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Related Concept Videos

Correlation and Causation01:27

Correlation and Causation

Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
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Cause and Effect01:53

Cause and Effect

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Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Granger causality--statistical analysis under a configural perspective.

Alexander von Eye1, Wolfgang Wiedermann, Eun-Young Mun

  • 1Michigan State University, 316 Psychology Building, East Lansing, MI, 48824-1116, USA, voneye@msu.edu.

Integrative Psychological & Behavioral Science
|August 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces Configural Frequency Analysis (CFA) to explore Granger causality, revealing specific patterns of aggressive impulses that predict physical aggression in adolescents.

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Last Updated: May 8, 2026

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Area of Science:

  • Social Sciences
  • Psychology
  • Statistics

Background:

  • Granger causality, based on regression models, traditionally assesses causal relationships between time series.
  • Existing methods may not capture context-specific or category-dependent causal effects.
  • A need exists for analytical approaches that consider the configural nature of data in causality assessment.

Purpose of the Study:

  • To extend Granger causality testing by incorporating a configural perspective.
  • To introduce Configural Frequency Analysis (CFA) as a novel method for Granger causation analysis.
  • To explore context-specific causal relationships within data.

Main Methods:

  • Adoption of a configural perspective for causality analysis.
  • Application of Configural Frequency Analysis (CFA) with derived base models.
  • Parallel specification of CFA models to traditional regression-based Granger causality models.

Main Results:

  • Demonstrated the utility of CFA in analyzing Granger causation from a configural viewpoint.
  • An example using adolescent aggression data identified a specific pattern of change in aggressive impulses.
  • This specific pattern was found to Granger-cause changes in physical aggression against peers.

Conclusions:

  • Configural Frequency Analysis offers a valuable extension to Granger causality testing.
  • Causality can be context-dependent, affecting specific categories or data sectors.
  • The findings highlight the importance of considering configural patterns in developmental aggression research.