The nature of human resource development (HRD) has been, and remains, a contested topic - the debate was sparked in part by Monica Lee's seminal 2001 paper which refused to define the discipline of HRD, but has been accentuated by increasing globalization, political unrest, inequality and the erosion of boundaries. Should HRD now be seen as more than 'training,' or a sub-function of large western bureaucracy? This book represents a very wide view of HRD: that it is at the core of our 'selves' and our relationships, and that we continually co-create ourselves, our organisations and societies. These ideas are hung upon a model of Holistic Agency, and supported from sources as diverse as evolutionary psychology, science fiction, the challenges of transitional economies, and the structural uncertainties of contemporary society. Examining the tensions between self and other, agency and structure, the book draws inspiration from an almost-autoethnographic approach. This yields a text that is personal, entertaining, and easier to read than many academic tomes - yet considers the depth and development of the human condition, and locates HRD within that.

Built environment students are not always familiar with the range of different research approaches they could be using for their projects. Whether you are undertaking a postgraduate doctoral programme or facing an undergraduate or masters dissertation, this book provides general advice, as well as 13 detailed case studies from 16 universities in 7 countries, to help you get to grips with quantitative and qualitative methods, mixed methods of data collection, action research, and more.

The classic edition of What If There Were No Significance Tests? highlights current statistical inference practices. Four areas are featured as essential for making inferences: sound judgment, meaningful research questions, relevant design, and assessing fit in multiple ways. Other options (data visualization, replication or meta-analysis), other features (mediation, moderation, multiple levels or classes), and other approaches (Bayesian analysis, simulation, data mining, qualitative inquiry) are also suggested. The Classic Edition's new Introduction demonstrates the ongoing relevance of the topic and the charge to move away from an exclusive focus on NHST, along with new methods to help make significance testing more accessible to a wider body of researchers to improve our ability to make more accurate statistical inferences. Part 1 presents an overview of significance testing issues. The next part discusses the debate in which significance testing should be rejected or retained. The third part outlines various methods that may supplement significance testing procedures. Part 4 discusses Bayesian approaches and methods and the use of confidence intervals versus significance tests. The book concludes with philosophy of science perspectives. Rather than providing definitive prescriptions, the chapters are largely suggestive of general issues, concerns, and application guidelines. The editors allow readers to choose the best way to conduct hypothesis testing in their respective fields. For anyone doing research in the social sciences, this book is bound to become "must" reading. Ideal for use as a supplement for graduate courses in statistics or quantitative analysis taught in psychology, education, business, nursing, medicine, and the social sciences, the book also benefits independent researchers in the behavioral and social sciences and those who teach statistics.

The classic edition of What If There Were No Significance Tests? highlights current statistical inference practices. Four areas are featured as essential for making inferences: sound judgment, meaningful research questions, relevant design, and assessing fit in multiple ways. Other options (data visualization, replication or meta-analysis), other features (mediation, moderation, multiple levels or classes), and other approaches (Bayesian analysis, simulation, data mining, qualitative inquiry) are also suggested. The Classic Edition's new Introduction demonstrates the ongoing relevance of the topic and the charge to move away from an exclusive focus on NHST, along with new methods to help make significance testing more accessible to a wider body of researchers to improve our ability to make more accurate statistical inferences. Part 1 presents an overview of significance testing issues. The next part discusses the debate in which significance testing should be rejected or retained. The third part outlines various methods that may supplement significance testing procedures. Part 4 discusses Bayesian approaches and methods and the use of confidence intervals versus significance tests. The book concludes with philosophy of science perspectives. Rather than providing definitive prescriptions, the chapters are largely suggestive of general issues, concerns, and application guidelines. The editors allow readers to choose the best way to conduct hypothesis testing in their respective fields. For anyone doing research in the social sciences, this book is bound to become "must" reading. Ideal for use as a supplement for graduate courses in statistics or quantitative analysis taught in psychology, education, business, nursing, medicine, and the social sciences, the book also benefits independent researchers in the behavioral and social sciences and those who teach statistics.

Prepares readers to analyze data and interpret statistical results using the increasingly popular R more quickly than other texts through LessR extensions which remove the need to program. By introducing R through less R, readers learn how to organize data for analysis, read the data into R, and produce output without performing numerous functions and programming first. Readers can select the necessary procedure and change the relevant variables without programming. Quick Starts introduce readers to the concepts and commands reviewed in the chapters. Margin notes define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number to the initial introduction. Scenarios highlight the use of a specific analysis followed by the corresponding R/lessR input and an interpretation of the resulting output. Numerous examples of output from psychology, business, education, and other social sciences demonstrate how to interpret results and worked problems help readers test their understanding. www.lessRstats.com website features the lessR program, the book's 2 data sets referenced in standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, PDF slides for each chapter, solutions to the book's worked problems, links to R/lessR videos to help readers better understand the program, and more. New to this edition: o upgraded functionality and data visualizations of the lessR package, which is now aesthetically equal to the ggplot 2 R standard o new features to replace and extend previous content, such as aggregating data with pivot tables with a simple lessR function call.

Originally published in 1986, this book's focal point is a field study which asks whether the social childrearing context of daycare transmits to young children values different from those within America's dominant value tradition of individualism. Daycare critics were concerned that this social childrearing within daycare would weaken the family and promote collectivist rather than individualistic values, and thereby threaten the social continuity of America's values. Through participant observation four daycare teachers' interactions as they emphasize children's individual learning experiences and children's social learning experiences are examined. By focusing on the actions and words of daycare teachers and their children in their daily activities over time, this field study provides a conceptual model for an initial understanding of the relationship of daycare to the continuity of America's values.

This book examines the conduct and purposes of educational research. It looks at values of researchers, at whose interests are served by the research, and the inclusion or exclusion of practitioners and subjects of research. It asks if educational research should be explicitly committed to promoting equality and inclusion, and whether that requires research to be more aware of the cultural and global contexts of research questions. It explores the ethical challenges encountered in the conduct of research and the potential ethical and social justice constraints imposed by comparative research rankings. Next, it discusses the research funding aspects of the above issues both philosophically and historically, thus examining the changing sources, patterns, and effects of educational research funding over time. Since the conduct of most educational research increasingly requires institutional and financial support, the question is whether funding shapes the content of research, and what counts as research. The book discusses if funding is a factor in the shift of efforts of researchers from pure or basic research to more applied research, and if it encourages the development of large research teams, to the detriment of individual scholars. It looks at the ownership of the content, results, and data of publicly funded research. Finally, it tries to establish whether scholars solicit funding to support research projects, or generate research projects to attract funding. This publication, as well as the ones that are mentioned in the preliminary pages of this work, were realized by the Research Community Philosophy and History of the Discipline of Education: Purposes, Projects, and Practices of Educational Research.

Bringing together scholars from around the world, this collection examines many of the historical developments in making data visible through charts, graphs, thematic maps, and now interactive displays. Today, we are used to seeing data portrayed in a dizzying array of graphic forms. Virtually any quantified knowledge, from social and physical science to engineering and medicine, as well as business, government, or personal activity, has been visualized. Yet the methods of making data visible are relatively new innovations, most stemming from eighteenth- and nineteenth-century innovations that arose as a logical response to a growing desire to quantify everything-from science, economics, and industry to population, health, and crime. Innovators such as Playfair, Alexander von Humboldt, Heinrich Berghaus, John Snow, Florence Nightingale, Francis Galton, and Charles Minard began to develop graphical methods to make data and their relations more visible. In the twentieth century, data design became both increasingly specialized within new and existing disciplines-science, engineering, social science, and medicine-and at the same time became further democratized, with new forms that make statistical, business, and government data more accessible to the public. At the close of the twentieth century and the beginning of the twenty-first, an explosion in interactive digital data design has exponentially increased our access to data. The contributors analyze this fascinating history through a variety of critical approaches, including visual rhetoric, visual culture, genre theory, and fully contextualized historical scholarship.

Research data are everywhere. In our everyday interactions, through social media, credit cards and even public transport, we generate and use data. The challenge for sociologists is how to collect, analyse and make best use of these vast arrays of information. The chapters in this book address these challenges using varied perspectives and approaches: The economics of big data and measuring the trajectories of recently arrived communities Social media and social research Researching 'elites', social class and 'race' across space and place Innovations in qualitative research and use of extended case studies Developing mixed method approaches and social network analysis Feminist quantitative methodology Teaching quantitative methods The book provides up to date and accessible material of interest to diverse audiences, including students and teachers of research design and methods, as well as policy analysis and social media.

This handbook provides a comprehensive overview of state-of-the-art, innovative approaches to qualitative research for organizational scholars. Individual chapters in each area are written by experts in a variety of fields, who have contributed some of the most innovative studies themselves in recent years. An indispensable reference guide to anyone conducting high-impact organizational research, this handbook includes innovative approaches to research problems, data collection, data analysis and interpretation, and application of research findings. The book will be of interest to scholars and graduate students in a wide variety of disciplines, including anthropology, organizational behavior, organizational theory, social psychology, and sociology

Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, Measurement Error: Models, Methods, and Applications provides an overview of the main techniques and illustrates their application in various models. It describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems to regression models to more complex mixed and time series models. The book covers correction methods based on known measurement error parameters, replication, internal or external validation data, and, for some models, instrumental variables. It emphasizes the use of several relatively simple methods, moment corrections, regression calibration, simulation extrapolation (SIMEX), modified estimating equation methods, and likelihood techniques. The author uses SAS-IML and Stata to implement many of the techniques in the examples. Accessible to a broad audience, this book explains how to model measurement error, the effects of ignoring it, and how to correct for it. More applied than most books on measurement error, it describes basic models and methods, their uses in a range of application areas, and the associated terminology.

This stimulating and refreshing study, written by one of the leading commentators in the field, provides novel answers to these crucial questions. "What's Wrong With Ethnography provides a fresh look at the rationale for and distinctiveness of ethnographic research in sociology, education and related fields, and succeeds in slaying a number of currently fashionable sacred cows. Relativism, critical theory, the uniqueness of the case study and the distinction between qualitative and quantitative research are all examined and found wanting as a basis for informed ethnography. The policy and political implications of ethnography are a particular focus of attention. The author compels the reader to reexamine some basic methodological assumptions in an exciting way", Martin Bulmer, London School of Economics.

First published in 1998. Routledge is an imprint of Taylor & Francis, an informa company.

This handbook provides a comprehensive overview of state-of-the-art, innovative approaches to qualitative research for organizational scholars. Individual chapters in each area are written by experts in a variety of fields, who have contributed some of the most innovative studies themselves in recent years. An indispensable reference guide to anyone conducting high-impact organizational research, this handbook includes innovative approaches to research problems, data collection, data analysis and interpretation, and application of research findings. The book will be of interest to scholars and graduate students in a wide variety of disciplines, including anthropology, organizational behavior, organizational theory, social psychology, and sociology

A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem - an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle - a framework for data's place within the research process and how data's role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management - covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data - an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data - explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis - covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data - many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage - deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data - digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data - addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data - as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." -Robert Buntrock, Chemical Information Bulletin

Empirical research has now become an essential component of software engineering yet software practitioners and researchers often lack an understanding of how the empirical procedures and practices are applied in the field. Empirical Research in Software Engineering: Concepts, Analysis, and Applications shows how to implement empirical research processes, procedures, and practices in software engineering. Written by a leading researcher in empirical software engineering, the book describes the necessary steps to perform replicated and empirical research. It explains how to plan and design experiments, conduct systematic reviews and case studies, and analyze the results produced by the empirical studies. The book balances empirical research concepts with exercises, examples, and real-life case studies, making it suitable for a course on empirical software engineering. The author discusses the process of developing predictive models, such as defect prediction and change prediction, on data collected from source code repositories. She also covers the application of machine learning techniques in empirical software engineering, includes guidelines for publishing and reporting results, and presents popular software tools for carrying out empirical studies.

This book seeks to establish the meaning of design research, its role in the field, and the characteristics that differentiate research in design from research in other fields. The author introduces a model to explain the relationship between the components of the ontological reality of design: the designed object, the designer, and the user. Addressing design research across disciplines, the author establishes a foundational understanding of research, and research paradigms, for the design disciplines. This will be crucial for the emerging field of design research to find its own identity and move forward, building its own knowledge base as it finds its positioning between science and art. The book will be of interest to scholars working in design history, design studies, graphic design, industrial design, interior design, architecture, fashion design, and service design.

This single-volume reference provides an alternative to traditional marketing research methods handbooks, focusing entirely on the new and innovative methods and technologies that are transforming marketing research and practice. Including original contributions and case studies from leading global specialists, this handbook covers many pioneering methods, such as: Methods for the analysis of user- and customer-generated data, including opinion mining and sentiment analysis Big data Neuroscientific techniques and physiological measures Voice prints Human-computer interaction Emerging approaches such as shadowing, netnographies and ethnographies Transcending the old divisions between qualitative and quantitative research methods, this book is an essential tool for market researchers in academia and practice.

The field of strategic management has developed significantly since its birth from "business policy" and "business planning" in the 1960s. Pioneering studies were essentially normative, prescriptive, and often based on in-depth case studies. The evolution of strategic management into a respected field of academic study resulted from the adoption of research methods previously employed in economics. Today, research in strategic management is likely to employ a mixture of methods borrowed from related and unrelated disciplines, such as politcal sciences, psychology, neuroscience, and behavioral economics, which can be confusing to researchers new to the field. This book provides the reader with a broad introduction to the array of qualitative and quantitative research methods required to investigate strategic management. Throughout the book, strong emphasis is placed on practical applications that transcend the mere analysis of the theoretical roots of single research methods. The underlying result is a book that encourages and aids readers to "learn by doing" - in applying the implications of each chapter to their own research. This text is vital reading for postgraduate students and researchers focused on business strategy.

Research in the humanities and social sciences thrives on critical reflections that unfold with each research project, not only in terms of knowledge created, but in whether chosen methodologies served their purpose. Ethics forms the bulwark of any social science research methodology and it requires continuous engagement and reengagement for the greater advancement of knowledge. Each chapter in this book will draw from the empirical knowledge created through intensive fieldwork and provide an account of ethical questions faced by the contributors, placing them in the context of contemporary debates surrounding the theory and practice of ethics. The chapters have been thematically organized into five sections: Feminist Ethics: Cross-Cultural Reflections and Its Implications for Change; Researching Physical and Sexual Violence in Non-Academic Settings: A Need for Ethical Protocols; Human Agency, Reciprocity, Participation and Activism: Meanings for Social Science Research Ethics; Emotions, Conflict and Dangerous Fields: Issues of "Safety" and Reflective Research; and Social Science Education: Training in Ethics or "Ethical Training" and "Ethical Publicizing." This inter-disciplinary volume will interest students and researchers in academic and non-academic settings in core disciplines of Anthropology, Sociology, Law, Political Science, International Relations, Geography, or inter-disciplinary degrees in Development Studies, Health Studies, Public Health Policy, Social Policy, Health Policy, Psychology, Peace and Conflict studies, and Gender Studies. The book features a foreword by His Holiness The Dalai Lama.

For the past ten years, Nancy MacKay's Curating Oral Histories (2006) has been the one-stop shop for librarians, curators, program administrators, and project managers who are involved in turning an oral history interview into a primary research document, available for use in a repository. In this new and greatly expanded edition, MacKay uses the life cycle model to map out an expanded concept of curation, beginning with planning an oral history project and ending with access and use. The book:-guides readers, step by step, on how to make the oral history "archive ready";-offers strategies for archiving, preserving, and presenting interviews in a digital environment;-includes comprehensive updates on technology, legal and ethical issues, oral history on the Internet, cataloging, copyright, and backlogs.

Published in 1996, this book advocates and persuasively exemplifies a qualitative sociology of childhood, spoken repeatedly through children's voices. After a long period of dormancy, interest in the sociology of childhood became a focus of attention and scholarly interest. Developments in practice by professionals working and learning in the fields of welfare, education, and youth and community studies have been paralleled by the emergence of specialist courses within sociology degrees. Yet the challenges raised by the sociology of childhood remain marginalised within the social sciences more generally. A Case of Neglect? provides an accessible reader and review of the field. Heard wherever possible through children's and young people's voices, it provides a penetrating insight into their understandings and experiences of their own and adults' worlds. It also provides a readable and absorbing review of qualitative applications in the sociology of childhood, and a counter to the common reliance on evidence derived from quantitative approaches. The fieldwork applications range across the often hidden worlds of children's and young people's involvement in prostitution, their experience of abuse, black children's experiences of social services, children's school cultures, naturist children and childlessness. Always arresting and sometimes poignant, A Case of Neglect? works towards a sociology which is both of and for childhood. This book was originally published as part of the Cardiff Papers in Qualitative Research series edited by Paul Atkinson, Sara Delamont and Amanda Coffey. The series publishes original sociological research that reflects the tradition of qualitative and ethnographic inquiry developed at Cardiff. The series includes monographs reporting on empirical research, edited collections focussing on particular themes, and texts discussing methodological developments and issues.

This volume highlights the integration of qualitative research methods into traditional journalism, offering new ways of expanding and enhancing news coverage. Designed for readers without prior experience in social science research, this collection presents a wide variety of qualitative techniques and their applications in journalistic practice. The work brings together contributions from professional journalists and journalism scholars who are highly experienced in conducting qualitative research. These experts demonstrate how valid, reliable qualitative procedures can be used to increase coverage and offer new insights. Written in a straightforward, reader-friendly style, features in this volume include: *real-world examples from contemporary newsrooms and interviews with practicing journalists who use the techniques of qualitative research in reporting; *a rationale for the use of qualitative methods in journalism, with an illustration of how various qualitative methods tie together; *step-by-step instructions for applying each methodology; *a solid foundation for understanding the history and theory behind qualitative research and its usefulness in journalism; *chapters on pairing qualitative and quantitative methods in journalism and on detailing partnerships between academics and professional journalists to facilitate newsroom research and reporting; and *a discussion of "objectivity" in qualitative research and in journalism that offers an ethic for journalists of today. The methodologies covered here include oral and life histories, textual analysis, focused interviews, ethnographies, focus groups, and case studies. In addition, a recently developed technique, civic mapping, is presented as a qualitative tool for reporting. Qualitative Research in Journalism is an indispensable resource for current and future journalists interested in enhancing their coverage of the news.

This handbook introduces the reader to the thought-provoking research on the neural foundations of human intelligence. Written for undergraduate or graduate students, practitioners, and researchers in psychology, cognitive neuroscience, and related fields, the chapters summarize research emerging from the rapidly developing neuroscience literature on human intelligence. The volume focusses on theoretical innovation and recent advances in the measurement, modelling, and characterization of the neurobiology of intelligence differences, especially from brain imaging studies. It summarizes fundamental issues in the characterization and measurement of general intelligence, and surveys multidisciplinary research consortia and large-scale data repositories for the study of general intelligence. A systematic review of neuroimaging methods for studying intelligence is provided, including structural and diffusion-weighted MRI techniques, functional MRI methods, and spectroscopic imaging of metabolic markers of intelligence.

Research collaboration in the form of networks, projects and centers has become one of the dominant modes of engaging in research, especially funded research, across all academic domains. However, there has been little research on the processes of such collaborations, particularly their affective dimensions. These, as this volume demonstrates and as researchers know well, are highly important, yet mostly not directly engaged with when scientists work together, even though they are experienced by everybody involved. This volume is the first to consider questions such as how the naming of projects impacts on their accompanying "affect-scapes," the policing or disciplining of emotions in research collaborations, their accompanying tensions and how these might be managed, and the challenges to trust between scientists that such collaborations present. Drawing on theories of affect and literature on collaboration, as well as on the contributors' experiences of being involved in large-scale research projects, the volume also importantly deals directly with some of the key emotions that occur during research collaborations such as blame, elation, frustration, alienation and belonging, and suggests some ways in which one might engage productively with the affective dimensions of research collaboration.