Generalisation of Mood's Test to Factorial Designs to Detect Heterogeneity of Variances

Generalisation of Mood's Test to Factorial Designs to Detect Heterogeneity of Variances

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Article ID: iaor2016126
Volume: 57
Issue: 4
Start Page Number: 535
End Page Number: 544
Publication Date: Dec 2015
Journal: Australian & New Zealand Journal of Statistics
Authors: ,
Keywords: experimental design, analysis of variance, factor analysis
Abstract:

Mood's test, which is a relatively old test (and the oldest non‐parametric test among those tests in its class) for determining heterogeneity of variance, is still being widely used in different areas such as biometry, biostatistics and medicine. Although it is a popular test, it is not suitable for use on a two‐way factorial design. In this paper, Mood's test is generalised to the 2 × 2 factorial design setting and its performance is compared with that of Klotz's test. The power and robustness of these tests are examined in detail by means of a simulation study with 10,000 replications. Based on the simulation results, the generalised Mood's and Klotz's tests can especially be recommended in settings in which the parent distribution is symmetric. As an example application we analyse data from a multi‐factor agricultural system that involves chilli peppers, nematodes and yellow nutsedge. This example dataset suggests that the performance of the generalised Mood test is in agreement with that of the generalised Klotz's test.

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