Missing Data

Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1452207909
Format: PDF, Kindle
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Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has relied on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

Fixed Effects Regression Models

Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1483389278
Format: PDF, ePub, Docs
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This book demonstrates 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 is 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. Learn more about "The Little Green Book" - QASS Series! Click Here

Applied Missing Data Analysis

Author: Craig K. Enders
Publisher: Guilford Press
ISBN: 1606236407
Format: PDF, ePub, Docs
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Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website (www.appliedmissingdata.com) includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists.

Understanding Regression Assumptions

Author: William D. Berry
Publisher: SAGE Publications
ISBN: 1506315828
Format: PDF, ePub
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Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.

Event History Analysis

Author: Paul D. Allison
Publisher: SAGE
ISBN: 9780803920552
Format: PDF, ePub, Mobi
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Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.

A Mathematical Primer for Social Statistics

Author: John Fox
Publisher: SAGE
ISBN: 1412960800
Format: PDF, Docs
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Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. This handy volume introduces the areas of mathematics that are most important to applied social statistics.

Missing Data

Author: Patrick E. McKnight
Publisher: Guilford Press
ISBN: 1606238205
Format: PDF, ePub, Mobi
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While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study’s conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study results. When statistical remedies are needed--such as deletion procedures, augmentation methods, and single imputation and multiple imputation procedures--the book also explains how to make sound decisions about their use. Patrick E. McKnight's website offers a periodically updated annotated bibliography on missing data and links to other Web resources that address missing data.

Interaction Effects in Multiple Regression

Author: James Jaccard
Publisher: SAGE
ISBN: 9780761927426
Format: PDF, ePub, Docs
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This is a practical introduction to conducting analyses of interaction effects in the context of multiple regression. This new edition expands coverage on the analysis of three-way interactions in multiple regression analysis.

Causal Analysis with Panel Data

Author: Steven E. Finkel
Publisher: SAGE
ISBN: 9780803938960
Format: PDF, Mobi
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Panel data — information gathered from the same individuals or units at several different points in time — are commonly used in the social sciences to test theories of individual and social change. This book highlights the developments in this technique in a range of disciplines and analytic traditions.

Mediation Analysis

Author: Dawn Iacobucci
Publisher: SAGE
ISBN: 141292569X
Format: PDF, Mobi
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Social science data analysts have long considered the mediation of intermediate variables of primary importance in understanding individuals' social, behavioural and other kinds of outcomes. In this book Dawn Iacobucci uses the method known as structural equation modeling (SEM) in modeling mediation in causal analysis. This approach offers the most flexibility and allows the researcher to deal with mediation in the presence of multiple measures, mediated moderation, and moderated mediation, among other variations on the mediation theme. The wide availability of software implementing SEM gives the reader necessary tools for modeling mediation so that a proper understanding of causal relationship is achieved.