1. Isaac’s Who Would You Fund Example
• Use this as an exercise to illustrate the
importance of collecting outcomes.
• Also used as a way to emphasize the
importance of comparison / contextual data.
• Based on real data I collected from two
different schools running the same program
during my time at LAYC.
January 28th 2015 Isaac Castillo - @Isaac_outcomes 1
2. How it Works
• Share next slide with group and introduce exercise.
• Audience is now a funder with the ability to write a single $1 million check to one
of two programs.
• Assume everything is the same between two programs/organizations except what
is shared.
• The second slide (with the data) has animation on it – the data points get shared
one at a time.
• After each data point, presenter asks audience members who they would fund
(who gets their $1 million) and why. Should get answers from several different
people.
• Add next set of data points, and continue to ask who people fund and why.
• Only last set of data includes outcome information. And by that point, the answer
of who to fund becomes clearer (but people may still disagree).
• The last slide points out the importance of comparison information and context.
The last slide may or may not be used depending on time and sophistication level
of the audience. The dotted lines are the comparison groups.
• The last slide also illustrates the importance of not overlooking programs that keep
things stable – as the counterfactual or comparison condition may be that things
would get worse without the program.
January 28th 2015 Isaac Castillo - @Isaac_outcomes 2
3. Who Would You Fund?
• You have $1 million to provide funding to a
tutoring program for “at-risk” youth.
• You need to pick one of two programs to fund
– but you can only pick one!
• Assume everything else is equal aside from
the information provided on the next page.
– Same service population
– Same areas of service
– Same tutoring approach
February 2015 Isaac Castillo - @isaac_outcomes 3
4. Which Program Would You Fund?
February 2015 Isaac Castillo - @isaac_outcomes 4
Program # 1 Program # 2
Served 500 “at-risk” youth Served 50 “at-risk” youth
Provided 2,500 total hours
of tutoring
Provided 2,500 total hours
of tutoring
Each youth received
average of 5 hours of
tutoring
Each youth received
average of 50 hours of
tutoring
5 % of youth showed
improved math grades on
report cards
90 % of youth showed
improved math grades on
report cards
5. Does this Change Things?
February 2015 Isaac Castillo - @isaac_outcomes 5
MathProficiency
Time
Program 1
Comparison Group
Program 1
MathProficiency
Program 2
Comparison Group
Program 2
Time
6. Isaac’s Contact Information
6February 2015 @Isaac_outcomes
Isaac D. Castillo
Deputy Director
DC Promise Neighborhood Initiative
On Twitter: @Isaac_outcomes
Email: Isaac.Castillo@dcpni.org