Dear folks,
We would like to correlate a questionnaire data set (shown in the
attached snapshot, first column - these are count data for the
frequency of an activity) with our group GLM analysis. However, as you
can see, we have a very non-normal and long tailed distribution. We
had a few questions:
1. Will automatic outlier de-weighting be sufficient to account for
the non-normalcy of the data? What's the mechanism of automatic
de-weighting?
2. If it isn't sufficient, would it be recommended to use another
transformation (such as square root or log transform) to make the data
more normal before running the GLM?
3. Would it be better to create bins/groups from the continuous
measure and then to analyze differences among those groups with the
GLM?
Thanks in advance.
--
Best Regards
Xue, Feng Ph.D.
Psychology Department
University of Southern California
Los Angeles CA, 90089
Mobile: +1 213-249-1040
==============================================
National Key Laboratory of Cognitive Neuroscience and Learning
Beijing Normal University
Beijing, China. 100875
Mobile: +86 13810154455
web: http://psychbrain.bnu.edu.cn
|