Process of Analyzing Data from HRD Crew

I'm analyzing the data collected in February. There are many ways to analyze this data such as:
1. Description in the user levels.
2. Number of responses in each user level.
3. Number of responses in each HRD unit.
4. Number of responses in each general job category, leadership, producer, clerical.
5. Number of responses from the iPod Learning Community members.
6. Total number of responses.
7. Number of no responses.

Also, I can compare the data between Oct. and Feb.
1. Description in the user levels.
2. Number of responses in each user level.
3. Number of responses from each HRD unit
4. Number of no responses of those in and those not in the iPod Learning Community
5. Pattern of increase/decrease in user level of those who were in the iPod Learning Community from the Feb. list, the Oct. list, or both times.
6. Number of no responses
7. Comparison in number of responses from the 3 general categories, leadership, producer and clerical/maintenance group.
8. Change of level of each respondent.

I see there is a need to:
1. Add user levels from Feb.
2. Provide a learning opportunity, support, and/or coach those who are unaware or are No User to help them reach the next level.

AHAs!
I'm amazed how much analysis can occur with this longitudinal data.
I emailed the request two times. I wish the number of respondents were higher.
I'm thinking that many who did not respond were more likely to be in the No User Level than the Varied or Advanced Levels.
I'm curious to the reason some did not respond: didn't want to bother, "don't do surveys" (as one member shared, didn't want to write they were no users, forgot, didn't realize a No User is still an important response to a survey, hesistant to respond to a survey when person did not know what pod technology was.
One of HRD's crew meeting after the Oct. survey highlighted pod technology and participated in small group conversations about pod technology.
With qualitative data, levels were determined and categories were created from what was written by the respondents.
If users added more description, I wonder if it would have fitted into another user level.
I accepted those that came in after the one week time. One was received 12 days after last request date.
Data is very interesting!
Data analysis takes a long time.
There's a need to be accurate in the data analysis, whether qualitative or quantitative.
Since in some ways, I'm more cautious, it takes me longer to complete tasks.
Most critical need to be shared.
I wish I could complete this analysis before tomorrow's iPod Learning Community.