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Direction Dependence in Statistical Modeling. Methods of Analysis. Edition No. 1

  • ID: 5186708
  • Book
  • January 2021
  • 432 Pages
  • John Wiley and Sons Ltd
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hypotheses - A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations - A review of methods of direction dependence following the copula-based tradition of Sungur and Kim - A presentation of extensions of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.
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ABOUT THE EDITORS

NOTES ON CONTRIBUTORS

ACKNOWLEDGMENTS

PREFACE

PART I: FUNDAMENTAL CONCEPTS OF DIRECTION DEPENDENCE

1.            From Correlation to Direction Dependence Analysis: 1888-2018

Yadolah Dodge and Valentin Rousson

2.            Direction Dependence Analysis: Statistical Foundations and Applications

Wolfgang Wiedermann, Xintong Li, and Alexander von Eye

3.            The Use of Copulas for Directional Dependence Modeling

Engin Sungur

PART II: DIRECTION DEPENDENCE IN CONTINUOUS VARIABLES

4.            Asymmetry Properties of the Partial Correlation Coefficient: Foundations for Covariate-Adjustment in Distribution-based Direction Dependence Analysis

Wolfgang Wiedermann

5.            Recent Advances in Semi-Parametric Methods for Causal Discovery

Shohei Shimizu and Patrick Blöbaum

6.            Assumption Checking for Directional Causality Analyses

Phillip K. Wood

7.            Complete Dependence: A survey

Santi Tasena

PART III: DIRECTION DEPENDENCE IN CATEGORICAL VARIABLES

8.            Locating Direction Dependence using Log-Linear Modeling, Configural Frequency Analysis, and Prediction Analysis

Alexander von Eye and Wolfgang Wiedermann

9.            Recent Development on Asymmetric Association Measures for Contingency Tables

Xiaonan Zhu, Zheng Wei and Tonghui Wang

10.          Analysis of asymmetric dependence for three-way contingency tables using the subcopula approach

Daeyoung Kim and Zheng Wei

PART IV: APPLICATIONS AND SOFTWARE

11.          Distribution-based Causal Inference: A Review and Practical Guidance for Epidemiologists

Tom Rosenström and Regina García-Velázquez

12.          Determining causality in relation to early risk factors for ADHD: The case of breastfeeding duration

Joel T. Nigg, Diane D. Stadler, Alexander von Eye and Wolfgang Wiedermann

13.          Direction of Effect between Intimate Partner Violence and Mood Lability: A Granger Causality Model

G. Anne Bogat, Alytia A. Levendosky, Jade Kobayashi and Alexander von Eye

14.          On the Causal Relation of Academic Achievement and Intrinsic Motivation: An Application of Direction Dependence Analysis using SPSS Custom Dialogs

Xintong Li and Wolfgang Wiedermann

Index 

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Wolfgang Wiedermann
Daeyoung Kim
Engin A. Sungur
Alexander von Eye Michigan State University, USA.
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