If pre-industrial societies were held together by common values, sentiments, and norms, equally shared by all, what holds modern societies, with their complex division of labor and non-cohesive social structure, together? What did this new social order mean for the autonomy of the individual? Durkheim argued that class conflict is not inherent in a capitalist society, as Marx contended, but that the unfettered growth of state power would lead to the extinction of individuality. Only in a free society that promotes voluntary bonds between its members, Durkheim suggested, can individuality prosper.In this new edition, the first since 1984, world-renowned Durkheim scholar Steven Lukes revisits and revises the original translation to enhance clarity, accuracy, and fluency for the contemporary reader. Lukes also highlights Durkheim’s arguments by putting them into historical context with a timeline of important information. For students and scholars, this edition of The Division of Labor is essential reading and key to understanding the relevance of Durkheim’s ideas today.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

    Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.

    Drawing on in-depth observations of black and white middle-class, working-class, and poor families, Unequal Childhoods explores this fact, offering a picture of childhood today. Here are the frenetic families managing their children's hectic schedules of "leisure" activities; and here are families with plenty of time but little economic security. Lareau shows how middle-class parents, whether black or white, engage in a process of "concerted cultivation" designed to draw out children's talents and skills, while working-class and poor families rely on "the accomplishment of natural growth," in which a child's development unfolds spontaneously—as long as basic comfort, food, and shelter are provided. Each of these approaches to childrearing brings its own benefits and its own drawbacks. In identifying and analyzing differences between the two, Lareau demonstrates the power, and limits, of social class in shaping the lives of America's children.The first edition of Unequal Childhoods was an instant classic, portraying in riveting detail the unexpected ways in which social class influences parenting in white and African-American families. A decade later, Annette Lareau has revisited the same families and interviewed the original subjects to examine the impact of social class in the transition to adulthood.

    Each contributor is either an originator or a leading proponent of one of the systems, and each presents the basic principles of the system in a clear and straightforward manner, discussing it in the context of the other systems. Theory chapters include a case example that guides you through the problem, evaluation, treatment, and follow-up process. Accompanying CURRENT PSYCHOTHERAPIES is CASE STUDIES IN PSYCHOTHERAPY, each case demonstrates the basic techniques and methods of the theory being illustrated. This edition retains classic case studies by Harold Mosak, Carl Rogers, Albert Ellis, Arnold Lazarus, and Peggy Papp.

    Each chapter focuses on one theory or group of theories, providing brief biographies that shed light on how the theories were formed.

    This book provides an applications-oriented introduction to multivariate data analysis for the non-statistician, by focusing on the fundamental concepts that affect the use of specific techniques.

    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.

    It also stresses the importance of ethics in research and taking the time to properly design and think through any research endeavor.

    Appropriate for students from a wide variety of backgrounds, this comprehensive book captures women's own experiences through direct quotations and an emphasis on empirical research. Known for her balance of scholarship and readability, as well as for her inclusion of women from diverse backgrounds, Margaret Matlin continues to lead the way for the Psychology of Women course. Matlin has meticulously updated this edition to reflect the most current research, and continues to exhibit a genuine interest in and understanding of the students for whom the book is written. Her text includes a chapter on old age, and discussions of topics such as welfare issues, pregnancy and women's retirement, which are central in many women's lives, but not consistently covered in other texts.

    In the Efe community in Zaire, infants routinely use machetes with safety and some skill, although U.S.middle-class adults often do not trust young children with knives. What explains these marked differences in the capabilities of these children?Until recently, traditional understandings of human development held that a child's development is universal and that children have characteristics and skills that develop independently of cultural processes. Barbara Rogoff argues, however, that human development must be understood as a culturalprocess, not simply a biological or psychological one. Individuals develop as members of a community, and their development can only be fully understood by examining the practices and circumstances of their communities.

    Essentially, you make an initial guess, and then get more data to improve it. Bayes Theorem, or Bayes Rule, has a ton of real world applications, from estimating your risk of a heart attack to making recommendations on Netflix But It Isn't That Complicated This book is a short introduction to Bayes Theorem. It is only 15 pages long, and is intended to show you how Bayes Theorem works as quickly as possible. The examples are intentionally kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions. If you want to learn the basics of Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.

    Covering all of the fundamentals in a straightforward, student-friendly manner, it is ideal for undergraduate- and graduate-level courses across the social sciences and also serves as an indispensable guide for researchers. Striking a balance between specific techniques and the underlying logic of scientific inquiry, this book provides a lucid treatment of the four major approaches to research: experimentation, survey research, field research, and the use of available data. Richly developed examples of empirical research and an emphasis on the research process enable students to better understand the real-world application of research methods. The authors also offer a unique chapter (13) advocating a multiple-methods strategy.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    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.