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This book explores the fundamentals of multidimensional scaling (MDS) and how this analytic method can be used in applied setting for educational and psychological research. The book tries to make MDS more accessible to a wider audience in terms of the language and examples that are more relevant to educational and psychological research and less technical so that the readers are not overwhelmed by equations. The goal is for readers to learn the methods described in this book and immediately start using MDS via available software programs. The book also examines new applications that have previously not been discussed in MDS literature. It should be an ideal book for graduate students and researchers to better understand MDS. Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research is divided into three parts. Part I covers the basic and fundamental features of MDS models pertaining to applied research applications. Chapters in this section cover the essential features of data that are typically associated with MDS analysis such as preference ration or binary choice data, and also looking at metric and non-metric MDS models to build a foundation for later discussion and applications in later chapters. Part II examines specific MDS models and its applications for education and psychology. This includes spatial analysis methods that can be used in MDS to test clustering effect of items and individual differences MDS model (INDSCAL). Finally, Part III focuses on new applications of MDS analysis in these research fields. These new applications consist of profile analysis, longitudinal analysis, mean-level change, and pattern change. The book concludes with a historical review of MDS development as an analytical method and a look to future directions.
The 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.
This book focuses on experimental research in two disciplines that have a lot of common ground in terms of theory, experimental designs used, and methods for the analysis of experimental research data: education and psychology. Although the methods covered in this book are also frequently used in many other disciplines, including sociology and medicine, the examples in this book come from contemporary research topics in education and psychology. Various statistical packages, commercial and zero-cost Open Source ones, are used. The goal of this book is neither to cover all possible statistical methods out there nor to focus on a particular statistical software package. There are many excellent statistics textbooks on the market that present both basic and advanced concepts at an introductory level and/or provide a very detailed overview of options in a particular statistical software programme. This is not yet another book in that genre. Core theme of this book is a heuristic called the question-design-analysis bridge: there is a bridge connecting research questions and hypotheses, experimental design and sampling procedures, and common statistical methods in that context. Each statistical method is discussed in a concrete context of a set of research question with directed (one-sided) or undirected (two-sided) hypotheses and an experimental setup in line with these questions and hypotheses. Therefore, the titles of the chapters in this book do not include any names of statistical methods such as ‘analysis of variance’ or ‘analysis of covariance’. In a total of seventeen chapters, this book covers a wide range of topics of research questions that call for experimental designs and statistical methods, fairly basic or more advanced.
By uniting key concepts and methods from education, psychology, statistics, econometrics, medicine, language, and forensic science, this textbook provides an interdisciplinary methodological approach to study human learning processes longitudinally. This longitudinal approach can help to acquire a better understanding of learning processes, can inform both future learning and the revision of educational content and formats, and may help to foster self-regulated learning skills. The initial section of this textbook focuses on different types of research questions as well as practice-driven questions that may refer to groups or to individual learners. This is followed by a discussion of different types of outcome variables in educational research and practice, such as pass/fail and other dichotomies, multi-category nominal choices, ordered performance categories, and different types of quantifiable (i.e., interval or ratio level of measurement) variables. For each of these types of outcome variables, single-measurement and repeated-measurements scenarios are offered with clear examples. The book then introduces cross-sectional and longitudinal interdependence of learning-related variables through emerging network-analytic methods and in the final part the learned concepts are applied to different types of studies involving time series. The book concludes with some general guidelines to give direction to future (united) educational research and practice. This textbook is a must-have for all applied researchers, teachers and practitioners interested in (the teaching of) human learning, instructional design, assessment, life-long learning or applications of concepts and methods commonly encountered in fields such as econometrics, psychology, and sociology to educational research and practice.
This unique text covers the core research methods and the philosophical assumptions that underlie various strategies, designs, and methodologies used when researching cultural issues. It teaches readers why and for what purpose one conducts research on cultural issues so as to give them a better sense of the thinking that should happen before they go out and collect data. More than a "methods text", it is about all the steps that go into doing cross-cultural research. It discusses how to select the most appropriate methods for data analysis and which approach to use, and details quantitative, qualitative, and mixed methods for experimental lab studies and ethnographic field work.
Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis.
Fundamentals and Frontiers of Medical Education and Decision-Making brings together international experts to consider the theoretical, practical, and sociocultural foundations of health professions education. In this volume, the authors review the foundational theories that have informed the early transition to competency-based education. Moving beyond these monolithic models, the authors draw from learning and psychological sciences to provide a means to operationalize competencies. The chapters cover fundamental topics including the transition from novices to experts, the development of psychomotor skills in surgery, the role of emotion and metacognition in decision-making, and how practitioners and laypeople represent and communicate health information. Each section provides chapters that integrate and advance our understanding of health professions education and decision- making. Grounded in psychological science, this book highlights the fundamental issues faced by healthcare professionals, and the frontiers of learning and decision-making. It is important reading for a wide audience of healthcare professionals, healthcare administrators, as well as researchers in judgment and decision-making.
This book provides an introduction to test equating, scaling and linking, including those concepts and practical issues that are critical for developers and all other testing professionals. In addition to statistical procedures, successful equating, scaling and linking involves many aspects of testing, including procedures to develop tests, to administer and score tests and to interpret scores earned on tests. Test equating methods are used with many standardized tests in education and psychology to ensure that scores from multiple test forms can be used interchangeably. Test scaling is the process of developing score scales that are used when scores on standardized tests are reported. In test linking, scores from two or more tests are related to one another. Linking has received much recent attention, due largely to investigations of linking similarly named tests from different test publishers or tests constructed for different purposes. In recent years, researchers from the education, psychology and statistics communities have contributed to the rapidly growing statistical and psychometric methodologies used in test equating, scaling and linking. In addition to the literature covered in previous editions, this new edition presents coverage of significant recent research. In order to assist researchers, advanced graduate students and testing professionals, examples are used frequently and conceptual issues are stressed. New material includes model determination in log-linear smoothing, in-depth presentation of chained linear and equipercentile equating, equating criteria, test scoring and a new section on scores for mixed-format tests. In the third edition, each chapter contains a reference list, rather than having a single reference list at the end of the volume The themes of the third edition include: * the purposes of equating, scaling and linking and their practical context * data collection designs * statistical methodology * designing reasonable and useful equating, scaling, and linking studies * importance of test development and quality control processes to equating * equating error, and the underlying statistical assumptions for equating
This collection brings together the principal sources in the development of the techniques of social network analysis, from early metaphorical statements in Simmel and Radcliffe-Brown through the more systematic explorations in sociology and social anthropology, to contemporary formalizations. A new introduction explores the history of Social Networks and highlights the arguments of those who treat social network analysis as a loose, qualitative approach as well as those who see its potential in technical, mathematical uses. The thematically organized coverage includes: * Part I: Conceptualizing Social Networks * Part II: Topics and Developments in Graph Theory * Part III: Further Mathematical Models for Networks * Part IV: Applications: Family and Community * Part V: Applications: Corporate Power and Economic Structures * Part VI: Applications: Political, Protest, and Policy Networks * Part VII: Applications: Knowledge, Reputation, and Diffusion
The subject of management research methodology is enthralling and complex. A student or a practitioner of management research is beguiled by uncertainties in the search and identification of the research problem, intrigued by the ramifications of research design, and confounded by obstacles in obtaining accurate data and complexities of data analysis. Management Research Methodology: Integration of Principles, Methods and Techniques seeks a balanced treatment of all these aspects and blends problem-solving techniques, creativity aspects, mathematical modelling and qualitative approaches in order to present the subject of Management Research Methodology in a lucid and easily understandable way.