1. Introduction to The Many Uses of Data
Larry Condelli, Ph.D.
Mary Ann Corley, Ph.D.
American Institutes for Research
2/23/2019
Mary Ann Corley American Institutes for Research

2. Data: A Carrot or a Stick?
Data Can Be Used. . .
To highlight, clarify, and explain what’s happening in your program, OR
To show what’s not happening in your program.
2/23/2019
Mary Ann Corley American Institutes for Research

3. How Do You Use Your NRS Data?
Look at your NRS reports each year;
Make statements about how these tables do not really reflect what goes on in the classroom;
Attempt to explain them to your publics;
Put results away in your files; and
Hope that the NRS will go away before next year?
2/23/2019
Mary Ann Corley American Institutes for Research

4. Using Data to Your Advantage
(i.e., as a carrot)
is up to you.
2/23/2019
Mary Ann Corley American Institutes for Research

5. Data Tells You Where you’ve been; Where you are;
Where you’re going, and
How to get there.
2/23/2019
Mary Ann Corley American Institutes for Research

6. Data That which separates successful adult education programs from
those that are not successful in
their reform efforts!
That which can help you design a quality program to ensure that your learners meet their goals.
2/23/2019
Mary Ann Corley American Institutes for Research

7. Did You Know That Data Can. . .
Guide you to improve instruction?
Measure program success and effectiveness?
Tell you if what you are doing is making a difference?
Tell you which programs are getting the results you want—and which are not?
Get to the root causes of problems, such as poor retention?
Sell your board, your funders, and your community on the value of your program?
2/23/2019
Mary Ann Corley American Institutes for Research

8. The same data can be used for accountability,
Attendance/Enrollment Numbers and Patterns,
Student Test Scores and Learning Gains,
Student Drop-out/Completion Rates,
Teacher Characteristics,
Student Demographics,
Program Spending,
can be used for
accountability,
program promotion/marketing, and
program management and improvement.
2/23/2019
Mary Ann Corley American Institutes for Research

9. Functions of Data
Help us replace hunches and hypotheses with facts concerning the changes that are needed
(program management and improvement);
Help us identify root causes of problems
Help us identify whether goals are being met
(accountability);
Tell our funders, our boards, and our communities about the value of our programs and the return on their investments (marketing).
2/23/2019
Mary Ann Corley American Institutes for Research

10. Properly Used Data
Can “help identify and uncover powerful solutions to your program’s biggest problems.”
But first you need to “take your data and torture it until it confesses.” 
(Victoria Bernhardt, Data Analysis for Comprehensive School Improvement, 1998)
2/23/2019
Mary Ann Corley American Institutes for Research

11. Quote of the Day “People without information cannot act.
People with information cannot help but act.”
(Ken Blanchard)
2/23/2019
Mary Ann Corley American Institutes for Research

12. Barriers to Using Data
Your program’s/school’s culture does not focus on data;
Gathering data is perceived to be a waste of time;
Staff lack adequate orientation and training in the value of data collection;
Staff have had negative experiences with data collection;
Staff are not aware of other programs’ successes in using data;
Staff think that data is collected “just for the state or the feds.”
2/23/2019
Mary Ann Corley American Institutes for Research

13. If you know why, you can figure out how. . .
Focusing the Data
If you know why,
you can
figure out how. . .
(W. Edwards Deming)
2/23/2019
Mary Ann Corley American Institutes for Research

14. Student Ethnicity by Site
2/23/2019
Mary Ann Corley American Institutes for Research

15. Program Components by Data Function
Program Accountability
Program Marketing/ Promotion
Program Improvement
Student Enrollment
Are students coming?
So what?
How can we improve?
Student Retention
Are students staying?
Learning Gains
Are students learning?
2/23/2019
Mary Ann Corley American Institutes for Research

16. A Data Use Model for Program Management and Improvement
Two Components of the Model:
Data Analysis
and
Program Improvement
2/23/2019
Mary Ann Corley American Institutes for Research

17. 4 Steps to Data Analysis Identify issues or topics;
Develop questions to address the selected issues or topics;
Plan analyses; and
Analyze and interpret the data.
2/23/2019
Mary Ann Corley American Institutes for Research

18. 3 Steps to Program Improvement
Develop a plan for initiating change;
Implement the plan; and
Evaluate whether the change has made a difference.
2/23/2019
Mary Ann Corley American Institutes for Research

19. Model: Using Data for Program Management and Improvement
2/23/2019
Mary Ann Corley American Institutes for Research

20. Focusing the Question Break the question into inputs and outputs
Inputs (what your program contributes):
Hours of instruction per week
Teacher education, experience, full-time/part-time
Instructional Curriculum
Outputs (outcomes, results, ROI):
Improved test scores
Advances to next educational level
Earned GED credentials
Improved attendance
2/23/2019
Mary Ann Corley American Institutes for Research

21. Focusing/Refining the Question
Poor Question:
Is my program effective for all students?
Good Question:
Do different types of students in my program achieve their goals?
Better Question:
How do attainment of a GED credential, entry into employment, and educational gain differ by student age and ethnicity?
2/23/2019
Mary Ann Corley American Institutes for Research

22. Focusing/Refining the Question
Poor Question:
Does my program have good teachers?
Good Question:
Does student learning differ by teacher?
Better Question:
Do students in classes taught by instructors who have more teaching experience have higher test scores than those taught by new teachers?
2/23/2019
Mary Ann Corley American Institutes for Research

23. Focusing/Refining the Question
Poor Question:
Is my program helping the most needy adult learners?
Good Question:
Are low-literate students learning less in my program than other students?
Better Question:
Are literacy and beginning level ABE students advancing levels at the same rate as students who enter my program at other levels?
2/23/2019
Mary Ann Corley American Institutes for Research

24. Developing a Data Analysis Plan
What data do you already have that will answer your question?
What additional data, if any, will you need to answer your question?
Where are you going to get the additional data?
What’s your plan for obtaining the data you need—and what’s your timeline?
2/23/2019
Mary Ann Corley American Institutes for Research

25. Analyzing and Interpreting Your Data
Keep your original question in mind.
Look for patterns and differences.
Use appropriate data and statistics.
Disaggregate the data.
Consider data quality.
Draw appropriate conclusion(s).
Remember serendipity: be open to the unexpected.
2/23/2019
Mary Ann Corley American Institutes for Research

26. Averages and Variation
Mean: the average score (add up the scores and divide by the total number of scores)
Median: the score that falls dead-center within the distribution (e.g., half the scores fall above it and half fall below it)
Mode: the score that occurs most frequently in the distribution
Range: the difference between the lowest and highest scores
2/23/2019
Mary Ann Corley American Institutes for Research

27. Mean, Median, Mode, Range, SD
State
Average hours
per Student
State 1 24
State 2 33
State 3 42
State 4 45
State 5
State 6 48
State 7 85
State 8 91
State 9
102
State 10
126
State 11
176
State 12
185
State 13
202
Mean: hours
Median: 85 hours
Mode: hours
Range: 178 (24-202)
Standard Deviation: 62.0
2/23/2019
Mary Ann Corley American Institutes for Research

28. Presenting Your Data Frequency Tables
Show numbers and percentages by category, e.g., ethnicity, gender, age
Simple frequency table versus a two-way table, or cross-tabulation, e.g., ethnicity by age
2/23/2019
Mary Ann Corley American Institutes for Research

29. Presenting Your Data Graphs and Charts Bar Chart: Pie Chart:
Categories displayed as bars, e.g., enrollees by age
Pie Chart:
Shows a slice of the pie in proportion to the whole, e.g., various ethnicities of total enrolled students
Line Chart:
Data form a continuous measure (not categories), e.g., pre-test and posttest scores
2/23/2019
Mary Ann Corley American Institutes for Research

30. Communication Strategies
Presenting Your Data
Communication Strategies
Article by Education Reporter in Local Newspaper
Public Meeting or News Conference Presented by Superintendent or Dean
Newsletters
Special Events, e.g., Open House
Web Sites
Annual Report
2/23/2019
Mary Ann Corley American Institutes for Research