Disaggregate and visualize
Same mean, but not the same distribution
Chart shows this
Immersion Service Considerations
Interested in change over time.
Pre- and Post-surveys are common.
Want information on
- Learning gains
- Impact
- Changes in perspective
Challenges
- Measuring change
- Finding statistical significance
Often looking at data like this
Q1 - Change!!
Q2 - No Change??
Often looking at data like this
Q1 - Change!!
Q2 - No Change??
Disaggregate and visualize to see patterns in data
Visual patterns worth recognizing
Summary statistics for no change and churn can look similar
Difference can be seen using charts
The churn pattern can
- Indicate confusion
- Suggest varying reactions to challenging environment
- Reveal opportunities for discussion topics for future sessions
Is this change significant?
Visual statistics can help
- Generate 19 randomized versions of data
- Add the real data to the set to get a total of 20 data sets
- Generate a chart for each set and shuffle them
- Show panel of 20 charts to a naive audience
If an observer can identify the real data,
the pattern is significant at a p-value of 0.05
If an observer can identify the real data,
the pattern is significant at a p-value of 0.05
Real Data
Group moved towards SD with a p-vale <= 0.05
Q: I feel that social problems are not my concern.
Group moved towards SD with a p-value <= 0.05
Beauty of procedure is that it finds the major changes
Future direction - look at larger datasets
Final Thought
Use on-line tools to create data with continuous variables
Sliders:
Fin
Kerri S. Warren, PhD
kwarren@rwu.edu
Becky L. Spritz, PhD
bspritz@rwu.edu
Andrew M. Staroscik, PhD
ams@staroscik.com