PT Journal
AU Hermanussen, M
   Aßmann, C
   Groth, D
TI Chain Reversion for Detecting Associations in Interacting Variables-St. Nicolas House Analysis
SO International journal of environmental research and public health : IJERPH / Molecular Diversity Preservation International
PY 2021
VL 18
IS 4
PU MDPI AG
DI 10.3390/ijerph18041741
WP https://macau.uni-kiel.de/receive/macau_mods_00001196
LA en
DE Network Graphs; Association Chains; Bivariate Correlation Coefficients; Data Matrices; St. Nicolas House Analysis
SN 1661-7827
AB (1) Background: We present a new statistical approach labeled as "St. Nicolas House Analysis" (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic "association chains" defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.
PI Basel, Switzerland
ER