Some methodological issues
Posted: Mon Dec 19, 2016 7:42 pm
Dear mates
I would be grateful if you could help me again.
Question 1
I think I have a problematic construct in my model. It is composed of 5 items (5 point Likert Scale). Original scale shows these results:
- AVE = 0.121; CR = 0.055; Alpha = 0.719
After deleting three items with < 0.40 outer loadings, AVE and CR improves (both > 0.70) but Alpha gets worse. After this, final report shows:
- AVE = 0.579; CR = 0.734; Alpha = 0.276.
This is not the only construct in which I see Alpha goes down when AVE improves. I think this last result allows me to report that AVE > 0.50 and CR > 0.50, and use this scale in my study. However, I don't know how to interpret this Alpha coefficient.
What I sould care about? This construct comes from a validated scale that have been used in other context with good results.
EDIT: I just read this post and I think I understand now: viewtopic.php?f=5&t=4003&p=13024&hilit=composite+reliability+alpha#p13024
Question 2
I have a model with four constructs of 3 items each. After running PLS Algorithm, results suggest that I have to delete at least two items, resulting in construct of two items. Final report is OK (AVE and CR). I have been told that construct needs at least three items to be considered. However, I don't know if this affirmation is only when you want to develop a scale, so that you keep at least three items in the case they are poor and need to drop them.
In any case, I wanted to ask you if there is any problem when using constructs of two items, being originally four.
Question 3
When it is advisable to remove an item with outer loading between 0.40 and 0.70? I have read that it depends on content validity. However, when I delete an item with an outer loading of 0.50, AVE changes from 0.55 to 0.65. Is this enough to consider remove the item? I ask this becase the significance of path loading changes depending on whether or not that item is included.
I hope you understand my two questions. Your answers are very important for my research.
Thank you in advance.
I would be grateful if you could help me again.
Question 1
I think I have a problematic construct in my model. It is composed of 5 items (5 point Likert Scale). Original scale shows these results:
- AVE = 0.121; CR = 0.055; Alpha = 0.719
After deleting three items with < 0.40 outer loadings, AVE and CR improves (both > 0.70) but Alpha gets worse. After this, final report shows:
- AVE = 0.579; CR = 0.734; Alpha = 0.276.
This is not the only construct in which I see Alpha goes down when AVE improves. I think this last result allows me to report that AVE > 0.50 and CR > 0.50, and use this scale in my study. However, I don't know how to interpret this Alpha coefficient.
What I sould care about? This construct comes from a validated scale that have been used in other context with good results.
EDIT: I just read this post and I think I understand now: viewtopic.php?f=5&t=4003&p=13024&hilit=composite+reliability+alpha#p13024
Question 2
I have a model with four constructs of 3 items each. After running PLS Algorithm, results suggest that I have to delete at least two items, resulting in construct of two items. Final report is OK (AVE and CR). I have been told that construct needs at least three items to be considered. However, I don't know if this affirmation is only when you want to develop a scale, so that you keep at least three items in the case they are poor and need to drop them.
In any case, I wanted to ask you if there is any problem when using constructs of two items, being originally four.
Question 3
When it is advisable to remove an item with outer loading between 0.40 and 0.70? I have read that it depends on content validity. However, when I delete an item with an outer loading of 0.50, AVE changes from 0.55 to 0.65. Is this enough to consider remove the item? I ask this becase the significance of path loading changes depending on whether or not that item is included.
I hope you understand my two questions. Your answers are very important for my research.
Thank you in advance.