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Incomplete Categorical Data Design by Guo-Liang Tian,Man-Lai Tang Pdf
A self-contained, systematic introduction, this book shows you how to draw valid statistical inferences from survey data with sensitive characteristics. It guides you in applying the non-randomized response approach in surveys and new non-randomized response designs. The techniques covered integrate the strengths of existing approaches, including randomized response models, incomplete categorical data design, the EM algorithm, the bootstrap method, and the data augmentation algorithm. All R codes for the examples are available online.
Incomplete Categorical Data Design by Guo-Liang Tian,Man-Lai Tang Pdf
Respondents to survey questions involving sensitive information, such as sexual behavior, illegal drug usage, tax evasion, and income, may refuse to answer the questions or provide untruthful answers to protect their privacy. This creates a challenge in drawing valid inferences from potentially inaccurate data. Addressing this difficulty, non-rando
Adaptive Survey Design by Barry Schouten,Andy Peytchev,James Wagner Pdf
Adaptive survey designs (ASDs) provide a framework for data-driven tailoring of data collection procedures to different sample members, often for cost and bias reduction. People vary in how likely they are to respond and in how they respond. This variation leads to opportunities to selectively deploy design features in order to control both nonresponse and measurement errors. ASD aims at the optimal matching of design features and the characteristics of respondents given the survey budget. Such a goal is sensible, but ASD requires investment in more advanced technical systems and management infrastructure and asks for the collection of relevant auxiliary data. So what are current best practices in ASD? And is ASD worthwhile when the same auxiliary data are employed in the estimation afterwards? In this book, the authors provide answers to these questions, and much more.
Generalized Linear Models for Categorical and Continuous Limited Dependent Variables by Michael Smithson,Edgar C. Merkle Pdf
Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages.The book provides br
Analysis of Integrated Data by Li-Chun Zhang,Raymond L. Chambers Pdf
The advent of "Big Data" has brought with it a rapid diversification of data sources, requiring analysis that accounts for the fact that these data have often been generated and recorded for different reasons. Data integration involves combining data residing in different sources to enable statistical inference, or to generate new statistical data for purposes that cannot be served by each source on its own. This can yield significant gains for scientific as well as commercial investigations. However, valid analysis of such data should allow for the additional uncertainty due to entity ambiguity, whenever it is not possible to state with certainty that the integrated source is the target population of interest. Analysis of Integrated Data aims to provide a solid theoretical basis for this statistical analysis in three generic settings of entity ambiguity: statistical analysis of linked datasets that may contain linkage errors; datasets created by a data fusion process, where joint statistical information is simulated using the information in marginal data from non-overlapping sources; and estimation of target population size when target units are either partially or erroneously covered in each source. Covers a range of topics under an overarching perspective of data integration. Focuses on statistical uncertainty and inference issues arising from entity ambiguity. Features state of the art methods for analysis of integrated data. Identifies the important themes that will define future research and teaching in the statistical analysis of integrated data. Analysis of Integrated Data is aimed primarily at researchers and methodologists interested in statistical methods for data from multiple sources, with a focus on data analysts in the social sciences, and in the public and private sectors.
Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits by Anonim Pdf
Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits tackles how to gather and analyze data relating to stigmatizing human traits. S.L. Warner invented RRT and published it in JASA, 1965. In the 50 years since, the subject has grown tremendously, with continued growth. This book comprehensively consolidates the literature to commemorate the inception of RR. Brings together all relevant aspects of randomized response and indirect questioning Tackles how to gather and analyze data relating to stigmatizing human traits Gives an encyclopedic coverage of the topic Covers recent developments and extrapolates to future trends
Big Data and Social Science by Ian Foster,Rayid Ghani,Ron S. Jarmin,Frauke Kreuter,Julia Lane Pdf
Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.
Handbook of Item Response Theory by Wim J. van der Linden Pdf
Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume 3: Applications presents applications of item response theory to practical testing problems. While item response theory may be known primarily for its advances in theoretical modeling of responses to test items, equal progress has been made in its providing innovative solutions to daily testing problems. This third volume in a three-volume set highlights the major applications. Specifically, this volume covers applications to test item calibration, item analysis, model fit checking, test-score interpretation, optimal test design, adaptive testing, standard setting, and forensic analyses of response data. It describes advances in testing in areas such as large-scale educational assessment, psychological testing, health measurement, and measurement of change. In addition, it extensively reviews computer programs available to run any of the models and applications in Volume One and Three. Features Includes contributions from internationally acclaimed experts with a history of advancing applications of item response theory Provides extensive cross-referencing and common notation across all chapters in this three-volume set Underscores the importance of treating each application in a statistically rigorous way Reviews major computer programs for item response theory analyses and applications. Wim J. van der Linden is a distinguished scientist and director of research and innovation at Pacific Metrics Corporation. Dr. van der Linden is also a professor emeritus of measurement and data analysis at the University of Twente. His research interests include test theory, adaptive testing, optimal test assembly, parameter linking, test equating, and response-time modeling as well as decision theory and its applications to problems of educational decision making.
Multivariate Analysis for the Behavioral Sciences, Second Edition by Kimmo Vehkalahti,Brian S. Everitt Pdf
Multivariate Analysis for the Behavioral Sciences, Second Edition is designed to show how a variety of statistical methods can be used to analyse data collected by psychologists and other behavioral scientists. Assuming some familiarity with introductory statistics, the book begins by briefly describing a variety of study designs used in the behavioral sciences, and the concept of models for data analysis. The contentious issues of p-values and confidence intervals are also discussed in the introductory chapter. After describing graphical methods, the book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models, logistic regression, and survival analysis. There are further chapters covering longitudinal data and missing values, before the last seven chapters deal with multivariate analysis, including principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, and cluster analysis. Features: Presents an accessible introduction to multivariate analysis for behavioral scientists Contains a large number of real data sets, including cognitive behavioral therapy, crime rates, and drug usage Includes nearly 100 exercises for course use or self-study Supplemented by a GitHub repository with all datasets and R code for the examples and exercises Theoretical details are separated from the main body of the text Suitable for anyone working in the behavioral sciences with a basic grasp of statistics
Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. CategoricalData Analysis was among those chosen. A valuable new edition of a standard reference "A 'must-have' book for anyone expecting to do research and/orapplications in categorical data analysis." –Statistics in Medicine on Categorical Data Analysis,First Edition The use of statistical methods for categorical data hasincreased dramatically, particularly for applications in thebiomedical and social sciences. Responding to new developments inthe field as well as to the needs of a new generation ofprofessionals and students, this new edition of the classicCategorical Data Analysis offers a comprehensiveintroduction to the most important methods for categorical dataanalysis. Designed for statisticians and biostatisticians as well asscientists and graduate students practicing statistics,Categorical Data Analysis, Second Edition summarizes thelatest methods for univariate and correlated multivariatecategorical responses. Readers will find a unified generalizedlinear models approach that connects logistic regression andPoisson and negative binomial regression for discrete data withnormal regression for continuous data. Adding to the value in thenew edition is coverage of: Three new chapters on methods for repeated measurement andother forms of clustered categorical data, including marginalmodels and associated generalized estimating equations (GEE)methods, and mixed models with random effects Stronger emphasis on logistic regression modeling of binaryand multicategory data An appendix showing the use of SAS for conducting nearly allanalyses in the book Prescriptions for how ordinal variables should be treateddifferently than nominal variables Discussion of exact small-sample procedures More than 100 analyses of real data sets to illustrateapplication of the methods, and more than 600 exercises An Instructor's Manual presenting detailed solutions to allthe problems in the book is available from the Wiley editorialdepartment.
Multilevel Modeling Using Mplus by Holmes Finch,Jocelyn Bolin Pdf
This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.
Ordered Regression Models by Andrew S. Fullerton,Jun Xu Pdf
Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R. This book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable. Web Resource More detailed examples are available on a supplementary website. The site also contains JAGS, R, and Stata codes to estimate the models along with syntax to reproduce the results.
Randomized Response and Related Methods by James Alan Fox Pdf
Randomized response is a data collection strategy specifically designed for surveys of a sensitive nature. By establishing a probabilistic connection between question and answer, randomized response and related methods protect respondents who are asked to disclose personal information. Covering a half century of theoretical and applied research, the Second Edition of James Alan Fox’s Randomized Response and Related Methods significantly updates and expands what was, at the time, the first comprehensive and practical guide to randomized response.
Bayesian Psychometric Modeling by Roy Levy,Robert J. Mislevy Pdf
A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.
Generalized Structured Component Analysis by Heungsun Hwang,Yoshio Takane Pdf
Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new approach and apply it to their own research. The book emphasizes conceptual discussions throughout while relegating more technical intricacies to the chapter appendices. Most chapters compare generalized structured component analysis to partial least squares path modeling to show how the two component-based approaches differ when addressing an identical issue. The authors also offer a free, online software program (GeSCA) and an Excel-based software program (XLSTAT) for implementing the basic features of generalized structured component analysis.