Timeline for Running ANOVA - must I remove outliers?
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13 events
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| Dec 21, 2021 at 3:50 | comment | added | Statsquestionboy | @BruceET Wait. I think I've just realised something I was doing didn't make sense. If you analyse outliers, then remove them, am I right in thinking it doesn't make sense to analyse this now-filtered data for more outliers, only to remove them again? It creates some sort of loop, as the definition of outliers keeps changing. You just care about what is considered an outlier in the initial data, right? Something that shows an outlier in the subsequent analysis doesn't count as an actual outlier? | |
| Dec 21, 2021 at 3:03 | comment | added | BruceET | Thanks for additional information. You might consider @Dave's comments and my Answer about using ranks to mitigate effects of outliers without censoring. // Maybe this information will prompt yet other ideas. | |
| Dec 21, 2021 at 2:42 | comment | added | Statsquestionboy | @BruceET Hmm, well, it's a 2 x 2 Stroop task measuring reaction time as the dependent variable, with posture and congruence as dependent variables, each with two levels. I'm running it in SPSS, a standard within subjects repeated measures ANOVA. When I generate boxplots to assess outliers of reaction time, I get like 4 or 5 out of around 200. I've read that ANOVAs are robust against normality violations, but not outliers. I want to know whether there are effects of posture and congruence on reaction time, mainly. But will I be able to determine any main effects with these outliers? | |
| Dec 20, 2021 at 20:44 | answer | added | Geoffrey Johnson | timeline score: 0 | |
| Dec 20, 2021 at 19:32 | answer | added | BruceET | timeline score: 1 | |
| Dec 20, 2021 at 6:08 | comment | added | BruceET | With more information about your proposed experimental design, the type of data, and your objectives, it might be easier to give more relevant advice . [(i) "$2\times 2$ repeated measures" could mean several different things. (ii) How do the outliers arise? What do they mean? What do you most want to know from your data?] An ANOVA on overall ranks would not ensure exact normality of residuals, but it would lessen the effect of outliers without censoring them. A regression approach as suggested by @Dave might work. It might be possible to find an appropriate metric for a permutation test, Etc. | |
| Dec 20, 2021 at 5:11 | comment | added | Dave | There are alternatives, such as proportional odds ordinal regression (generalization of Kruskal-Wallis). However, it might be the case that your “outliers” are perfectly consistent with the normality assumption. In a normal distribution (standard ANOVA assumption), what is the probability of getting a point that meets your definition of an outlier? | |
| Dec 20, 2021 at 4:39 | comment | added | Statsquestionboy | @Dave By that, do you mean not running an ANOVA? I'm not sure else what I could do. Do you have any suggestions, if I'm determined to stick with this type of ANOVA? Or is it simply not possible, in your mind? | |
| Dec 20, 2021 at 4:38 | comment | added | Dave | Then you change your modeling approach to reflect the reality of the data, rather than changing your data to reflect the assumptions of a mathematical procedure. | |
| Dec 20, 2021 at 4:35 | comment | added | Statsquestionboy | @Dave but you're supposed to check assumptions of ANOVA before you do them, right? See here: statistics.laerd.com/spss-tutorials/… The fact mine has violated one of these key assumptions... isn't that a problem? My analysis will no longer be valid, right? So I'm not sure what to do. I have to report the main effects, simple effects, etc. But then presumably I'd be all 'but yeah all this is nonsense, because of the outliers, so...' which doesn't sound ideal. | |
| Dec 20, 2021 at 4:18 | comment | added | Dave | That ANOVA is sensitive to extreme observations is why it is important to keep them. | |
| S Dec 20, 2021 at 4:10 | review | First questions | |||
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| S Dec 20, 2021 at 4:10 | history | asked | Statsquestionboy | CC BY-SA 4.0 |