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Secondary Data in Mixed Methods Research by Daphne C. Watkins, the latest contribution to the Mixed Methods Research Series, offers unique and necessary instruction in this growing topic. With the increasing amount of secondary data available through journals and repositories, researchers have a trove of sources for new investigations at their fingertips, but few books to guide them. This brief text provides readers with a step-by-step procedure for incorporating secondary data into various mixed methods research designs, as well as identifying key characteristics of existing datasets that make them good candidates for mixed methods projects and giving ideas for new uses of secondary data. Introductory chapters help the reader understand the “what” and “why” of secondary data. Subsequent chapters address the use of secondary data in convergent, exploratory sequential, explanatory sequential, and other complex research designs. The final chapters delve into writing and reporting on projects before, during, and after the project. Quotes throughout the chapter help readers remember key bits of knowledge, while learning objectives and summaries in each chapter structure the reading experience. Application questions at the end of each chapter help readers recall information and apply it to their own research projects. By emphasizing how to use existing qualitative and quantitative datasets in mixed methods research, Secondary Data in Mixed Methods Research will help readers answer new and ongoing questions in social science research.

This volume is a comprehensive overview of Content Analysis (CA), whose extraordinary potential is operationally flexible, compatible with different techniques, theoretically creative, and multidisciplinary at its core. Also, CA keeps tradition and innovation together: as technology advances, CA can more efficiently perform its typical functions and proves its worth in new fields. The book illustrates the main characteristics, perspectives, plurality of objects, and contexts of use of CA, focusing on the various practical strategies that it entails and on their combinations. The aim is to provide readers with a concrete guide, presenting a research notebook that explores a unique empirical-methodological heritage – such working style is replicable and the goal is to transmit it as clearly as possible.

The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website (www.appliedmissingdata.com) includes data sets and analysis examples from the book, up-to-date software information, and other resources. New to This Edition •Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables. •New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics. •Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. •Most of the book is entirely new.

Les méthodes qualitatives occupent une place grandissante en sciences humaines et sociales, constituant depuis leurs origines américaines un courant aussi riche que diversifié.Cet ouvrage, véritable manuel, permet d'appréhender aussi bien l'histoire que les enjeux épistémologiques et applications pratiques de ces méthodes dans leur diversité (analyses thématique, à l'aide des catégories, en mode écriture...).Réactualisé dans cette 5e édition, il cible toute la fécondité du dénominateur commun à ces méthodes : une démarche rigoureuse de reformulation, d'explicitation ou de théorisation des données d'enquête participant de la découverte et de la construction de sens.

Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background.Highlights of the new edition include: More learning tools to strengthen readers'understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings. Greater emphasis on the use of R packages; readers can download the R code from the Routledge website. Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4). A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6). Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10). Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences. Professionals in these areas will also appreciate the book's accessible introduction.

Este livro clássico, citado mais de 1.900 vezes, oferece um entendimento profundo do modelo de Rasch. Os autores examinam as propriedades fundamentais do modelo e demonstram seu uso com uma variedade de exemplos nas áreas de educação, psicologia e saúde. Um glossário e numerosas ilustrações ajudam os leitores a compreenderem melhor esse modelo. Os leitores aprendem a aplicar a análise de Rasch para que possam fazer suas próprias análises e interpretar os resultados obtidos. Os autores apresentam uma visão geral acessível que não requer formação matemática. Este livro destina-se aos cursos de pós-graduação em Medição, Teoria de Resposta ao Item, Métodos de Pesquisa (avançados) ou Análise Quantitativa ministrados em Psicologia, Educação, Desenvolvimento Humano, Administração de Empresas e outras ciências sociais ou da saúde. Profissionais dessas áreas também se beneficiarão com a introdução acessível apresentada no livro.

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.

The use of mixed methods designs for conducting research has become a major trend in social science research. Renowned methodological experts Janice Morse and Linda Niehaus present a guide to intermediate and experienced researchers on the possibilities inherent in mixed method research. They offer the basic principles of conducting this kind of study, then examine a wide variety of design options available to the researcher, including their strengths and weaknesses and when to use them. Providing examples from a variety of disciplines, examining potential threats to validity, and showing the relationship between method and theory, the book will be a valuable addition to the methodologist's library and a useful text in courses in research design.

Cited over 1900 times, this classic text facilitates a deep understanding of the Rasch model. The authors review the crucial properties of the model and demonstrate its use with a variety of examples from education, psychology, and health. A glossary and numerous illustrations aid the reader's understanding. Readers learn how to apply Rasch analysis so they can perform their own analyses and interpret the results. The authors present an accessible overview that does not require a mathematical background. Highlights of the new edition include: -More learning tools to strengthen readers'understanding including chapter introductions, boldfaced key terms, chapter summaries, activities, and suggested readings. -Divided chapters (4, 6, 7 & 8) into basic and extended understanding sections so readers can select the level most appropriate for their needs and to provide more in-depth investigations of key topics. -A website at www.routledge.com/9780415833424 that features free Rasch software, data sets, an Invariance worksheet, detailed instructions for key analyses, and links to related sources. -Greater emphasis on the role of Rasch measurement as a priori in the construction of scales and its use post hoc to reveal the extent to which interval scale measurement is instantiated in existing data sets. -Emphasizes the importance of interval level measurement data and demonstrates how Rasch measurement is used to examine measurement invariance. -Insights from other Rasch scholars via innovative applications (Ch. 9). -Extended discussion of invariance now reviews DIF, DPF, and anchoring (ch. 5). -Revised Rating Scale Model material now based on the analysis of the CEAQ (ch.6). -Clarifies the relationships between Rasch measurement, True Score Theory, and Item Response Theory by reviewing their commonalities and differences (Ch.13). -Provides more detail on how to conduct a Rasch analysis so readers can use the techniques on their own (Appendix B). Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences, professionals in these areas also appreciate the book‘s accessible introduction.

This volume presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These methods include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. This volume contains the following five sections: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models). Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.

Lineare Strukturgleichungsmodelle sind aus der verhaltenswissenschaftlichen Forschung nicht mehr wegzudenken. Sie sind ein nützliches Werkzeug, um Hypothesen über Beziehungen zwischen Variablen zu prüfen und – mehr noch – Implikationen kausaler Strukturen zu testen. In diesem Buch werden Grundlagen, Hintergründe und die Vorgehensweisen bei der Spezifizierung von Kausalmodellen diskutiert. Dies wird an einem empirischen Beispiel und unter Verwendung des lavaan-Pakets innerhalb der Software R illustriert. Der Schwerpunkt des Buchs liegt hierbei weniger auf methodischen und statistischen Aspekten eines Modells, sondern auf der adäquaten Übersetzung der theoretischen Vorstellungen in ein Modell. Ein besonderer Stellenwert wird dem Verständnis der kausalen Implikationen von Modellen, der theoretische Bedeutung von Variablen, der Diagnostik einer Fehlanpassung an die Daten und modernen Konzepten wie dem Instrumentalvariablen-Ansatz aus der Ökonometrie und dem d-separation-Konzept, eingeräumt. Somit bietet das Buch sowohl einen Einstieg für Anfänger/innen als auch interessante Inhalte für Fortschrittene.

Focusing on research designs for projects that collect both qualitative and quantitative data, this practical book discusses strategies for bringing qualitative and quantitative methods together so that their combined strengths accomplish more than is possible with a single method. The approach is broadly interdisciplinary, reflecting the interest in mixed methods research of social scientists from anthropology, communication, criminal justice, education, evaluation, nursing, organizational behavior, psychology, political science, public administration, public health, sociology, social work, and urban studies. In contrast to an'anything goes'approach or a naïve hope that'two methods are better than one,'the author argues that projects using mixed methods must pay even more attention to research design than single method approaches. The book's practical emphasis on mixed methods makes it useful both to active researchers and to students who intend to pursue such a career.

One central and enduring image of the social science researcher is of an individual who commits a great deal of time to collecting original, primary data from a field of enquiry. This approach is often underpinned by a sincerely held belief that key research questions can only be explored by the collection of ever new, and ever greater amounts of data, or that already existing data are insufficient for researchers to test their ideas. Yet such an approach to social science research can be problematic not least because the collection of primary data can be an expensive, time-consuming, and even wasteful approach to social enquiry. Secondary analysis can serve many purposes, as well as being a valid approach in its own right. However, despite its widespread application, secondary analysis is often undervalued or perceived to be the preserve of only those interested in the re-use of large-scale survey data. Highlighting both the theory and practice of secondary analysis and the use of secondary sources, this collection considers the nature of secondary analysis as a research tool; reflects on the definitional debates surrounding terms such as secondary analysis, data re-use and restudies; illustrates how secondary analysis is used in social science research; and finally reviews the practical, methodological and ethical aspects of secondary analysis. Volume One: Using Secondary Sources and Secondary Analysis Volume Two: Quantitative Approaches to Secondary Analysis Volume Three: Qualitative Data and Research in Secondary Analysis Volume Four: Ethical, Methodological and Practical Issues in Secondary Analysis

This is a book for any researcher using any kind of survey data. It introduces the latest methods of assessing the quality and validity of such data by providing new ways of interpreting variation and measuring error. By practically and accessibly demonstrating these techniques, especially those derived from Multiple Correspondence Analysis, the authors develop screening procedures to search for variation in observed responses that do not correspond with actual differences between respondents. Using well-known international data sets, the authors exemplify how to detect all manner of non-substantive variation having sources such as a variety of response styles including acquiescence, respondents′ failure to understand questions, inadequate field work standards, interview fatigue, and even the manufacture of (partly) faked interviews.

Este libro fue pensado para todos aquellos profesionales, estudiantes, docentes e investigadores de las Ciencias Sociales y la Comunicación Social que requieran de las herramientas necesarias para llevar a cabo un proceso completo de aplicación del método de encuesta. Se desarrollan a lo largo de la obra, las implicancias tanto del método de encuesta como de la estadística. Se trata de una propuesta pedagógica basada en los aportes brindados por diferentes metodólogos, sociólogos, estadísticos y comunicólogos. En consonancia con lo planteado por otros investigadores, se ubica a la encuesta en el marco más amplio de la metodología de la investigación social; los sondeos de opinión, la teoría estadística y los métodos de selección de muestras. Se concibe a la encuesta como una etapa posterior a la definición teórica del objeto de estudio. En este sentido, el libro ubica a la encuesta en el marco de un proceso de investigación, sea este de carácter académico o institucional, y pone énfasis en la definición conceptual del objeto de estudio como condición necesaria para la posterior operacionalización de variables. Se entiende que sin el procedimiento de operacionalización de variables no es posible establecer los datos pertinentes que permitirán luego construir preguntas acordes para un cuestionario.

This book introduces a new data analysis technique that addresses long standing criticisms of the current standard statistics. Observation Oriented Modelling presents the mathematics and techniques underlying the new method, discussing causality, modelling, and logical hypothesis testing. Examples of how to approach and interpret data using OOM are presented throughout the book, including analysis of several classic studies in psychology. These analyses are conducted using comprehensive software for the Windows operating system. - Describes the problems that statistics are meant to answer, why popularly used statistics often fail to fully answer the question, and how OOM overcomes these obstacles - Chapters include examples of statistical analysis using OOM

This book proposes a revolutionary new theory of construct measurement – called C-OAR-SE – for the social sciences. The acronym is derived from the following key elements: construct definition; object representation; attribute classification; rater entity identification; selection of item type; enumeration and scoring. The new theory is applicable to the design of measures of constructs in: • Management • Marketing • Information Systems • Organizational Behavior • Psychology • Sociology C-OAR-SE is a rationally rather than empirically-based theory and procedure. It can be used for designing measures of the most complex and also the most basic constructs that we use in social science research. C-OAR-SE is a radical alternative to the traditional empirically-based psychometric approach, and a considerable amount of the book's content is devoted to demonstrating why the psychometric approach does not produce valid measures. The book argues that the psychometric approach has resulted in many misleading findings in the social sciences and has led to erroneous acceptance – or rejection – of many of our main theories and hypotheses, and that the C-OAR-SE approach to measurement would correct this massive problem. The main purpose of this book is to introduce and explain C-OAR-SE construct measurement theory in a way that will be understood by all social science researchers and that can be applied to designing new, more valid measures. Featuring numerous examples, practical applications, end-of-chapter questions, and appendices, the book will serve as an essential resource for students and professional researcher alike.

The Reviewer's Guide to Quantitative Methods in the Social Sciences is designed for evaluators of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its thirty-one 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 book updates readers on each technique's key principles, appropriate usage, underlying assumptions, and limitations. It thereby assists reviewers to offer constructive commentary on works they evaluate, and also serves as an indispensable author's reference for preparing sound research manuscripts and proposals. Key features include: The chapters cover virtually all of the popular classic and emerging quantitative techniques, thus helping reviewers to evaluate a manuscript's methodological approach and its data analysis. In addition, the volume serves as an indispensable reference tool for those designing their own research. For ease of use, all chapters follow the same structure: the opening page of each chapter defines and explains the purpose of that statistical method the next one or two pages provide a table listing various criteria that should be considered when evaluating and applying that methodological approach to data analysis the remainder of each chapter contains numbered sections corresponding to the numbered criteria listed in the opening table. Each section explains the role and importance of that particular criterion. Chapters are written by methodological and applied scholars who are expert in the particular quantitative method being reviewed.

This book has been replaced by Applied Missing Data Analysis, Second Edition, ISBN 978-1-4625-4986-3.

Written in an accessible style, this book facilitates a deep understanding of the Rasch model. Authors Bond and Fox review the crucial properties of the Rasch model and demonstrate its use with a wide range of examples including the measurement of educational achievement, human development, attitudes, and medical rehabilitation. A glossary and numerous illustrations further aid the reader's understanding. The authors demonstrate how to apply Rasch analysis and prepare readers to perform their own analyses and interpret the results. Updated throughout, highlights of the Second Edition include: a new CD that features an introductory version of the latest Winsteps program and the data files for the book's examples, preprogrammed to run using Winsteps; a new chapter on invariance that highlights the parallels between physical and human science measurement; a new appendix on analyzing data to help those new to Rasch analysis; more explanation of the key concepts and item characteristic curves; a new empirical example with data sets demonstrates the many facets of the Rasch model and other new examples; and an increased focus on issues related to unidimensionality, multidimensionality, and the Rasch factor analysis of residuals. Applying the Rasch Model is intended for researchers and practitioners in psychology, especially developmental psychologists, education, health care, medical rehabilitation, business, government, and those interested in measuring attitude, ability, and/or performance. The book is an excellent text for use in courses on advanced research methods, measurement, or quantitative analysis. Significant knowledge of statistics is not required.

The study of social dynamics using quantitative methodology is complex and calls for cutting-edge technical and methodological approaches in social science research. This book presents the existing statistical models and methods available for understanding social change over time. It provides step-by-step instructions for designing and conducting longitudinal research, with special focus on the longitudinal analysis of both quantitative outcomes (for the modeling of change in continuous variables) and qualitative outcomes (for the modeling of events occurring over time). Readers will learn how to study change in variables over time and how to formulate and estimate multivariate longitudinal models to predict such change, mainly using cross-sectional, cross-time and event history analyzes. This text also teaches how to design and implement a study using longitudinal data from the selection and collection of appropriate variables to the most effective ways to analyze and present data for publication in top social science journals.

Clinically oriented professionals and students need to understand and evaluate the research and statistics in professional articles, especially given today's emphasis on evidence-based practice. This book demonstrates how the research approach and design help determine the appropriate statistical analysis. Understanding and Evaluating Research in Applied and Clinical Settings features:•short, independent, chapters that do not have to be read in order;•a guide to understanding why a particular statistic was selected;•an emphasis on effects sizes including measures of risk potency;•numerous cross-disciplinary examples to illustrate the material; and•methods to help determine practical and clinical significance and their relation to meta-analysis and evidence-based practice.This book is intended for practitioners and students in psychology, education, counseling, mental and allied health, nursing, and medicine, and as a text for courses on understanding research methods and statistics.

Such diverse thinkers as Lao-Tze, Confucius, and U.S. Defense Secretary Donald Rumsfeld have all pointed out that we need to be able to tell the difference between real and assumed knowledge. The systematic review is a scientific tool that can help with this difficult task. It can help, for example, with appraising, summarising, and communicating the results and implications of otherwise unmanageable quantities of data. This book, written by two highly-respected social scientists, provides an overview of systematic literature review methods: Outlining the rationale and methods of systematic reviews; Giving worked examples from social science and other fields; Applying the practice to all social science disciplines; It requires no previous knowledge, but takes the reader through the process stage by stage; Drawing on examples from such diverse fields as psychology, criminology, education, transport, social welfare, public health, and housing and urban policy, among others. Including detailed sections on assessing the quality of both quantitative, and qualitative research; searching for evidence in the social sciences; meta-analytic and other methods of evidence synthesis; publication bias; heterogeneity; and approaches to dissemination.

Click ′Additional Materials′ for downloadable samples'The 24 chapters in this Handbook span a wide range of topics, presenting the latest quantitative developments in scaling theory, measurement, categorical data analysis, multilevel models, latent variable models, and foundational issues. Each chapter reviews the historical context for the topic and then describes current work, including illustrative examples where appropriate. The level of presentation throughout the book is detailed enough to convey genuine understanding without overwhelming the reader with technical material. Ample references are given for readers who wish to pursue topics in more detail. The book will appeal to both researchers who wish to update their knowledge of specific quantitative methods, and students who wish to have an integrated survey of state-of- the-art quantitative methods.'—Roger E. Millsap, Arizona State University'This handbook discusses important methodological tools and topics in quantitative methodology in easy to understand language. It is an exhaustive review of past and recent advances in each topic combined with a detailed discussion of examples and graphical illustrations. It will be an essential reference for social science researchers as an introduction to methods and quantitative concepts of great use.'—Irini Moustaki, London School of Economics, U.K.'David Kaplan and SAGE Publications are to be congratulated on the development of a new handbook on quantitative methods for the social sciences. The Handbook is more than a set of methodologies, it is a journey. This methodological journey allows the reader to experience scaling, tests and measurement, and statistical methodologies applied to categorical, multilevel, and latent variables. The journey concludes with a number of philosophical issues of interest to researchers in the social sciences. The new Handbook is a must purchase.'—Neil H. Timm, University of PittsburghThe SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource. The handbook is divided into six sections: • Scaling• Testing and Measurement• Models for Categorical Data• Models for Multilevel Data• Models for Latent Variables• Foundational Issues These sections, comprising twenty-four chapters, address topics in scaling and measurement, advances in statistical modeling methodologies, and broad philosophical themes and foundational issues that transcend many of the quantitative methodologies covered in the book. The Handbook is indispensable to the teaching, study, and research of quantitative methods and will enable readers to develop a level of understanding of statistical techniques commensurate with the most recent, state-of-the-art, theoretical developments in the field. It provides the foundations for quantitative research, with cutting-edge insights on the effectiveness of each method, depending on the data and distinct research situation.

This clever scheme builds on Gorard's previous book, Quantitative Methods in Educational Research. He has revised the original book in the light of experience and feedback, and has reworked it so that it includes more social science examples. Four chapters are entirely new.

Introduction to Quantitative Research Methods is a student-friendly introduction to quantitative research methods and basic statistics. It uses a detective theme throughout the text to show how quantitative methods have been used to solve real-life problems. The book focuses on principles and techniques that are appropriate to introductory level courses in media, psychology and sociology. Examples and illustrations are drawn from historical and contemporary research in the social sciences. The original CD-ROM accompanying the book and its content are no longer available.

This major inter-disciplinary collection, edited by two of the best respected figures in the field, provides a superb general introduction to this subject. Chapters include discussions of fieldwork methodology, analyzing discourse, the advantages and pitfalls of team approaches, the uses of computers, and the applications of qualitative data analysis for social policy. Shrewd and insightful, the collection will be required reading for students of the latest thinking on research methods.

In textbooks and courses in statistics, substantive and measurement issues are rarely, if at all, considered. Similarly, textbooks and courses in measurement virtually ignore design and analytic questions, and research design textbooks and courses pay little attention to analytic and measurement issues. This fragmentary approach fosters a lack of appreciation of the interrelations and interdependencies among the various aspects of the research endeavor. Pedhazur and Schmelkin's goal is to help readers become proficient in these aspects of research and their interrelationships, and to use that information in a more integrated manner. The authors offer extensive commentaries on inputs and outputs of computer programs in the context of the topics presented. Both the organization of the book and the style of presentation allow for much flexibility in choice, sequence, and degree of sophistication with which topics are dealt.