1. The Research Experience for Teachers Program
The Correlation Between Free and Reduced-Price Lunches and High School Academic Performance in North Carolina Summer 2015
Introductions
Danilo Morales, Western Vance High School
Matt Charles, Mitchell High School

2. Hypothesis:
Talk about motivation- Dan’s experience in the Philippines and the U.S.
Look for trends, not for solutions
County poverty rates affect the county-wide academic performance of high schools.

3. Data Poverty data from U.S. Census Bureau
EOC, SAT, and ACT scores and graduation and dropout rates from NCDPI
Compiled for school years through
NC has 115 school districts – 100 county and 15 city school systems. We only have poverty data for counties, so we consolidated city and county school systems to get totals for each county.
Charter schools were excluded from this data analysis
Only 4 year cohort graduation rate was analyzed – did not consider 5year cohort grad rate
Only 9-12 duplicated dropout rates were analyzed. We ignored 7-12 – middle school – dropout rates
Did not look for individual statistics – only county-wide averages were considered.

4. Measuring Poverty in Schools
Free and reduced lunch participation is a stand-in for poverty rate in schools
Pearson correlation coefficient (r) 0.748
Free and reduced lunch directly related to the economics of a county. Therefore, we are also measuring the achievement based on poverty by way of free and reduced lunch.

5. Increase in Participation in Free and Reduced Lunch

6. Pearson Correlation Coefficients by Year and Subject
Subject (as y)
Year
%Free and Reduced (as x)
English I EOC % Proficient
2006/07
2007/08
2008/09
-0.723
2009/10
-0.532
2010/11
-0.636
2011/12
-0.519
2012/13 -
2013/14
Biology EOC % Proficient
-0.65
-0.486
-0.451
-0.434
-0.708
-0.726
Math EOC % Proficient (Algebra / /12 and Math / /14)
-0.619
-0.63
Subject (as y)
Year
%Free and Reduced (as x)
ACT Composite
2006/07 -
2007/08
2008/09
2009/10
2010/11
2011/12
-0.715
2012/13
-0.794
2013/14
-0.729
SAT M+CR
-0.768
-0.353
-0.35
-0.772
-0.351
-0.306
Graduation Rates
-0.564
-0.476
-0.389
-0.362
-0.4
-0.386
Subject (as y)
Year
%Free and Reduced (as x)
Dropout Rates
2006/07
2007/08
2008/09
0.0832
2009/10
0.3405
2010/11
0.3146
2011/12
0.2674
2012/13
0.4446
2013/14
0.2
Checked to see if the previous year’s trend could be predictive of the next years results. We used the linear regression from the scatterplot to check this – there was no clear trend that appeared.
No obvious trend could be observed in graduation rates and dropout rates.
We could not, with our data, make sense of the trends for SAT performance. We thought that since the SAT is a test students must pay for that the number or percentage of students taking the test would have dropped after the (the year of the economic recession) school year, but that idea was not supported by the data.
One observation that really caught our eye was that over time, the correlation between free and reduced lunch participation and EOC scores becomes weaker and weaker – until a change in the curriculum or testing standards happens, at which point the correlation becomes much stronger again. The common core curriculum was implemented along with more rigorous testing standards in the school year. Our data leads us to believe that when anything new is implemented, it is the poorer counties that have the hardest time adjusting to the new content.

7. Results
What impact on academic performance was observed?

8. Can the trend from one year predict results for the next year?
Graduation Rate 2007
Predicted Grad based on 2006
70.8
74.3
75.8
74.0
81.6
71.6
64.3
67.8
80.9
71.9
76.3
72.7
62.2
70.5
60.4
66.3
61.7
69.1
72.5
73.6
69.8
70.7
72.4
74.4
67.4
73.2
81.8
75.7
77.6
75.2
67.3
80.4
79.9
72.6
72.1
64.4
71.2
76.5
67.9
73.0
63.9
Common sense says no, but we wanted to check anyways. The scatterplot on the left plots the linear regression of the graduation rate vs. % participation in free and reduced lunches. The linear regression formula from SY was used to predict graduation rates for (a sample of 25 counties is shown in the middle). The graph on the right plots the predicted grad rates vs. the actual grad rates. The slope of the regression line was monitored over time, with no trend becoming obvious. The trend from one year cannot be used to determine the next year’s results

9. How Graduation Rates and Dropout Rates Tied to Poverty

10. Biology EOC Results Before and After Common Core

11. Math EOC Results Before and After Common Core

12. Biology EOC
Biology EOC

13. Algebra 1 EOC
Math 1 EOC

14. Conclusions
Free and reduced lunch participation is an appropriate indicator for poverty level within a school system
Yearly results are in no way indicative of the next years results
A county’s graduation rates and dropout rates are not closely related to their percentage of free and reduced lunch students
An increase in the participation in free and reduced lunch was not strong enough to significantly influence the educational outcomes of the students
Over time, test scores become less correlated to poverty, unless a significant change in curriculum or testing standards is implemented. In this case, the counties with higher levels of poverty are most negatively impacted.

15. Acknowledgements Mr. Eric Pierce, principal, Western Vance High School
Mr. Mark Woody, principal, Mitchell High School
Dr. Rahman Tashakkori
Dr. Mitch Parry
Dr. Mary Beth Searcy
Computer Science Department, Appalachian State University
National Science Foundation

16. Questions?