1. Our Future Revisited Logan Contreras Master of Public Policy Candidate, 2012 12/6/10 An expounding on potential factors related to academic performance

2. Summary of Past Findings In my midterm project, I explored the effect of expenditure on academic performance in unified school districts in eastern Los Angeles County. – There did not appear to be any relationship between the magnitude of expenditure and performance, as measured by average district API. – However there was a clear difference in scores among the districts.

3. New Directions The following study will be an exploration of potential factors related to academic performance. This analysis will take place in part at the school level, and in part at the district level. Suspected factors include: – Proximity to the district – Differences in ethnic demographics – Number of schools – Average classroom size – Student to teacher ratios

5. Potential Factors: Proximity From my anecdotal experiences in Glendora Unified, I suspected that the proximity of a school to the district office might affect the quality of the school, thereby affecting academic performance

9. Potential Factors: Proximity It appears that my initial suspicion is not supported by the analysis (at least not with 2006 data). As such, I will not analyze the proximity data in the remaining districts.

10. Potential Factors: Ethnicity Racial studies require a lot of judgment calls, due to self report and poor categorization. Because “Hispanic” can be of any race, it is difficult to account for Hispanics without double counting white’s and blacks. But while they are not mutually exclusive, it is fairly rare to have a child who identifies as Hispanic and Asian.

11. Potential Problems: Filipinos There is the potential for Filipino students to self identify as Hispanic and/or Asian. – The proportion of Filipino students, while not insignificant, is fairly small in the districts in question. – As such, I will classify Filipinos as Asian, aggregating Asian, Pacific Islander, and Filipino into a single class: Total Asians Enrolled

15. Potential Factors: Ethnicity At the school level, there is a large variation in how ethnicity and academic performance interact. Looking at these factors region wide will give a consistent scale and broader perspective.

17. Potential Factors: Number of Schools In the process of working with the data, I noticed that the districts with the highest APIs tended to be quite small in area, while those with the lowest APIs tended to be relatively large. I suspect that the number of schools in a district might relate to academic performance.

19. Potential Factors: Student to Teacher Ratios I suspected that a small student to teacher ratio might be related to better academic performance. Conversely a large student to teacher ration might be related to worse academic performance.

21. Potential Factors: Average Classroom Size Finally to take into account variation in class size, I suspect that a low classroom size might relate to better academic performance. Conversely, a large classroom size might relate to worse academic performance.

23. Conclusions There appears to be a strong relationship between the number of Hispanic students and the number of Asian students within a district, to how that district performs academically on average – That is, the larger the ratio of Hispanics to Asians, the lower the average API tends to be for that district – There could be either cultural or economic factors (property value, median income) that relate

24. Conclusions While not as clear, there also appears to be a relationship between the number of schools in a district to academic performance – That is, the smaller the number of schools, the higher the API tends to be – It may be that smaller communities can afford to be small because higher residential taxes due to higher land values

25. Conclusions There does not appear to a clear relationship between performance and student to teacher ratios or average classroom size. – In the extremes, however, it appears that the lower student to teacher ratios and lower average classroom sizes actually correspond to lower APIs. – This may be due to a larger incidence of special education classes with fewer students per class.

26. My (very simple) Model

27. My Metadata

28. Sources United States Census Bureau American Factfinder http://factfinder.census.gov/home/saff/main.html?_lang=en UCLA Mapshare http://gis.ats.ucla.edu/Mapshare/ RAND California http://www.ca.rand.org/stats/education/education.html United States Geological Survey http://seamless.usgs.gov/website/seamless/viewer.htm

29. Sources http://factfinder.census.gov/home/saff/main.ht ml?_lang=en http://factfinder.census.gov/home/saff/main.ht ml?_lang=en UCLA Mapshare UCLA Mapshare http://gis.ats.ucla.edu/Mapshare/ Rand California Rand California http://www.ca.rand.org/stats/education/educati on.html http://www.ca.rand.org/stats/education/educati on.html USGS http://seamless.usgs.gov/website/seamless/vie wer.htm