"It's good to worry about mistakes when doing stats..."
Following those Tweets on the JASP Twitter I was wondering if I could get an additional opinion on my statistical decision-making?
I studied a team which spent a whole year at an isolated and confined research station. The team was made up of 11 people, 10 of whom participated in the study. 1 team member had to leave the station early due to psychological complications so they did not complete the study – their data will be used as a case study instead of as a group study.
I did cognitive assessments with my team at three time points (autumn, polar night, midnight sun) and well-being questionnaires at two additional points (after arrival, spring).
My current statistical approach has been: if the data are non-normally distributed, I use a Friedman's test and Wilcoxon for follow up as suggested by Field (2009, p. 579-580) in R. For normally distributed data in this within-subjects design, I chose a parametric ANOVA with a Huyn-Feldt correction in JASP. The Huynh-Feldt correction decreases the ANOVA’s chance of erroneously finding an effect that is not present at all (Type I error) despite having my small sample and allows me to use ordinal DV, such as my questionnaire data (Stiger et al., 1998). For ANOVA effect size, I will report omega squared (ω2) because it is reliable with small sample sizes (Levine & Hullet, 2002). I've also been reporting the Vovk-Sellke maximum p-ratio.
I remember enquiring on this forum and EJ saying small sample size was not an issue so I supplemented the above frequentist statistics with Bayesian analyses in JASP. I've usually done a within-subjects ANOVA with paired samples t-tests for follow up. There are no previous studies on teams like mine from which I could derive information to form a subjective prior.
For the JASP Bayesian ANOVA I've been reporting BF10, BF(M), BF(01), P(M), P(M|data), %error. For the t-tests I have been reporting –additionally– the credibility interval. I've been illustrating my PhD chapter with pizza plots for Bayes and bar or line graphs for frequentist stats.
Does this sound okay? Should I do anything else?