<span>"This is a first-class book dealing with one of the most important areas of current research in applied statistics&#8230;the methods described are widely applicable&#8230;the standard of exposition is extremely high." <br />--Short Book Reviews from the International Statistical Institute</span></p> <span>"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research." <br />--TED GERBER, Sociology, University of Arizona</span></p> <span>"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."<br />--PAUL SWANK, Houston School of Nursing, University of Texas, Houston</span></p> <span>Popular in the <em>First Edition</em> for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as: </span></p> <span>* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3<br />* New section on multivariate growth models in Chapter 6 <br />* A discussion of research synthesis or meta-analysis applications in Chapter 7<br />* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators</span></p> <span>While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:</span></p> <span>* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case <br />* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model <br />* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)</span></p> <span>The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.<br /></p><span> </p></span></span>

This long awaited successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation:

In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software. Highlights of the Second Edition include: * Review of latent change (growth) analysis models at an introductory level * Coverage of the popular Mplus program * Updated examples of LISREL and EQS * A CD that contains all of the text's LISREL, EQS, and Mplus examples. A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

This is the fourth edition of the standard introductory text and complete reference for scientists in all disciplines, as well as engineers. This fully revised version includes important updates on articles and books as well as information on a crucial new topic: how to create transparencies and computer projections, both for classrooms and professional meetings. The text maintains its user-friendly, example-based, visual approach, gently easing readers into the secrets of Latex with The Short Course. Then it introduces basic ideas through sample articles and documents. It includes a visual guide and detailed exposition of multiline math formulas, and even provides instructions on preparing books for publishers.

This book will show how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book will be appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data.

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis. Key Features: • Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis. • Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies • Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data. Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.

Statistics-driven thinking is ubiquitous in modern society. In this study of the history of statistics, which begins with probability theory in the 17th century, Alain Desrosieres shows how the evolution of modern statistics has been inextricably bound up with the knowledge and power of governments. He traces the complex reciprocity between modern governments and the mathematical artifacts that both dictate the duties of the state and measure its successes.

This investigation of the overwhelming appeal of quantification in the modern world discusses the development of cultural meanings of objectivity over two centuries. How are we to account for the current prestige and power of quantitative methods? The usual answer is that quantification is seen as desirable in social and economic investigation as a result of its successes in the study of nature. Theodore Porter is not content with this. Why should the kind of success achieved in the study of stars, molecules, or cells be an attractive model for research on human societies? he asks. And, indeed, how should we understand the pervasiveness of quantification in the sciences of nature? In his view, we should look in the reverse direction: comprehending the attractions of quantification in business, government, and social research will teach us something new about its role in psychology, physics, and medicine. Drawing on a wide range of examples from the laboratory and from the worlds of accounting, insurance, cost-benefit analysis, and civil engineering, Porter shows that it is "exactly wrong" to interpret the drive for quantitative rigor as inherent somehow in the activity of science except where political and social pressures force compromise. Instead, quantification grows from attempts to develop a strategy of impersonality in response to pressures from outside. Objectivity derives its impetus from cultural contexts, quantification becoming most important where elites are weak, where private negotiation is suspect, and where trust is in short supply.

西奥多·赛泽在《一个称作学校的地方》二十周年纪念版的序言里，简练精辟地评述了美国20世纪80年代初两项重大教育工程的性质。一项是全美优质教育委员会的报告，题为“处在危机中的国家”。这是专门回应当时“公众普遍的看法，认为我们的教育系统存在着严重的问题”。另一项则是：美国学校教育史上规模最大和最复杂的调查研究之一，即学校教育研究”，本书就是这一调研的综合报告。 《一个称作学校的地方》的研究和写作从20世纪70年代末延伸到80年代初。这是一个有深度、有收获、令人筋疲力尽的学习过程。成千上万的研究数据是从一批有代表性的美国中小学校收集上来的。在本书第一版的附录A里，我们列举了一系列的技术报告和其他原始资料，显示了这些数据的广泛性。列在附录B里的工作人员大多数是在采集数据的最后阶段雇用的半时工。因为纪念版需要给新序言和后序留出空间，所以删去了原版中的这两个附录，以及原版的序和其他的一些前面部分。原版里为27000人—那些为我们的调研提供了数据的学生、家长、教师及学校领导人—所致的献词仍保留在新版里。

Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.

This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.