Page 1 of 1

Posted: Wed Dec 02, 2015 12:46 pm
Dear all,

I have a a question. My primary data has 472 observations after removal of straight lining, and I found there were more than 30 outliers (univariate outlier, manifest variable). I do not think removal of these outlier is a wise solution. Shall I keep it as a subgroup and run a multi group analysis? Or other treatment I can do?
Thank you all.
Thomas

Posted: Wed Dec 02, 2015 3:20 pm
I have run PLS, here is my findings
Fullset (include outlier)
Construct A: AVE =0.697 ; CR = 0.920 , Cronbach's Alpha = 0.891
Construct B: AVE = 0.756; CR = 0.925, Cronbach's Alpha = 0.892
Construct C:AVE = 0.691; CR = 0.870, Cronbach's Alpha = 0.779
Construct D:AVE = 0.887; CR = 0.959, Cronbach's Alpha = 0.936
Path: A-->D: 0.456 (p<0.001)
Path B--> D: 0.204 (p<0.001)
Path C-->D: 0.132 (ns)
D adjusted R-square: 0.5, Q-square: 0.441

Dataset without outlier (totally 30 outlier out of 481 data)
Construct A: AVE =0.736 ; CR = 0.933 , Cronbach's Alpha = 0.910
Construct B: AVE = 0.776; CR = 0.933, Cronbach's Alpha = 0.904
Construct C:AVE = 0.733 CR = 0.892, Cronbach's Alpha = 0.819
Construct D:AVE = 0.917; CR = 0.959, Cronbach's Alpha = 0.954
Path: A-->D: 0.381 (p<0.001)
Path B--> D: 0.236 (p<0.001)
Path C-->D: 0.189 (p<0.01)
D adjusted R-square: 0.514, Q-square: 0.441

Outlier Dataset
Construct A: AVE =0.495 ; CR = 0.826 , Cronbach's Alpha = 0.738
Construct B: AVE = 0.662; CR = 0.887, Cronbach's Alpha = 0.836
Construct C:AVE = 0.418; CR = 0.586, Cronbach's Alpha = 0.418
Construct D:AVE = 0.732; CR = 0.891, Cronbach's Alpha = 0.817
Path: A-->D: 0.673 (p<0.001)
Path B--> D: 0.117 (ns)
Path C-->D: -0.008 (ns)
D adjusted R-square: 0.552, Q-square: 0.441

However, I run the muligroup analysis:
PLS-MGA:
A-->D, path mean diff = 0.292 (ns)
B-->D, path mean diff = 0.119 (ns)
C-->D, path mean diff = 0.197 (ns)

So, shall i drop the 30 outliers for better reliability and validity as well as R-square? However, it has no sig difference in path coefficient...please help.

Posted: Wed Dec 02, 2015 5:14 pm
I would always be careful with excluding outliers. You need to explain, why you think that the outliers are not meaningful and not just extreme cases of the population. Just because there are some very unsatisfied customers in a customer satisfaction survey does not make the worth excluding. You would lose valuable information.
However, given the lower reliability of your measures it seems that these respondents have different response styles and interpret the measurement items differently (or act randomly?). You really need to investigate the outliers to judge their situation.