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Nonparametric Evaluation of Learning Curves by Hierarchical Contingeney Analysis
Author(s) -
Lienert G. A.,
Longo N.
Publication year - 1988
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710300103
Subject(s) - bonferroni correction , univariate , multivariate analysis of variance , nonparametric statistics , statistics , mathematics , contingency table , analysis of variance , factorial analysis , multivariate statistics
Hierarchical contingency analysis (HCA) is derived from the Perli‐Hommel‐Lehmacher (1986) closed test procedure for nonparametrical evaluation of learning curves of a 2 x 2‐factorial experiment. By HCA, univariate main effects are detected without Bonferroni alpha adjustment, as is shown by a numerical example from gold fish shock avoidance conditioning. Alternative approaches to nonparametrical evaluation of MANOVA designs with and without repeated measurements are discussed.