Hi,
I am working on Student Retention data and was trying multiple explorations and analytical models. I would be glad if anyone can suggest me which models(or) explorations would be best to analyze the student retention data which is having the common variables like gender,ethnicity, retention rate(aggregated measure) and many others.
Thanks in advance,
Thanuja
This type of analysis is usually trying to identify combinations of categorical measures (age, gender, ethnicity etc) that predict a binary outcome (retained or not retained)
logistic regression could be one suitable statistical techinque but I guess you would need to have the visual stats add on for that.
What questions are to be answered by the analysis? It is somewhat difficult to suggest a model without knowing what the model should represent.
This type of analysis is usually trying to identify combinations of categorical measures (age, gender, ethnicity etc) that predict a binary outcome (retained or not retained)
logistic regression could be one suitable statistical techinque but I guess you would need to have the visual stats add on for that.
Hi Ballard,
1) What are the factors that are affecting the Student Retention Rate?
2) How and why these factors impact the Student Retention Rate?
3) What will be the retention rate with the existing conditions?
4) Any other ideas to increase student retention rate?
These are some, any additional analysis which can help the institution will be useful.
-Thanuja
A decision tree would be a good starting point if you do not have Visual Statistics.
Hi All,
I do have Visual Statistics add-on to Visual Analytics. I am using 7.1 version.
The target is a binary variable having 0 and 1.
-Thanuja
There are some online demos for logistic regression, decision trees and clustering using visual statistics here - http://www.sas.com/en_us/software/analytics/visual-statistics.html
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