Psychological methodology Books
Taylor & Francis Ltd Progress in Self Psychology V 19 Explorations in
Book SynopsisThe contributors to Explorations in Self Psychology, volume 19 of the Progress in Self Psychology series, wrestle with two interrelated questions at the nexus of contemporary discussions of technique: How authentic and relationally invested should the self psychologically informed analyst be, and what role should self-disclosure play in the treatment process? The responses to these questions embrace the full range of clinical possibilities. Dudley and Walker argue that empathically based interpretation precludes self-disclosure whereas Miller argues in favor of authentic self-expression and against the self psychologist''s frustrating attempt to decenter from frustration or anger. Consideration of the utility of a consistently empathic stance continues with Weisel-Barth''s clinical presentation and the discussions that it elicits about management of her patient''s primary destructiveness. Lenoff''s critical rereading of Kohut''s Examination of the Relationship BetweenTable of ContentsPart I: Theory.Dudley, Walker, "To Be or Not To Be?": The Question of Authenticity, Therapist Subjectivity, and the Role of Interpretive Moments in Treatment. Lenoff, Consequences of "Empathy": Rereading Kohut's (1959) "Examination of the Relationship Between Mode of Observation and Theory." Martinez, Twinship Selfobject Experience and Homosexuality. Miller, Empathy and Self Expression: Confessions of a Sometimes Angry Self Psychologist. Ornstein, Survival and Recovery: Psychoanalytic Reflections. Rieveschl, Cowan, Selfhood and the Dance of Empathy. Shoshani, Shoshani, Psychic Survival versus Psychic Freedom: Reflections on Symington's Theory of Narcissism. Part II: Clinical. Smaller, Working with Adolescents: A Time for "Reconsideration." Stern, A Case of Sexual (Dis-)Orientation with Thoughts on Sexuality, Sexual Orientation, and Psychoanalysis. Shane, Wiesel-Barth, Aron, & Stolorow, Panel: The Role of the Relationship in the Therapeutic Process. Part III: Applied.Childs, Death in Venice: A Selfobject Perspective on Thomas Mann's Homolimerence. Elson, John Adams and Benjamin Rush Exchange Dreams. Rass, Making Contact with the Perception World of a Child: Undetected Disabilities in Sensory Motor Integration and the Effects on the Development of Self-Esteem. Schulz, Tragic Man and Narcissistic Disturbance in the Films of Ingmar Bergman. Wada, The Applicability of Self Psychology to Psychotherapy with the Elderly: With Emphasis on Twinship Selfobject Needs and Empathy as a Mode of Observation. Part IV: Personal Memoir.Goldberg, A Personal and Professional Reminiscence of Heinz Kohut.
£80.74
Cambridge University Press The Cambridge Handbook of Research Methods and
Book SynopsisIn a time where new research methods are constantly being developed and science is evolving, researchers must continually educate themselves on cutting-edge methods and best practices related to their field. The second of three volumes, this Handbook provides comprehensive and up-to-date coverage of a variety of issues important in developing, designing, and collecting data to produce high-quality research efforts. First, leading scholars from around the world provide an in depth explanation of various advanced methodological techniques. In section two, chapters cover general important methodological considerations across all types of data collection. In the third section, the chapters cover self-report and behavioral measures and their considerations for use. In the fourth section, various psychological measures are covered. The final section of the handbook covers issues that directly concern qualitative data collection approaches. Throughout the book, examples and real-world research efforts from dozens of different disciplines are discussed.
£58.89
Cambridge University Press Pragmatism and Methodology
Book SynopsisDesigned for social scientists working with big data sets, this book maps out the cycle of research, from epistemology and ethical questions to data collection and analysis. It introduces a unique mixed methods approach by integrating qualitative and quantitative methods. This book is also available as Open Access on Cambridge Core.Trade Review'This book offers a more than welcome clarification of the role of pragmatism as a framework for several methodological contexts. It is very important for qualitative, mixed methods, and big data research, and for the methodological foundation of psychological research. It is clearly written and very accessible for various audiences.' Uwe Flick, Freie Universität Berlin, Germany'We must congratulate the authors for this bountiful attempt to cross the divide between the quantitative and qualitative research communities. Their choice of philosophic pragmatism as the vehicle for this uniting is right on target, and their concern with ethical implications adds vital dimension to the ongoing dialogues. This is a vitally needed reflection.' Kenneth J. Gergen, Swarthmore College, USA'This remarkable book insists that social research should work on expanding human possibilities in ethical, imaginative, and responsive ways. Building on early American pragmatism, the authors argue that complex multifarious human interests can only be addressed by mixing different research methods. Their stance greatly contributes to current debates on truth, post-truth, epistemology, and useful knowledge, among other fundamental issues.' Ivana Markova, University of Stirling, UK'This book should be used in introductory research methods courses, to complement the standard texts. Students will enjoy this book, which presents a clear and elegant introduction to research methodology from a pragmatist perspective, with a leaning toward mixed methodology.' Fathali M. Moghaddam, Georgetown University, USA'Informative, insightful, and eminently practical. Gillespie, Glăveanu, and de Saint Laurent capture the spirit of pragmatist thinking, redevelop it in the context of contemporary problems, and present it to us in a way that is timely and reinvigorates social scientific inquiry.' Kieran O'Doherty, University of Guelph, CanadaTable of ContentsPreface; 1. Pragmatism; 2. Epistemology: How We Know; 3. Theory: What We Know; 4. Creating Questions; 5. Eliciting and Transforming Data; 6. Mixing Qualitative And Quantitative Methods; 7. Multi-Resolution Research; 8. Ethics; 9. Expanding Human Possibilities; References; Index.
£22.99
Cambridge University Press Foundations of MATLAB Programming for Behavioral Sciences
£109.55
Cambridge University Press Foundations of MATLAB Programming for Behavioral Sciences
£37.99
Cambridge University Press Research Methods and Applied Statistics
£114.38
Cambridge University Press Research Methods and Applied Statistics
£29.99
Cambridge University Press Pursuing Competitive Grants
£28.00
Cambridge University Press Direction Dependence Analysis
Book Synopsis
£30.48
Cambridge University Press Rethinking Clinical Research
Book SynopsisChallenging traditional methodologies, this analysis uncovers biases in health research. Covering fundamental principles, tools, and ethics, it offers in-depth case examples. Aimed at students and professionals, it promotes critical evaluation for a nuanced understanding of evidence production.
£30.99
Cambridge University Press Politicians Manipulating Statistics
Book SynopsisExplore Billig and Marinho's highly original study of politicians misusing statistics, misleading the public and manipulating statisticians. This book also highlights how the British and French statistical agencies aim to combat this increasingly serious problem. Tailored for all audiences, it is a clearly written, insightful, and witty work.
£25.99
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 Handbook of Regression Modeling in People
Book SynopsisDespite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best swiss army knife' we have for answering these kinds of questions.This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a sweet spot' where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wTable of Contents1. The Importance of Regression in People Analytics. 2. The Basics of the R Programming Language. 3. Statistics Foundations. 4. Linear Regression for Continuous Outcomes. 5. Binomial Logistic Regression for Binary Outcomes. 6. Multinomial Logistic Regression for Nominal Category Outcomes. 7. Ordinal Logistic Regression for Ordered Category Outcomes. 8. Modeling Explicit and Latent Hierarchical Structure in Data. 9. Survival Analysis for Modeling the Occurrence of Singular Events Over Time. 10. Alternative Technical Approaches in R and Python. 11. Power Analysis to Estimate Required Sample Sizes for Inferential Modeling. 12. Further Exercises for Practice.
£65.54
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
£17.99
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.
£128.25
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
£60.00
Taylor & Francis IBM SPSS Statistics 27 Step by Step
Book SynopsisIBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference, seventeenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Table of ContentsPreface; 1. An Overview of IBM® SPSS® 2A. IBM SPSS Statistics Processes for PC 2B. IBM SPSS Statistics Processes for Mac 3. Creating and Editing a Data File 4. Managing Data 5. Graphs and Charts: Creating and Editing 6. Frequencies 7. Descriptive Statistics 8. Crosstabulation and χ2 Analyses 9. The Means Procedure 10. A Priori Power Analysis: What Sample Size Do I Need? 11. Bivariate Correlation 12. The t Test Procedure 13. The One-Way ANOVA Procedure 14. General Linear Model: Two-Way ANOVA 15. General Linear Model: Three-Way ANOVA 16. Simple Linear Regression 17. Multiple Regression Analysis 18. Nonparametric Procedures 19. Reliability Analysis 20. Multidimensional Scaling 21. Factor Analysis 22. Cluster Analysis 23. Discriminant Analysis 24. General Linear Models: MANOVA and MANCOVA 25. G.L.M.: Repeated-Measures MANOVA 26. Logistic Regression 27. Hierarchical Log-Linear Models 28. Nonhierarchical Log-Linear Models 29. Residuals: Analyzing Left-Over Variance; Data Files Glossary References
£65.54
Taylor & Francis Ltd The Handbook of Teaching Qualitative and Mixed
Book SynopsisThe Handbook of Teaching Qualitative and Mixed Research Methods: A Step-by-Step Guide for Instructors presents diverse pedagogical approaches to teaching 71 qualitative and mixed methods.These tried-and-true methods are widely applicable to those teaching and those being trained in qualitative and mixed-methods research. The methods for data collection cover ethics, sampling, interviewing, recording observations of behavior, Indigenous and decolonizing methods and methodologies as well as visual and participatory methods. Methods for analyzing data include coding and finding themes, exploratory and inductive analysis, linguistic analysis, mixed-methods analysis, and comparative analysis. Each method has its own 1,500-word lesson (i.e., chapter) written by expert methodologists from around the globe. In these lessons, contributors give the reader a brief history of the method and describe how they teach it by including their best practicesâwith succinct, step-by-step instructionsâfocusing on student-centered experiential and active learning exercises.This comprehensive, one-of a-kind text is an essential reference for instructors who teach qualitative and/or mixed methods across the Social and Behavioral Sciences and other related disciplines, including Anthropology, Sociology, Education, and Health/Nursing research.Table of ContentsSection 1. Research Ethics 1. Teaching Ethics in Qualitative Research 2. Obtaining Consent: Why, What and How; Section 2. Sampling in Qualitative Research 3. Teaching Qualitative Sample Size Estimation 4. Non-probability Sampling 5. Collecting Data from Networked Populations: Snowball and Respondent-driven Sampling 6. Sampling Design for Qualitative Research: Teaching Theoretical Sampling for Cross-cultural Research; Section 3. Interviewing as Data Collection 7. Teaching Interviewing: How to Guide Conversations for Qualitative Research 8. Cognitive Interviewing for Questionnaire Design and Evaluation 9. Ethnographic Interviewing: The Life of Language among Families 10. Free-list Interviewing 11. Qualitative Interviewing in the Study of Culture 12. Personal Network Analysis 13. A Strategy to Practice Moderating in Focus Groups 14. Focus Group Interviewing Method; Section 4. Observations as Data Collection 15. Participant Observation: How to Be a Participant and Observer at the Same Time 16. Observation as Data Collection 17. How Behavior Observations Enrich Qualitative Researchers 18. Ethnography: A First Overarching Look 19. Rapid Ethnographic Assessment 20. Teaching Field Notes 21. Ethnographic Writing 22. Teaching Reflexivity and Positionality 23. Autoethnography: Sensitizing the "I"24. Social Media Data Analysis 25. Shooting Video for Social Science Research; Section 5. Indigenous and Decolonizing Methods 26. A Brief Introduction to Critical Indigenous Research Methodologies 27. Ethically Engaging Marginalized Communities in Research 28. Talanoa Research Methodology: Strategies for Teaching an Indigenous Methodology 29. Centering Indigenous Practice: Talking Circles as Participatory Action Research 30. Indigenous Data Sovereignty and Governance 31. A Black Feminist Life History Method: How to Center Black Women’s Voices 32. Sister–girl Talk: A Method for Group Interviewing Black Women 33. Community-engaged Partnership Research 34. Theater as Ethnographic Method; Section 6. Visual and Participatory Methods 35. Photovoice and Participatory Visual Elicitation Methods 36. Teaching Ethnographic Photovoice as a Method for Studying Marginalized Groups 37. Digital Storytelling as a Tool for Critical Narrative Research 38. Introduction to Interactive 3D e-Participatory Methods through the Lens of Urban Planning Projects 39. Art-based Methods for Qualitative Research with Younger Children 40. Reflecting on Place: Group Sketch Mapping; Section 7. Building Blocks and Basis of Analysis 41. Transcription: Understanding Its Analytical Power 42. Handling Multilingual Data: Transcription and Translation 43. Teaching Theme Identification 44. Finding Themes Using the Cutting and Sorting Approach: Guided Exercises with Interview Data 45. Creating Visual Variables: A First Step in Systematic Analysis of Videotaped Data 46. Developing a Codebook for a Case Participant 47. Coding in Action: Applying Codes at Various Levels 48. Teaching Intercoder Reliability Assessment 49. Teaching Qualitative Content Analysis 50. Context Matters: Conducting Word-based Analysis in Qualitative Research; Section 8. Grounded Theory, Phenomenology and Narrative Analysis 51. Doing Grounded Theory: Key Steps for Design, Data Collection, and Analysis 52. Phenomenological Interpretation: A Creative Lesson on Lived Experience 53. Narrative Analysis 54. Narrative Analysis: The Narrated, Non-narrated, and the Disnarrated 55. Teaching Meaning and Idioms in Life History Narratives; Section 9. Linguistic Analysis 56. Corpus Linguistics and Its Role in Qualitative Research 57. Teaching about Variation 58. Analyzing Language as Actions-in-interaction 59. Teaching Indexicality through a Focus on Identity 60. Teaching Discourse Analysis Using Political Texts 61. Using Critical Discourse Analysis to Understand Texts in Context; Section 10. Network Analysis and Cultural Domain Analysis 62. Mixed Semantic Network Analysis to Explore Discoursive and Cultural Landscapes 63. Walking in Your Footsteps: Using Qualitative Methods in Whole Network Analysis 64. Mapping the Structure of a Cultural Domain: Cultural Domain Analysis 65. Measuring Cultural Consonance; Section 11. Modeling and Comparative Analysis 66. Topic Models: Modeling from Text Corpora 67. Agent-based Modeling in Mixed Methods Research 68. Teaching Meta-themes: A Mixed Methods Approach 69. Teaching Comparative Ethnography: Two Examples from the Environmental Governance Field 70. Uncovering Causal Complexity with Qualitative Comparative Analysis 71. Concepts before Numbers: Teaching Effective Calibration Practices for Qualitative Comparative Analysis
£39.89
Taylor & Francis Ltd Body Image in Eating Disorders
Body Image in Eating Disorders explores issues relating to the prevention, clinical diagnosis, and psychological treatment of distortions of body image in eating disorders. It presents a multifactorial model of indicators for diagnosis and treatment, considering psychological, sociocultural, and family indicators. Based on original empirical research with women and girls suffering from eating disorders, the book draws attention to limitations and dilemmas related to psychological diagnosis and treatment of people with eating disorders including anorexia readiness syndrome, bulimia, and bigorexia. The book proposes an integrative psychodynamic approach to the diagnosis and treatment of body image disorders and presents case studies illustrating examples of application of integration of psychodynamic therapy and psychodrama in psychological treatment of young people suffering from eating disorders. It considers risk factors including abnormal body image for the
£37.04
Taylor & Francis Ltd Undertaking Capstone and Final Year Projects in
Book SynopsisUndertaking Capstone and Final Year Projects in Psychology serves a seminal purpose in guiding its readers to create a capstone project. The text employs traditional and emerging methodologies and methods in order to posit an exhaustive approach that the psychology students can adopt to see their project to fruition.The text aims at fortifying the reader's skills through the structure of its chapters as they begin to work on their capstone or final year project. The chapters collectively explore the varied aspects that are involved in the completion of a final year project, that is, beginning from the inception of the idea to laying the foundation, designing the project, analysing the data, and, finally, presenting the findings. The text guides the reader through each step and provides further guidance on approaching the idea, coming up with the research question, positioning it within the epistemological and ontological context, and constructing the theoreticTable of Contents1. The foundation. 2. Developing Skills. 3. Getting ready, set, go. 4. The interest. 5. Positioning. 6. Methodology spectrum. 7. Methods. 8. The analysis (empirical only). 9. Presentation. 10. Making impact with your research.
£31.34
Taylor & Francis Longitudinal Structural Equation Modeling
Book SynopsisLongitudinal Structural Equation Modeling is a comprehensive resource that reviews structural equation modeling (SEM) strategies for longitudinal data to help readers determine which modeling options are available for which hypotheses. This accessibly written book explores a range of models, from basic to sophisticated, including the statistical and conceptual underpinnings that are the building blocks of the analyses. By exploring connections between models, it demonstrates how SEM is related to other longitudinal data techniques and shows when to choose one analysis over another. Newsom emphasizes concepts and practical guidance for applied research rather than focusing on mathematical proofs, and new terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues and each chapter also includes examples of each model type, descriptions of model extensions, commeTrade Review"This is a "must have" volume on examining change from a SEM perspective. It is thoughtfully put together beginning with a number of basic principles/concepts in the latent variable approach to change (e.g., longitudinal measurement invariance, linear and nonlinear growth). It then moves into a number of intermediate approaches (cross-lagged panel models, latent class, latent transition, and latent growth mixture models). The final chapters provide more advanced topics (time series and dynamic structural equation models, survival analysis, and missing data). The various topics covered are extensive, clearly presented, and well supported with examples and references that readers can use to work through the analyses."Ronald H. Heck, University of Hawaii"This book offers a schematic, comprehensive, and well-structured resource for understanding, applying, and teaching most of the techniques related to Longitudinal SEM. The book follows a specific flow based on the difficulties of the topics. It starts with a clear introduction to latent variable modeling, then moves on widely used longitudinal applications (e.g., measurement invariance, cross-lagged panel models), and finally offers chapters on more advanced and recent topics (e.g., LST, Mixture Modeling, and DSEM). The structure of the book also allows the reader to directly access the topics of interest. Both from an applied and teaching perspective, it is difficult to think of a more complete and better structured book on longitudinal SEM."Enrico Perinelli, University of Trento (Italy)"I've cited Jason Newsom's first edition of Longitudinal Structural Equation Modeling many times, and his second edition continues the tradition of clear, accessible presentations that cover both the basics of analysis and modeling strategies for longitudinal data and extra details that experts would appreciate. An impressive, authoritative work."Rex Kline, Concordia UniversityTable of ContentsContentsList of FiguresList of TablesPreface to the Second EditonPreface to the First EditionAcknowledgementsExample Data SetsChapter 1. Review of Some Key Latent Variable PrinciplesChapter 2. Longitudinal Measurement InvarianceChapter 3. Structural Models for Comparing Dependent Means and Proportions Chapter 4. Fundamental Concepts of Stability and Change Chapter 5. Cross-Lagged Panel ModelsChapter 6. Latent State-Trait ModelsChapter 7. Linear Latent Growth Curve Models Chapter 8. Nonlinear Latent Growth Curve ModelsChapter 9. Nonlinear Latent Growth Curve ModelsChapter 10. Latent Class and Latent TransitionChapter 11. Growth Mixture Models Chapter 12. Intensive Longitudinal Models: Time Series and Dynamic Structural Equation Models Chapter 13. Survival Analysis Models Chapter 14. Missing Data and Attrition Appendix A: Notation Appendix B: Why Does the Single Occasion Scaling Constraint Approach Work? Appendix C: A Primer on the Calculus of ChangeGlossaryIndex
£68.39
Taylor & Francis Ltd Applied Regularization Methods for the Social
Book SynopsisResearchers in the social sciences are faced with complex data sets in which they have relatively small samples and many variables (high dimensional data). Unlike the various technical guides currently on the market, Applied Regularization Methods for the Social Sciences provides and overview of a variety of models alongside clear examples of hands-on application. Each chapter in this book covers a specific application of regularization techniques with a user-friendly technical description, followed by examples that provide a thorough demonstration of the methods in action.Key Features: Description of regularization methods in a user friendly and easy to read manner Inclusion of regularization-based approaches for a variety of statistical analyses commonly used in the social sciences, including both univariate and multivariate models Fully developed extended examples using multiple software packages, including R, SAS, and SPSS
£42.74
Taylor & Francis Principles and Methods of Social Research
Book SynopsisThrough a multi-methodology approach, Principles and Methods of Social Research, Fourth Edition covers the latest research techniques and designs and guides readers toward the design and conduct of social research from the ground up. Applauded for its comprehensive coverage, the breadth and depth of content of this new edition is unparalleled.Explained with updated applied examples useful to the social, behavioral, educational, and organizational sciences, the methods described are relevant to contemporary researchers. The underlying logic and mechanics of experimental, quasi-experimental, and non-experimental research strategies are discussed in detail. Introductory chapters cover topics such as validity and reliability furnish readers with a firm understanding of foundational concepts. The book has chapters dedicated to sampling, interviewing, questionnaire design, stimulus scaling, observational methods, content analysis, implicit measures, dyadic and group methods,Table of ContentsAcknowledgements and DedicationPrefacePart IIntroduction to Social Research MethodsChapter 1. Basic ConceptsScience and Daily LifeFrom Theory, Concept, or Idea to OperationRole of Theory in Scientific InquiryConclusion and OverviewReferencesChapter 2. Internal and External ValidityCausationDistinguishing Internal and External ValidityBasic Issues of Internal ValidityBasic Issues of External ValidityConclusionReferencesChapter 3. Measurement ReliabilityClassical Measurement TheoryContemporary Measurement TheoryConclusionReferencesChapter 4. Measurement ValidityTypes of Measurement ValidityThe Multitrait-Multimethod MatrixThreats to Measurement ValidityConclusionReferencesPart II. Research Design Strategies: Experiments, Quasi-Experiments, and NonexperimentsChapter 5. Designing Experiments: Variations on the BasicsBasic Variations in Experimental DesignExpanding the Number of Experimental TreatmentsBlock Designs: Incorporating a Nonexperimental FactorRepeated Measure Designs and CounterbalancingConclusionReferencesChapter 6. Constructing Laboratory ExperimentsSteps for Constructing an ExperimentTypes of Experimental ManipulationsManipulation and Attention ChecksAssignment of Participants to Conditions: Randomization ProceduresRealism and Engagement in an ExperimentRole-Playing Simulations and Analogue ExperimentsConclusionReferencesChapter 7. External Validity of Laboratory ExperimentsGeneralizability Across ParticipantsExperimenter Expectancy and BiasThree Faces of External ValidityConclusionReferencesChapter 8. Conducting Experiments Outside the LaboratoryResearch Settings and Issues of ValidityConstructing a Field ExperimentThe Internet as a Site for Experimental ResearchConclusionReferencesChapter 9. Quasi-Experiments and Applied ResearchQuasi-experimental Methods in Applied ContextsQuasi-experimental DesignsThe Use of Archival Data in Longitudinal ResearchConclusionReferencesChapter 10. Nonexperimental Research: Correlational DesignBivariate Correlation and RegressionMultiple RegressionUsing Regression to Test MediationUses and Misuses of Correlational AnalysisConclusionReferencesChapter 11. Advanced Multivariate Correlational DesignMultilevel ModelsStructural Equation ModelsModeling Longitudinal DataConclusionReferencesPart IIIData Collecting MethodsChapter 12. Survey Studies: Design and SamplingSelection vs. AssignmentCensus and Survey BasicsRandom SamplingNonrandom SamplingOther Sampling IssuesTypes of Survey StudiesMissing DataConclusionReferencesChapter 13. Systematic Observational MethodsThree Aspects of NaturalismObserver Involvement in the Naturalistic SettingCoding ObservationsConclusionReferencesChapter 14. Content AnalysisContent Analysis BasicsConducting a Content AnalysisSummary of the General ParadigmRepresentative ExamplesConclusionReferencesChapter 15. InterviewingModes of AdministrationDeveloping the InterviewConducting the InterviewGroup Interviews and Focus GroupsConclusionReferencesChapter 16. Construction of Questionnaires and Rating ScalesQuestionnairesConstructing Rating ScalesConclusionReferencesChapter 17. Scaling Stimuli: Social PsychophysicsScaling StimuliStimulus Scaling TechniquesMultidimensional Scaling ModelsConclusionReferencesChapter 18. Indirect and Implicit Measures of Cognition and AffectIndirect MeasuresInformation Processing: Attention and MemoryPriming: Processing Without Awareness or IntentSocial PsychophysiologyConclusionReferencesChapter 19. Methods for Assessing Dyads and GroupsDyadic DesignsDesigns to Study Group StructuresDesigns to Study Multiple GroupsMeasuring Group Process and OutcomesConclusionReferencesPart IVConcluding PerspectivesChapter 20. Synthesizing Research Results: Meta-AnalysisReplicability of FindingsMeta-AnalysisStages in the Meta-Analysis ProcessInterpreting the Meta-AnalysisConclusionReferencesChapter 21. Social Responsibility and Ethics in Social ResearchEthics of Research PracticesThe Regulatory Context of Research Involving Human ParticipantsEthics of Data ReportingEthical Issues Related to the Products of Scientific ResearchConclusionReferencesGlossary
£80.74
Taylor & Francis Factor Analysis and Dimension Reduction in R
Book SynopsisFactor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods.The social scientist''s toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book's coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularizTable of ContentsPART I: MULTIVARIATE ANALYSIS OF FACTORS AND COMPONENTS Chapter 1: Factor Analysis: Purposes and Research Questions Chapter 2: Dealing with the Assumptions and Limitations of Factor Analysis Chapter 3: Fundamental Concepts and Functions in Factor Analysis Chapter 4: Quick Start: Principal Axis Factoring (FA) in R Chapter 5: Quick Start: Confirmatory Factor Analysis in R Chapter 6. Quick Start: Principal Components Analysis (PCA) in R Chapter 7: Oblique and Higher Order Factor Models Chapter 8: Factor Analysis for Binary, Ordinal, and Mixed Data Chapter 9: FA in Greater Detail Chapter 10: PCA in Greater Detail PART II: ADDITIONAL TOOLS FOR DIMENSION REDUCTION Chapter 11: Sixteen Additional Methods for Dimension Reduction (DimRed) Chapter 12: Metrics for Comparing and Evaluating Dimension Reduction Models Chapter 13: Recipes: An Alternative System for Dimension Reduction Chapter14: Factor Analysis for Neural Models Chapter 15: Factor Analysis for Time Series Data APPENDICES I. Datasets used in this volume 2. Introduction to R and RStudio
£63.64
Taylor & Francis Ltd Regression Analysis
Book SynopsisThis thoroughly practical and engaging textbook is designed to equip students with the skills needed to undertake sound regression analysis without requiring high-level math.Regression Analysis covers the concepts needed to design optimal regression models and to properly interpret regressions. It details the most common pitfalls, including three sources of bias not covered in other textbooks. Rather than focusing on equations and proofs, the book develops an understanding of these biases visually and with examples of situations in which such biases could arise. In addition, it describes how âholding other factors constantâ actually works and when it does not work. This second edition features a new chapter on integrity and ethics, and has been updated throughout to include more international examples. Each chapter offers examples, exercises, and clear summaries, all of which are designed to support student learning to help towards producing responsible research.This is the textbook the author wishes he had learned from, as it would have helped him avoid many research mistakes he made in his career. It is ideal for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand regressions. Additional digital supplements are available at: www.youtube.com/channel/UCenm3BWqQyXA2JRKB_QXGyw.Table of Contents1. Introduction 2. Regression analysis basics 3. Essential tools for regression analysis 4. What does "holding other factors constant" mean? 5. Standard errors, hypothesis tests, p-values, and aliens 6. What could go wrong when estimating causal effects? 7. Strategies for other regression objectives 8. Methods to address biases 9. Other methods besides Ordinary Least Squares 10. Time-series models 11. Some really interesting research 12. How to conduct a research project 13. The ethics of regression analysis 14. Summarizing thoughts Appendix of background statistical tools
£37.99
Taylor & Francis Ltd Bayesian Multilevel Models for Repeated Measures
Book SynopsisThis comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many random effects'. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking Table of ContentsPrefaceAcknowledgments1. Introduction: Experiments and Variables2. Probabilities, Likelihood, and Inference3. Fitting Bayesian Regression Models with brms4. Inspecting a ‘Single Group’ of Observations using a Bayesian Multilevel Model5. Comparing Two Groups of Observations: Factors and Contrasts6. Variation in Parameters (‘Random Effects’) and Model Comparison7. Comparing Many Groups, Interactions, and Posterior Predictive Checks8. Varying Variances, More about Priors, and Prior Predictive Checks9. Quantitative Predictors and their Interactions with Factors10. Logistic Regression and Signal Detection Theory Models11. Multiple Quantitative Predictors, Dealing with Large Models, and Bayesian ANOVA12. Multinomial and Ordinal Regression13. Writing up Experiments: An investigation of the Perception of Apparent Speaker Characteristics from Speech Acoustics
£47.49
Taylor & Francis Statistical Power Analysis
Book SynopsisStatistical Power Analysis explains the key concepts in statistical power analysis and illustrates their application in both tests of traditional null hypotheses (that treatments or interventions have no effect in the population) and in tests of the minimum-effect hypotheses (that the population effects of treatments or interventions are so small that they can be safely treated as unimportant). It provides readers with the tools to understand and perform power analyses for virtually all the statistical methods used in the social and behavioral sciences.Brett Myors and Kevin Murphy apply the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. This book starts with a review of the key concepts that underly statistical power. It goes on to show how to perform and interpret power analyses, and the ways to use them to diagnose and plan research. We discuss the uses of power analysis in correlation and Table of Contents1. The Power of Statistical Tests 1.1 The Structure of Statistical Tests 1.1.1 Null Hypotheses vs. Nil Hypotheses1.1.2 Understanding Conditional Probability 1.2 The Mechanics of Power Analysis 1.2.1 Understanding Sampling 1.2.2 Distributions d vs. delta vs. g 1.3 Statistical Power of Research in the Social and Behavioral Sciences Power and the Replication Crisis 1.4 Using Power Analysis The Meaning of Statistical Significance 1.5 Hypothesis Tests vs. Confidence Intervals Accuracy in Parameter Estimation 1.6 What Can We Learn from a Null Hypothesis Test? 1.7 Summary 2. A Simple and General Model for Power Analysis 2.1 The General Linear Model, the F Statistic, and Effect Size 2.1.1 Effect Size 2.2 Understanding Linear Models 2.3 The F Distribution and Power 2.3.1 Confidence Intervals for PV and d 2.4 Using the Noncentral F Distribution to Assess Power 2.5 Translating Common Statistics and ES Measures into F 2.5.1 Worked Example – Hierarchical Regression 2.5.2 Worked Examples Using the d Statistic 2.6 Defining Large, Medium and Small Effects 2.7 Nonparametric and Robust Statistics 2.8 From F to Power Analysis 2.9 Analytic and Tabular Methods of Power Analysis 2.10 Using the One-Stop F Table 2.11 Simple and General Software for Power Analysis 2.12 R code for Power Analysis for Traditional and Modern Hypothesis Tests 2.13 Summary 3. Power Analyses for Minimum-Effect Tests 3.1 Nil Hypothesis Testing 3.2 The Nil Hypothesis is Almost Always Wrong 3.2.1 Polar Bear Traps: Why Type I Error Control is a Bad Investment 3.3 The Nil may not be True, but it is Often Fairly Accurate 3.4 Minimum-Effect tests as Alternatives to Traditional Null Hypothesis Tests 3.5 Sometimes a Point Hypothesis is also a Range Hypothesis 3.6 How do you Know the Effect Size? 3.7 Testing the Hypothesis that Treatment Effects are Negligible 3.8 Using the One-Stop Tables to Assess Power for Minimum-Effect Tests 3.9 A Worked Example of Minimum-Effect Testing 3.10 Type I Errors in Minimum-Effect Tests 3.11 Summary 4. Using Power Analyses 4.1 Estimating the Effect Size 4.2 Using the One-Stop Tables and the R Code/Shiny Web app to Perform Power Analyses 4.2.1 Worked Example: Calculating F-equivalents and Power 4.3 Four Applications of Statistical Power Analysis 4.4 Calculating Power 4.5 Determining Sample Sizes 4.6 A Few Simple Approximations for Determining Sample Size Needed 4.7 Determining the Sensitivity of Studies 4.8 Determining Appropriate Decision Criteria 4.8.1 Finding a Sensible Alpha 4.9 Post-Hoc Power Analysis Should be Avoided 4.10 Summary 5. Correlation and Regression 5.1 The Perils of Working with Large Samples 5.2 Multiple Regression 5.2.1 Testing Minimum-Effect Hypotheses in Multiple Regression 5.3 Power in Testing for Moderators 5.3.1 Power Analysis for Moderators 5.4 Implications of Low Power in Tests for Moderators 5.5 If You Understand Regression, You Will Understand (Almost) Everything 5.6 Summary 6. t-Tests and the One-Way Analysis of Variance 6.1 The t Test 6.2 The t distribution vs the Normal Distribution 6.3 Independent Groups t Test 6.3.1 Determining an Appropriate Sample Size 6.4 One- Versus Two Tailed Tests 6.4.1 Re-analysis of Smoking Reduction Treatments: One-Tailed Tests 6.5 Repeated Measures or Dependent t Test 6.6 The Analysis of Variance 6.6.1 Retrieving Effect Size Information from F Ratios 6.7 Which Means Differ? 6.8 Designing a One-way ANOVA Study 6.9 Summary 7. Multi-Factor ANOVA Designs 7.1 The Factorial Analysis of Variance 7.1.2 Calculating PV from F and df in Multi-Factor ANOVA: Worked Example 7.2 Factorial ANOVA from Means and Standard Deviations 7.2.1 Reconstructing ANOVA results from descriptive statistics: A Worked Example 7.2.2 Eta squared vs. partial eta squared 7.3 General Design Principles for Multifactor ANOVA 7.4 Fixed, Mixed and Random Models 7.5 Summary 8. Studies with Multiple Observations for Each Subject: Repeated-Measures and Multivariate Analyses 8.1 Randomized Block ANOVA: An Introduction to Repeated Measures Designs 8.2 Independent Groups versus Repeated Measures 8.3 Complexities in Estimating Power in Repeated-Measures Designs 8.4 Mixed Designs: Split Plot Factorial ANOVA 8.4.1 Estimating Power for a Split Plot Factorial ANOVA 8.5 Power for Within-Subject vs. Between-Subject Factors 8.6 Split-Plot Designs with Multiple Repeated-Measures Factors 8.7 The Multivariate Analysis of Variance 8.8 Summary 9. Power Analysis for Multilevel Studies 9.1 What do Multilevel Analyses Tell You? 9.2 The Multilevel Equation 9.3 Are Multilevel Models Necessary? – The Intraclass Correlation 9.4 An Illustration of Multilevel Analysis 9.5 Remember, It’s All Regression 9.6 Effect Sizes in Multilevel Analysis 9.6.1 R code for obtaining R2 and pseudo-R2 estimates 9.7 Power for What? 9.8 Using Changes in Model Fit as a Basis for Power Analysis in Multilevel Modeling 9.9 R code for calculating critical chi squared values and power for minimum-effect comparisons of models 9.10 Sample Size – Some General Guidance 9.11 Summary 10. The Implications of Power Analyses 10.1 Tests of the Traditional Null Hypothesis 10.2 Tests of Minimum-Effect Hypotheses 10.2.1 Type I Errors in Minimum-Effect Tests Revisited 10.2.2 Statistical Power and the Replication Crisis 10.3 Power Analysis: Benefits, Costs, and Implications for Hypothesis Testing 10.4 Direct Benefits of Power Analysis 10.4.1 Is HARKing a Serious Problem? 10.5 Indirect Benefits of Power Analysis 10.6 Costs Associated With Power Analysis 10.7 Implications of Power Analysis: Can Power be too High? 10.8 Summary 11. Appendix A – Translating Statistics into F and PV Values 12 Appendix B - One Stop F Table 13. Appendix C- One Stop PV Table 14. Appendix D – dferr Needed for Power of .80 for Nil and Minimum-Effect Hypothesis Tests
£49.39
Taylor & Francis Making Sense of Statistics
Book SynopsisMaking Sense of Statistics, Eighth Edition, is the ideal introduction to the concepts of descriptive and inferential statistics for students undertaking their first research project. It presents each statistical concept in a series of short steps, then uses worked examples and exercises to enable students to apply their own learning.It focuses on presenting the why, as well as the how of statistical concepts, rather than computations and formulas. As such, it is suitable for students from all disciplines regardless of mathematical background. Only statistical techniques that are almost universally included in introductory statistics courses, and widely reported in journals, have been included. This conceptual book is useful for all study levels, from undergraduate to doctoral level across disciplines. Once students understand and feel comfortable with the statistics presented in this book, they should find it easy to master additional statistical concepts.Table of ContentsIntroduction: What is Research?; Part A: The Research Context 1. The Empirical Approach to Knowledge 2. Types of Empirical Research 3. Scales of Measurement 4. Descriptive, Correlational, and Inferential Statistics; Part B: Sampling 5. Introduction to Sampling 6. Variations on Random Sampling 7. Sample Size 8. Standard Error of Mean and Central Limit Theorem; Part C: Descriptive Statistics 9. Frequencies, Percentages, and Proportions 10. Shapes of Distributions 11. The Mean: An Average 12. Mean, Median, and Mode 13. Range and Interquartile Range 14. Standard Deviation 15. Z Score; Part D: Correlational Statistics 16. Correlation 17. Pearson r 18. Scattergram 19. Coefficient of Determination 20. Multiple Correlation; Part E: Inferential Statistics 21. Introduction to Null Hypothesis 22. Decisions About the Null Hypothesis 23. Limits of Significance Testing and Practical Implications; Part F: Means Comparison 24. Introduction to the t Test 25. Independent Samples t Test 26. Dependent Samples t Test 27. One Sample t Test 28. Reporting the Results of t Tests: Display of Outcomes 29. One-Way ANOVA 30. Two-Way ANOVA; Part G: Predictive Significance 31. Chi-Square 32. Effect Size 33. Simple and Multiple Linear Regressions; Appendix A. Computations Appendix B. Notes on Interpreting Pearson r and Linear Regression Appendix C. Table of Random Numbers
£118.75
Taylor & Francis Ltd Making Sense of Statistics
Book SynopsisMaking Sense of Statistics, Eighth Edition, is the ideal introduction to the concepts of descriptive and inferential statistics for students undertaking their first research project. It presents each statistical concept in a series of short steps, then uses worked examples and exercises to enable students to apply their own learning.It focuses on presenting the why, as well as the how of statistical concepts, rather than computations and formulas. As such, it is suitable for students from all disciplines regardless of mathematical background. Only statistical techniques that are almost universally included in introductory statistics courses, and widely reported in journals, have been included. This conceptual book is useful for all study levels, from undergraduate to doctoral level across disciplines. Once students understand and feel comfortable with the statistics presented in this book, they should find it easy to master additional statistical concepts.Table of ContentsIntroduction: What is Research?; Part A: The Research Context 1. The Empirical Approach to Knowledge 2. Types of Empirical Research 3. Scales of Measurement 4. Descriptive, Correlational, and Inferential Statistics; Part B: Sampling 5. Introduction to Sampling 6. Variations on Random Sampling 7. Sample Size 8. Standard Error of Mean and Central Limit Theorem; Part C: Descriptive Statistics 9. Frequencies, Percentages, and Proportions 10. Shapes of Distributions 11. The Mean: An Average 12. Mean, Median, and Mode 13. Range and Interquartile Range 14. Standard Deviation 15. Z Score; Part D: Correlational Statistics 16. Correlation 17. Pearson r 18. Scattergram 19. Coefficient of Determination 20. Multiple Correlation; Part E: Inferential Statistics 21. Introduction to Null Hypothesis 22. Decisions About the Null Hypothesis 23. Limits of Significance Testing and Practical Implications; Part F: Means Comparison 24. Introduction to the t Test 25. Independent Samples t Test 26. Dependent Samples t Test 27. One Sample t Test 28. Reporting the Results of t Tests: Display of Outcomes 29. One-Way ANOVA 30. Two-Way ANOVA; Part G: Predictive Significance 31. Chi-Square 32. Effect Size 33. Simple and Multiple Linear Regressions; Appendix A. Computations Appendix B. Notes on Interpreting Pearson r and Linear Regression Appendix C. Table of Random Numbers
£45.59
Taylor & Francis Ltd Applied Statistics for the Social and Health
Book SynopsisCovering basic univariate and bivariate statistics and regression models for nominal, ordinal, and interval outcomes, Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with fundamental skills to estimate, interpret, and publish quantitative research using contemporary standards.Reflecting the growing importance of Big Data in the social and health sciences, this thoroughly revised and streamlined new edition covers best practice in the use of statistics in social and health sciences, draws upon new literatures and empirical examples, and highlights the importance of statistical programming, including coding, reproducibility, transparency, and open science.Key features of the book include: interweaving the teaching of statistical concepts with examples from publicly available social and health science data and literature excerpts; thoroughly integrating the teaching of statistiTrade Review"This book is a teacher’s dream. Not only does it provide a comprehensive discussion of statistics as it is actually practiced by working researchers in the social and health sciences, it also provides detailed guidance on how to carry out such analyses using Stata, one of the best available and widely used statistical packages. Finally it provides numerous examples drawn directly from the research literature. I know of no other book like it."Richard Campbell, Emeritus Professor of Public Health, University of Illinois at Chicago, USA "I taught a year-long graduate level statistics course to first year sociology, education, policy analysis and demography Ph.D. students for more than 40 years. I always pieced together material from several different textbooks, software manuals, and published articles, since no one volume met the need to provide entering graduate students with appropriate content coverage at the right difficulty level. The 2nd edition of Rachel Gordon’s book, with its excellent update, meets these needs better than any other volume I have seen."George Farkas, Distinguished Emeritus Professor of Education, University of California, Irvine, USA "I have used the first edition of Rachel Gordon’s Applied Statistics for the Social and Health Sciences in my multidisciplinary graduate-level statistics course since I began teaching it around 5 years ago. Gordon’s ability to translate complex information into practical, real-world examples that are applicable and engaging for students across the social sciences and health disciplines has helped her textbook stand out from others. The second edition enhances this even further, bringing the material fully up-to-date with recent advances, and displaying a much-needed focus on developing students’ coding skills as well as their statistical knowledge. I anticipate Gordon’s second edition becoming a standard textbook in the field for years to come."Jeffrey E. Stokes, Assistant Professor of Gerontology & Undergraduate Director of Aging Studies Program, University of Massachusetts Boston, USA "I have used—and loved—the first edition of this book for nearly a decade. However, I was thrilled to see that the new edition promises to retain the rigor and clarity of purpose of the first edition, but in a more focused and streamlined package. I look forward to adopting this book for my introductory and advanced regression courses for the next decade and beyond."Jeffrey M. Timberlake, Professor of Sociology, University of Cincinnati, USA Table of ContentsPart I: Getting ready; 1 Considering Examples of Scholarly Publications Modeling Social and Health Variables; 2 Planning and Starting a Quantitative Research Project with Existing Data;; Part II: Describing the data; 3 Graphing and Summarizing Individual Variables; 4 Introducing Population Estimation and Hypothesis Testing; 5 Estimating and Testing the Association between Two Variables; ; Part III: Estimating and presenting linear regression models; 6 Introducing the Linear Regression Model with Two Continuous Variables; 7 Considering Nonlinearity and Nonconstant Variance; 8 Including Categorical Predictor Variables; 9 Including More Than One Predictor Variable in the Model; 10 Considering Interactions among Predictor Variables; ; Part IV: Estimating and presenting generalized linear models; 11 Introducing the Generalized Linear Regression Model; 12 Analyzing Dichotomous Outcomes; 13 Analyzing Multi-Category Outcomes and Offering a Roadmap to Additional Models
£39.99
Taylor & Francis Ltd Models for NonModelers
Book SynopsisModels for Non-Modelers focuses not on how to design models but on how to understand and critically appraise them. Data and statistical models are widely used in disciplines such as epidemiology, climate science and systems design, but it can be difficult for those without the necessary training to understand and implement them.This book is for non-modelers, especially social scientists. Through extensive examination of some common models both in visual and text form, this book shows these non-modellers how to understand the problems, both in the logic and implementation of such models. It includes in-depth worked examples and boxed text for more technical aspects. It does not require the reader to have in-depth mathematical knowledge. Also working through some common models in epidemiology and climate change scholarship, it examines AI and the problem of causality.This book will be suitable for graduate students and researchers in the social sciences who woulTrade Review"Models for Non-Modelers is a very timely and up-to-date comment on a central theme of scientific work, the use of models. It helps the interested reader to better understand important challenges of knowledge production in an era of critical debate regarding the concept of truth. Furthermore, to understand the research community’s strengths and limitations to produce new knowledge, which can be useful for handling complex environmental, economic and societal challenges like climate change and pandemics. It is highly recommended for a broad audience spanning from the interested public to students and researchers in development studies, environmental sciences, public health, and many other fields of science." -- Per-Olof Östergren, Professor in Social Medicine, Lund University, Sweden"This is a mind-opening critical, and pedagogical, scrutiny of scientific reasoning about the fateful issues of our time, from "Limits to Growth" to climate change and artificial intelligence." --Göran Therborn, Professor Emeritus of Sociology, University of Cambridge, UK"Models walk a fine balance between simplicity and complexity to make it possible for us to summarize, make sense of, and sometimes predict a complicated world. How to illuminate the importance of models for scientists’ work and its communication to the public? Göran Djurfeldt’s answer is to ’teach by illustration.’ Occam’s razor of parsimony guides the author both in his style of presentation and in his approach to models themselves. This book is enlivened by both subject-specific and mathematical nuance at key intersections." -- Christopher Swader, Associate Professor of Sociology, Lund University, SwedenTable of Contents1. Limits to Growth: A Mother of all Models? 2. Epidemiological Models 3. Weather and Climate Models 4. Artificial Intelligence 5. Spatial Data and Models 6. Tragedy of the Commons 7. Conclusions: Whereto from Here?
£43.99
Taylor & Francis Ltd Burnout
Book SynopsisBurnout: A Guide to Identifying Burnout and Pathways to Recovery is the first complete self-help guide to burnout, based on groundbreaking new research. Burnout is widespread among high achievers in the workplace, and the problem is becoming more prevalent and profound in its impact. This book contains new evidence-based tools for readers to work out for themselves whether they have burnout and generate a plan for recovery based on their personal situation. Chapters show readers how to recognise their own burnout patterns and how far they may have travelled into burnout territory, and provide research-based management approaches to help them regain their passions and build their resilience. Offering fascinating new insights into the biology of burnout, and stories from people who have rebounded from it, the book acts as a complete guide for anyone who suspects they may have burnout, for their friends and families, and for health professionals and employerTrade Review'I cried reading parts of this book. Profoundly insightful, with information that is superbly liberating about a phenomenon that imprisons too many of us. Highly recommend.' Dr. Sonia Henry, bestselling author of Going Under 'No one wants to cause burnout in others or be the victim of burnout. Avoiding both calamities requires a much deeper and clearer understanding of what causes burnout and what cures it. This book does precisely that.' Adrian Piccoli, Professor, Gonski Institute for Education'A must for every senior executive and all HR professionals.' Warren Hogan, banker and economist'Lawyers need to read this book: burnout doesn’t have to mean the end of your career.' Alice Cooney, Principal Solicitor, Office of Public Prosecutions VictoriaTable of ContentsPART 1 What is burnout? 1. Burnout: Forerunners and variants 2. Burnout: Its modern history 3. Defining and identifying burnout 4. The Sydney studies 5. What burnout is NOT 6. Burnout versus depression 7. The biology of burnout PART 2 Causes of burnout: The seed and the soil 8. The workers’ dilemma 9. The toxic workplace 10. Occupations at high risk 11. Forgotten caregivers 12. Predisposing factors 13. Perfectionism PART 3 Overcoming burnout and rekindling the flame 14. Resolving burnout: An introduction 15. For managers 16. For workers and caregivers 17. De-stressing 18. Managing perfectionism 19. Pulling it all together 20. A final note and a personal story of Grace under pressure PART 4 Appendices Appendix A: The Sydney Burnout Measure (SBM) Appendix B: Workplace triggers Appendix C: Perfectionism scale Appendix D: Resources for workers and caregivers
£18.99
Taylor & Francis Ltd Multilevel Modeling Using R
Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data.The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class models. The thir
£52.24
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.
£46.54
Taylor & Francis Narratives of Recovery from Mental Illness
Book SynopsisNarratives of Recovery from Mental Illness presents research that challenges the prevailing view that recovery from âmental illnessâ must take place within the boundaries of traditional mental health services. While Watts and Higgins accept that medical treatment may be a vital start to some peopleâs recovery, they argue that mental health problems can also be resolved through everyday social interactions, and through peer and community support.Using a narrative approach, this book presents detailed recovery stories of 26 people who received various diagnoses of âmental illnessâ and were involved in a mutual help group known as âGROWâ. Drawing on an in-depth analysis of each story, chapters offer new understandings of the journey into mental distress and a progressive entrapment through a combination of events, feelings, thoughts and relationships. The book also discusses the process of ongoing personal liberation and healing which assists recovery, and suggests that friendshTrade ReviewAs someone who is undergoing the recovery process with many years lived experience of mental distress I have no doubt that this book demonstrates a profound and deep understanding of the person-centred recovery process and will in my opinion become a seminal read that puts forth evidenced-based research about the transformative power of peer support that challenges the medical model. The authors, Agnes Higgins and Mike Watts, along with the 26 co-authors, have produced a piece of work that will be a source of hope and inspiration for people with lived experience of mental illness and emotional distress, as it was for me.Eugene Egan, The Institute of Mental Health BlogRead the ful review: https://imhblog.wordpress.com/2017/09/08/book-review-narratives-of-recovery-from-mental-illness-the-role-of-peer-support-by-eugene-egan/Reading these stories it strikes me that I have, indeed, been hopeful in my own way. Each turn I have deliberately taken in my life has involved new and exciting thoughts about the future. That’s a product of hope. Each life-change has also involved support from many other people. Mutual help is very much part of a healthy mental health process as outlined in the book.Padraig O'Morain, The Irish Times, November 5, 2017Read the full review: https://www.irishtimes.com/life-and-style/health-family/narratives-of-hope-in-mental-illness-1.3275055 Table of ContentsSection 1 1. Genesis of the book and setting the context 2. The medicalisation of human distress 3. Towards Recovery: beyond the psychiatric system 4. Towards equality and reciprocity: mutual help/mutual support 5. Generating recovery narratives for this study Section 2 6. Personal journeys into severe emotional distress 7. Attempting to Escape from Distress and Terror 8. A Time of Healing: Struggling through Fear to Encounter Hope and Trust 9. A Time of Healing: the healing power of reciprocal relationship 10. A Time of Healing: Leadership, Choosing ‘Goodness’ New identities and Resilience 11. A time of Growth: successful social involvements 12. Flourishing Selves and a Re-enchantment with life Section 3 13. Recovery through mutual help: recovery processes revisited 14. An exploration of recovery through graphic illustrations 15. Journey’s End and New Beginnings
£37.04
Taylor & Francis Ltd Methodological Issues in Psychology
Book SynopsisMethodological Issues in Psychology is a comprehensive text that challenges current practice in the discipline and provides solutions that are more useful in contemporary research, both basic and applied. This book begins by equipping the readers with the underlying foundation pertaining to basic philosophical issues addressing theory verification or falsification, distinguishing different levels of theorizing, or hypothesizing, and the assumptions necessary to negotiate between these levels. It goes on to specifically focus on statistical and inferential hypotheses including chapters on how to dramatically improve statistical and inferential practices and how to address the replication crisis. Advances to be featured include the author''s own inventions, the a priori procedure and gain-probability diagrams, and a chapter about mediation analyses, which explains why such analyses are much weaker than typically assumed. The book also provides aTable of Contents Part I: General methodological issues A Philosophical Foundation The Reality Underneath the Reality: Examples from the Hard Sciences The TASI Taxonomy and Implications Why We Should Not Engage Null Hypothesis Significance Testing How to Think About Replicating Findings The A Priori Procedure (APP) Gain-Probability Diagrams The Unfortunate Dependence of Much Social Science on Mediation Analysis Part II: Measurement issues The Classical Theory and Implications Potential Performance Theory Auxiliary Validity Unit Validity and Why Units Matter A Tripartite Parsing of Variance Shocking Measurement Implications
£43.69
Taylor & Francis Ltd Experimental Design in Psychology
Book SynopsisThis text is about doing science and the active process of reading, learning, thinking, generating ideas, designing experiments, and the logistics surrounding each step of the research process. In easy-to-read, conversational language, Kim MacLin teaches students experimental design principles and techniques using a tutorial approach in which students read, critique, and analyze over 75 actual experiments from every major area of psychology. She provides them with real-world information about how science in psychology is conducted and how they can participate.Recognizing that students come to an experimental design course with their own interests and perspectives, MacLin covers many subdisciplines of psychology throughout the text, including IO psychology, child psychology, social psychology, behavioral psychology, cognitive psychology, clinical psychology, health psychology, educational/school psychology, legal psychology, and personality psychology, among others. ParTable of ContentsPreface Permissions Acknowledgements Part 1 - Basic Principles in Experimental Design 1. An Introduction to Scientific Inquiry 2. The Psychological Literature 3. Basic Experimental Design in Psychology 4. Advanced Design Techniques 5. Using Experimental Design to Control Variables 6. Control of Subject Variables 7. Design Critiques 8. Ethics of Experimental Research 9. The Research Process Part 2- Analysis of Experiments 10. The Look of Love 11. False Memories for Fake News 12. Emotions and Chronic Fatigue 13. Temperature and Loneliness 14. Violent Media 15. Aggression and Schizophrenia 16. Workplace Deviance 17. Controlling Racial Prejudice 18. Mandela Effect 19. False Confessions 20. Androgens and Toy Preference 21. Language-Trained Chimpanzees 22. Peer Excellence and Quitting 23. Remembering and Eyes 24. Non-Suicidal Self-Injury 25. Police Responses to Criminal Suspects 26. Sleep Final, Final Note Appendix A & B Glossary References Name Index Subject Index
£87.39
CRC Press Geocomputation with Python
Book SynopsisGeocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.Key features: Showcases the integration of vector and raster datasets operations. Provides explanation of each line of code in the book to minimize surprises. Includes example datasets and meaningful operations to illustrate the applied nature of geographic research. Another uniq
£53.19
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
£73.14
Taylor & Francis Ltd Applications of Regression for Categorical
Book SynopsisThis book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it's ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy Table of Contents1. Introduction 2. Introduction to R Studio and Packages 3. Overview of OLS Regression and Introduction to the General Linear Model 4. Describing Categorical Variables and Some Useful Tests of Association 5. Regression for Binary Outcomes 6. Regression for Binary Outcomes – Moderation and Squared Terms 7. Regression for Ordinal Outcomes 8. Regression for Nominal Outcomes 9. Regression for Count Outcomes 10. Additional Outcome Types 11. Special Topics: Comparing Between Models and Missing Data
£58.89
Taylor & Francis Ltd Research Methods in Applied Behavior Analysis
Book SynopsisResearch Methods in Applied Behavior Analysis, third edition, is a practical and accessible text that provides the beginning researcher with a clear description of how behavior analysts conduct applied research and submit it for publication.In a sequence of ten logical steps, this text covers the elements of single-case research design and the practices involved in organizing, implementing, and evaluating research studies. This revision covers important new topics for consideration when designing a study, including ecological validity, procedural fidelity, and the consecutive controlled case series design, which includes replications of single-cases and the statistical analysis of accumulated studies. Also included are chapter summaries, specific tips for master's and doctoral researchers, and recommended procedures for BCBA consultants.Rich with details from the authors' vast experience and numerous examples from published research, this text is
£37.99
CRC Press Introduction to Political Analysis in R
Book SynopsisIntroduction to Political Analysis in R is a comprehensive guide for students and researchers eager to delve into the intersection of data science, statistics, and political science. Aimed at equipping readers with the essential quantitative skills to analyze political data, the book bridges practical coding techniques in R with foundational statistical concepts, emphasizing real-world applications in politics. The text adopts a progressive structure, beginning with the basics of R and data manipulation before advancing to more complex topics such as data visualization, spatial analysis, text analysis, and modeling. Through accessible language and engaging examplesâranging from U.S. election forecasting to global development trendsâit demystifies complex analytical methods. Each chapter integrates coding exercises and real-world datasets to reinforce learning, fostering independent data analysis skills. Designed for undergraduate political science majors, this book is also a valuable resource for anyone seeking to understand data-driven political analysis, whether for academic research, professional development, or personal curiosity. Key features include: Integrates data science and statistics with a political science focus, offering hands-on coding practice using the R programming language. Provides real-world datasets and step-by-step exercises, enabling students to directly apply concepts to political phenomena such as gerrymandering. Features a progressive chapter structure, progressing from foundational data handling to advanced methods like text analysis, spatial mapping, and linear modeling. Emphasizes accessible coding for beginners, fostering self-sufficiency in data analysis without requiring prior statistical expertise. Bridges theory and application with examples that engage studentsâ interest in politics while developing transferable analytical skills.
£51.29
Taylor & Francis Ltd How to Use SPSS
Book SynopsisHow to Use SPSS is designed with the novice computer user in mind and for people who have no previous experience using SPSS. Each chapter is divided into short sections that describe the statistic being used, important underlying assumptions, and how to interpret the results and express them in a research report.The book begins with the basics, such as starting SPSS, defining variables, and entering and saving data. It covers all major statistical techniques typically taught in beginning statistics classes, such a descriptive statistics, graphing data, prediction and association, parametric inferential statistics, nonparametric inferential statistics and statistics for test construction.More than 275 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. The book includes a glossary of statistical terms and practice exercises. A complete set of online resources including video tutorials and output files for students, and PowerPoint slides and test bank questions for instructors, make How to Use SPSS the definitive, field-tested resource for learning SPSS.New to this edition: Fully updated to the reflect SPSS version 29. Every screen shot has been recaptured. New video supplements for all practice exercises. References to significance levels have been updated to reflect the new SPSS output format. Effect size is now shown in output for many procedures and reference to some effect size has been moved from Appendix A to be more integrated into the chapters. Sample results sections now also include effect size where SPSS directly calculates effect size. A new section covering the EXPLORE command has been added to Chapter 3. Table of ContentsPreface to the Twelfth Edition 1. Getting Started 2. Entering and Modifying Data 3. Descriptive Statistics 4. Graphing Data 5. Prediction and Association 6. Basic Parametric Inferential Statistics and t-tests 7. ANOVA Models 8. Nonparametric Inferential Statistics 9. Test Construction Appendix A. Effect Size Appendix B. Practice Exercise Data Sets Appendix C. Sample Data Files Used in Text Appendix D. SPSS Syntax Basics Appendix E. Glossary Appendix F. Selecting the Appropriate Inferential Test Appendix G. Answer Key
£56.99
Taylor & Francis Ltd Evaluating What Works
Book SynopsisThose who work in allied health professions and education aim to make people's lives better. Often, however, it is hard to know how effective this work has been: would change have occurred if there was no intervention? Is it possible we are doing more harm than good? To answer these questions and develop a body of knowledge about what works, we need to evaluate interventions. Objective intervention research is vital to improve outcomes, but this is a complex area, where it is all too easy to misinterpret evidence. This book uses practical examples to increase awareness of the numerous sources of bias that can lead to mistaken conclusions when evaluating interventions. The focus is on quantitative research methods, and exploration of the reasons why those both receiving and implementing intervention behave in the ways they do. Evaluating What Works: Intuitive Guide to Intervention Research for Practitioners illustrates how different research designs can overcome these issuesTable of Contents1. Introduction 2. Why observational studies can be misleading 3. How to select an outcome measure 4. Improvement due to nonspecific effects of intervention 5. Limitations of the pre-post design: biases related to systematic change 6. Estimating unwanted effects with a control group 7. Controlling for selection bias: randomized assignment to intervention 8. The researcher as a source of bias 9. Further potential for bias: volunteers, dropouts, and missing data 10. The randomized controlled trial as a method for controlling biases 11. The importance of variation 12. Analysis of a two-group RCT 13. How big a sample do I need? Statistical power and type II errors 14. False positives, p-hacking and multiple comparisons 15. Drawbacks of the two-arm RCT 16. Moderators and mediators of intervention effects 17. Adaptive Designs 18. Cluster Randomized Controlled Trials 19. Cross-over designs 20. Single case designs 21. Can you trust the published literature? 22. Pre-registration and Registered Reports 23. Reviewing the literature before you start 24. Putting it all together 25. Comments on exercises 26. References
£43.69
Taylor & Francis Ltd Multivariate Statistical Methods
Book SynopsisMultivariate Statistical Methods: A Primer offers an introduction to multivariate statistical methods in a rigorous yet intuitive way, without an excess of mathematical details. In this fifth edition, all chapters have been revised and updated, with clearer and more direct language than in previous editions, and with more up-to-date examples, exercises, and references, in areas as diverse as biology, environmental sciences, economics, social medicine, and politics.Features A concise and accessible conceptual approach that requires minimal mathematical background. Suitable for a wide range of applied statisticians and professionals from the natural and social sciences. Presents all the key topics for a multivariate statistics course. The R code in the appendices has been updated, and there is a new appendix introducing programming basics for R. The data from examples and exercises are available on a companion website.This
£133.00
Taylor & Francis Ltd Conducting Contextual Research
Book SynopsisThis innovative book proposes an entirely new approach to social research, presenting practical ways to discover people's life contexts in order to understand why they do what they do, which is essential for any forms of research that need to understand people.Taking a novel approach that goes beyond traditional categorisations of qualitative and quantitative research, the book starts by discussing the real basis of all research methods in social relationships, before detailing the methods for finding out about a person's life contexts in very practical terms, accompanied by suggested questions, advice, and research tricks to help you progress. The various life contexts are then worked through chapter by chapter. Drawing on the rich and varied research experiences of all the authors, examples are given throughout, with later chapters focusing on specific research areas.Conducting Contextual Research is essential reading for postgraduate students an
£41.79
Taylor & Francis Ltd Preparing Literature Reviews
Book SynopsisPreparing Literature Reviews is an accessible guide that provides easy to follow step-by-step advice on how to prepare qualitative and quantitative literature reviews. The 6th edition of this bestselling book retains its original lauded features of clear style and organization while bringing new chapters into the fold to address mixed-methods and theoretical frameworks in conducting reviews of literature.The text walks the reader through both the conceptualization and the writing stages, offering templates for fostering different phases of designing and drafting required to complete the process of conducting a review of literature from start to finish, in empirical articles, theses, or dissertations. New examples of literature reviews utilizing various approaches have been added, and there is also additional coverage of concepts such as literature organization, plagiarism, reviewing, topic selection, and writing. Commentary on literature in a variety of fields as
£63.64
