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Robert E. McGrath offers a comprehensive survey of quantitative methods and concepts in psychology that covers everything needed at the graduate level and beyond.
`I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology' - David C Howell, Professor Emeritus, University of Vermont Quantitative psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area. Drawing on a global scholarship, the Handbook is divided into seven parts: Part One: Design and Inference: addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance. Part Two: Measurement Theory: begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item-response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis. Part Three: Scaling Methods: covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next. Part Four: Data Analysis: includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models. Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis. Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.
Looks in detail at the problems involved in attempting to reconcile qualitative and quantitative methods, providing both theoretical and methodological guidance as well as practical examples of how methods can be fruitfully combined.
When seeking to test specific hypotheses in large data sets, social and behavioral scientists often construct models. Although useful in such situations, many phenomena of interest do not occur in large samples and do not lend themselves to precise measurement. In addition, a focus on hypothesis testing can constrict the potential use of models as organizing devices for emerging patterns -- summaries of what we believe we know about the dynamics of situation. This book bridges the gap between "quantitative" and "qualitative" modelers to reconcile the need to impose rigor and to understand the influence of context. Although there are many different uses for models, there is also the realistic possibility of doing credible research without their use. A critical reexamination of the assumptions used in quantitatively-oriented models, however, suggests ways to increase their effectiveness as organizers of both quantitative and qualitative data. Students of methods in psychology, sociology, education, management, social work, and public health -- and their instructors -- are increasingly expected to become familiar with both quantitative and qualitative approaches. Unfortunately, they find few vehicles for communication regarding the implications of overlapping work between the two approaches. Using models as organizing devices for a better dialogue between assumptions and data might facilitate this communication process.
Quantitative consumer research has long been the backbone of consumer psychology producing insights with peerless validity and reliability. This new book addresses a broad range of approaches to consumer psychology research along with developments in quantitative consumer research. Experts in their respective fields offer a perspective into this rapidly changing discipline of quantitative consumer research. The book focuses on new techniques as well as adaptations of traditional approaches and addresses ethics that relate to contemporary research approaches. The text is appropriate for use with university students at all academic levels. Each chapter provides both a theoretical grounding in its topic area and offers applied examples of the use of the approach in consumer settings. Exercises are provided at the end of each chapter to test student learning. Topics covered are quantitative research techniques, measurement theory and psychological scaling, mapping sentences for planning and managing research, using qualitative research to elucidate quantitative research findings, big data and its visualization, extracting insights from online data, modeling the consumer, social media and digital market analysis, connectionist modeling of consumer choice, market sensing and marketing research, preparing data for analysis;, and ethics. The book may be used on its own as a textbook and may also be used as a supplementary text in quantitative research courses.
This proceedings volume highlights the latest research and developments in psychometrics and statistics. It represents selected and peer-reviewed presentations given at the 85th Annual International Meeting of the Psychometric Society (IMPS), held virtually on July 13-17, 2020. The IMPS is one of the largest international meetings on quantitative measurement in education, psychology and the social sciences. It draws approximately 500 participants from around the world, featuring paper and poster presentations, symposiums, workshops, keynotes, and invited presentations. Leading experts and promising young researchers have written the included chapters. The chapters address a wide variety of topics including but not limited to item response theory, adaptive testing, Bayesian estimation, propensity scores, and cognitive diagnostic models. This volume is the 9th in a series of recent works to cover research presented at the IMPS.
This concise reference serves as a companion to traditional research texts by focusing on such essentials as model construction, robust methodologies and defending a compelling hypothesis. Designed to wean Master's and doctorate-level students as well as new researchers from their comfort zones, the book challenges readers to engage in multi-method approaches to answering multidisciplinary questions. The result is a step-by-step framework for producing well-organized, credible papers based on rigorous, error-free data. The text begins with a brief grounding in the intellectual attitude and logical stance that underlie good research and how they relate to steps such as refining a topic, creating workable models and building the right amount of complexity. Accessible examples from psychology and business help readers grasp the fine points of observations, interviewing, simulations, interpreting and finalizing data and presenting results. Fleshed out with figures, tables, key terms, tips, and questions, this book acts as both a friendly lecturer and a multilevel reality check.
In this collection, international contributors come together to discuss how qualitative and quantitative methods can be used in psychotherapy research. The book considers the advantages and disadvantages of each approach, and recognises how each method can enhance our understanding of psychotherapy. Divided into two parts, the book begins with an examination of quantitative research and discusses how we can transfer observations into numbers and statistical findings. Chapters on quantitative methods cover the development of new findings and the improvement of existing findings, identifying and analysing change, and using meta-analysis. The second half of the book comprises chapters considering how qualitative and mixed methods can be used in psychotherapy research. Chapters on qualitative and mixed methods identify various ways to strengthen the trustworthiness of qualitative findings via rigorous data collection and analysis techniques. Adapted from a special issue of Psychotherapy Research, this volume will be key reading for researchers, academics, and professionals who want a greater understanding of how a particular area of research methods can be used in psychotherapy.
Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to todays complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.
This exciting new core textbook offers a clear and practical introduction to quantitative methods, taking a project-based approach. The author's extensive knowledge and straightforward writing style ensure that students are steered through the process step-by-step, from developing research questions and preparing data for analysis, to explaining how to present data in appropriate formats, avoid bias, and write up results and reports. Featuring a comprehensive pedagogical framework and companion website, readers are encouraged to follow practice analyses as they go, with examples given in both SPSS and Excel, and templates are provided for students' own research. In addition to covering the research project, chapters also cover the essential mathematical and statistical analyses that are a logical consequence of posing a quantitative research methods question. This is the perfect text for all social science students studying introductory modules on quantitative methods, research methods or statistics at undergraduate or postgraduate level. It also functions as an effective guide for undergraduate and postgraduate students faced with an independent research project.