Currently there are many introductory textbooks on educational measurement and psychometrics as well as R. However, there is no single book that covers important topics in measurement and psychometrics as well as their applications in R. The Handbook of Educational Measurement and Psychometrics Using R covers a variety of topics, including classical test theory; generalizability theory; the factor analytic approach in measurement; unidimensional, multidimensional, and explanatory item response modeling; test equating; visualizing measurement models; measurement invariance; and differential item functioning. This handbook is intended for undergraduate and graduate students, researchers, and practitioners as a complementary book to a theory-based introductory or advanced textbook in measurement. Practitioners and researchers who are familiar with the measurement models but need to refresh their memory and learn how to apply the measurement models in R, would find this handbook quite fulfilling. Students taking a course on measurement and psychometrics will find this handbook helpful in applying the methods they are learning in class. In addition, instructors teaching educational measurement and psychometrics will find our handbook as a useful supplement for their course.

In this book, Hackett introduces the traditional usage of the mapping sentence within quantitative research, reviews its philosophical underpinnings, and proposes the "declarative mapping sentence" as an instrument and approach to qualitative scholarship. With a helpful glossary and a range of illustrative tables, Hackett takes the reader through a straightforward introduction to mapping sentences and their construction, before discussing declarative mapping sentences and possible future research directions. This innovative direction for social research provides a flexible structure for research domain, and it allows qualitative research results to be uniformly sorted. Declarative Mapping Sentences in Qualitative Research will be essential reading for researchers, academics, and postgraduate students in the fields of qualitative psychology and psychological methods, as well as philosophical psychology and social science research methods.

* Provides an up to date reference point for ethnographic research conducted into healthcare research * Embodies an outline of major methodological approaches to health and well-being ethnography * Includes illustrative case-studies of ethnographical research within the healthcare setting. * Offers a holistic view of ethnography, taking a multi-disciplinary approach

* Provides an up to date reference point for ethnographic research conducted into healthcare research * Embodies an outline of major methodological approaches to health and well-being ethnography * Includes illustrative case-studies of ethnographical research within the healthcare setting. * Offers a holistic view of ethnography, taking a multi-disciplinary approach

Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book's updated website.

A New Way of Analyzing Object Data from a Nonparametric Viewpoint Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields, including statistics, medical imaging, computer vision, pattern recognition, and bioinformatics. The book begins with a survey of illustrative examples of object data before moving to a review of concepts from mathematical statistics, differential geometry, and topology. The authors next describe theory and methods for working on various manifolds, giving a historical perspective of concepts from mathematics and statistics. They then present problems from a wide variety of areas, including diffusion tensor imaging, similarity shape analysis, directional data analysis, and projective shape analysis for machine vision. The book concludes with a discussion of current related research and graduate-level teaching topics as well as considerations related to computational statistics. Researchers in diverse fields must combine statistical methodology with concepts from projective geometry, differential geometry, and topology to analyze data objects arising from non-Euclidean object spaces. An expert-driven guide to this approach, this book covers the general nonparametric theory for analyzing data on manifolds, methods for working with specific spaces, and extensive applications to practical research problems. These problems show how object data analysis opens a formidable door to the realm of big data analysis.

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

Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem gives a number of perspectives from top methodologists and applied researchers on the best ways to attempt to answer Age-Period-Cohort related questions about society. Age-Period-Cohort (APC) analysis is a fundamental topic for any quantitative social scientist studying individuals over time. At the same time, it is also one of the most misunderstood and underestimated topics in quantitative methods. As such, this book is key reference material for researchers wanting to know how to deal with APC issues appropriately in their statistical modelling. It deals with the identification problem caused by the co-linearity of the three variables, considers why some currently used methods are problematic and suggests ideas for what applied researchers interested in APC analysis should do. Whilst the perspectives are varied, the book provides a unified view of the subject in a reader-friendly way that will be accessible to social scientists with a moderate level of quantitative understanding, across the social and health sciences.

Age, Period and Cohort Effects: Statistical Analysis and the Identification Problem gives a number of perspectives from top methodologists and applied researchers on the best ways to attempt to answer Age-Period-Cohort related questions about society. Age-Period-Cohort (APC) analysis is a fundamental topic for any quantitative social scientist studying individuals over time. At the same time, it is also one of the most misunderstood and underestimated topics in quantitative methods. As such, this book is key reference material for researchers wanting to know how to deal with APC issues appropriately in their statistical modelling. It deals with the identification problem caused by the co-linearity of the three variables, considers why some currently used methods are problematic and suggests ideas for what applied researchers interested in APC analysis should do. Whilst the perspectives are varied, the book provides a unified view of the subject in a reader-friendly way that will be accessible to social scientists with a moderate level of quantitative understanding, across the social and health sciences.

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB (R) routines used for the examples are available on the authors' website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

Item Response Theory clearly describes the most recently developed IRT models and furnishes detailed explanations of algorithms that can be used to estimate the item or ability parameters under various IRT models. Extensively revised and expanded, this edition offers three new chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo methods. It includes discussions on issues related to statistical theory, numerical methods, and the mechanics of computer programs for parameter estimation, which help to build a clear understanding of the computational demands and challenges of IRT estimation procedures.

Devising tests that evaluate a nation's educational standing and implement efficacious educational reforms requires a careful balance among the contributions of technology, psychometrics, test design, and the learning sciences. Unlike other forms of adaptive testing, multistage testing (MST) is highly suitable for testing educational achievement because it can be adapted to educational surveys and student testing. Computerized Multistage Testing: Theory and Applications covers the methodologies, underlying technology, and implementation aspects of this type of test design. The book discusses current scientific perspectives and practical considerations for each step involved in setting up an MST program. It covers the history of MST, test design and implementation for various purposes, item pool development and maintenance, IRT-based and classical test theory-based methodologies for test assembly, routing and scoring, equating, test security, and existing software. It also explores current research, existing operational programs, and innovative future assessments using MST. Intended for psychologists, social scientists, and educational measurement scientists, this volume provides the first unified source of information on the design, psychometrics, implementation, and operational use of MST. It shows how to apply theoretical statistical tools to testing in novel and useful ways. It also explains how to explicitly tie the assumptions made by each model to observable (or at least inferable) data conditions. Winner of the 2016 AERA Award for Significant Contribution to Educational Measurement and Research Methodology The 2016 American Education Research Association (AERA) Div. D award committee for Significant Contributions to Educational Measurement and Research Methodology has recognized unanimously this collaborative work advancing the theory and applications of computerized MST. This annual award recognizes published research judged to represent a significant conceptual advancement in the theory and practice of educational measurement and/or educational research methodology. The 2016 award was made under the heading: Measurement, Psychometrics, and Assessment. This collective work, published in 2014 as an edited volume titled Computerized Multistage Testing: Theory and Applications, was cited by the committee both for the originality of the conceptual foundations presented in support of multistage testing and for arguing persuasively for its potential impact on the practice of educational measurement.

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity. Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.

This volume explores the abiding intellectual inertia in scientific psychology in relation to the discipline's engagement with problematic beliefs and assumptions underlying mainstream research practices, despite repeated critical analyses which reveal the weaknesses, and in some cases complete inappropriateness, of these methods. Such paradigmatic inertia is especially troublesome for a scholarly discipline claiming status as a science. The book offers penetrating analyses of many (albeit not all) of the most important areas where mainstream practices require either compelling justifications for their continuation or adjustments - possibly including abandonment - toward more apposite alternatives. Specific areas of concern addressed in this book include the systemic misinterpretation of statistical knowledge; the prevalence of a conception of measurement at odds with yet purporting to mimic the natural sciences; the continuing widespread reliance on null hypothesis testing; and the continuing resistance within psychology to the explicit incorporation of qualitative methods into its methodological toolbox. Broader level chapters examine mainstream psychology's systemic disregard for critical analysis of its tenets, and the epistemic and ethical problems this has created. This is a vital and engaging resource for researchers across psychology, and those in the wider behavioural and social sciences who have an interest in, or who use, psychological research methods.

This book critically examines the work of a number of pioneers of social psychology, including legendary figures such as Kurt Lewin, Leon Festinger, Muzafer Sherif, Solomon Asch, Stanley Milgram, and Philip Zimbardo. Augustine Brannigan argues that the reliance of these psychologists on experimentation has led to questions around validity and replication of their studies. The author explores new research and archival work relating to these studies and outlines a new approach to experimentation that repudiates the use of deception in human experiments and provides clues to how social psychology can re-articulate its premises and future lines of research. Based on the author's 2004 work The Rise and Fall of Social Psychology, in which he critiques the experimental methods used, the book advocates for a return to qualitative methods to redeem the essential social dimensions of social psychology. Covering famous studies such as the Stanford Prison Experiment, Milgram's studies of obedience, Sherif's Robbers Cave, and Rosenhan's expose of psychiatric institutions, this is essential and fascinating reading for students of social psychology, and the social sciences. It's also of interest to academics and researchers interested in engaging with a critical approach to classical social psychology, with a view to changing the future of this important discipline.

This book critically examines the work of a number of pioneers of social psychology, including legendary figures such as Kurt Lewin, Leon Festinger, Muzafer Sherif, Solomon Asch, Stanley Milgram, and Philip Zimbardo. Augustine Brannigan argues that the reliance of these psychologists on experimentation has led to questions around validity and replication of their studies. The author explores new research and archival work relating to these studies and outlines a new approach to experimentation that repudiates the use of deception in human experiments and provides clues to how social psychology can re-articulate its premises and future lines of research. Based on the author's 2004 work The Rise and Fall of Social Psychology, in which he critiques the experimental methods used, the book advocates for a return to qualitative methods to redeem the essential social dimensions of social psychology. Covering famous studies such as the Stanford Prison Experiment, Milgram's studies of obedience, Sherif's Robbers Cave, and Rosenhan's expose of psychiatric institutions, this is essential and fascinating reading for students of social psychology, and the social sciences. It's also of interest to academics and researchers interested in engaging with a critical approach to classical social psychology, with a view to changing the future of this important discipline.

This book covers statistical consequences of breaches of research integrity such as fabrication and falsification of data, and researcher glitches summarized as questionable research practices. It is unique in that it discusses how unwarranted data manipulation harms research results and that questionable research practices are often caused by researchers' inadequate mastery of the statistical methods and procedures they use for their data analysis. The author's solution to prevent problems concerning the trustworthiness of research results, no matter how they originated, is to publish data in publicly available repositories and encourage researchers not trained as statisticians not to overestimate their statistical skills and resort to professional support from statisticians or methodologists. The author discusses some of his experiences concerning mutual trust, fear of repercussions, and the bystander effect as conditions limiting revelation of colleagues' possible integrity breaches. He explains why people are unable to mimic real data and why data fabrication using statistical models stills falls short of credibility. Confirmatory and exploratory research and the usefulness of preregistration, and the counter-intuitive nature of statistics are discussed. The author questions the usefulness of statistical advice concerning frequentist hypothesis testing, Bayes-factor use, alternative statistics education, and reduction of situational disturbances like performance pressure, as stand-alone means to reduce questionable research practices when researchers lack experience with statistics.

There is a recent surge in the use of randomized controlled trials (RCTs) within education globally, with disproportionate claims being made about what they show, 'what works', and what constitutes the best 'evidence'. Drawing on up-to-date scholarship from across the world, Taming Randomized Controlled Trials in Education critically addresses the increased use of RCTs in education, exploring their benefits, limits and cautions, and ultimately questioning the prominence given to them. While acknowledging that randomized controlled trials do have some place in education, the book nevertheless argues that this place should be limited. Drawing together all arguments for and against RCTs in a comprehensive and easily accessible single volume, the book also adds new perspectives and insights to the conversation; crucially, the book considers the limits of their usefulness and applicability in education, raising a range of largely unexplored concerns about their use. Chapters include discussions on: The impact of complexity theory and chaos theory. Design issues and sampling in randomized controlled trials. Learning from clinical trials. Data analysis in randomized controlled trials. Reporting, evaluating and generalizing from randomized controlled trials. Considering key issues in understanding and interrogating research evidence, this book is ideal reading for all students on Research Methods modules, as well as those interested in undertaking and reviewing research in the field of education.

This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many 'random effects'. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write-up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to 'translate' their skills with more traditional models to a Bayesian framework, will benefit greatly from the lessons in this text.

* Explores the overlap/parallels between the work of clinicians and qualitative researchers. * Suggests how postmodern therapeutic approaches can be integrated into methods of data collection and analysis. * Examines a range of postmodern therapies, including collaborative language systems, narrative therapy, and solution-focused brief therapy. * Offers an innovative and unique way of enhancing the skills of the qualitative researcher, with an emphasis on reflective practice.

"Describes recent developments and surveys important topics in the areas of multivariate analysis, design of experiments, and survey sampling. Features the work of nearly 50 international leaders."

* Explores the overlap/parallels between the work of clinicians and qualitative researchers. * Suggests how postmodern therapeutic approaches can be integrated into methods of data collection and analysis. * Examines a range of postmodern therapies, including collaborative language systems, narrative therapy, and solution-focused brief therapy. * Offers an innovative and unique way of enhancing the skills of the qualitative researcher, with an emphasis on reflective practice.

There is a recent surge in the use of randomized controlled trials (RCTs) within education globally, with disproportionate claims being made about what they show, 'what works', and what constitutes the best 'evidence'. Drawing on up-to-date scholarship from across the world, Taming Randomized Controlled Trials in Education critically addresses the increased use of RCTs in education, exploring their benefits, limits and cautions, and ultimately questioning the prominence given to them. While acknowledging that randomized controlled trials do have some place in education, the book nevertheless argues that this place should be limited. Drawing together all arguments for and against RCTs in a comprehensive and easily accessible single volume, the book also adds new perspectives and insights to the conversation; crucially, the book considers the limits of their usefulness and applicability in education, raising a range of largely unexplored concerns about their use. Chapters include discussions on: The impact of complexity theory and chaos theory. Design issues and sampling in randomized controlled trials. Learning from clinical trials. Data analysis in randomized controlled trials. Reporting, evaluating and generalizing from randomized controlled trials. Considering key issues in understanding and interrogating research evidence, this book is ideal reading for all students on Research Methods modules, as well as those interested in undertaking and reviewing research in the field of education.

Factor 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, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.

This book, originally published in 1970, concerns the new technique of computer simulation in psychology at the time. Computer programs described include models of learning, problem-solving, pattern recognition, the use of language, and personality. More general topics are discussed including the evaluation of such models, the relation of the field to cybernetics, and the problem posed by consciousness. Today it can be read and enjoyed in its historical context.