Psychological methodology Books

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

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

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

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

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

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

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

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

Read more less

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

Read more less

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

Read more less

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

Read more less

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

Read more less

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

Read more less

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

Read more less

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

Read more less

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

Read more less

Book SynopsisThis book is a how-to guide on statistical analyses designed for undergraduates and others new to the subject. It uses a conceptual framework, starting with the most basic concepts of statistics and moving up through the capacity to perform bivariate regression.Written in an easy-going and clear style, it uses policing data to illustrate concepts. Easily identified Main Take-Aways and Key Terms features aid student understanding. Designed to combat the fear of mathematics and statistics often held by students in the social sciences, plain verbiage, multiple examples, and clear demonstrations combine to achieve the actualization and proper contextualized use of univariate and bivariate statistics. This work also serves as a launching pad for further study in statistics.As an accessible introduction to statistics in criminal justice and criminology, this text will appeal to both students and instructors in introductory criminal justice and criminology stati

Read more less

Book SynopsisThis expanded second edition of Scientific Method shows how science works, fails to work or pretends to work by looking at examples from physics, biomedicine, psychology, sociology and economics.Scientific Method aims to help curious readers understand the idea of science, not by learning a list of techniques but through examples both historical and contemporary. Staddon affirms that if the reader can understand successful studies as well as studies that appear to be scientific but are not, they will become a better judge of the âœscienceâ in circulation today. To this end, this new edition includes a new chapter, What is Science?, which points out that science, like any human activity, has its own set of values, with truth being the core. Other new chapters focus on the emergence of AI and machine learning, science and diversity, and behavioral economics. The book also includes textual features such as bullet-points and text boxes on topical issues.Scientific M

Read more less

Book SynopsisThis book is the first hands-on roadmap for conducting rigorous experimental research on second language speech processing and spoken word recognition.Isabelle Darcy expertly defines key concepts and offers a detailed step-by-step guide to designing empirical psycholinguistic research in this complex, interdisciplinary area. The book covers the following: setting up an efficient workflow to enhance reproducibility of findings; determining a methodology; selecting experimental controls and designing stimuli; collecting data using an array of methodological tools; addressing common challenges; preparing and analyzing data; preregistering the study; and sharing data transparently in accordance with Open Science practices. Darcy provides everything needed to design and carry out robust behavioral studies on L2 speech processing, in a laboratory or online.This book will be an invaluable practical resource for researchers and advanced students in second language speech learn

Read more less

Book SynopsisEthical Issues in Psychology: A Critical Introduction offers readers a clear review of current ethical practices and ideas in psychology and goes on to challenge some of the agreed wisdom on ethics.Ethical issues within psychology are not easy to resolve and debates continue as we encounter new dilemmas. This book introduces ethics and their importance, and uses examples from psychological research to consider key ethical issues; ethical principles and guidelines for psychologists, including BPS guidelines; ethics in practice in psychology; ethical problems within psychology, such as racism; and methods for ethical research, including socially sensitive research, internet-mediated research, and the use of animals in psychological research. Fully up-to-date, this book considers recent challenges for researchers and teachers including privacy and consent dilemmas in the use of social media for psychological research, the rise of the open science movement and an awareness

Read more less

Book SynopsisAt the beginning of writing a thesis, many questions arise, for example:â How do I know that I have formulated a relevant research problem?â Have I chosen the right empirical method?â Are interviews or observations appropriate?â How should I structure my text to get my point across in the best way?â What exactly is a theory?â How can the quality of my work be assessed?Crafting Your Thesis is a broad and accessible handbook in qualitative methods that gives you clear and concise answers to these questions â and many more. The book can be used both in introductory university courses, where you as a student encounter questions of method for perhaps the first time, and right up to Masterâs thesis level, where it gives a quick overview of different available qualitative methods and highlights questions that must be dealt with when crafting the thesis.

Read more less

Book SynopsisPattern Theory is a groundbreaking exploration of the concept of pattern across a range of disciplines, including science, neuroscience, psychology, and social sciences.This book examines the meaning and implications of pattern, presenting a comprehensive body of theory that unifies concepts of form, order, and regularity and connects them to memory and perception. By challenging existing orthodoxies and linking evidence from brain and mind function, it outlines a robust theoretical framework around pattern searching and matching, pattern activation, and the continuity of pattern nexuses. This in-depth study of pattern theory and pattern thinking delves into the cognitive basis of patterns, their impact on reasoning and learning, and the social and collaborative nature of pattern recognition, expression, and representation. It also addresses philosophical issues and implications surrounding shared pattern thinking and introduces a broad conceptual basis for pattern inq

Read more less

Book SynopsisThis book explores the current developments and future implications of psychotherapeutic theories, research methodologies, and practices in this rapidly advancing digital economy.This book is an invaluable resource for those interested in: The effects of our âinformation economyâ on our brains, consciousness, inner world and the way as psychotherapists we conceptualise The promise of autonomous psychotherapy programmes that integrate âtherapy with the actual relationship experiences of the individual userâ Whether traditional psychotherapy can provide the best antidote to the ills of our digital age An overarching concern is that we will no longer be able to control technology. Hence, the need to be clearer not only regarding the effect of the digital era on the processes of the psychological therapies but the effects on us, as people who are clients/patients and psychological therapists - perhaps before it is too late, if isnât already.This book has been developed from a special issue of the European Journal of Psychotherapy & Counselling.

Read more less

Book SynopsisFull of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variablesTrade Review"While the book has some of the clearest geometric explanations written on the topic, in terms of inertia possessed by clouds of individuals and variables, its primary function is to operate as a step-by-step walk through on how to visualize, analyze and portray the results of analyses in R. This is accomplished via thought-provoking examples, ranging from wine ratings, decathlons to high-dimensional text-mining and genomic breeding. Data and code are available online, enabling fast cut-and-paste implementation…the book makes an excellent self-tutorial or teaching aid for the whole gamut of students and researchers working in applied fields. The authors are to be congratulated for their contribution to making the implementation of complex analyses ideas simple and implementable in practice."—Donna Ankherst, in Biometrics, September 2018"In the days of "big data" every researcher should be able to summarize and explain multivariate data sets. The purpose of "Exploratory Multivariate Analysis by Example using R" is to provide the practitioner with a sound understanding of, and the tools to apply, an array of multivariate technique (including Principal Components, Correspondence Analysis, and Clustering). The focus is on descriptive techniques, whose purpose is to explore the data from different perspectives, trying to find patterns, but without going into the realm of inferential statistics, with its formal tests of hypotheses, confidence intervals and other more advanced topics. This seems to be the right choice for the audience of non-statisticians to whom the book is directed. The second edition of the book includes a more extensive treatment of missing data and a new chapter on multivariate data visualization - both of which I consider very welcome additions.In summary, I consider "Exploratory Multivariate Analysis by Example using R" to be a good introduction, with an applied slant, to the fundamental multivariate techniTable of ContentsPrefacePrincipal Component Analysis (PCA)Correspondence Analysis (CA)Multiple Correspondence Analysis (MCA)ClusteringVisualisationAppendix

Read more less

Book SynopsisQualitative Inquiry in Neoliberal Times is written from the perspective that the scholarly lives of academics are changing, constantly in flux, and increasingly bound to the demands of the market a context in which the university has increasingly morphed into a business enterprise, one that treats students as consumers to be marketed to, education as something to be purchased, and research as something to be capitalized on for financial gain. The effects of this market-orientation of scholarly life, especially on those in the social sciences and humanities, are ones that demand serious examination. At the same time, qualitative inquiry itself is changing and evolving within and against the rhythms of this new normal'.This volume engages with these emerging debates in qualitative research over new materialism, ''data'', public policy, research ethics, public scholarship, and the corporate university in the neoliberal age. World-renowned contributors from the United STrade ReviewQualitative Inquiry in Neoliberal Times is an extremely important and necessary book in our current post-anthropocentric neoliberal condition, a circumstance in which a rhizomatic, immanent capitalism has changed everything. To quote Harry Torrance, author of chapter 5, "neo-liberalism already operates with a more sophisticated theory of change than empirical social science." The multiplicities, diversities, and unthought possibilities embedded within qualitative research serve as points from/through which this all invasive performance can hopefully be countered and challenged.Gaile S. Cannella, Independent Critical Qualitative Research and Policy Studies Scholar and Research Professor at Arizona State UniversityMarket based values permeate audit culture with its performance metrics that increasingly infiltrate higher education. This collection might help us negotiate what is already here with the threat of more to come if we recognize the stakes: the place of the university in the politics of knowledge and the forms of governmentality we will abide. We incalculable subjects have much work to do.Patti Lather, Professor Emerita, Ohio State University. Author of (Post)Critical Methodologies: The Science Possible After the Critiques (Routledge, 2017)Table of ContentsIntroduction Norman K. Denzin & Michael D. GiardinaSection I: Theory, Data, and Entanglements1 Qualitative inquiry, research marketplaces, and neoliberalism: Adding some +s (pluses) to our thinking about the mess in which we find ourselves Julianne Cheek 2 Post qualitative research: The next generation Elizabeth Adams St.Pierre 3 Qualitative methodology and the new materialisms: ‘A little of Dionysus’s blood?’ Maggie MacLure 4 The importance of small form: ‘Minor’ data and ‘BIG’ neoliberalism Mirka Koro-Ljungberg, Anna Montana Cirell, Byoung-gyu Gong, & Marek Tesar 5 Be careful what you wish for: Data entanglements in qualitative research, policy, and neoliberal governance Harry TorranceSection II: Ethics, Politics, and Resistance6 Feminist poststructuralisms and the neoliberal university Bronwyn Davies, Margaret Somerville, & Lise Claiborne 7 Leaky privates: Resisting the neoliberal university and mobilizing movements for public scholarship Michelle Fine 8 Assembling a we in critical qualitative inquiry Stacy Holman Jones 9 Trickster as resistance: Impacts of neoliberalism on Indigenous research and Indigenous methodologies Roe Bubar & Doreen E. Martinez 10 Turning against each other in neoliberal times: The discourses of Othering and how they threaten our scholarship Kristi Jackson 11 Communicative methodology and social impact Aitor Gomez Coda All I really need to know about qualitative research I learned in high school: The 2016 Qualitative High commencement address Johnny Saldaña

Read more less

Book SynopsisCompositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information. Examples are found in various fields, including geology, medicine, chemistry, agriculture, economics, social science, etc. This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.Trade Review"(…This book) avoids cumbersome theoretical digressions and only presents to the reader the essential basic concepts for the application of CODA, using ratios and logratios that retain most of the original data structure and, subsequently, may lead to proper conclusions. … The simplification of the analysis and the straightforward interpretability of results is, clearly, one of the primary values of the publication. In addition, the emphasis on the general application of weights in the calculus of most of the operations and methodologies used throughout the book deserves a special mention.. … Altogether, the book and the easyCODA R package may represent a promising instrument for introducing CODA in the fat and oils field, where fatty acid compositions have been treated until now exclusively by classical multivariate techniques without considering their compositional structure. Predicting the future is risky, but the book may represent an essential instrument for CODA spreading since it represents just what many practitioners were expecting to initiate their experience in this promising new statistical field of compositional data analysis."—A. Garrido Fernández in Gracas y Aceites – International Journal of Fats and Oils, July-September 2019"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."—Jan Graffelman in the Biometrical Journal, March 2019"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."—Professor Eric Grunsky, Department of Earth and Environmental Sciences, University of WaterlooWaterloo, Ontario, Canada"Clearly the best introduction to compositional data analysis"—Professor John Bacon-Shone"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."- Victor Moreno in ISCB, June 2019"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."—Jan Graffelman in the Biometrical Journal, March 2019"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."—Professor Eric Grunsky, University of Waterloo, Ontario, Canada"Clearly the best introduction to compositional data analysis"—Professor John Bacon-Shone"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."- Victor Moreno in ISCB, June 2019Table of ContentsWhat are compositional data, and why are they special? Geometry and visualization of compositional data. Logratio transformations. Properties and distributions of logratios. Regression models involving compositional data. Dimension reduction using logratio analysis. Clustering of compositional data. The problem of zeros, with some solutions. Simplifying the task: variable selection. Case study: Fatty acids of marine amphipods. Appendix A: Theory of compositional data analysis. Appendix B: Commented Bibliography. Appendix C: Computational examples using the R package easyCODA. Appendix D: Epilogue.

Read more less

Book SynopsisThe question of whether someone is psychologically healthy or mentally ill, and the fundamental nature of mental health underlying that question has been debated in cultural, academic, and clinical settings for millennia. This book provides an overview of how people have conceptualized and understood mental illness through the ages. The book begins by looking at mental illness in humanity's evolutionary past then moves through the major historical epochs: the mythological, the Classical, the Middle Ages, the Renaissance, the Enlightenment, and modern, and the postmodern. At each point, it focuses on major elements that emerged regarding how people judged sanity and insanity and places major emphasis on the growing fields of psychiatry and psychology as they emerged and developed. As the book moves into the twenty-first century, Dr. Jenkins presents his integrated model of knowledge, a systemic, holistic model of the psyche that creates a conceptual foundation for underTrade Review"In this deeply probing work, Paul Jenkins investigates and analyses the constructs of mental health and mental illness. Drawing from multiple perspectives based in a rich and deep knowledge of the relevant scholarship from an array of relevant fields, Jenkins deconstructs and assesses the theories and underlying epistemologies that have been applied to mental health and mental illness. He takes the reader on an insightful historical voyage through the development of these concepts over time, concluding with an integrative synthesis grounded in a new variation on the biopsychosocial model. This is a book that should be read by every thoughtful student or practitioner in a mental health field."Jay L. Lebow, PhD, ABPP, senior scholar and clinical professor, The Family Institute at Northwestern University"In this deeply probing work, Paul Jenkins investigates and analyses the constructs of mental health and mental illness. Drawing from multiple perspectives based in a rich and deep knowledge of the relevant scholarship from an array of relevant fields, Jenkins deconstructs and assesses the theories and underlying epistemologies that have been applied to mental health and mental illness. He takes the reader on an insightful historical voyage through the development of these concepts over time, concluding with an integrative synthesis grounded in a new variation on the biopsychosocial model. This is a book that should be read by every thoughtful student or practitioner in a mental health field."Jay L. Lebow, PhD, ABPP, senior scholar and clinical professor, The Family Institute at Northwestern University"In his "exploration of the past, present, and future" Jenkins articulates the history with poise and clarity. Covering not only key schools of thought in the changing views of mental health and mental illness but important historical thinkers as well, Jenkins puts the history into context and illuminates the "good" and the "bad" in past and present views. More importantly, he uses the analysis of past and present perspectives to prognosticate about future directions for psychology and psychiatry in their ongoing attempts to comprehend the complexities of mental health and mental illness. All this is clearly done with an eye toward fostering appropriate understanding and treatment. This volume would make an excellent core source in a history of psychology course emphasizing the development of clinical psychology and psychiatry. It should be considered a must for practitioners."R. E. Osborne, Texas State University. Highly recommended- CHOICE, December 2021 Vol. 59 No. 4.Table of ContentsIntroduction; Ch. 1 Prehistory; Ch. 2 The Mythological Era; Ch. 3 The Classical Era; Ch. 4 The Middle Ages; Ch. 5 The Renaissance; Ch. 6 The Enlightenment; Ch. 7 The Modern Age; Ch. 8 The Postmodern Age; Ch. 9 The Twenty-First Century; Ch. 10 Now and Into the Future.

Read more less

Book SynopsisA Step-by-Step Guide to Qualitative Data Coding is a comprehensive qualitative data analysis guide. It is designed to help readers to systematically analyze qualitative data in a transparent and consistent manner, thus promoting the credibility of their findings. The book examines the art of coding data, categorizing codes, and synthesizing categories and themes. Using real data for demonstrations, it provides step-by-step instructions and illustrations for analyzing qualitative data. Some of the demonstrations include conducting manual coding using Microsoft Word and how to use qualitative data analysis software such as Dedoose, NVivo and QDA Miner Lite to analyze data. It also contains creative ways of presenting qualitative findings and provides practical examples.After reading this book, readers will be able to: Analyze qualitative data and present their findings Select an appropriate qualitative analysis tool<Table of Contents1. Introduction to Qualitative Data Analysis2. Review of Qualitative Approaches and their Data Analysis Methods3. Understanding the Art of Coding Qualitative Data4. Preparing Data to Code5. Reflecting, Acknowledging and Bracketing Your Perspectives and Preconceptions6. Documenting Personal Reflections and Analytical Process7. Manually Assigning Codes to Data8. Developing Categories and Themes9. Connecting Themes and Developing Tables and Diagrams10. Using QDA Miner Lite to Analyze Qualitative Data11. Using NVivo 12 to Analyze Qualitative Data12. Using Dedoose to Analyze Qualitative Data13. Presenting Qualitative Findings14. Ensuring Credibility of the Analysis Process and FindingsAppendix

Read more less

Book SynopsisAn Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. IntroTrade ReviewPraise for the Third Edition: Overall, this new edition remains a highly useful and compact introduction to a large number of seemingly disparate regression models. Depending on the background of the audience, it will be suitable for upper-level undergraduate or beginning post-graduate courses.—Christian Kleiber, Statistical Papers (2012) 53 The comments of Lang in his review of the second edition, that ‘This relatively short book gives a nice introductory overview of the theory underlying generalized linear modelling. …’ can equally be applied to the new edition. … three new chapters on Bayesian analysis are also added. … suitable for experienced professionals needing to refresh their knowledge … .—Pharmaceutical Statistics, 2011 The chapters are short and concise, and the writing is clear … explanations are fundamentally sound and aimed well at an upper-level undergrad or early graduate student in a statistics-related field. This is a very worthwhile book: a good class text and a practical reference for applied statisticians. —Biometrics This book promises in its introductory section to provide a unifying framework for many statistical techniques. It accomplishes this goal easily. … Furthermore, the text covers important topics that are frequently overlooked in introductory courses, such as models for ordinal outcomes. … This book is an excellent resource, either as an introduction to or a reminder of the technical aspects of generalized linear models and provides a wealth of simple yet useful examples and data sets.—Journal of Biopharmaceutical Statistics, Issue 2 This book aims to provide an overview of the key issues in generalized linear models (GLMs), including assumptions, estimation methods, different link functions, and a Bayesian approach. Applications of the book concern different types of data, such as continuous, categorical, count, correlated, and time-to-event data. The book contains theoretical and applicable examples of different type of GLMs. The first five chapters introduce the basics of linear models and the relations between different distributions. The following chapters explain GLMs in respect to different types of link function. One of the most important features of the book is the statistical software codes in each chapter, which make it more practical, as well as the last chapter that focuses on examples of Bayesian analysis.- Morteza Hajihosseini in ISCB, June 2019 Table of ContentsIntroduction. Model Fitting. Exponential Family and Generalized. Linear Models.Estimation. Inference. Normal Linear Models. Binary Variables and Logistic Regression. Nominal and Ordinal Logistic Regression. Poisson Regression and Log-Linear Models.Survival Analysis. Clustered and Longitudinal Data. Bayesian Analysis. Markov Chain Monte Carlo Methods. Example Bayesian Analyses. Postface. Appendix.

Read more less

Book SynopsisThis bestselling text provides a practical guide to structural equation modeling (SEM) using the Amos Graphical approach. Using clear, everyday language, the text is ideal for those with little to no exposure to either SEM or Amos. The author reviews SEM applications based on actual data taken from her own research. Each chapter walks readers through the steps involved (specification, estimation, evaluation, and post hoc modification) in testing a variety of SEM models. Accompanying each application is: an explanation of the issues addressed and a schematic presentation of hypothesized model structure; Amos input and output with interpretations; use of the Amos toolbar icons and pull-down menus; and data upon which the model application was based, together with updated references pertinent to the SEM model tested.Thoroughly updated throughout, the new edition features: All new screen shots featuring Amos Version 23. DescriptiTrade Review"Having used Byrne's SEM texts for decades, this updated AMOS edition clearly continues her winning streak. With trademark accessibility of writing and helpful software illustrations, this remains an indispensable companion for any course using AMOS." - Gregory R. Hancock, University of Maryland, USA "Byrne's trademark clarity and practicality are on full display in this new edition of her bestselling book on using Amos for structural equation modeling. Unlike typical guides for SEM software, Byrne embeds her coverage in realistic and telling examples that take the reader beyond the simple how-tos to guidance on strategy and interpretation." - Rick H. Hoyle, Duke University, USA "When Barbara Byrne teaches, all analyses become simple. She is exceptionally skillful in teaching complex matters in an accessible way. With this book she confirms her international reputation. She will continue to show students and scholars how to conduct sophisticated analyses using AMOS." –Fons van de Vijvar, Tilburg University, Netherlands "Barbara M. Byrne’s book on how to use Amos for SEM analyses is a great resource for students and experienced researchers. Written in a clear, accessible way, it also teaches essential concepts, not just skills." - Rex Kline, Concordia University, Canada "Dr. Byrne writes at a perfect level which is friendly to the novice, but also makes it clear that this is an advanced technique that the reader will benefit from learning. ...The audiences that I think would be attracted to this book [are] faculty/researchers, graduate students and undergraduate students. ... Many of my past students have retained their edition of the text for future reference." – Brian Lawton, George Mason University, USA "It is the best book on the market for our course. ... I can use it not only as a textbook but also as a reference. ... It works well for the market ... across a range of disciplines in both the social and natural sciences. ...This book is really appropriate for postgraduate and research students. ...It is a starter book for SEM. Many people begin using AMOS and this is the perfect companion. ... I would recommend it to anyone looking to incorporate SEM into their research." – Rob Angell, Cardiff University, Wales "Dr. Byrne did an exceptional job in presenting the SEM terms and concepts in an understandable way, in adopting various examples from social science disciplines, and walking through readers with a variety of SEM applications using AMOS! ... [Market] Applied researchers who want to conduct SEM analyses using AMOS, no matter whether they have strong background in SEM or not. It is also a good supplementary text for graduate courses in SEM." – Yanyun Yang, Florida State University, USA "The book is appropriate for graduate students in the social and behavioral sciences ... to help with the execution of statistical analysis. Students and researchers in varied social and behavioral programs will be interested in using the book. ...It is a great book to assist with AMOS." – Julian Montoro-Rodriquez, California State University, San Bernardino, USA Table of ContentsSection 1: Introduction 1. Structural Equation Modeling: The Basics 2. Using the Amos Program Section 2: Single-Group Analyses Confirmatory Factor Analytic Models 3.. Application 1: Testing the Factorial Validity of a Theoretical Construct (First-Order CFA Model) 4. Application 2: Testing the Factorial Validity of Scores from a Measurement Scale (First-Order CFA Model) 5. Application 3: Testing the Validity of Scores from a Measurement Scale (Second-Order CFA Model) Full Latent Variable Model 6. Application 5: Testing the Validity of a Causal Structure Section 3: Multiple-Group Analyses Confirmatory Factor Analytic Models 7. Application 5: Testing Factorial Invariance of Scales from a Measurement Scale (First-Order CFA Model) 8. Application 6: Testing Invariance of Latent Mean Structures (First-Order CFA Model) Full Latent Variable Model 9. Application 7: Testing Invariance of a Causal Structure Section 4: Other Important Applications 10. Application 8: Testing Evidence of Construct Validity: The Multitrait-Multimethod Model 11. Application 9: Testing Change Over Time: The Latent Growth Curve Model Section 5: Other Important Topics 12. Application 10: Use of Bootstrapping in Addressing Non-normal Data 13. Application 11: Addressing the Issues of Incomplete Data

Read more less

Book SynopsisThe Reviewer's Guide to Quantitative Methods in the Social Sciences provides evaluators of research manuscripts and proposals in the social and behavioral sciences with the resources they need to read, understand, and assess quantitative work. 35 uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The second edition of this valuable resource updates readers on each technique's key principles, appropriate usage, underlying assumptions and limitations, providing reviewers with the information they need to offer constructive commentary on works they evaluate. Written by methodological and applied scholars, this volume is also an indispensable author's reference for preparing sound research manuscripts and proposals.Trade ReviewUpdated and even more useful, this much-needed volume fills a gap for review consultation and instructional purposes. Highly recommended! Michael G. Vaughn, Saint Louis UniversityThe first edition of this book belongs on every reviewer’s bookshelf. The second edition is even better and covers topics missed in the first.David L. Streiner, McMaster UniversityAs an editor for more than 20 years, I had long wrestled with what graduate students and reviewers need to understand and address when evaluating the quality of the quantitative analyses reported in manuscripts. That problem is made even more frustrating by the range of quantitative methods populating the educational research literature. Thanks to this outstanding volume those nagging concerns have largely been put to rest. What these respected editors have compiled are 35 insightful chapters, each devoted to a particular quantitative method and written by an acknowledged expert. Each chapter not only succinctly overviews a given technique, but also delineates the musts and shoulds of its reporting, which are summarized in an easily referenced table. I plan to make this essential guide required reading for all my graduate students and for every editorial board member.Patricia A. Alexander, Jean Mullan Professor of Literacy, Distinguished Scholar-Teacher, University of Maryland. Senior Editor, Contemporary Educational PsychologyGreg Hancock and his colleagues have done it again. The second edition of The Reviewer’s Guide to Quantitative Methods in the Social Sciences offers 35 chapters written by top-of-the line quantitative researchers who inspire, instruct, and illuminate. The chapters provide key information that is essential in evaluating a wide swath of methods including basic and multivariate statistics, research design, statistical inference procedures, psychometrics, latent variable methods, modeling and more. Every social scientist would benefit from this gem of a volume that cannot help but leave readers more informed, enlightened, and empoweredLisa L. Harlow, Professor of Psychology, University of Rhode IslandThe Reviewer’s Guide to Quantitative Methods in the Social Sciences, Second Edition, is an essential resource for editors, reviewers, researchers, and graduate students who desire to produce and disseminate accurate and meaningful quantitative research. Each of the 35 chapters provides a comprehensive overview of a particular aspect of quantitative study design and/or analytic technique, indicating where (within a research report) and how critical information should be conveyed. Of particular value are the recommendations for appropriate language to use in describing and interpreting quantitative findings, including admonitions against common reporting fallacies, gaps, and other improprieties. It goes without saying that all editors and reviewers should consult this authoritative guide as a de facto set of rigorous standards for evaluating quantitative inquiry, and emerging researchers would benefit tremendously from working with these insightful chapters as they develop a fundamental understanding of designing, conducting, and reporting their work.John Norris, Senior Research Director, Educational Testing ServiceThis book is not only of immense value to reviewers, but also a great help to academic writers developing an adequate account of their research outcomes and striving for accountability and transparancy. In 35 chapters (of which 5 are new to the 2nd edition) experts introduce in a very systematic way a large array of methodological techniques, ranging from well-known statistical analyses to more recent advanced ones, from design issues to psychometric analyses. All chapters are centered around a discussion of the desiderata or key elements that are required for a proper report of the technique, even indicating where to put it in a paper, and thus providing convenient evaluation criteria for reviewers and writers alike.This book is an invaluable reference guide, explaining the issues applied researchers are always wondering about: assumptions, computational options and interpretations.Rob Schoonen, Professor of Applied Linguistics, Radboud UniversityTable of Contents1. Analysis of Variance: Between-Groups Designs Robert A. Cribbie and Alan J. Klockars 2. Analysis of Variance: Repeated Measures Designs Lisa M. Lix and H. J. Keselman 3. Canonical Correlation Analysis Xitao Fan and Timothy R. Konold 4. Cluster Analysis Dena A. Pastor and Monica K. Erbacher 5. Correlation and Other Measures of Association Jill L. Adelson, Jason W. Osborne, and Brittany F. Crawford 6. Effect Sizes and Confidence Intervals Fiona Fidler and Geoff Cumming 7. Event History and Survival Analysis Paul D. Allison 8. Factor Analysis: Exploratory and Confirmatory Deborah L. Bandalos and Sara J. Finney 9. Generalizability Theory Amy Hendrickson and Ping Yin 10. Interrater Reliability and Agreement William T. Hoyt 11. Item Response Theory and Rasch Modeling R.J. De Ayala 12. Latent Class Analysis Karen M. Samuelsen and C. Mitchell Dayton 13. Latent Growth Curve Models Kristopher J. Preacher 14. Latent Transition Analysis David Rindskopf 15. Latent Variable Mixture Models Gitta Lubke 16. Logistic Regression and Extensions Ann A. O’Connell and K. Rivet Amico 17. Log-Linear Analysis Ronald C. Serlin and Michael A. Seaman 18. Mediation and Moderation Paul E. Jose 19. Meta-Analysis S. Natasha Beretvas 20. Monte Carlo Simulation Methods Daniel McNeish, Stephanie Lane, and Patrick Curran 21. Multidimensional Scaling Cody S. Ding and Se-Kang Kim 22. Multilevel Modeling D. Betsy McCoach 23. Multiple Regression Ken Kelley and Scott E. Maxwell 24. Multitrait-Multimethod Analysis Keith F. Widaman 25. Multivariate Analysis of Variance Keenan A. Pituch 26. Nonparametric Statistics Michael A. Seaman 27. Power Analysis Kevin R. Murphy 28. Propensity Scores and Matching Methods Elizabeth A. Smart 29. Reliability and Validity Ralph O. Mueller 30. Research Design Sharon Anderson Dannels 31. Single-Subject Design and Analysis Andrew L. Egel, Christine H. Barthold, Jennifer Lee Kuou, and Fayez S. Maajeeny 32. Social Network Analysis Tracy Sweet 33. Structural Equation Modeling Ralph O. Mueller and Gregory R. Hancock 34. Structural Equation Modeling: Multisample Covariance and Mean Structures Richard G. Lomax 35. Survey Sampling, Administration, and Analysis Laura M. Stapleton

Read more less

Book SynopsisThis text teaches readers how to plan, conduct, and write a research project and select and interpret data through its integrated approach to quantitative research methods. Although not a statistics book, students learn to master which technique to use when and how to analyze and interpret results, making them better consumers of research. Organized around the steps of conducting a research project, this book is ideal for those who need to analyze journal articles. With teaching experience in various departments, the authors know how to address the research problems faced by behavioral and social sciences students. Independent sections and chapters can be read in any order allowing for flexibility in assigning topics.Adopters applaud the bookâs clarity and applied interdependent approach to research. The book emphasizes five research approaches: randomized experimental, quasi-experimental, comparative, associational, and descriptive. These five approaches lead to threeTrade Review"Gliner, Morgan, and Leech have built upon and enhanced their previous work in designing this clear and comprehensive 3rd edition, perfect for a quantitative research design course, or as a go-to text for the quantitatively-focused researcher." —Erica Eckert, Kent State University, USA"We have used this textbook for a number of years. It is clear, well-organized, and written at a level suited to doctoral students who do applied work using a variety of research methodologies. The latest edition improves upon an already good book by adding sections from research articles that exemplify points the authors are making, tables and diagrams for visual learners, and references to effect size and power that reinforce the need for consideration of the research design from multiple perspectives. Our students do a good job of writing a research proposal using the information from this book." —Kathy Green, University of Denver, USA"This third edition is a comprehensive and well-written book for graduate students and researchers doing research in education and other applied areas. The book provides not only an in-depth discussion of research methods concepts, but also a lot of real life examples for illustration. It is an excellent reference source for anyone doing research." —Michael C. W. Yip, The Hong Kong Institute of Education, Hong Kong"This is an indispensable text for students of applied research and even though I have many years of experience myself, I find it very helpful in doing my own research projects and teaching research to graduate students. It sets out key messages about how to do research properly, and in this 3rd edition bases much of its advice on practical case studies." —Paul Kiff, The Research Academy, UK"This is an excellent textbook for any education or social science course on quantitative methods. The authors break down the elements of the research process into easily digestible segments that use examples from published research studies to demonstrate each step. This is a must for faculty introducing graduate students to quantitative methods and also as an easy to use reference for the experienced researcher." —Mark Kretovics, Kent State University, USATable of ContentsPart I. Introductory Chapters 1. Definitions, Purposes, and Dimensions of Research 2. Planning a Quantitative Research Project Part II: Quantitative Research Approaches, Questions, and Designs 3. Variables, Research Questions, and Hypotheses 4. Research Approaches 5. Randomized Experimental and Quasi-Experimental Designs 6. Single-Subject Designs 7. Non-experimental Approaches/Designs 8. Internal Validity Part III: Sampling, Measurement and Data Collection 9. Sampling and Introduction to External Validity 10. Measurement and Descriptive Statistics 11. Measurement Reliability 12. Measurement Validity 13. Types of Data Collection Techniques 14. Ethical Issues in Conducting the Study 15. Practical Issues in Data Collection and Coding Part IV: Data Analysis and Interpretation 16. Making Inferences from Sample Data I: The Null Hypothesis Significance Testing Approach 17. Making Inferences From Sample Data II: The Evidence-Based Approach 18. General Design Classifications for Selection of Difference Statistical Methods 19. Selection of Appropriate Statistical Methods: Integration of Design and Analysis 20. Data Analysis and Interpretation – Basic Difference Questions 21. Analysis and Interpretation of Basic Associational Research Questions 22. Analysis and Interpretation of Complex Research Questions Part V: Evaluating and Writing Research Reports 23. Evaluating Research Validity: Part I 24. Evaluating Research Validity: Part II 25. Evaluating Research for Evidence-Based Practice 26. Writing the Research Report

Read more less

Book SynopsisWith each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, AHandbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language.Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. The authors begin each chapter with descriptions of the data and the statistical techniques to be used. The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis. The core of the book centers on how to use Stata to perform analyses and how to interpret the results. The chapters conclude with several exercises based on data sets from different disciplines. A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study.Trade Review“…In this recent edition more exercises have been included at the end of each chapter. The exercises make use of data sets in the examples and further data sets that are downloadable from the Internet, giving the reader the opportunity to explore. The index is clear to follow and throughout the book there are plenty of references. The book is very accessible for the Stata novice or those requiring a concise summary of Stata’s functionality.” —Diane Berry (University College London), Journal of the Royal Statistical Society “…The book is clearly written and it provides a good introduction to the Stata language. … The book excels in giving very clear and concise descriptions of a number of important, modern statistical methodologies. …” —Michael Evans (University of Toronto), International Statistical Review, 2007 “Rabe-Hesketh and Everitt provide an authoritative overview of modern methods of applied statistics. … they cover the major methods of biostatistics together with explanations of how to perform these analyses using Stata. The authors also provide valuable references to other texts … The many examples and effective graphics add greatly to the value of this publication. … This book is well written and carefully copyedited. … owners of the [previous] editions would benefit from the updated material … a valuable overview of modern statistical methods. …” —The Stata Journal, Vol. 7, No. 2, 2007 "For Stata users who wish to keep up with the latest changes in the software, this handbook is an excellent reference." – Timothy J. Robinson, University of Wyoming, in Journal of the American Statistical Association, March 2008, Vol. 103, No. 481Praise for previous editions: “…a useful compendium of examples of Stata analyses and data management tricks…covers the gamut of major statistical applications, from the most basic to some quite sophisticated…extremely useful, well-written, and worth the price.” —The American Statistician “The book is very well written and is both an excellent introduction to Stata and a useful reference book on applied statistics. I would certainly recommend it to anybody interested in this package.” —Significance, December 2004 “… the handbook starts with a brief, but very useful, introduction to Stata including getting help with Stata, data management, writing simple programs, and how to keep Stata up to date. Particularly useful will be the introduction to the new graphics facilities which have changed out of all recognition from that available in previous versions. … The main strengths of this well-written and useful handbook lie in the broad range of statistical methods it covers and in the clear step-by-step examples.” —Statistics in Medicine“…In this recent edition more exercises have been included at the end of each chapter. The exercises make use of data sets in the examples and further data sets that are downloadable from the Internet, giving the reader the opportunity to explore. The index is clear to follow and throughout the book there are plenty of references. The book is very accessible for the Stata novice or those requiring a concise summary of Stata’s functionality.” —Diane Berry (University College London), Journal of the Royal Statistical Society “…The book is clearly written and it provides a good introduction to the Stata language. … The book excels in giving very clear and concise descriptions of a number of important, modern statistical methodologies. …” —Michael Evans (University of Toronto), International Statistical Review, 2007 “Rabe-Hesketh and Everitt provide an authoritative overview of modern methods of applied statistics. … they cover the major methods of biostatistics together with explanations of how to perform these analyses using Stata. The authors also provide valuable references to other texts … The many examples and effective graphics add greatly to the value of this publication. … This book is well written and carefully copyedited. … owners of the [previous] editions would benefit from the updated material … a valuable overview of modern statistical methods. …” —The Stata Journal, Vol. 7, No. 2, 2007 Praise for previous editions:“…a useful compendium of examples of Stata analyses and data management tricks…covers the gamut of major statistical applications, from the most basic to some quite sophisticated…extremely useful, well-written, and worth the price.” —The American Statistician “The book is very well written and is both an excellent introduction to Stata and a useful reference book on applied statistics. I would certainly recommend it to anybody interested in this package.” —Significance, December 2004 “… the handbook starts with a brief, but very useful, introduction to Stata including getting help with Stata, data management, writing simple programs, and how to keep Stata up to date. Particularly useful will be the introduction to the new graphics facilities which have changed out of all recognition from that available in previous versions. … The main strengths of this well-written and useful handbook lie in the broad range of statistical methods it covers and in the clear step-by-step examples.” —Statistics in MedicineTable of ContentsA Brief Introduction to Stata. Data Description and Simple Inference: Female Psychiatric Patients. Multiple Regression: Determinants of Pollution in U.S. Cities. Analysis of Variance I: Treating Hypertension. Analysis of Variance II: Effectiveness of Slimming Clinics. Logistic Regression: Treatment of Lung Cancer and Diagnosis of Heart Attacks. Generalized Linear Models: Australian School Children. Summary Measure Analysis of Longitudinal Data: Treatment of Post-Natal Depression. Random Effects Models: Thought Disorder and Schizophrenia. Generalized Estimating Equations: Epileptic Seizures and Chemotherapy. Some Epidemiology. Survival Analysis: Retention of Heroin Addicts in Methadone Maintenance Treatment. Maximum Likelihood Estimation: Age of Onset of Schizophrenia. Principal Components Analysis: Hearing Measurement using an Audiometer. Cluster Analysis: Tibetan Skulls and Determinants of Pollution in U.S. Cities. Appendix. References. Index.

Read more less

Book SynopsisThis new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion.Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book’s concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis.Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.Trade Review"An excellent volume on an array of next-steps for learning in the multilevel arena. The result is a superb resource on the state-of-the-art and the cutting edge of multilevel models. Each chapter brings the reader up to speed on an issue by providing a valuable synthesis of the existing analytic and empirical statistical work often enhanced by sound thinking and guidance. ... Aside from providing a valuable reference for researchers and instructors, it provides a deep well of possibilities for future statistical work in this area." - Jason T. Newsom, Portland State University, USA, in The American Statistician"Perhaps the most interesting aspect of this handbook is its focus on a range of non-standard multilevel analysis topics such as structural equation modelling (SEM), item response theory (IRT), mixture models and dyadic data analysis. It is the timely inclusion of these topics ... which distinguishes this handbook from competing books. ... Overall I enjoyed this handbook and would recommend it to advanced applied researchers with a firm grounding in multilevel analysis." - George Leckie, University of Bristol, UK in Journal of the Royal Statistical Society"This is a wonderful addition to the field of multilevel modeling. It is a state-of-the-art contribution from the frontiers of the field. Chapters are written by leading authorities and cover a wide array of models from introductory to more advanced. This book will become an essential reference resource." - George A. Marcoulides, University of California, Riverside, USA"The Handbook … covers a wide range of topics, both technical and applied; and the chapters address some of the most crucial and controversial issues in the field of multilevel modeling. This book is sure to become a classic reference, and I plan to keep it within an arms’ length of my computer at all times!" - Betsy McCoach, University of Connecticut, USA"This book presents a wide range of well-selected topics, like multilevel latent variable models, longitudinal data analysis, multilevel models for ordinal outcomes, design, model fit, bootstrapping, and missing data. Especially useful are the examples and the accompanying software codes." - Rolf Steyer, University of Jena, Germany "An outstanding set of authors who should advance the field’s understanding about ... multilevel modeling ... the coverage is excellent... .I would... recommend it to students who are doing dissertations on multilevel analysis... [and] in programs that are training methodologists. ...An excellent resource." - Ron Heck, University of Hawaii – Manoa, USA"It makes an important contribution to the field by bringing together many top experts to produce a ‘one-stop’ source for cutting-edge advanced MLM procedures. I would purchase this book and ... recommend it ... for students with strong quantitative interests using MLM... psychologists, child developmental, educational, and sociological researchers, to name just a few, would find relevance in this work." - Noel A. Card, University of Arizona, USA"A useful contribution to a rapidly developing area [that] promises to further fuel growth and interest in this area. ...The breadth of coverage and ... depth of treatment ... is a real strength.... [it] fills a gap in the material currently available." - Scott L. Thomas, Claremont Graduate University, USA"An excellent volume on an array of next-steps for learning in the multilevel arena. The result is a superb resource on the state-of-the-art and the cutting edge of multilevel models. Each chapter brings the reader up to speed on an issue by providing a valuable synthesis of the existing analytic and empirical statistical work often enhanced by sound thinking and guidance. ... Aside from providing a valuable reference for researchers and instructors, it provides a deep well of possibilities for future statistical work in this area." - Jason T. Newsom, Portland State University, USA, in The American Statistician"Perhaps the most interesting aspect of this handbook is its focus on a range of non-standard multilevel analysis topics such as structural equation modelling (SEM), item response theory (IRT), mixture models and dyadic data analysis. It is the timely inclusion of these topics ... which distinguishes this handbook from competing books. ... Overall I enjoyed this handbook and would recommend it to advanced applied researchers with a firm grounding in multilevel analysis." - George Leckie, University of Bristol, UK in Journal of the Royal Statistical Society"This is a wonderful addition to the field of multilevel modeling. It is a state-of-the-art contribution from the frontiers of the field. Chapters are written by leading authorities and cover a wide array of models from introductory to more advanced. This book will become an essential reference resource." - George A. Marcoulides, University of California, Riverside, USA"The Handbook … covers a wide range of topics, both technical and applied; and the chapters address some of the most crucial and controversial issues in the field of multilevel modeling. This book is sure to become a classic reference, and I plan to keep it within an arms’ length of my computer at all times!" - Betsy McCoach, University of Connecticut, USA"This book presents a wide range of well-selected topics, like multilevel latent variable models, longitudinal data analysis, multilevel models for ordinal outcomes, design, model fit, bootstrapping, and missing data. Especially useful are the examples and the accompanying software codes." - Rolf Steyer, University of Jena, Germany "An outstanding set of authors who should advance the field’s understanding about ... multilevel modeling...the coverage is excellent... .I would... recommend it to students who are doing dissertations on multilevel analysis... [and] in programs that are training methodologists. ...An excellent resource." - Ron Heck, University of Hawaii – Manoa, USA"It makes an important contribution to the field by bringing together many top experts to produce a ‘one-stop’ source for cutting-edge advanced MLM procedures. I would purchase this book and ... recommend it ... for students with strong quantitative interests using MLM... psychologists, child developmental, educational, and sociological researchers, to name just a few, would find relevance in this work." - Noel A. Card, University of Arizona, USA"A useful contribution to a rapidly developing area [that] promises to further fuel growth and interest in this area. ...The breadth of coverage and ... depth of treatment ... is a real strength.... [it] fills a gap in the material currently available." - Scott L. Thomas, Claremont Graduate University, USATable of ContentsPart 1. Introduction. J. Hox, J.K. Roberts, Multilevel Analysis: Where We Were and Where We Are. Part 2. Multilevel Latent Variable Modeling (LVM). B. Muthén, T. Asparouhov, Beyond Multilevel Regression Modeling: Multilevel Analysis in a General Latent Variable Framework. A. Kamata, B. Vaughn, Multilevel IRT Modeling. J. Vermunt, Mixture Models for Multilevel Data Sets. Part 3. Multilevel Models for Longitudinal Data. J. Hox, Panel Modeling: Random Coefficients and Covariance Structures. R.D. Stoel, F.G. Garre, Growth Curve Analysis using Multilevel Regression and Structural Equation Modeling. Part 4. Special Estimation Problems. D. Hedeker, R. J. Mermelstein, Multilevel Analysis of Ordinal Outcomes Related to Survival Data. E.L. Hamaker, I. Klugkist, Bayesian Estimation of Multilevel Models. H. Goldstein, Bootstrapping in Multilevel Models. S. van Buuren, Multiple Imputation of Multilevel Data. J. Kim, C.M. Swoboda, Handling Omitted Variable Bias in Multilevel Models: Model Specification Tests and Robust Estimation. J.K. Roberts, J.P. Monaco, H. Stovall, V. Foster, Explained Variance in Multilevel Models. E.L. Hamaker, P. van Hattum, R.M. Kuiper, H. Hoijtink, Model Selection Based on Information Criteria in Multilevel Modeling. M. Moerbeek, S. Teerenstra, Optimal Design in Multilevel Experiments. Part 5. Specific Statistical Issues. J. Algina, H. Swaminathan, Centering in Two-Level Nested Designs. S.N. Beretvas, Cross-Classified and Multiple Membership Models. D.A. Kenny, D.A. Kashy, Dyadic Data Analysis using Multilevel Modeling.

Read more less

Book SynopsisModeled after Barbara Byrne’s other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using Mplus Versions 5 & 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed illustrated and annotated testing of the hypothesized and post hoc models explanation and interpretation of all Mplus input and output files important caveats pertinent to the SEM application under study a description of the data and reference upon which the model was based the corresponding data and syntax files available under "Supplementary Material" below The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models.Intended for researchers, practitioners, and students who use SEM and Mplus, this book is an ideal resource for graduate level courses on SEM taught in psychology, education, business, and other social and health sciences and/or as a supplement for courses on applied statistics, multivariate statistics, intermediate or advanced statistics, and/or research design. Appropriate for those with limited exposure to SEM or Mplus, a prerequisite of basic statistics through regression analysis is recommended.Trade Review"Barbara Byrne has published another winner--a practically oriented, thorough, and accessible resource for students and researchers who want to harness the power of Mplus for their SEM analyses. The writing is clear and engaging. I anticipate assigning the book in my graduate SEM course and recommending it to fellow researchers. This book will be a valuable resource for moving from knowing about SEM to using it." - Rick H. Hoyle, Duke University, USA"This book provides a good starting point to newcomers to Mplus. It focuses, as it should for an introductory text, on the basics of 'classical' SEM. If you are new to SEM, plan on using Mplus, and are looking for an introductory text with minimal statistical jargon, this is it." - Albert Maydeu-Olivares, University of Barcelona, Spain"A solid introduction to the use of Mplus for SEM. All of the common types of structural equation models are illustrated using real examples, building the Mplus syntax from start to finish. The book is an excellent and readable guide for researchers and students who want to learn more about SEM in the context of Mplus." - Roger E. Millsap, Arizona State University, USA"A hallmark of Byrne's books is their accessibility to new users. … Byrne has done a great service to the field by bringing thousands of students and researchers to structural equation modeling through her clear writing and accessible examples. This book will be another contribution along those same lines. .... I field many, many questions … that could be answered by simply referring the asker to a book like Byrne's." - Kristopher J. Preacher, University of Kansas, USA"The book is targeted to non-mathematical readers, and hence it focuses on the applications of SEM. It does this very nicely, beginning from the part that covers the basic ideas of SEM and shows how to get started with the Mplus. Overall, this book is an excellent resource for a beginner interested in SEM with Mplus." -Kimmo Vehkalahti, Department of Social Research, Statistics, University of Helsinki, Finland"Through the use of illustrative examples, this much-needed and well-written book provides an accessible presentation of SEM with Mplus. Those new to SEM and/or Mplus will find Byrne’s book extremely useful as a companion textbook and long-term reference guide." - Sara J. Finney, James Madison University, USATable of ContentsPart 1: Introduction. 1. Structural Equation Models: The Basics. 2. Using the Mplus Program. Part 2: Single-Group Analyses. Confirmatory Factor Analytic Models 3. Testing the Factorial Validity of a Theoretical Construct (1st-order CFA Model). 4. Testing the Factorial Validity of Scores from a Measuring Instrument (1st-order CFA Model). 5. Testing the Validity of Scores from a Measuring Instrument (2nd-order CFA Model). The Full Latent Variable Model 6. Testing the Validity of a Causal Structure. Part 3: Multiple-Group Analyses. Confirmatory Factor Analytic Models 7. Testing for the Factorial Equivalence of a Measuring Instrument (Analysis of Covariance Structures). 8. Testing for the Equivalence of Latent Factor Means (Analysis of Mean and Covariance Structures). The Full Latent Variable Model 9. Testing for the Equivalence of a Causal Structure (Analysis of Covariance Structures). Part 4: Other Important Topics. 10. Testing Evidence of Construct Validity: The Multitrait-multimethod Model. 11. Testing Change Over Time: The Latent Growth Curve Model. 12. Testing Within- and Between-level Variability: The Multilevel Model.

Read more less

Book SynopsisThis is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.This book will help readers do just that. Sample computer code in R, SPSS, and SAS at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal.Organized according to various techniques, chapters 1 – 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 - 6 cover common statistical procedures -- analysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 - 11 review the new statistical procedures -- multi-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (HLM) and/or multilevel modeling and SEM taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the book‘s unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.Trade Review"This book extends earlier landmark texts by adding sample-size estimation for multilevel and longitudinal designs, meta-analysis, and structural-equation modeling. It is written thoughtfully and understandably. Readers will benefit enormously from the inclusion of computer code (in R, SAS and SPSS) for conducting the power analyses described. I recommend the book very highly to any researcher who wants to design research in the social sciences." - John B. Willett, Harvard University, USA"The author skillfully blends simple explanations of core concepts with more advanced material in a way that will make the work attractive to a range of readers in psychology and related disciplines. This text will be useful for postgraduate quantitative methods courses and for researchers. The coverage - from t tests through to multilevel models and SEM - is impressive. I found the examples of R, SPSS, and SAS code invaluable." - Thom Baguley, Nottingham Trent University, UK"This is a long-awaited, comprehensive book on power analysis after Cohen’s (1988) seminal book. The updated content accompanied by sample computer code is well suited for quantitative researchers in the social and behavioral sciences." - Wei Pan, Duke University, USA"This book provides a more comprehensive treatment of power analysis than any other work. ... This is likely to be the "go to" book for more complex designs. ... I found the writing style clear. ... The primary audience for this book would be all investigators who seek external funding for their work." - Warren W. Tryon, Fordham University, USA"This book would be good for departments of psychology, sociology, social work, nursing and public health. Most of the PhD programs in these departments have an advanced research methods course that could use this book. ... The Cohen book has been the standard in the field for over 20 years. ... This book would make a very nice update on a classic." - Jay Maddock, University of Hawaii, USA"This book is] a valuable extension beyond what is currently provided by other books on power. This book would contribute significantly to the field, most notably by covering the advanced and more complex techniques. …Liu and his work are well known in this field.…[This] book…could serve as the primary text for a …course on power. ...This book would basically have the field to itself." – Geoff Cumming, La Trobe University, AustraliaTable of Contents1. Introduction 2. Statistical Power 3. Power of Confidence Interval 4. Analysis of Variance 5. Linear Regression 6. Multivariate Analysis 7. Multi-level Models 8. Complex Multi-level Models 9. Meta-analysis 10. Structural Equation Models 11. Longitudinal Studies Appendix A. Cumulative Distribution Function for t, F, or x Appendix B. R Tutorial

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less