Are Magnet Schools Attracting All Families Equally ? Naralys Estevez ’06 Cities, Suburbs, and Schools research project at Trinity College, Hartford CT July 18, 2005

Sheff and School Segregation 1996 Sheff vs. O’Neill ruled that that the state must desegregate schools in metropolitan Hartford to address educational inequalities

Sheff and School Segregation 1996 Sheff vs. O’Neill ruled that that the state must desegregate schools in metropolitan Hartford to address educational inequalities Finding a remedy remains a challenge to this day

Magnet Schools as a Solution? 2003 Sheff settlement focused on inter-district magnet schools as a key desegregation remedy Popular voluntary approach, with 19 magnet schools for

Magnet Schools as a Solution? 2003 Sheff settlement focused on inter-district magnet schools as a key desegregation remedy Popular voluntary approach, with 19 magnet schools for Not Pictured: MLC (Bloomfield), EHG & CT IB Acad (East Hartford), GPA (Manchester) Learning Corridor

Magnet School Controversy Magnet schools are designed to attract a special mix of students

Magnet School Controversy Magnet schools are designed to attract a special mix of students But critics charge that magnets “cream” the best students from neighborhood schools - race - socio-economic status - achievement

Magnet School Controversy Magnet schools are designed to attract a special mix of students But critics charge that magnets “cream” the best students from neighborhood schools - race - socio-economic status - achievement Although designed to equalize society, magnet schools may create an unexpected second tier of inequality

Research Question Are magnet schools attracting all families equally?

Research Question Are magnet schools attracting all families equally?

Research Question Are magnet schools attracting all families equally? My study uses Geographic Information System (GIS) to conduct spatial analysis of the “creaming” controversy by using smaller geographic units of analysis.

Research Question Are magnet schools attracting all families equally? My study uses Geographic Information System (GIS) to conduct spatial analysis of the “creaming” controversy by using smaller geographic units of analysis. GIS training funded by National Institute for Technology & Liberal Arts Education (NITLE) and Trinity College Academic Computing

Four Levels of Investigating Creaming 1.Individual magnet school

Four Levels of Investigating Creaming 1.Individual magnet school 2.Categories of inequality Race Socio-economic status Achievement

Four Levels of Investigating Creaming 1.Individual magnet school 2.Categories of inequality Race Socio-economic status Achievement 3. Geographic unit of analysis School District Neighborhood

Four Levels of Investigating Creaming 1.Individual magnet school 2.Categories of inequality Race Socio-economic status Achievement 3. Geographic unit of analysis School District Neighborhood 4. Stages of magnet admissions process Application (Which students apply?) Acceptance (Which applicants are accepted?) Enrollment (Which students actually enroll?)

Sample Statistical Test of Creaming

1.Individual magnet school Montessori Magnet School ( ) 2.Categories of inequality Race Socio-economic status Achievement 3. Geographic unit of analysis School District Neighborhood 4. Stages of magnet admissions process Application (Which students apply?) Acceptance (Which applicants are accepted?) Enrollment (Which students actually enroll?)

Sample Statistical Test of Creaming District Enroll (Bloomfield 5yr) Total White Students Observed Percent 12, %

Sample Statistical Test of Creaming District Enroll (Bloomfield 5yr)MMS Applicants (Blmfd 5yr) Total White StudentsTotal White Applicants Observed PercentObserv Pct 12, % %

Sample Statistical Test of Creaming District Enroll (Bloomfield 5yr)MMS Applicants (Blmfd 5yr) Total White StudentsTotal White Applicants Observed PercentObserv Pct Expected 12, % % 5.25

Sample Statistical Test of Creaming District Enroll (Bloomfield 5yr)MMS Applicants (Blmfd 5yr) Total White StudentsTotal White Applicants Observed PercentObserv Pct Expected 12, % % 5.25 Calculate Chi-Square goodness of fit test to determine statistical significance Chi-Square = 1.516P>.05

Sample Statistical Test of Creaming District Enroll (Bloomfield 5yr)MMS Applicants (Blmfd 5yr) Total White StudentsTotal White Applicants Observed PercentObserv Pct Expected 12, % % 5.25 Calculate Chi-Square goodness of fit test to determine statistical significance Chi-Square = 1.516P>.05 Difference in percentage of White Bloomfield MMS applicants, compared to Whites in the district, is not statistically significant

Sample Statistical Test of Creaming

Non-White Applicant Results are reciprocal (e.g. when Whites are less likely to apply, then Non-Whites are more likely to apply)

Purple = Significant White Dots = White Students < less likely to apply > more likely to apply Significant Differences in MMS Applications and School District Enrollment, by Race, Sample Statistical Test of Creaming

Future Statistical Tests

1.Individual magnet school 2.Categories of inequality Race Socio-economic status Achievement 3. Geographic unit of analysis School District Neighborhood 4. Stages of magnet admissions process Application (Which students apply?) Acceptance (Which applicants are accepted?) Enrollment (Which students actually enroll?)

Future Statistical Tests 3. Geographic unit of analysis School District Neighborhood Census Block Group = 1500 residents With Census 2000 demographic data for race, SES, educational attainment Requires magnet applicant addresses

Future Statistical Tests Limitations and Factors to Consider

Future Statistical Tests Limitations and Factors to Consider Census data – only available every decade Application form data – some magnets do not ask race Application process – depends upon marketing (varies by magnet management and individual school) - student eligibility lottery requirements (eg GHAA audition) - type of lottery system (open to all vs. selected districts only) Achievement analysis- Requires compiled CMT scores