Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Normal view MARC view ISBD view

Statistics and causality : methods for applied empirical research / [edited by] Wolfgang Wiedermann and Alexander von Eye.

Contributor(s): Wiedermann, Wolfgang [editor.] | Eye, Alexander von [editor].
Material type: TextTextSeries: Wiley series in probability and statistics.Publisher: New Jersey : John Wiley, ©2016Description: xxvi, 438 pages : illustrations ; 25 cm.ISBN: 9781118947043 .Subject(s): Statistics -- Methodology | Causation | Quantitative research -- MethodologyAdditional physical formats: Online version:: Statistics and causalityDDC classification: 001.422
Contents:
Part IBASES OF CAUSALITY. 1. Causation and the Aims of Inquiry / Ned Hall -- 2. Evidence and Epistemic Causality / Michael Wilde, Jon Williamson -- Part II DIRECTIONALITY OF EFFECTS. 3. Statistical Inference for Direction of Dependence in Linear Models / Yadolah Dodge, Valentin Rousson -- 4. Directionality of Effects in Causal Mediation Analysis / Wolfgang Wiedermann, Alexander Eye -- 5. Direction of Effects in Categorical Variables: A Structural Perspective / Alexander Eye, Wolfgang Wiedermann -- 6. Directional Dependence Analysis Using Skew-Normal Copula-Based Regression / Seongyong Kim, Daeyoung Kim -- 7. Non-Gaussian Structural Equation Models for Causal Discovery / Shohei Shimizu -- 8. Nonlinear Functional Causal Models for Distinguishing Cause from Effect / Kun Zhang, Aapo Hyvarinen -- Part III GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING. 9. Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity / Peter C M Molenaar, Lawrence L Lo -- 10. Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models / Ingrid Koller, Claus H Carstensen, Wolfgang Wiedermann, Alexander von Eye -- 11. Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences / Katerina Hlavkov-Schindler, Valeriya Naumova, Sergiy Pereverzyev -- 12. Unmeasured Reciprocal Interactions: Specification and Fit Using Structural Equation Models / Phillip K Wood -- Part IV COUNTERFACTUAL APPROACHES AND PROPENSITY SCORE ANALYSIS. 13. Log-Linear Causal Analysis of Cross-Classified Categorical Data / Kazuo Yamaguchi -- 14. Design- and Model-Based Analysis of Propensity Score Designs / Peter M Steiner -- 15. Adjustment when Covariates are Fallible / Steffi Pohl, Marie-Ann Sengewald, Rolf Steyer -- 16. Latent Class Analysis with Causal Inference: The Effect of Adolescent Depression on Young Adult Substance Use Profile / Stephanie T Lanza, Megan S Schuler, Bethany C Bray -- Part V DESIGNS FOR CAUSAL INFERENCE. 17. Can We Establish Causality with Statistical Analyses? The Example of Epidemiology / Ulrich Frick, Jurgen Rehm.
Summary: A one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: - New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories - End-of-chapter bibliographies that provide references for further discussions and additional research topics - Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic. Wolfgang Wiedermann, PhD, is Assistant Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri, Columbia. His research interests include the development of methods for direction dependence analysis and causal inference, the development and evaluation of methods for person-oriented research, and methods for intensive longitudinal data. Alexander von Eye, PhD, is Professor Emeritus of Psychology at Michigan State University. His research interests include statistical methods, categorical data analysis, and human development. Dr. von Eye is Section Editor for the Encyclopedia of Statistics in Behavioral Science and is the coauthor of Log-Linear Modeling: Concepts, Interpretation, and Application, both published by Wiley.
Tags from this library: No tags from this library for this title. Log in to add tags.
Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
001.422 W644 (Browse shelf) Available 137746
Total holds: 0

Includes bibliographical references and index.

Part IBASES OF CAUSALITY.
1. Causation and the Aims of Inquiry / Ned Hall --
2. Evidence and Epistemic Causality / Michael Wilde, Jon Williamson --
Part II DIRECTIONALITY OF EFFECTS.
3. Statistical Inference for Direction of Dependence in Linear Models / Yadolah Dodge, Valentin Rousson --
4. Directionality of Effects in Causal Mediation Analysis / Wolfgang Wiedermann, Alexander Eye --
5. Direction of Effects in Categorical Variables: A Structural Perspective / Alexander Eye, Wolfgang Wiedermann --
6. Directional Dependence Analysis Using Skew-Normal Copula-Based Regression / Seongyong Kim, Daeyoung Kim --
7. Non-Gaussian Structural Equation Models for Causal Discovery / Shohei Shimizu --
8. Nonlinear Functional Causal Models for Distinguishing Cause from Effect / Kun Zhang, Aapo Hyvarinen --
Part III GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING.
9. Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity / Peter C M Molenaar, Lawrence L Lo --
10. Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models / Ingrid Koller, Claus H Carstensen, Wolfgang Wiedermann, Alexander von Eye --
11. Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences / Katerina Hlavkov-Schindler, Valeriya Naumova, Sergiy Pereverzyev --
12. Unmeasured Reciprocal Interactions: Specification and Fit Using Structural Equation Models / Phillip K Wood --
Part IV COUNTERFACTUAL APPROACHES AND PROPENSITY SCORE ANALYSIS.
13. Log-Linear Causal Analysis of Cross-Classified Categorical Data / Kazuo Yamaguchi --
14. Design- and Model-Based Analysis of Propensity Score Designs / Peter M Steiner --
15. Adjustment when Covariates are Fallible / Steffi Pohl, Marie-Ann Sengewald, Rolf Steyer --
16. Latent Class Analysis with Causal Inference: The Effect of Adolescent Depression on Young Adult Substance Use Profile / Stephanie T Lanza, Megan S Schuler, Bethany C Bray --
Part V DESIGNS FOR CAUSAL INFERENCE.
17. Can We Establish Causality with Statistical Analyses? The Example of Epidemiology / Ulrich Frick, Jurgen Rehm.

A one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: - New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories - End-of-chapter bibliographies that provide references for further discussions and additional research topics - Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic. Wolfgang Wiedermann, PhD, is Assistant Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri, Columbia. His research interests include the development of methods for direction dependence analysis and causal inference, the development and evaluation of methods for person-oriented research, and methods for intensive longitudinal data. Alexander von Eye, PhD, is Professor Emeritus of Psychology at Michigan State University. His research interests include statistical methods, categorical data analysis, and human development. Dr. von Eye is Section Editor for the Encyclopedia of Statistics in Behavioral Science and is the coauthor of Log-Linear Modeling: Concepts, Interpretation, and Application, both published by Wiley.

There are no comments for this item.

Log in to your account to post a comment.
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in


Visitor Counter