Two-way Anova

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by Lazzftw » Wed Oct 20, 2021 10:50 am

Dear all,

I'm working on analyzing the effects of social cycling on cyclists' stress levels.
An experiment with a total of 21 participants was conducted. The Experiment was about a comparison between single cycling vs group cycling.
Single rides and group rides were performed while measuring the GSR (stress) data, in order to notice if there are any effects or changes on stress when riding in a group compared to riding alone.
Every participant has to participate in a single ride, followed then by group rides, changing the position each time ( riding in front, middle, in the back).

The plan was to use a simple t-test for analyzing (single vs group).
But we also noticed interesting effects between the stress values inside the groups depending on the position (front vs back, front vs middle..)

My supervisor meant that I should use analyze 2 factors using two-way ANOVA:
One factor would be cycling (single, group) and the other factor is position (front, middle, back) with stress values as the fixed factor.

I'm new to statistical analyses, and my question is: is the two-way ANOVA the right approach for my case? Because while adding the data i noticed 'single' will only have one position, while 'group' would have (front, mid, and back), meaning that group would be repeated many 3 times more than single. could that be a problem?

Apologies for the long question and if anything doesn't make sense.
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by jonathon » Thu Oct 21, 2021 12:06 am

yes, i think you're correct. i don't think ANOVA can handle this.

i'd be inclined to perform it as two separate analyses. a t-test comparing single vs group, and a one-way anova comparing front, middle, back (only looking at the 'group' condition).

perhaps others will have a different suggestion, but that seems to me the most straight forward way of tackling it.


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by reason180 » Tue Nov 02, 2021 1:51 am


I would consider having one of your analyses be a one-factor repeated-measures ANOVA on change (change-from-baseline) scores. For each participant, compute three change-from-baselines scores: Front-position stress minus single-ride stress, middle-position stress minus single-ride stress, and back-position stress minus single-ride stress. The repeated-measures ANOVA measures whether there is significant variation across the means of the three conditions. Additionally, a single-sample t test would reveal whether, overall, the mean change-from-baseline is significantly different from zero.
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