I am running an experiment testing reaction times under different conditions. I have a data sample located here and I have added a graphical plot of my data below in order to ease your understanding:
I would like to check whether weak emotion recognition has significantly higher error rates than all other conditions. I was told that the proper way to do this was a repeated measurement ANOVA. I have found out that this can be done via R's
I am interfacing with R via RPy and you may see my exact code under this notebook.
I am getting the following resulting summary:
Error: ID Df Sum Sq Mean Sq F value Pr(>F) Residuals 6 0.022 0.003666 Error: Within Df Sum Sq Mean Sq F value Pr(>F) COI 6 0.02628 0.004379 3.468 0.0083 ** Residuals 36 0.04547 0.001263 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
How does this help me address my issue?
Additionally, in a discussion resulting from this other question I have been told that while anova is acceptable in some cases (such as this one) linear models such as the one generated by
nlme::lme() are preferable.
I have used that function in this other notebook, and the output reads as follows:
Fixed effects: ER ~ COI Value Std.Error DF t-value p-value (Intercept) 0.01928571 0.01514819 36 1.273137 0.2111 COIem-hard 0.07028571 0.01899579 36 3.700069 0.0007 COIsc-11 0.00403687 0.01899579 36 0.212514 0.8329 COIsc-15 0.01700000 0.01899579 36 0.894935 0.3768 COIsc-19 0.00417857 0.01899579 36 0.219974 0.8271 COIsc-23 0.00432488 0.01899579 36 0.227676 0.8212 COIsc-27 0.00417857 0.01899579 36 0.219974 0.8271
how am I to interpret those p-values in the context of the point I'm trying to make? Also, why is my first COI (COIem-easy) absent from the list?
As a general point I would also be very happy to hear which of these 2 approaches you advise I should use.