The Research Experience for Teachers Program http://www.cs.appstate.edu/ret 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

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.

Data Poverty data from U.S. Census Bureau EOC, SAT, and ACT scores and graduation and dropout rates from NCDPI Compiled for school years 2006-07 through 2013-14. 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.

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.

Increase in Participation in Free and Reduced Lunch

Pearson Correlation Coefficients by Year and Subject Subject (as y) Year %Free and Reduced (as x) English I EOC % Proficient 2006/07 -0.72172 2007/08 -0.69037 2008/09 -0.723 2009/10 -0.532 2010/11 -0.636 2011/12 -0.519 2012/13 - 2013/14 Biology EOC % Proficient -0.56429 -0.57019 -0.65 -0.486 -0.451 -0.434 -0.708 -0.726 Math EOC % Proficient (Algebra 1 2006/07-2011/12 and Math 1 2012/13-2013/14) -0.65593 -0.65083 -0.619 -0.49778 -0.477743 -0.41378 -0.62002 -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 -0.81841 -0.79336 -0.768 -0.353 -0.35 -0.772 -0.351 -0.306 Graduation Rates -0.39683 -0.55994 -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 0.222713 2007/08 0.169247 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 2008-09 (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 2012-13 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.

Results What impact on academic performance was observed?

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 2006-07 was used to predict graduation rates for 2007-08 (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

How Graduation Rates and Dropout Rates Tied to Poverty

Biology EOC Results Before and After Common Core

Math EOC Results Before and After Common Core

Biology EOC 2011-2012 Biology EOC 2012-2013

Algebra 1 EOC 2011-2012 Math 1 EOC 2012-2013

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.

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

Questions?