The Logic of Causal Order

Author: James A. Davis

Publisher: SAGE

ISBN: 9780803925533

Category: Medical

Page: 72

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This monograph is not statistical. It looks instead at pre-statistical assumptions about dependent variables and causal order. Professor Davis spells out the logical principles that underlie our ideas of causality and explains how to discover causal direction, irrespective of the statistical technique used. He stresses throughout that knowledge of the "real world" is important and repeatedly challenges the myth that causal problems can be solved by statistical calculations alone.

Causal Analysis with Panel Data

Author: Steven E. Finkel

Publisher: SAGE

ISBN: 9780803938960

Category: Medical

Page: 98

View: 611

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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

Category: Mathematics

Page: 85

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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.

Multilevel Modeling

Author: Douglas A. Luke

Publisher: SAGE

ISBN: 9780761928799

Category: Mathematics

Page: 79

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A practical introduction to multi-level modelling, this book offers an introduction to HLM & illustrations of how to use this technique to build models for hierarchical & longitudinal data.

Fixed Effects Regression Models

Author: Paul D. Allison

Publisher: SAGE Publications

ISBN: 1483389278

Category: Social Science

Page: 136

View: 9189

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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

Propensity Score Analysis

Author: Shenyang Guo,Mark W. Fraser

Publisher: SAGE

ISBN: 1452235007

Category: Mathematics

Page: 421

View: 1922

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Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.

The SAGE Handbook of Quantitative Methodology for the Social Sciences

Author: David Kaplan

Publisher: SAGE Publications

ISBN: 1483365875

Category: Social Science

Page: 528

View: 3098

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The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.

Structural Equation Modeling

Foundations and Extensions

Author: David Kaplan

Publisher: SAGE Publications

ISBN: 148334259X

Category: Social Science

Page: 272

View: 7581

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Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face. Intended Audience While the book assumes some knowledge and background in statistics, it guides readers through the foundations and critical assumptions of SEM in an easy-to-understand manner.

Quantile Regression

Author: Lingxin Hao,Daniel Q. Naiman

Publisher: SAGE Publications

ISBN: 1483316904

Category: Social Science

Page: 136

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Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research

Applied Regression

An Introduction

Author: Colin Lewis-Beck,Michael Lewis-Beck

Publisher: SAGE Publications

ISBN: 1483381498

Category: Social Science

Page: 120

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Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. Authors Colin Lewis-Beck and Michael Lewis-Beck then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.

Hierarchical Linear Models

Applications and Data Analysis Methods

Author: Stephen W. Raudenbush,Anthony S. Bryk

Publisher: SAGE

ISBN: 9780761919049

Category: Mathematics

Page: 485

View: 5284

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Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: 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; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.

Missing Data

Author: Paul D. Allison

Publisher: SAGE Publications

ISBN: 1452207909

Category: Social Science

Page: 104

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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.

The Reviewer’s Guide to Quantitative Methods in the Social Sciences

Author: Gregory R. Hancock,Ralph O. Mueller

Publisher: Routledge

ISBN: 1135172994

Category: Education

Page: 448

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The Reviewer’s Guide is designed for reviewers of research manuscripts and proposals in the social and behavioral sciences, and beyond. Its uniquely structured chapters address traditional and emerging quantitative methods of data analysis.

Agent-Based Models

Author: Nigel Gilbert,Professor Nigel Gilbert

Publisher: SAGE

ISBN: 1412949645

Category: Social Science

Page: 98

View: 345

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Aimed at readers with minimal experience in computer programming, this brief book provides a theoretical and methodological rationale for using ABM in the social sciences. It goes on to describe some carefully chosen examples from different disciplines, illustrating different approaches to ABM. It concludes with practical advice about how to design and create ABM, a discussion of validation procedures, and some guidelines about publishing articles based on ABM.

Theory-Based Data Analysis for the Social Sciences

Author: Carol S. Aneshensel

Publisher: SAGE

ISBN: 1412994357

Category: Social Science

Page: 446

View: 8470

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This book presents a method for bringing data analysis and statistical technique into line with theory. The author begins by describing the elaboration model for analyzing the empirical association between variables. She then introduces a new concept into this model, the focal relationship. Building upon the focal relationship as the cornerstone for all subsequent analysis, two analytic strategies are developed to establish its internal validity: an exclusionary strategy to eliminate alternative explanations, and an inclusive strategy which looks at the interconnected set of relationships predicted by theory. Using real examples of social research, the author demonstrates the use of this approach for two common forms of analysis, multiple linear regression and logistic regression. Whether learning data analysis for the first time or adding new techniques to your repertoire, this book provides an excellent basis for theory-based data analysis.

Doing Quantitative Research in the Social Sciences

An Integrated Approach to Research Design, Measurement and Statistics

Author: Thomas R Black

Publisher: SAGE

ISBN: 1446223639

Category: Social Science

Page: 768

View: 6713

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This original textbook provides a comprehensive and integrated approach to using quantitative methods in the social sciences. Thomas R Black guides the student and researcher through the minefield of potential problems that may be confronted, and it is this emphasis on the practical that distinguishes his book from others which focus exclusively on either research design and measurement or statistical methods. Focusing on the design and execution of research, key topics such as planning, sampling, the design of measuring instruments, choice of statistical text and interpretation of results are examined within the context of the research process. In a lively and accessible style, the student is introduced to researc design issues alongside statistical procedures and encouraged to develop analytical and decision-making skills.

The SAGE Handbook of Regression Analysis and Causal Inference

Author: Henning Best,Christof Wolf

Publisher: SAGE

ISBN: 1473908353

Category: Social Science

Page: 424

View: 1063

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'The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.' - John Fox, Professor, Department of Sociology, McMaster University 'The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.' - Ben Jann, Executive Director, Institute of Sociology, University of Bern 'Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.' -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Causality in Sociological Research

Author: Jakub Karpinski

Publisher: Springer Science & Business Media

ISBN: 9400904959

Category: Social Science

Page: 192

View: 5005

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The general treatment of problems connected with the causal conditioning of phenomena has traditionally been the domain of philosophy, but when one examines the relationships taking place in the various fields, the study of such conditionings belongs to the empirical sciences. Sociology is no exception in that respect. In that discipline we note a certain paradox. Many problems connected with the causal conditioning of phenomena have been raised in sociology in relatively recent times, and that process marked its empirical or even so-called empiricist trend. That trend, labelled positivist, seems in this case to be in contradiction with a certain type of positivism. Those authors who describe positivism usually include the Humean tradition in its genealogy and, remembering Hume's criticism of the concept of cause, speak about positivism as about a trend which is inclined to treat lightly the study of causes and confines itself to the statements on co-occurrence of phenomena.

Statistical Models and Causal Inference

A Dialogue with the Social Sciences

Author: David A. Freedman,David Collier,Jasjeet S. Sekhon

Publisher: Cambridge University Press

ISBN: 0521195004

Category: Mathematics

Page: 399

View: 8627

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David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.