000K  utf8
1100  $c2021
1500  eng
2050  urn:nbn:de:gbv:8:3-2021-00353-5
2051  10.3390/ijerph18041741
3000  Hermanussen, Michael
3010  Aßmann, Christian
3010  Groth, Detlef
4000  Chain Reversion for Detecting Associations in Interacting Variables-St. Nicolas House Analysis  [Hermanussen, Michael]
4209  (1) Background: We present a new statistical approach labeled as "St. Nicolas House Analysis" (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic "association chains" defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.
4950  https://doi.org/10.3390/ijerph18041741$xR$3Volltext$534
4950  https://nbn-resolving.org/urn:nbn:de:gbv:8:3-2021-00353-5$xR$3Volltext$534
4961  https://macau.uni-kiel.de/receive/macau_mods_00001196
5051  333.7
5550  Association Chains
5550  Bivariate Correlation Coefficients
5550  Data Matrices
5550  Network Graphs
5550  St. Nicolas House Analysis