- Thread starter LZC
- Start date

I basically want to know if there's a significant difference in helmet use between riders of each vehicle type, and then also within each group. I thought about doing a ttest for the latter for each group but that just increases the error. Maybe an anova for the former? I guess the question is do I really need to do a comparative test or is a regression more informative?

Thank you!!

Helmet Usage ~ Bicycle Type A + Bicycle Type B,

where 1) one of the bicycle types is left out as the reference category,

2) Bicycle Type A and Bicycle Type B are binary dummy variables.

The omnibus likelihood-ratio test for the estimated logit model will answer the same question as the aforementioned chi-square test (about the overall relationship)... Next, polish the logit model by keeping only statistically significant terms. Once you are done, the model will show you the direction of the effects. You will see which group is more helmet-friendly than another and by how much.

Helmet Usage ~ Bicycle Type A + Bicycle Type B,

where 1) one of the bicycle types is left out as the reference category,

2) Bicycle Type A and Bicycle Type B are binary dummy variables.

The omnibus likelihood-ratio test for the estimated logit model will answer the same question as the aforementioned chi-square test (about the overall relationship)... Next, polish the logit model by keeping only statistically significant terms. Once you are done, the model will show you the direction of the effects. You will see which group is more helmet-friendly than another and by how much.

Also, many people strongly encourage controlling for family-wise error when making multiple comparisons in order to lessen the risk for type I errors (rejecting the null when it is true). So if you are planning on comparing the differences in outcome via multiple group comparisons, it may be prudent to investigation the correction.

Thanks and welcome to the forum.