posterior Distribution 4.6.2 Interval Summaries of the Posterior Distribution 4.7 Introduction to Stan and Example 4.8 An Alternative Algorithm: Variational Bayes 4.8.1 Evidence Lower Bound (ELBO) 4.8.2 Variational Bayes Diagnostics 4.9 Summary II. Bayesian Model Building 5. Bayesian Linear and Generalized Models 5.1 The Bayesian Linear Regression Model 5.1.1 Non-informative Priors in the Linear Regression Model 5.2 Bayesian Generalized Linear Models 5.2.1 The Link Function 5.3 Bayesian Logistic Regression 5.4 Bayesian Multinomial Regression 5.5 Bayesian Poisson Regression 5.6 Bayesian Negative Binomial Regression 5.7 Summary 6. Model Evaluation and Comparison 6.1 The Classical Approach to Hypothesis Testing and Its Limitations 6.2 Model Assessment 6.2.1 Prior Predictive Checking 6.2.2 Posterior Predictive Checking 6.3 Model Comparison 6.3.1 Bayes Factors 6.3.2 The Deviance Information Criterion (DIC) 6.3.3 Widely Applicable Information Criterion (WAIC) 6.3.4 Leave-One-Out Cross-Validation 6.3.5 A Comparison of WAIC and LOO 6.4 Summary 7. Bayesian Multilevel Modeling 7.1 Revisiting Exchangeability 7.2 Bayesian Random Effects Analysis of Variance 7.3 Bayesian Intercepts as Outcomes Model 7.4 Bayesian Intercepts and Slopes as Outcomes Model 7.5 Summary 8. Bayesian Latent Variable Modeling 8.1 Bayesian Estimation for the CFA 8.1.1 Priors for CFA Model Parameters 8.2 Bayesian Latent Class Analysis 8.2.1 The Problem of Label-Switching and a Possible Solution 8.2.2 Comparison of VB to the EM Algorithm 8.3 Summary III. Advanced Topics and Methods 9. Missing Data From a Bayesian Perspective 9.1 A Nomenclature for Missing Data 9.2 Ad Hoc Deletion Methods for Handling Missing Data 9.2.1 Listwise Deletion 9.2.2 Pairwise Deletion 9.3 Single Imputation Methods 9.3.1 Mean Imputation 9.3.2 Regression Imputation 9.3.3 Stochastic Regression Imputation 9.3.4 Hot Deck Imputation 9.3.5 Predictive Mean Matching 9.4 Bayesian Methods for Multiple Imputation 9.4.1 Data Augmentation 9.4.2 Chained Equations 9.4.3 EM Bootstrap: A Hybrid Bayesian/Frequentist Methods 9.4.4 Bayesian Bootstrap Predictive Mean Matching 9.4.5 Accounting for Imputation Model Uncertainty 9.5 Summary 10. Bayesian Variable Selection and Sparsity 10.1 Introduction 10.2 The Ridge Prior 10.3 The Lasso Prior 10.4 The Horseshoe Prior 10.5 Regularized Horseshoe Prior 10.6 Comparison of Regularization Methods 10.6.1 An Aside: The Spike-and-Slab Prior 10.7 Summary 11. Model Uncertainty 11.1 Introduction 11.2 Elements of Predictive Modeling 11.2.1 Fixing Notation and Concepts 11.2.2 Utility Functions for Evaluating Predictions 11.3 Bayesian Model Averaging 11.3.1 Statistical Specification of BMA 11.3.2 Computational Considerations 11.3.3 Markov Chain Monte Carlo Model Composition 11.3.4 Parameter and Model Priors 11.3.5 Evaluating BMA Results: Revisiting Scoring Rules 11.4 True Models, Belief Models, and M-Frameworks 11.4.1 Model Averaging in the M-Closed Framework 11.4.2 Model Averaging in the M-Complete Framework 11.4.3 Model Averaging in the M-Open Framework 11.5 Bayesian Stacking 11.5.1 Choice of Stacking Weights 11.6 Summary 12. Closing Thoughts 12.1 A Bayesian Workflow for the Social Sciences 12.2 Summarizing the Bayesian Advantage 12.2.1 Coherence 12.2.2 Conditioning on Observed Data 12.2.3 Quantifying Evidence 12.2.4 Validity 12.2.5 Flexibility in Handling Complex Data Structures 12.2.6 Formally Quantifying Uncertainty List of Abbreviations and Acronyms References Author Index Subject Index
£55.09
Guilford Publications Ordinary Magic Second Edition
Book SynopsisFully updated with key advances in theory, methods, and research, the second edition of this landmark work features an expanded conceptual framework and a more global perspective on threats to human development, including climate change, war, poverty, racial injustice, and pandemics. Pioneering resilience expert Ann S. Masten illuminates the ordinary but powerful processes that allow many children exposed to trauma and adversity to survive, adapt, and even thrive. The book traces fundamental adaptive systems that have evolved and function synergistically at the neurobiological, psychological, social, community, and cultural levels. Using a range of case examples to illustrate complex concepts, Masten provides a cogent resilience framework for promoting healthy development in children at risk. New to This Edition *Advances in neurobiology, more international (including non-Western) findings and examples, new discussions of cultural identity development, up-to-date intervention research, and more. *Heightened focus on the interactions of multiple systems--including families, schools, culture, and communities--in supporting children's resilience. *Increased attention to the impact of structural inequality, poverty, and intergenerational trauma. *Coverage of rapidly emerging threats--the risks posed to children by multisystem, cascading disasters, such as the COVID-19 pandemic.
£52.24
Guilford Publications Categorical Data Analysis with Structural Equation Models
Book SynopsisMultivariate categorical outcomes, such as Likert scale responses and disease diagnoses, require specialized structural equation modeling (SEM) software to be analyzed properly. Providing needed skills for applied researchers and graduate students, this book leads readers from regression analysis with categorical outcomes to complex SEMs with latent variables for categorical indicators. The initial section sets the stage by demonstrating regression analyses for binary, ordered, or count outcomes using R, with comparable SAS code at the companion website. Chapters then reanalyze the same data using Mplus and R lavaan to show how univariate models for categorical outcomes can be estimated and interpreted with SEM programs. Subsequently, the book turns to multivariate models, discussing path models, confirmatory factor models, and latent variable path models with categorical outcomes. Concluding chapters cover advanced SEM with categorical outcomes, including growth models, latent class models, and survival models. Worked-through examples and annotated Mplus and lavaan code are featured throughout.
£59.84
SAGE Publications Inc Generalized Linear Models for Bounded and Limited
Book SynopsisThis book introduces researchers and students to the concepts and generalized linear models for analyzing quantitative random variables that have one or more bounds. Examples of bounded variables include the percentage of a population eligible to vote (bounded from 0 to 100), or reaction time in milliseconds (bounded below by 0). The human sciences deal in many variables that are bounded. Ignoring bounds can result in misestimation and improper statistical inference. Michael Smithson and Yiyun Shou′s book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.Trade ReviewThis book provides a thorough and accessible look at an important class of statistical models. It communicates intuition well and shows through numerous examples that understanding how to analyze bounded outcome variables is useful for applied researchers. -- Jeff HardenThe authors are leaders in the world-wide effort to extend and tailor the generalized linear model to variables that are bounded and not normally distributed. The discussion of models for data recorded as proportions is worth the price of admission. -- Paul JohnsonTable of Contents1. Introduction and Overview Overview of this Book The Nature of Bounds on Variables The Generalized Linear Model Examples 2. Models for Singly-Bounded Variables GLMs for singly-bounded variables Model Diagnostics Treatment of Boundary Cases 3. Models for Doubly-Bounded Variables Doubly-Bounded Variables and \Natural" Heteroskedasticity The Beta Distribution: Definition and Properties Modeling Location and Dispersion Estimation and Model Diagnostics Treatment of Cases at the Boundaries 4. Quantile Models for Bounded Variables Introduction Quantile regression Distributions for Doubly-Bounded Variables with Explicit Quantile Functions The CDF-Quantile GLM 5. Censored and Truncated Variables Types of censoring and truncation Tobit models Tobit Model Example Heteroskedastic and Non-Gaussian Tobit Models 6. Extensions and Conclusions Extensions and a General Framework Absolute Bounds and Censoring Multi-Level and Multivariate Models Bayesian Estimation and Modeling Roads Less Traveled and the State of the Art References
£29.44
Taylor & Francis Inc Longitudinal Data Analysis
Book SynopsisAlthough many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data.After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data. Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters. Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature. They seamlessly blend theory with applications and include examples and case studies from various disciplines. Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future. Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.Trade ReviewThe scope is remarkable, and the degree of integration and polish is admirable. The contributors include many of the most innovative researchers in the field, and happily, many of the clearest writers as well. … a lively text with clear-eyed positions and well-argued recommendations on how to analyze data of specific structures. … [material] is all accessible, well explained, and well illustrated using examples … a very good book … an excellent resource for a graduate class for statisticians or biostatisticians, and as a reference for quantitatively minded researchers.—Statistics in Medicine, 2011The volume’s editors have assembled a world-class panel of contributors; many have made seminal contributions to the field (this includes the editors themselves). Immediately apparent is the uniformity of notation and writing style not typically found in volumes of this kind. The editors clearly have taken great care to ensure a whole document rather than a disjointed patchwork typical of similar collections. Chapters reflect contributor diversity while suppressing distracting idiosyncrasies. … Experienced researchers and those new to the field will find useful material here. … several chapters provide fresh insights. For graduate students and new researchers, the book provides a useful introduction and comprehensive reference material for the topics it covers. Case studies and software enable readers to implement some methods described in the book, with supplemental datasets and programs appearing on a useful website. … A strong inaugural volume for Chapman & Hall’s new series on modern statistical methods, Longitudinal Data Analysis provides an outstanding model for future entries.—Biometrics, September 2010… Longitudinal Data Analysis is the first book to collect and sort through many of the most important developments. The authors make clear the assumptions of the statistical methods and their consequences. Coupled with an abundance of examples, the book guides the practitioner about when to apply one method as opposed to another. The book has remarkable breadth and contains material that would likely be new even to those that analyze longitudinal data on a regular basis. Longitudinal Data Analysis would be useful for applied statisticians looking to expand their analytical toolkit and statistical researchers familiar with the area but looking for a good reference. …an excellent text for a special topics course for Ph.D. students in statistics. It has a good balance of statistical theory and applications, with a large number of real data examples and case studies to illustrate how to use the methods described therein. …a well organized, excellent overview of the state of the art in modeling longitudinal data and would make a useful supplement to the library of anyone that analyzes this type of data.—Journal of the American Statistical Association, June 2010…a concise but complete encyclopedia on longitudinal data analysis. The editors have made a great effort to produce a volume providing a comprehensive and up-to-date view of the theory and application of longitudinal data analysis. … One of the strengths of the book is the organizational structure and the fact that the book has been written by well-known experts in the field. … I find this book very useful for statisticians and researchers in many fields where the interest relies on studying the change of an outcome or multiple outcomes over time. Many of the chapters include examples and case studies in different disciplines and some of this material can be found in the website of this book (http://www.biostat.harvard.edu/ fitzmaur/lda). I would like to congratulate the editors and all the contributing authors for preparing this comprehensive handbook on many interesting and complementary aspects of the theory and applications of longitudinal data analysis. This handbook will have, without any doubt, an important place on the shelf of those statisticians and applied researchers working with longitudinal data.—Journal of Applied Statistics, Vo. 36, No. 10, October 2009This is public-service broadcasting at its best. Many of the leading internationally recognized experts in the field have been assembled to write a series of expository articles on an important area of modern statistics. … Care has clearly been taken to make the book hang together—it’s not like some ‘edited tomes’ consisting of a set of papers stapled together. There is a mixture of theory and applications with real data, some of which is available on a website. In my opinion the book will be a must-have for anyone seriously involved with repeated measures or longitudinal data.—International Statistical Review, 2009Other longitudinal data books do not have the breadth of this one. … I highly recommend this book to anyone interested in learning about modern methods for longitudinal data analysis. I think it would make a particularly good book for a Ph.D.-level reading course or as a supplement to a longitudinal data textbook in a graduate-level course. I especially recommend this book to statistical researchers, as it makes a great reference book.—Journal of Biopharmaceutical Statistics, Issue 4, 2009Table of ContentsIntroduction and Historical Overview. Parametric Modeling of Longitudinal Data. Nonparametric and Semiparametric Methods for Longitudinal Data. Joint Models for Longitudinal Data. Incomplete Data. Index.
£154.56
Guilford Publications Transformative Research and Evaluation
Book SynopsisFrom distinguished scholar Donna M. Mertens, this book provides a framework for making methodological decisions and conducting research and evaluations that promote social justice. The transformative paradigm has emerged from—and guides—a broad range of social and behavioral science research projects with communities that have been pushed to the margins, such as ethnic, racial, and sexual minority group members and children and adults with disabilities. Mertens shows how to formulate research questions based on community needs, develop researcher–community partnerships grounded in trust and respect, and skillfully apply quantitative, qualitative, and mixed-methods data collection strategies. Practical aspects of analyzing and reporting results are addressed, and numerous sample studies are presented. Student- and Instructors-Friendly Features Include:*Commentary on the sample studies that explains what makes them transformative.*Explanations of key concepts related to oppression, social justice, and the role of research and evaluation.*Questions for Thought to stimulate critical self-reflection and discussion.*Advance chapter organizers and chapter summaries.Trade ReviewI've been searching for the right text for my master's-level course entitled Research in Community Settings. I've just finished reading this book, and I think it is terrific! It is just right for a course in which students will be engaging in community-university partnerships as well as learning to think critically about research.--Robb Travers, PhD, Department of Psychology, Wilfrid Laurier University, Waterloo, Ontario, CanadaAs the world becomes more interconnected and diverse, our research methods need to keep up with changes. Mertens writes beautifully about conducting research in the service of social justice, using a transformative lens and focusing clearly on each step in the process. This book illustrates why Mertens is so highly regarded for her writing that links research methods theory with practice. I highly recommend this book for researchers and graduate students across the social and human sciences.--John W. Creswell, Department of Educational Psychology, University of Nebraska-LincolnMertens focuses on using research and evaluation to transform people’s lives and communities. Including historically excluded and marginalized people in the processes of generating and using knowledge is transformative not only for research participants, but also for the researchers and evaluators who engage with them. The genius of Mertens's comprehensive approach resides in the way she makes transformative inquiry accessible and inclusive yet intellectually robust and methodologically rigorous. She invites us into the transformative process, changing the ways we both think about and conduct research and evaluation. This book is for anyone open to seriously engaging the transformative power of inclusive inquiry.--Michael Q. Patton, author of Utilization-Focused Evaluation, Fourth EditionMertens gets to the heart of how to ground research and evaluation projects in a social justice framework, in line with multiple disciplinary perspectives that embrace a transformative paradigm. This book will challenge students--including those who have not previously questioned their basic societal beliefs and assumptions--to explore diversity and oppression issues in new ways. I appreciated the inclusion of truly diverse groups, including ethnocultural minority groups and others, in the discussions of the various research and evaluation projects. The international examples and perspectives were very refreshing, too.--Gary W. Harper, Department of Psychology, DePaul University With the increase in awareness of diversity, a book like this one is long overdue. This is the first book I have read that comprehensively discusses how to approach evaluation using strategies that are culturally appropriate and inclusive of diverse communities. Mertens provides a holistic and inclusive way of thinking about research methodology. She does a wonderful job of explaining the transformative paradigm in lay language.--Katrina L. Bledsoe, Associate Project Director, Walter R. McDonald & Associates, Washington, DCThis important book assembles the complex array of procedures, perspectives, and priorities associated with transformative research and evaluation. It blends the many voices and illustrations available in the literature to assist the reader in identifying what is possible in this important new paradigm. Significant cross-cultural and international examples of studies that truly warrant the label of 'transformative' are incorporated.--Melvin E. Hall, Department of Educational Psychology, Northern Arizona University- This is the first book that specifically addresses research methodology for the transformative perspective....Although the book prioritizes an educational focus, it has relevance beyond education and scholars from management disciplines can add a practical and penetrating reading to their research toolbox....The audience encompasses novice researchers and evaluators, advanced undergraduate and beginning graduate students, more experienced teachers and evaluators....This book reminds us all that social research is embedded within a system of values and the myth of objective and value-free research should be discharged. It demonstrates how to blend science and action in an attempt to solve specific problems and promote social change. --Organizational Research Methods, 11/1/2008Table of ContentsIntroductionThe Intersection of Applied Social Research and Program EvaluationParadigmsRationale for the Transformative ParadigmBreadth of Transformative Paradigm Applicability1. Resilience, Resistance, and Complexities That Challenge In This ChapterHuman Rights AgendaThe Transformative Paradigm as a Metaphysical UmbrellaNeed for Transformative Research and EvaluationExamples of Transformative Research and EvaluationExamples of Shifting ParadigmsNeed for the Transformative Paradigm and Scholarly LiteratureNeed for the Transformative Paradigm and Public PolicyComplexities That ChallengeEthical ImpetusStriving for Improved ValiditySummary2. The Transformative Paradigm: Basic Beliefs and Commensurate Theories In This ChapterParadigms and Basic Belief SystemsThe Transformative Paradigm and Its Basic Belief SystemsTheories Commensurate with the Transformative ParadigmPolitics and PowerSummary3. Self, Partnerships, and RelationshipsIn This ChapterHuman Relations as Factors Contributing to Research Validity and RigorKnowing YourselfKnowing Yourself in Relation to the CommunityCultural CompetenceStrategies for Developing Relationships/PartnershipsTypes of Partnerships/RelationshipsChallenges in Relationships/PartnershipsRecognizing the Complexity of Culturally Competent WorkPurposes of PartnershipsExamples of Points of Interaction in the Research ProcessBuilding CapacitySummary4. Developing the Focus of Research/Evaluation StudiesIn This ChapterPurposes for the Gathering of Information at This Stage of the InquirySources That Support the Need for Research and EvaluationTheoretical FrameworksMaking Use of SourcesSummary5. A Transformative Research and Evaluation Model In This ChapterCyclical Models: Indigenous PeoplesCyclical Model: PARCyclical Models: Immigrant CommunitiesShort-Term Research and EvaluationTypes of Transformative Research and EvaluationTransformative Intervention ApproachesSummary6. Quantitative, Qualitative, and Mixed Methods In This ChapterMixed- and Multiple-Methods ApproachesCase StudiesEthnographyPhenomenology Participatory Action ResearchAppreciative InquiryExperimental and Quasi-Experimental DesignsSurvey Design and Correlational and Causal-Comparative StudiesGender Analysis: A Mixed-Methods Approach with Potential Transfer to OtherGroups That Experience DiscriminationRigor in the Process of Research and EvaluationSummary7. Participants: Identification, Sampling, Consent, and ReciprocityIn This ChapterSocial Justice: Dimensions of Diversity and Cultural CompetenceRationale for Sampling StrategiesRecruitment of ParticipantsProtection of Human Participants and Ethical Review BoardsSummary8. Data-Collection Methods, Instruments, and StrategiesIn This ChapterReliability and Validity/Dependability and CredibilityLanguage as a Critical IssuePlanning Data-Collection StrategiesSpecific Data-Collection StrategiesSummary9. Data Analysis and InterpretationIn This ChapterTransformative Theories as Guides to Data Analysis and InterpretationInvolving the Community in Analysis and In
£47.49
Stata Press Multilevel and Longitudinal Modeling Using Stata,
Book SynopsisMultilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes.Table of ContentsVolume II: V. Models for categorical responses 10. Dichotomous or binary responses 11. Ordinal responses 12. Nominal responses and discrete choice VI. Models for counts 13. Counts VII. Models for survival or duration data; Introduction to models for survival or duration data (part VII) 14. Discrete-time survival 15. Continuous-time survival VIII. Models with nested and crossed random effects 16. Models with nested and crossed random effects
£66.49
Stata Press Maximum Likelihood Estimation with Stata, Fifth
Book SynopsisMaximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s commands for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming.The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml’s noteworthy features: Linear constraints Four optimization algorithms (Newton–Raphson, DFP, BFGS, and BHHH) Observed information matrix (OIM) variance estimator Outer product of gradients (OPG) variance estimator Huber/White/sandwich robust variance estimator Cluster–robust variance estimator Complete and automatic support for survey data analysis Direct support of evaluator functions written in Mata When appropriate options are used, many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator.In later chapters, you will learn how to take advantage of Mata, Stata's matrix programming language. For ease of programming and potential speed improvements, you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata.In the final chapter, the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit, linear regression, Weibull regression, the Cox proportional hazards model, random-effects regression, and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model, a model that is not available otherwise in Stata.The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book, users will be able to write their own estimation commands---commands that look and behave just like the official estimation commands in Stata.Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use, you need this book.Table of ContentsTheory and practice The likelihood-maximization problem Likelihood theory The maximization problem Estimation with mlexp Syntax Normal linear regression Initial values Restricted parameters Robust standard errors The probit model Specifying derivatives Additional estimation features Wrapping up Introduction to ml The probit mode Normal linear regression Robust standard errors Weighted estimation Other features of method-gf0 evaluators Limitations Overview of ml The terminology of ml Equations in ml Likelihood-evaluator methods Tools for the ml programmer Common ml options Maximizing your own likelihood functions Appendix: More about scalar parameters Method lf The linear-form restrictions Examples The importance of generating temporary variables as doubles Problems you can safely ignore Nonlinear specifications The advantages of lf in terms of execution speed Methods lf0, lf1, and lf2 Comparing these methods Outline of evaluators of methods lf0, lf1, and lf2 Summary of methods lf0, lf1, and lf2 Examples Methods d0, d1, and d2 Comparing these methods Outline of method d0, d1, and d2 evaluators Summary of methods d0, d1, and d2 Panel-data likelihoods Other models that do not meet the linear-form restrictions Debugging likelihood evaluators ml check Using the debug methods ml trace Setting initial values ml search ml plot ml init Interactive maximization The iteration log Pressing the Break key Maximizing difficult likelihood functions Final results Graphing convergence Redisplaying output Writing do-files to maximize likelihoods The structure of a do-file Putting the do-file into production Writing ado-files to maximize likelihoods Writing estimation commands The standard estimation-command outline Outline for estimation commands using ml Using ml in noninteractive mode Advice Writing ado-files for survey data analysis Program properties Writing your own predict command Mata-based likelihood evaluators Introductory examples Evaluator function prototypes Utilities Random-effects linear regression Ado-file considerations Mata’s moptimize() function Introductory examples Restricting the estimation sample Estimation preliminaries Estimation Results Estimation commands Regression redux Other examples The logit model The probit model Normal linear regression The Weibull model The Cox proportional hazards model The random-effects regression model The seemingly unrelated regression model A bivariate Poisson regression model Epilogue Syntax of mlexp Syntax of ml Syntax of moptimize() Likelihood-evaluator checklists Method lf Method d0 Method d1 Method d2 Method lf0 Method lf1 Method lf2 Listing of estimation commands The logit model The probit model The normal model The Weibull model The Cox proportional hazards model The random-effects regression model The seemingly unrelated regression model A bivariate Poisson regression model References
£56.99
Templeton Foundation Press,U.S. Research On Altruism & Love
Book SynopsisResearch on Altruism and Love is a compendium of annotated bibliographies reviewing literature and research studies on the nature of love. An essay introduces each of the annotated bibliographies.A variety of literature either directly related to science-and-love issues or supporting literature for those issues is covered in the Religious Love Interfaces with Science section. This annotated bibliography is unique in that it approaches the field from a decidedly religious perspective. It includes classical expositions of love that continue to influence contemporary scholars, including Platos' work on eros, the work and words of Jesus, Aristotle, Augustine of Hippo, Martin Luther, Kierkegaard, and Ghandi, among others. The contemporary discussion includes Anders Nygren's theological arguments in his classic, Agape and Eros; Pitirim Sorokin; and others. An issue that often emerges in this literature is the question of the nature and definition of love.A second annotated bibliography features current empirical research in the field of Personality and Altruism, with a focus on social psychology. Among the topics covered are the altruistic personality, altruistic behavior, empathy, helping behavior, social responsibility, and volunteerism. Methodologies are diverse, and studies include experiments, local and national surveys, naturalistic observation, and combinations of these.The Evolutionary Biology annotated bibliography covers the most significant works on altruism and love in the field of biology and evolutionary psychology.The fourth and final annotated bibliography in this volume is entitled Sociology of Faith-Based Volunteerism. Here the focus is on literature on the interface of helping behavior and religious organizations, as well as major pieces on voluntary associations.
£21.59
Springer Nature Switzerland AG Image Schemas and Concept Invention: Cognitive, Logical, and Linguistic Investigations
Book SynopsisIn this book the author's theoretical framework builds on linguistic and psychological research, arguing that similar image-schematic notions should be grouped together into interconnected family hierarchies, with complexity increasing with regard to the addition of spatial and conceptual primitives. She introduces an image schema logic as a language to model image schemas, and she shows how the semantic content of image schemas can be used to improve computational concept invention. The book will be of value to researchers in artificial intelligence, cognitive science, psychology, and creativity.Table of ContentsCreating Concepts: Considerations from Psychology and Artificial Intelligence.- Image Schemas: Spatiotemporal Relationships Used as Conceptual Building Blocks.- Formal Structure: Image Schemas as Families of Theories.- Introducing ISLFOL: A Logical Language for Image Schemas.- Modelling Conceptualisations: Combining Image Schemas to Model Event Conceptualisations.- Generating Concepts: How Image Schemas Can Help Guide Computational.- Conceptual Blending.- Defining Concepts: Experiment on the Role of Image Schemas in Object Conceptualisation.- Identifying Image Schemas: Experiment Towards Automatic Image Schema Extraction.- Discussion and Conclusions.
£80.99
Johns Hopkins University Press Persuasion and Healing
Book Synopsis
£28.35
American Psychological Association Qualitative Research in Psychology
Book SynopsisThis updated edition of Qualitative Research in Psychology brings together a diverse group of scholars to illuminate the value that qualitative methods bring to studying psychological phenomena in depth and in context.Table of ContentsPreface to the Second EditionPaul M. Camic Part 1. Laying the Foundations: The Pluralistic Approaches of Qualitative Inquiry Chapter 1. Going Around the Bend and Back: Qualitative Inquiry in Psychological ResearchPaul M. Camic Chapter 2. Choosing a Qualitative Method: A Pragmatic, Pluralistic PerspectiveChris Barker and Nancy Pistrang Chapter 3. Narrative in Qualitative Psychology: Approaches and Methodological ConsequencesMichael Bamberg Chapter 4. Information Power: Sample Content and Size in Qualitative StudiesKirsti Malterud, Volkert Siersma, and Ann Dorrit Guassora Part 2. Methodologies for Qualitative Researchers: Helping to Understand the World Around Us Chapter 5. Participation, Power, and Solidarities Behind Bars: A 25-Year Reflection on Critical Participatory Action Research on College in PrisonMichelle Fine, Maria Elena Torre, Kathy Boudin, and Cheryl Wilkins Chapter 6. Doing Narrative ResearchMichael Murray Chapter 7. Discursive Psychology: Capturing the Psychological World as It UnfoldsJonathan Potter Chapter 8. Interpretative Phenomenological AnalysisJonathan A. Smith and Megumi Fieldsend Chapter 9. Situational Analysis: Mapping Relationalities in PsychologyRachel Washburn, Adele Clarke, and Carrie Friese Chapter 10. What Lies Beneath? Eliciting Grounded Theory Through the Analysis of Video-Recorded Verbal and Nonverbal InteractionsColin Griffiths Chapter 11. Under Observation: Line Drawing as an Investigative Method in Focused EthnographyAndrew Causey Part 3. Developing and Expanding Qualitative Research Chapter 12. Into the Ordinary: Lessons Learned From a Mixed-Methods Study in the Homes of People Living With DementiaEmma Harding, Mary Pat Sullivan, Keir X. X. Yong, and Sebastian J. Crutch Chapter 13. Using Qualitative Research for Intervention Development and EvaluationLucy Yardley, Katherine Bradbury, and Leanne Morrison Chapter 14. Qualitative Meta-Analysis: Issues to Consider in Design and Review Kathleen M. Collins and Heidi M. Levitt
£63.90
American Psychological Association Essentials of Thematic Analysis
Book SynopsisThe brief, practical texts in the Essentials of Qualitative Methods series introduce social science and psychology researchers to key approaches toqualitative methods, offering exciting opportunities to gather in-depth qualitative data and to develop rich and useful findings. In this book, Gareth Terry and Nikki Hayfield introduce readers to reflexive thematic analysis, a method of analyzing interview and focus group transcripts, qualitative survey responses, and other qualitative data. Central to this method is the recognition that we are all situated in a particular context, and that we see and speak from that position.This leads researchers to produce knowledge that represents situated truths, providinginsights intopeople''s perspectives on a given topic.About the Essentials of Qualitative Methods book series: Even for experienced researchers, selecting and correctly applying the right method can be challenging. In this groundbreaking series, leading experts in qualitative methods provide clear, crisp, and comprehensive descriptions of their approach, including its methodological integrity, and its benefits and limitations. Each book includes numerous examples to enable readers to quickly and thoroughly grasp how to leverage these valuable methods.Trade ReviewThis is a clear and accessible guide to thematic analysis that will greatly appeal to students and researchers who are developing their analytic practice. The authors walk the reader through the steps of analysis, which are illustrated with clear commentary and helpful examples. -- Abigail Locke, PhD, Professor of Critical Social & Health Psychology, Keele University, Keele, UKTerry and Hayfield demystify reflexive thematic analysis with analogies from everyday life and offer hacks for fixing common mistakes. This book is a must-read for students and researchers conducting thematic analysis. -- Adam Jowett, PhD, School of Psychological, Social & Behavioural Sciences, Coventry University, Coventry, UK
£21.84
American Psychological Association Essentials of Interpretative Phenomenological
Book SynopsisA step-by-step guide to a research method that investigates how people make sense of their lived experience in the context of their personal and social worlds.Trade ReviewThis is an excellent guide to the theory and practice of interpretative phenomenological analysis; it is well written, carefully organized, engaging, and accessible. The authors have a remarkable knack for anticipating and effectively answering the questions that are typically raised by novice researchers as well as guiding them through the intricacies of qualitative research. -- Steen Halling, PhD, Professor Emeritus, Department of Psychology, Seattle University, Seattle, WA, United StatesFrom setting the background context to interpretative phenomenological analysis (IPA) through to offering exemplar studies, this book gives a thorough introduction to the steps a novice researcher will need to follow to conduct their first study using this method. The clear and concise summary also serves as an excellent refresher for anyone returning to IPA as an analytic method after some time away. An excellent addition to qualitative methodology bookshelves. -- Jane Montague, PhD, School of Psychology, University of Derby, Derby, United KingdomTable of ContentsSeries Foreword—Clara E. Hill and Sarah Knox Chapter 1: What Is Interpretative Phenomenological Analysis? A Note on Terminology Theoretical Underpinnings of IPA Chapter 2: Designing an IPA Study Choosing a Topic and a Research Question Determining What Type of Data to Collect Sampling and Recruiting Participants Practical and Ethical Considerations Chapter 3: Collecting Data Rationale for an Interview Guide How to Design an Interview Guide Conducting the Interview Transcription Chapter 4: Analyzing the Data: Starting With the First Case Step 1. Reading and Exploratory Notes Step 2. Formulating Experiential Statements Step 3. Finding Connections and Clustering Experiential Statements Step 4. Compiling the Table of Person Experiential Themes Some More Thoughts on Clustering and Compiling One Case or More Than One Case? Chapter 5: Cross-Case Analysis Chapter 6: Writing Up the Study Results Section Other Sections of an IPA Manuscript Chapter 7: Variations on the Method and More Complex Designs Chapter 8: Methodological Integrity Chapter 9: Summary and Conclusions Deciding Whether IPA Is the Right Methodology for You Concluding Words Appendix: Exemplar Studies References Index About the Authors About the Series Editors
£21.84
