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Neurophysiological data were analyzed in a repeated measures ANOVA using the within-subject factor "correctness" and the between-subject factor "group".
We found higher prevalence of LSI using the normalised within-subjects model, compared to a conventional Gaussian between-subjects model.
Sessions were used as the within-subject factor and treatment as the between-subjects factor.
Use the subject line "TechCrunchPitch".
Use the subject line SHARE YOUR LOCATION.
Use the subject line "My office picture".
The repeated measures analysis of variance (ANOVA) was used to detect the within-subject (side-turning) and between-subject effects (gender, age, and BMI) on various parameters related to turning ability.
Fig. 1 Study design for Experiments 1 and 2. Accuracy was assessed using both a within-subject and a between-subjects comparison.
The correlation coefficient was calculated for the relationship between the journey durations estimated from the two methods using both within-subject and between-subject methods.
A compound symmetry (co variance structure was used to model the within-subject errors.
Using a double-blind within-subject design the study was divided into two phases.
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CEO of Professional Science Editing for Scientists @ prosciediting.com