Using real-life case studies and written with a student-centered approach, this new edition provides students with the necessary knowledge and skills to enable them to undertake a piece of business research making the best use of IT where appropriate.

    Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.

    The ultimate guide to the ‘what’, ‘how’ and ‘why’ of literature reviewing, the second edition of the classic text shows how the literature review will unlock the full potential of one's research with:A thorough exploration of the literature review process from start to finishTwo brand new chapters explaining the different types of review and evidence and evaluationExtended examples that show you how to apply key techniques and proceduresA new ‘core skills’ framework to help you turn your research experience into employabilityA critical thinking focus that will help you construct convincing arguments and improve your research decisions In combining a critical, philosophical approach with an expertly selected body of practical examples, the Second Edition of Chris Hart's landmark text provides both the intellectual understanding and the technical skills required to produce sophisticated, robust literature reviews of the very highest standard.

    With it you can prime yourself with the key concepts of statistics before getting involved in the associated calculations. Using words and diagrams instead of figures, formulae and equations, Derek Rowntree makes statistics accessible to those who are non-mathematicians. And just to get you into the spirit of things. Rowntree has included questions in his argument; answer them as you go and you will be able to tell how far you have mastered the subject.

    Carolyn Ellis, the leading proponent of these methods, does not disappoint. She weaves both methodological advice and her own personal stories into an intriguing narrative about a fictional graduate course she instructs. In it, you learn about her students and their projects and understand the wide array of topics and strategies that fall under the label autoethnography. Through Ellis's interactions with her students, you are given useful strategies for conducting a study, including the need for introspection, the struggles of the budding ethnographic writer, the practical problems in explaining results of this method to outsiders, and the moral and ethical issues that get raised in this intimate form of research. Anyone who has taken or taught a course on ethnography will recognize these issues and appreciate Ellis's humanistic, personal, and literary approach toward incorporating them into her work. A methods text or a novel? The Ethnographic 'I' answers yes to both.

    Patton has created the most comprehensive, systematic and up-to-date review of qualitative methods available.Patton has retained and expanded upon the Exhibits that highlight and summarize major issues and guidelines, the summative sections, tables, and figures as well as the sage advice of the Sufi Master, Halcolm. This revision will help readers integrate and make sense of the great volume of qualitative works published in the past decade.

    It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    Offering an approach with a visual/graphical emphasis, this text offers a number of examples on the premise that students learn best by doing. This book features an emphasis on interpretation of results and critical thinking over calculations.

    Drugs, Behavior, and Modern Society, 6/e, examines the impact of drug-taking behavior on our society and our daily lives.  The use and abuse of a wide range of licit and illicit drugs are discussed from historical, biological, psychological, and sociological perspectives.  The use of Drugs in our lives and drug-taking behavior, legally restricted drugs in our society, legal drugs in our society, medicinal drugs, treatment, prevention, and education.  Forstudents, or people working with drug related topics in the fields of psychology and health.

    race relations, federal bureaucracies, Christian churches, and social scientists. This book continues to be required reading for all Americans, whatever their special interest.

    In total, 29 different approaches to coding are covered, ranging in complexity from beginner to advanced level and covering the full range of types of qualitative data from interview transcripts to field notes. For each approach profiled, Johnny Saldana discusses the method's origins in the professional literature, a description of the method, recommendations for practical applications, and a clearly illustrated example.Also included in the book is an introduction to how codes and coding initiate qualitative data analysis, their applications with qualitative data analysis software, the writing of supplemental analytic memos, and recommendations for how to best use the manual for particular studies.

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    Readerswill find information on frequency distributions; mean, median, and mode; range, variance, and standard deviation;probability; and more.-Emphasizes Microsoft Excel for number-crunching and computationsDownload a sample chapter.

    Grosjean describes the various strategies used by parents raising bilingual children.