A Note on Using the Nonparametric Levene Test When Population Means Are Unequal
This computer simulation study evaluates the robustness of the nonparametric Levene test of equal variances (Nordstokke & Zumbo, 2010) when sampling from populations with unequal (and unknown) means. Testing for population mean differences when population variances are unknown and possibly unequ...
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| Veröffentlicht in: | Practical assessment, research & evaluation research & evaluation, 2018-09, Vol.23 (13), p.13 |
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| creator | Shear, Benjamin R Nordstokke, David W Zumbo, Bruno D |
| description | This computer simulation study evaluates the robustness of the nonparametric Levene test of equal variances (Nordstokke & Zumbo, 2010) when sampling from populations with unequal (and unknown) means. Testing for population mean differences when population variances are unknown and possibly unequal is often referred to as the Behrens-Fisher problem when the populations are normally distributed, and the generalized Behrens-Fisher problem when the populations are non-normal. The nonparametric Levene test was developed to overcome reductions in power of the original Levene test of equal variances in the case of the generalized Behrens-Fisher problem. We use a Monte Carlo computer simulation to demonstrate that sampling from populations with unequal and unknown means can lead to incorrect (either inflated or decreased) Type I error rates of the nonparametric Levene test. Centering samples using either sample means or medians does not correct the Type I error rates. This note is intended to make applied researchers aware of this problem when testing for the equality of population variances with the NPL test and in general. |
| doi_str_mv | 10.7275/bwvg-d091 |
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Testing for population mean differences when population variances are unknown and possibly unequal is often referred to as the Behrens-Fisher problem when the populations are normally distributed, and the generalized Behrens-Fisher problem when the populations are non-normal. The nonparametric Levene test was developed to overcome reductions in power of the original Levene test of equal variances in the case of the generalized Behrens-Fisher problem. We use a Monte Carlo computer simulation to demonstrate that sampling from populations with unequal and unknown means can lead to incorrect (either inflated or decreased) Type I error rates of the nonparametric Levene test. Centering samples using either sample means or medians does not correct the Type I error rates. 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| source | ERIC - Full Text Only (Discovery); EZB-FREE-00999 freely available EZB journals; EBSCOhost Education Source |
| subjects | Computer Simulation Error of Measurement Monte Carlo Methods Nonparametric Statistics Population Sampling |
| title | A Note on Using the Nonparametric Levene Test When Population Means Are Unequal |
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