I just conducted an 2 factorial experiment that has 6 conditions (2 by 3). Specifically, my design is: IV1 = prior positive information (positive in A domain vs. control vs. positive in B domain) IV2 = negative information (negative in A domain vs. negative in B domain) DV = Blame judgment (7 point Likert type scale)
I assumed that if prior positive domain and negative domain do not match, prior positive domain can play a shield role to block negative information (i.e., if the target person has prior positive quality in A domain and later engage in negative behavior in B domain, people do not really blame the target).
The results of two way ANOVA supported some of my assumptions. However, I just found out that my DV is not normally distributed (Negatively skewed so much). The reason can be related to my research context. People may immediately place 7 in the scale (i.e., blame) when they receive negative information. I tied to transform the data (log, centered, standardized), but it was not corrected enough to reach normal distribution.
Then my questions are: 1. Is the two way ANOVA a wrong statistic technique to use in this situation? 2. If it is wrong, which statistical technique is appropriate?
NOTE: I am using SPSS and AMOS.
Thank you very much in advance for your help and support!