Diana,
After I read your posting I looked at your website and while I think I understand the overall question you are asking, I don’t understand the construction of
your dataset. To summarize the dataset: People rated 21 features of something and also rated their overall satisfaction, all on a 1-5 scale. Feature ratings were recoded 1-3=0, 4,5=1. There seem to have been 51 groups of people. Each group is analyzed separately
because different relationships may be expected in each group. This I don’t get: It seems that the feature ratings were converted to either proportions or “z’s” via an inverse normal distribution mapping of the proportions. Either way, haven’t you converted
your 51*n(g) dataset to a N=51 dataset.
Gene Maguin
From: SPSSX(r) Discussion [mailto:[hidden email]]
On Behalf Of Kornbrot, Diana
Sent: Tuesday, January 28, 2014 7:36 AM
To: [hidden email]
Subject: Statistics Challenge: Does analysis metric matter? Are normal based methods robust?
Greetings and apologies for cross-posting
It is often claimed that normal based methods such as linear regression are 'robust' and do not give misleading results, even when data are far from normally distributed.
To investigate this claim, several real data sets have been analysed: both using normal based methods and using methods based on various non-normal distributions. The first scenario, Scenario 1 is given below.
We want to compare the actual concordance of two alternative methods with the predictions of statistical practitioners, such as the committed users of this list. So we are asking for
your predictions about concordance for various scenarios.
Scenario 1: Multiple linear regression is performed with a raw and a transformed metric.
Predict % agreement between results from the 2 metrics
Analyst want to know which of 21 features significantly predict overall satisfaction
Raw metric is proportion of respondents favourable, p
BUT p is not & can not be normally distributed. So an alternative is the inverse normal, z, corresponding to p.
Best subset linear regression was conducted for 51 separate units: a. using p as metric. b. using z as metric.
Concordance Question: How much difference does it make?
Predict from all the significant predictors, what:
% same predictors significant at 95% cl for both p and z
% predictors only significant for p
% predictors only significant for z.
Please give your expert predictions at https://www.surveymonkey.com/s/9SY7V7Z
More details about project at: http://dianakornbrot.wordpress.com/projects/methods-matter/
Dissemination of Results
The actual concordance and a summary of the predicted concordance of experts will be published on 16 Feb 2014 at http://dianakornbrot.wordpress.com/projects/methods-matter/
Many thanks for reading this long screed. Comments on the project are very welcome.
best
Diana
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