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Dec 2017
Vol 14 No 4
BACK ISSUES


What Size Community Does it Take to Raise a Child’s Test Scores?

By Alan Haskvitz
 

By Winston Feng, with research done by students in Mr. Alan Haskvitz’s social studies class at Suzanne Middle School, CA
This research project was done in my eight grade social studies class a few years ago as part of the service learning requirement. The team was lead by Winston Feng who organized the data and worked with the other students to reach the hypothesis that small and large cities by and large don’t produce the best language arts test scores, especially in districts with larger minority populations. The students’ previous work had clearly shown that all test scores almost always followed the social economic demographics of the area. The higher the education and income level the higher the test scores. However, there has never been a study done about the relationship between community size and test scores. Thus the reason for the project. Of note is the fact that this was limited to California. It would be of interest to see if the same held true in other states. – Al Haskvitz

 

Objective
To see if there is a relationship between the population of a city and student achievement at the middle school level in California.

 

Methodology
First, we used US Census figures to find cities with populations in the 10, 000, 30,000, 50,000, 100,000, 150,000, and those between 200 and 250,000 ranges. We selected every community in California within those ranges.

 

The reason that we selected communities of that size was to provide a sequence of population increases and also present users with data that would be of value in both selecting a community to live in and to educational institutions wishing to pursue research in a different direction. We did not select cities over 250,000 populations because of they essentially were comprised of smaller communities with little cohesion and many overlapping school district boundaries.

 

We downloaded the test data directly from the website by California Standardized Testing organization. http://star.cde.ca.gov/star2003/viewreport.asp for the 2001-02 school year. We used the API is Academic Performance Index (API) scoring system as our standard due to the fact that API is the standard in comparing schools within the state. As we our eighth graders we used that grade level for our research in the fields of language arts, mathematics, and history.

 

Scoring
As there were five levels, the same as grades for students, we used a simple formula to turn the API scores into grades.
EXAMPLE:
School A Language Arts API scores
Advanced: 20 x 4 = 80 A Below basic: 8 x 1 = 8 D
Proficient: 35 x 3 = 105 B Far below basic: 5 x 0 = 0 F
Basic: 31 x 2 = 62 C Total 255
Adding the results (80+105+62+8=255) yield a score of 255 points for the API scores. To make an even clearer picture of the scores, we converted each subject’s API to Grade Point Average (GPA). In the GPA system, an A is equal to four points, a B is three, C will receive a two, D only deserve one point, and F will have no points. We did this by moving the decimal point two places to the left. Therefore a 255 API score will be equal to a 2.55 GPA score, or a B-/C+.

 

The averages were then grouped together by population and subject matter so that we would have a grade point average for each city by curriculum. Next we added them together to yield an overall report card grade for all subjects tested. We took both sets of statistics, the ones for individual curriculum scores and the combined scores and grouped them by population to see if there was a relationship between student middle school achievement scores on the California standardized test scores, API, and community population.
Summary of Data

 

Throughout the course of the compilation of school test scores per city, many tangible patterns were identified. Cities with 30,000 residents tend to score higher than cities with a lower or higher population. Cities with the greatest populations had the worst overall performance. The differences in the scores also were different based on community size with the largest differences in social studies and English and the least differences in math scores.

 

Population
English
Math
Social Studies
Average
10,000
2.03
1.97
1.91
1.97
30,000
2.15
2.1
2.01
2.09
50,000
2.04
2.11
1.97
2.04
100,000
1.9
1.93
1.89
1.91
150,000
1.91
1.9
1.87
1.89
200,000- 250,000
1.83
2.08
1.64
1.85
Differences
0.32
0.21
0.37
0.24

 

The chart illustrates the pattern of differences within the data.

 

Comparatively, the difference between the highest and lowest math scores is lower than the others. This anomaly is most likely caused by math being a universal subject that is not subject to as much interpretive knowledge of English as the other two curriculum areas.

 

English scores varied greatly among population centers. In cities with a smaller population, such as 10,000 or 30,000 residents, the scores were noticeably higher than others cities. The same held true to an even greater extent with social studies. We felt that the reason was in the nature of smaller communities. In very small communities a child can survive by going to local establishments and stay within the realm of that language. English, although taught at school, does not get the rigorous use because of the language isolation.

 

When a community reaches a population of about 10,000, students more frequently venture into areas where English is the medium of exchange. Thus the student is forced to use English more away from school. This hypothesis appears to be validated by the fact that the top scores in social studies and English were recorded in communities in the 30,000 range. Again, the student must venture even further into the town and use English to communicate on a daily basis.

Finally, the consistent drop off of scores for students in cities from 50,000 upwards confirms the hypothesis again, because in communities of these sizes the student has the ability to have enough people that have the same culture and thus limit the need to go outside the immediate neighborhood. Indeed, in larger cities students are not encouraged to go outside their immediate area while in smaller communities the safety standards are more relaxed.

 

 



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This entry was posted on Wednesday, August 1st, 2012 and is filed under *ISSUES, Alan Haskvitz, August 2012. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.
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