Psychological methodology Books, bestsellers and new releases

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.

Read more less

15 in stock

£65.54

Taylor & Francis Ltd Body Image in Eating Disorders

15 in stock

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

Read more less

15 in stock

£37.04

Taylor & Francis Ltd Applied Regularization Methods for the Social

15 in stock

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

Read more less

15 in stock

£42.74

Taylor & Francis Principles and Methods of Social Research

15 in stock

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

Read more less

15 in stock

£80.74

Taylor & Francis Factor Analysis and Dimension Reduction in R

15 in stock

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

Read more less

15 in stock

£63.64

Taylor & Francis Statistical Power Analysis

15 in stock

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

Read more less

15 in stock

£49.39

689 products

Taylor & Francis Studying ServiceLearning Innovations in Education Research Methodology

Taylor & Francis SOLUTIONS MANUAL to Accompany Research Design and Statistical Analysis 2e

Taylor & Francis Studying Educational and Social Policy Theoretical Concepts and Research Methods Sociocultural Political and Historical Studies in Education

Taylor & Francis Improving the First Year of College

Taylor & Francis Funds of Knowledge

Taylor & Francis Funds of Knowledge

Taylor & Francis Data Analytic Techniques for Dynamical Systems

Taylor & Francis Factor Analysis at 100

Taylor & Francis Statistical Methods for Communication Science Routledge Communication Series

Taylor & Francis An Introduction to Latent Variable Growth Curve Modeling

Taylor & Francis An Introduction to Latent Variable Growth Curve Modeling

Taylor & Francis ANOVA for the Behavioral Sciences Researcher

Taylor & Francis ANOVA for the Behavioral Sciences Researcher

Taylor & Francis A First Course in Structural Equation Modeling

Taylor & Francis A First Course in Structural Equation Modeling

Taylor & Francis Best Practices in Teaching Statistics and Research Methods in the Behavioral Sciences

Taylor & Francis International Handbook of Survey Methodology European Association of Methodology Series

Taylor & Francis International Handbook of Survey Methodology

Taylor & Francis Multilevel Analysis of Individuals and Cultures

Taylor & Francis Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences

Taylor & Francis Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences

Taylor & Francis Structural Equation Modeling with Mplus Basic Concepts Applications and Programming Multivariate Applications Series

Taylor & Francis Factor Analysis at 100

Taylor & Francis Statistical and Methodological Myths and Urban Legends

Taylor & Francis Statistical and Methodological Myths and Urban Legends

Taylor & Francis Statistical Power Analysis with Missing Data

Taylor & Francis An Introduction to Applied Multivariate Analysis

Taylor & Francis Introduction to Statistical Mediation Analysis

Taylor & Francis Handbook of Research Methods in Consumer Psychology

Taylor & Francis Schizotypy

Taylor & Francis The Elements of Inquiry

Taylor & Francis Mental Health and Punishments Critical Perspectives in Theory and Practice

Taylor & Francis Qualitative Methods in Organizational Research and Practice

Taylor & Francis Ltd Laboratory Psychology A Beginners Guide Cognitive Psychology

Taylor & Francis Ltd Laboratory Psychology

Taylor & Francis Doing Qualitative Analysis in Psychology

Taylor & Francis Mathematical Psychology and Psychophysiology

Taylor & Francis Multidimensional Scaling

Taylor & Francis Handbook of Research and Quantitative Methods in Psychology For Students and Professionals

Taylor & Francis New Statistical Procedures for the Social Sciences

Taylor & Francis The Compleat Academic

Taylor & Francis Ltd Dialectical Behavior Therapy for Eating Disorders

Taylor & Francis Ltd Handbook of Regression Modeling in People

Taylor & Francis Ltd Body Image in Eating Disorders

Taylor & Francis Ltd Applied Regularization Methods for the Social

Taylor & Francis Principles and Methods of Social Research

Taylor & Francis Factor Analysis and Dimension Reduction in R

Taylor & Francis Statistical Power Analysis