Direction Dependence in Statistical Models: Methods of Analysis

Direction Dependence in Statistical Models: Methods of Analysis
  • eBook:
    Direction Dependence in Statistical Models: Methods of Analysis
  • Author:
    Wolfgang Wiedermann, Daeyoung Kim, Engin A. Sungur, Alexander von Eye
  • Edition:
    1 edition
  • Categories:
  • Data:
    December 3, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    432 pages
  • Format:

Book Description
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  • A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations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.


Part I: Fundamental Concepts of Direction Dependence
1. From Correlation to Direction Dependence Analysis 1888–2018
2. Direction Dependence Analysis
3. The Use of Copulas for Directional Dependence Modeling

Part II: Direction Dependence in Continuous Variables
4. Asymmetry Properties of the Partial Correlation Coefficient
5. Recent Advances in Semi‐Parametric Methods for Causal Discovery
6. Assumption Checking for Directional Causality Analyses
7. Complete Dependence

Part III: Direction Dependence in Categorical Variables
8. Locating Direction Dependence Using Log‐Linear Modeling, Configural Frequency Analysis, and Prediction Analysis
9. Recent Developments on Asymmetric Association Measures for Contingency Tables
10. Analysis of Asymmetric Dependence for Three‐Way Contingency Tables Using the Subcopula Approach

Part IV: Applications and Software
11. Distribution‐Based Causal Inference
12. Determining Causality in Relation to Early Risk Factors for ADHD
13. Direction of Effect Between Intimate Partner Violence and Mood Lability
14. On the Causal Relation of Academic Achievement and Intrinsic Motivation

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