Robust ANOVA method of analysis is presented and comprehensive comparisons between the results obtained from
the robust analysis and the previous result obtained from the non-robust method of analysis are obtained. The robust
statistical method commonly used in the experimental design fields is the robust ANOVA. This exercise introduces
the R package called WRS2 package which function is to implement method from WRS package (the original
package) to create a more user-friendly manner. The analysis which involves using the statistical method (Robust
method) will be executed on R studio using the WRS2 package available on CRAN (Mair & Wilcox, 2015).
In this segment, analysis on ANOVA is being carried out. The previous analysis was about analysis of interaction
between the processing style and sematic priming to check its effect on the aggressiveness score. The statistical
robust method is introduced and series of analysis is carried out with the method in order to make some necessary
comparison between the previous (non-robust) results and the current (Robust) method of analysis.
Bringing into consideration, the first analysis conducted which has to do with the analysis of interaction between a
response variable (Aggressiveness score) and two factors namely: map condition and puzzle condition. The result
obtained from the robust method the p-value for the interaction between the map condition and the puzzle condition.
The results obtained from the statistical robust method shows that the p-value for the interaction between the map
condition and the puzzle condition is 0.4257 while the result obtained from the non-robust method shows that the
interaction between the map condition and puzzle condition has an assigned p-value of 0.51073. The p-value of the
robust analysis is slightly less than that of the non-robust analysis. However slight, following the hypothesis
significant rule, the smaller the p-value, the closer it gets to the region of rejecting the null hypothesis “the interaction
between the factors has no significant effect on the response variable”. Here, two cases are considered in making
the decision to accept or reject the null hypothesis because both statistical methods (robust and non-robust methods)
are not following same inequality direction. That is p-value of robust method