1. I love working with data.
Warm-Up Exercise
Reflect on this statement:
I love working with data.
Red/orange means you DISAGREE
Yellow means you are IN THE MIDDLE
Green means you AGREE

2. Using Data to Increase Attendance
Presented September 8, 2017, in Albuquerque, New Mexico, at the Every Day Matters: Building School-Based Plans to Reduce Chronic Absenteeism Conference
Using Data to Increase Attendance
Deborah Good
Director of Data and Research, Mission: Graduate

3. Presentation Overview
The Basics
Understanding the Problem
Continuous Improvement

4. The Basics

5. The Basics: Calculating Chronic Absence
# of days absent
# of days enrolled =
XX % days missed
SEVERE Chronic Absence (missing 20% or more of school days)
MODERATE Chronic Absence (missing % of school days)
AT-RISK Attendance (missing % of school days)
SATISFACTORY Attendance (missing less than 5% of school days)

6. Chronic Absence is Research-Based
Excused Absences
Chronic Absence +
Unexcused
Absences +
Suspensions

7. ADA versus Chronic Absence
Average Daily Attendance and Chronic Absence
Average Daily Attendance (percentage of students present on the average day)
% Chronic Absence (percentage of students who missed 10% or more of school days)

8. The Basics: Calculating Chronic Absence
# of days absent
# of days enrolled
CHALLENGE: Some data systems are not currently set up to calculate chronic absence rates
HELPFUL RESOURCE:
ALTERNATIVE: Use total number of absences for each student (excused + unexcused + suspensions), and establish cutoffs based on the # of school days to date.

9. The Basics: Calculating Chronic Absence
For example:
As of 40 days into the school year,
Any student with ____ or more absences (10%) is chronically absent
Any student with 8 or more absences (20%) is severely chronically absent 4
ALTERNATIVE: Use total number of absences for each student (excused + unexcused + suspensions), and establish cutoffs based on the # of school days to date.

10. The Basics: Data Best Practices
Source: StriveTogether (2015). Student Data Privacy Best Practices.

11. The Basics: Qualitative and Quantitative
Structured data
Numbers
Collected through assessments, surveys, databases
Analyzed with statistics
Presented through graphs and tables
Qualitative
Unstructured
Words and images
Collected through observations, surveys, interviews, focus groups
Analyzed with themes
Presented as summaries and quotes

12. Understanding the Problem

13. Understanding WHO is absent
Chronic Absence by Race/Ethnicity
30% of ALL students were chronically absent
% SATISFACTORY Attendance (missing less than 5% of school days)
% AT-RISK Attendance (missing % of school days)
TOO FEW STUDENTS
% MODERATE Chronic Absence (missing % of school days)
% SEVERE Chronic Absence (missing 20% or more of school days)
AFRICAN AMERICAN
(4 STUDENTS)
NATIVE AMERICAN
(294 STUDENTS)
WHITE
(35 STUDENTS)
HISPANIC
(392 STUDENTS)

14. Understanding WHEN they are absent

15. Understanding WHY are they absent?
Surveys
Focus Groups
Interviews
Observations
Self-Assessment
Attendance Codes

16. Understanding WHY are they absent?
What are all the different reasons that you have missed school this year?

17. Continuous Improvement

18. Continuous Improvement
Plan Do
Study
Adjust

19. Continuous Improvement: STUDY
Are we doing the right things?
Are we doing things right?
What data do we need to collect in order to answer these questions?

20. Performance Measures Answer Three Questions
Quantity
Quality
Effort
How much did we do?
# clients/customers served
# activities (by type of activity)
How well did we do it?
% common measures (e.g., client-staff ratio, % clients seen in their own language)
Responses to a satisfaction survey
Effect
Did we make a difference?
Changes in: Attitudes, Knowledge, Behavior, Circumstance
How much did we do?
How well did we do it?
Did we make a difference?

21. Results-Based Accountability
Websites
raguide.org resultsaccountability.com
resultsleadership.org

22. Performance Measures Example
What data can we use to evaluate whether PSAs and robocalls before holidays helps increase attendance?
How much did we do?
How well did we do it?
Did we make a difference?
Effort
Effect

23. Setting Performance Measures (excerpt from Attendance Success Plan)
How much did we do? 
How well did we do it?
Did we make a difference?
# of robocalls sent
# of flyers that went out
# and % of calls that reached a live person
% of student body that received the calls
Feedback from parents
# of days before holiday robocalls were sent
# of languages flyer was translated into
Comparison of last year’s attendance rate the day before the holiday compared with this year

24. Data Collection and Analysis Plan
Data- related Action Steps
By When Do We Plan to Get This Completed
Who Oversees the Action Step
Who Does the Work
Who Approves the work
Who Must Be Consulted
Who Must Be Informed
Design survey
September 15
 Phyllis
Joseph
Phyllis
Principal Robinson and Attendance Team members
Third period teachers
Administer survey to all 8th graders
September  
Violeta
Third period teachers
Principal Robinson
N/A
 etc.
Attendance Team Debrief re: Perf. Measures
Mid- December
Mid-April
 N/A

25. Questions? Thoughts?