Statistical Models For Causal Analysis
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Statistical Models For Causal Analysis
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Author : Robert D. Retherford
language : en
Publisher: John Wiley & Sons
Release Date : 2011-02-01
Statistical Models For Causal Analysis written by Robert D. Retherford and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-01 with Mathematics categories.
Simplifies the treatment of statistical inference focusing on how to specify and interpret models in the context of testing causal theories. Simple bivariate regression, multiple regression, multiple classification analysis, path analysis, logit regression, multinomial logit regression and survival models are among the subjects covered. Features an appendix of computer programs (for major statistical packages) that are used to generate illustrative examples contained in the chapters.
Statistical Models And Causal Inference
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Author : David A. Freedman
language : en
Publisher: Cambridge University Press
Release Date : 2010
Statistical Models And Causal Inference written by David A. Freedman and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Mathematics categories.
David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.
Statistical Approaches To Causal Analysis
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Author : Matthew McBee
language : en
Publisher: SAGE
Release Date : 2022-03
Statistical Approaches To Causal Analysis written by Matthew McBee and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03 with Psychology categories.
A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. McBee clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis.
Statistical Models And Causal Inference
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Author : David Freedman
language : en
Publisher:
Release Date : 2010
Statistical Models And Causal Inference written by David Freedman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Causation categories.
"David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views"--Provided by publisher.
Causal Analysis
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Author : Lawrence R. James
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1982-10
Causal Analysis written by Lawrence R. James and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982-10 with Business & Economics categories.
This book focuses specifically on confirmatory analysis - a quantitative technique used to illuminate causal relationships among organizational phenomena. The authors outline the conditions that must be met if causal inferences are to be drawn from nonexperimental data, and offer new tests for determining whether data meet those conditions. While analytic models and techniques of confirmatory analysis are stressed here, the authors also emphasize the importance of strong, well-developed theory as a prerequisite to the appropriate application of these powerful (but easily misused) tools.
Event History Analysis With Stata
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Author : Hans-Peter Blossfeld
language : en
Publisher: Psychology Press
Release Date : 2007-02-02
Event History Analysis With Stata written by Hans-Peter Blossfeld and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-02-02 with Business & Economics categories.
Event History Analysis With Stata provides an introduction to event history modeling techniques using Stata (version 9), a widely used statistical program that provides tools for data analysis. The book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models. T
Direction Dependence In Statistical Modeling
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Author : Wolfgang Wiedermann
language : en
Publisher: John Wiley & Sons
Release Date : 2020-12-03
Direction Dependence In Statistical Modeling written by Wolfgang Wiedermann and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-03 with Mathematics categories.
Covers the latest developments in direction dependence research Direction Dependence in Statistical Modeling: Methods of Analysis incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable relations. Having particular application in the fields of neuroscience, clinical psychology, developmental psychology, educational psychology, and epidemiology, direction dependence methods have attracted growing attention due to their potential to help decide which of two competing statistical models is more likely to reflect the correct causal flow. The book covers several topics in-depth, including: A demonstration of the importance of methods for the analysis of direction dependence hypotheses A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations A review of methods of direction dependence following the copula-based tradition of Sungur and Kim A presentation of extensions of direction dependence methods to the domain of categorical data An overview of algorithms for causal structure learning The book's fourteen chapters include a discussion of the use of custom dialogs and macros in SPSS to make direction dependence analysis accessible to empirical researchers.
Statistical Models For The Social And Behavioral Sciences
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Author : James H. Dwyer
language : en
Publisher: Oxford University Press, USA
Release Date : 1983
Statistical Models For The Social And Behavioral Sciences written by James H. Dwyer and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Business & Economics categories.
This pathbreaking graduate text in quantitative methodology attempts to provide a solution to a number of problems which have confronted empirical social science throughout its history -- the general overload of statistics and the apparents distinction between methods appropriate for analysis of variance and those appropriate for analysis of quasi-experiments -- proposing a linear model as the general and unified statistical model applicable to a full range of experimental designs. "An extremely sensible and usable step beyond its competitors. I will strongly recommend it to my students." --Steven Penrod, University of Wisconsin. "Deserves a place on that small shelf of valuable references for knowledgeable causal researchers." --Contemporary Psychology
Multilevel Modeling Of Social Problems
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Author : Robert B. Smith
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-02-26
Multilevel Modeling Of Social Problems written by Robert B. Smith and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-26 with Social Science categories.
Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models. This book innovatively: •Develops procedures for studying social, economic, and human development. • Uses typologies to group (i.e., classify or nest) the level of random macro-level factors. • Estimates models with Poisson, binomial, and Gaussian end points using SAS's generalized linear mixed models (GLIMMIX) procedure. • Selects appropriate covariance structures for generalized linear mixed models. • Applies difference-in-differences study designs in the multilevel modeling of intervention studies. •Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data. • Uses the Kenward-Rogers correction in mixed models of repeated measures. • Explicates differences between associational and causal analysis of multilevel models. • Consolidates research findings via meta-analysis and methodological critique. •Develops criteria for assessing a study's validity and zone of causality. Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures; policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.
Applied Bayesian Modeling And Causal Inference From Incomplete Data Perspectives
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Author : Andrew Gelman
language : en
Publisher: John Wiley & Sons
Release Date : 2004-09-03
Applied Bayesian Modeling And Causal Inference From Incomplete Data Perspectives written by Andrew Gelman and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-09-03 with Mathematics categories.
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.