To keep digging into data and analysis I enrolled into a Coursera specialization about Data Analysis and Interpretation by the Wesleyan University. Since part of the course requires to 1) write blog posts about analysis performed during the course and 2) that the submissions have deadline, I thought it was a good way to keep to a deadline and analyze some old data that I have collected and never looked at. Below is a short description of the data set I would like to analyze.
I started collecting these data during my last post-doc year but I could never finish the research. Therefore I thought it a good idea to dig-it-up again and see whether there is anything in it. I was trying to determine whether people with high perfectionists scores were behaving differently than people with low perfectionism scores in finding mismatches. It was my opinion that if perfectionists are characterized by an ability to look into things with a higher detail/scrutiny than non perfectionists, then they should have been faster or more accurate in finding differences/mismatches.
I thought that a good start was to measure reaction times and accuracy of responses during a comparative visual search task (e.g., Pomplum et al, 2001). The comparative visual search task displays two images which are mostly identical but for one detail (e.g. a difference in color or shape among the displayed objects, see picture for an example – the figure below shows an example in which I explicitly painted the difference/target green for sake of simplicity). Participants try to find the difference as quickly as possible, seventy-two times. At the end of the experimental session participants could go home and fill in an on-line version of a perfectionism questionnaire.
The dataset I will use contains three variables: 1) average reaction times, 2) average proportion of correct responses, 3) and total score at the perfectionism questionnaire. Sixty-eight participants participated, so there are 68 observations/columns in the dataset.
I am curious to see what will come out of the analysis.