In this chapter we introduce what to do when we are looking at "pass/fail" data and have "paired data," or a "within-subject design".
Data was gathered through a within-subject cross randomized and counterbalanced design, on both internet and paper-and-pencil formats.
As the data were derived from a within-subject cross-over design (where half of the subjects received the placebo tablet in the first session, while the placebo session for the other half of subjects was the second experimental session), we additionally tested for an effect of session order by adding this variable as a between-subject factor to the ANOVA.
Alternatively the data can be normalised on a within-subject level and integrated with data from all other subjects into a group table, to answer the question whether the previously outlined PC is significant in an analysis of pooled data as well.
Data collection was organized by a within-subject design, meaning that while each test-taker had to complete the whole test, one half was executed in PBA (first part) and the other half in CBA (second part).
We also performed a between-group (control vs. PD) repeated measures ANOVA with "Predictability" (blocked vs. random) as a within-subject factor, with data from left and right CM trials averaged for each condition on the absolute value of peak object roll (the rationale being explained in [29]).
To test the hypothesis that function of amplitude reduction was modified during attention, an additional Datatype (observed data vs. predicted data) within-subject factor was used.
Data were collected through a within-subjects and between-groups design based on eye-tracking technology, followed by a self-administered questionnaire.
Similar to our approach for cluster-randomised controlled trials, we sought to compute deflated standard errors for outcome data from studies with a within-subjects design based on reported test statistics, or on ratios of inflated to unadjusted standard errors obtained from similar studies included in the review.
A mixed model was developed for analyzing the data with within-subject treatments, and the pairwise comparisons among the treatments were performed to determine the P values.
