{"title":"Statistics For Social And Behavioral Sciences","description":"\u003cp\u003eDive into the world of social and behavioural statistics. This collection offers accessible guides and resources, perfect for students and researchers alike. Enhance your analytical skills and explore key methodologies.\u003c\/p\u003e","products":[{"product_id":"marginal-models-book-wicher-bergsma-9780387096094","title":"Marginal Models","description":"Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answering important research questions when dependent observations are involved, as the many real world examples in this book show.   In the social, behavioral, educational, economic, and biomedical sciences, data are often collected in ways that introduce dependencies in the observations to be compared. For example, the same respondents are interviewed at several occasions, several members of networks or groups are interviewed within the same survey, or, within families, both children and parents are investigated. Statistical methods that take the dependencies in the data into account must then be used, e.g., when observations at time one and time two are compared in longitudinal studies. At present, researchers almost automatically turn to multi-level models or to GEE estimation to deal with these dependencies. Despite the enormous potential and applicability of these recent developments, they require restrictive assumptions on the nature of the dependencies in the data. The marginal models of this book provide another way of dealing with these dependencies, without the need for such assumptions, and can be used to answer research questions directly at the intended marginal level. The maximum likelihood method, with its attractive statistical properties, is used for fitting the models.   This book has mainly been written with applied researchers in mind. It includes many real world examples, explains the types of research questions for which marginal modeling is useful, and provides a detailed description of how to apply marginal models for a great diversity of research questions. All these examples are presented on the book's website (www.cmm.st), along with user friendly programs.","brand":"WoB","offers":[{"title":"GB \/ LIKE_NEW \/ INTERNAL","offer_id":49535199052049,"sku":"GOR013589054","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52535696425233,"sku":"NLS9780387096094","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":52735189025041,"sku":"NIN9780387096094","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0387096094.jpg?v=1750910401"},{"product_id":"bayesian-item-response-modeling-book-jean-paul-fox-9781441907417","title":"Bayesian Item Response Modeling","description":"The modeling of item response data is governed by item response theory, also referred to as modern test theory. The eld of inquiry of item response theory has become very large and shows the enormous progress that has been made. The mainstream literature is focused on frequentist statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly for making further - provements in the statistical modeling process. The Bayesian approach has two important features that make it attractive for modeling item response data. First, it enables the possibility of incorpor- ing nondata information beyond the observed responses into the analysis. The Bayesian methodology is also very clear about how additional information can be used. Second, the Bayesian approach comes with powerful simulation-based estimation methods. These methods make it possible to handle all kinds of priors and data-generating models. One of my motives for writing this book is to give an introduction to the Bayesian methodology for modeling and analyzing item response data. A Bayesian counterpart is presented to the many popular item response theory books (e.g., Baker and Kim 2004; De Boeck and Wilson, 2004; Hambleton and Swaminathan, 1985; van der Linden and Hambleton, 1997) that are mainly or completely focused on frequentist methods. The usefulness of the Bayesian methodology is illustrated by discussing and applying a range of Bayesian item response models.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":49731268739345,"sku":"NGR9781441907417","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ NEW \/ INGRAM","offer_id":51026509431057,"sku":"NIN9781441907417","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52138095051025,"sku":"NLS9781441907417","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1441907416.jpg?v=1751209880"},{"product_id":"statistical-science-in-the-courtroom-book-joseph-l-gastwirth-9781461270461","title":"Statistical Science in the Courtroom","description":"In recent years, statistics has played in an increasing important role in the courtroom. Written at a popular level, this collection of articles by leading statistical consultants discusses the impact of testimony on the consultants, judges, and juries. 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It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. 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The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.   The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":49951240192273,"sku":"CIN038771264XG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52486257115409,"sku":"NLS9780387712642","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":53583126528273,"sku":"CIN038771264XVG","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/038771264X.jpg?v=1751228994"},{"product_id":"statistics-for-lawyers-book-michael-o-finkelstein-9781441959843","title":"Statistics for Lawyers","description":"This classic text, first published in 1990, is designed to introduce law students, law teachers, practitioners, and judges to the basic ideas of mathematical probability and statistics as they have been applied in the law. The third edition includes over twenty new sections, including the addition of timely topics, like New York City police stops, exonerations in death-sentence cases, projecting airline costs, and new material on various statistical techniques such as the randomized response survey technique, rare-events meta-analysis, competing risks, and negative binomial regression. The book consists of sections of exposition followed by real-world cases and case studies in which statistical data have played a role. The reader is asked to apply the theory to the facts, to calculate results (a hand calculator is sufficient), and to explore legal issues raised by quantitative findings. The authors' calculations and comments are given in the back of the book. As with previous editions, the cases and case studies reflect a broad variety of legal subjects, including antidiscrimination, mass torts, taxation, school finance, identification evidence, preventive detention, handwriting disputes, voting, environmental protection, antitrust, sampling for insurance audits, and the death penalty. A chapter on epidemiology was added in the second edition. In 1991, the first edition was selected by the University of Michigan Law Review as one of the important law books of the year.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50128437281041,"sku":"CIN144195984XG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":51504130064657,"sku":"CIN144195984XVG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ WELL_READ \/ SBYB","offer_id":52496635756817,"sku":"CIN144195984XA","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/144195984X.jpg?v=1750794774"},{"product_id":"explanatory-item-response-models-book-paul-de-boeck-9780387402758","title":"Explanatory Item Response Models","description":"This edited volume gives a new and integrated introduction to item re­ sponse models (predominantly used in measurement applications in psy­ chology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models. Moreover, this new framework aHows the domain of item response mod­ els to be co-ordinated and broadened to emphasize their explanatory uses beyond their standard descriptive uses. The basic explanatory principle is that item responses can be modeled as a function of predictors of various kinds. The predictors can be (a) char­ acteristics of items, of persons, and of combinations of persons and items; they can be (b) observed or latent (of either items or persons); and they can be (c) latent continuous or latent categorical. Thus, a broad range of models can be generated, including a wide range of extant item response models as weH as some new ones. Within this range, models with explana­ tory predictors are given special attention, but we also discuss descriptive models. Note that the 'item responses' that we are referring to are not just the traditional 'test data,' but are broadly conceived as categorical data from a repeated observations design. Hence, data from studies with repeated-observations experimental designs, or with longitudinal designs, mayaIso be modeled. 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The goal in this book is to put an array of tools at the fingertips of practitioners by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This book serves at least three audiences: (1) Students seeking a more in-depth understanding of applied sampling either through a second semester-long course or by way of a supplementary reference; (2) Survey statisticians searching for practical guidance on how to apply concepts learned in theoretical or applied sampling courses; and (3) Social scientists and other survey practitioners who desire insight into the statistical thinking and steps taken to design, select, and weight random survey samples.   Several survey data sets are used to illustrate how to design samples, to make estimates from complex surveys for use in optimizing the sample allocation, and to calculate weights. Realistic survey projects are used to demonstrate the challenges and provide a context for the solutions. The book covers several topics that either are not included or are dealt with in a limited way in other texts. These areas include: sample size computations for multistage designs; power calculations related to surveys; mathematical programming for sample allocation in a multi-criteria optimization setting; nuts and bolts of area probability sampling; multiphase designs; quality control of survey operations; and statistical software for survey sampling and estimation. An associated R package, PracTools, contains a number of specialized functions for sample size and other calculations. The data sets used in the book are also available in PracTools, so that the reader may replicate the examples or perform further analyses.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50293483340049,"sku":"CIN146146448XG","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/146146448X.jpg?v=1750923992"},{"product_id":"prove-it-with-figures-book-hans-zeisel-9780387948928","title":"Prove It with Figures","description":"\"Prove It With Figures\" displays some of the tools of the social and statistical sciences that have been applied to the proof of facts in the courtroom and to the study of questions of legal importance. It explains how researchers can extract the most valuable and reliable data that can conveniently be made available, and how these efforts sometimes go awry. In the tradition of Zeisel's \"Say It with Figures,\" a standard in the field of social statistics since 1947, it clarifies, in non-technical language, some of the basic problems common to all efforts to discern cause-and-effect relationships. Designed as a textbook for law students who seek an appreciation of the power and limits of empirical methods, the work also is a useful reference for lawyers, policymakers, and members of the public who would like to improve their critical understanding of the statistics presented to them. The many case histories include analyses of the death penalty, jury selection, employment discrimination, mass torts, and DNA profiling. Hans Zeisel was Professor of Law and Sociology Emeritus at the University of Chicago, where he pioneered the application of social science to the law. Earlier, he had a distinguished career in public opinion and market research. He has written on a wide variety of topics, ranging from research methodology and history to law enforcement, juries, and Sheakespeare. He was elected Fellow of the American Statistical Assoication and the American Association for the Advancement of Science, and in 1980 he was inducted into the Market Research Hall of Fame. David Kaye is Regents Professor at the Arizona State University, where he teaches evidence and related topics. 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This second edition includes many new problems reflecting current developments in the law, including a new chapter on epidemiology.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50350718877969,"sku":"CIN0387950079G","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":50954767696145,"sku":"CIN0387950079VG","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ LIKE_NEW \/ SBYB","offer_id":52819176325393,"sku":"CIN0387950079LN","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0387950079.jpg?v=1750697204"},{"product_id":"generalizability-theory-book-robert-l-brennan-9780387952826","title":"Generalizability Theory","description":"In 1972 a monograph by Cronbach, Gleser, Nanda, and Rajaratnam was published entitled The Dependability of Behavioral Measurements. That book incorporated, systematized, and extended their previous research into what came to be called generalizability theory, which liberalizes classical test theory, in part through the application of analysis of variance proce­ dures that focus on variance components. Generalizability theory is perhaps the most broadly defined measurement model currently in existence, and the Cronbach et al. (1972) treatment of the theory represents a major con­ tribution to psychometrics. However, as Cronbach et al. (1972, p. 3) state, their book is \"complexly organized and by no means simple to follow\" and, of course, it is nearly 30 years old. In 1983, ACT, Inc. published my monograph entitled Elements of Gen­ eralizability Theory, with a slightly revised version appearing in 1992. That treatment is considerably less comprehensive than Cronbach et al. (1972) but still detailed enough to convey much ofthe richness of the theory and to facilitate its application. 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Correct methods for computing the precision of the survey data and for making inferences to the target population are absolutely essential to sound decision making. Now in its second edition, Introduction to Variance Estimation has for more than twenty years provided the definitive account of the theory and methods for correct precision calculations and inference, including examples of modern, complex surveys in which the methods have been used successfully.   The book provides instruction on the methods that are vital to data-driven decision making in business, government, and academe. It will appeal to survey statisticians and other scientists engaged in the planning and conduct of survey research, and to those analyzing survey data and charged with extracting compelling information from such data. It will appeal to graduate students and university faculty who are focused on the development of new theory and methods and on the evaluation of alternative methods. Software developers concerned with creating the computer tools necessary to enable sound decision-making will find it essential.   Prerequisites include knowledge of the theory and methods of mathematical statistics and graduate coursework in survey statistics. Practical experience with real surveys is a plus and may be traded off against a portion of the requirement for graduate coursework.   This second edition reflects shifts in the theory and practice of sample surveys that have occurred since the content of the first edition solidified in the early 1980’s. Additional replication type methods appeared during this period and have featured prominently in journal publications. Reflecting these developments, the second edition now includes a new major chapter on the bootstrap method of variance estimation. This edition also includes extensive new material on Taylor series methods, especially as they apply to newer methods of analysis such as logistic regression or the generalized regression estimator. An introductory section on survey weighting has been added. Sections on Hadamard matrices and computer software have been substantially scaled back. Fresh material on these topics is now readily available on the Internet or from commercial sources.   Kirk Wolter is a Senior Fellow at NORC, Director of the Center for Excellency in Survey Research, and Professor in the Department of Statistics, University of Chicago. He is a Fellow of the American Statistical Association and a Member of the International Statistical Institute. He is a past president of the International Association of Survey Statisticians and a past chair of the Survey Research Methods Section of the American Statistical Association. 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