Modelling social and educational segregationHarvey Goldstein

University of Bristol

• We start from a suitable measure – here we consider FSM

• A Model based approach assumes an underlying process such that a suitable function of the parameters measures ‘segregation’.

• Traditional index construction uses definitions based upon observed proportions.

• A statistical model-based approach allows us to make inferences about underlying social processes by allowing random fluctuations that are unconnected with the difference of interest (function of parameters).

A simple example:

Suppose we have 2 schools with same number of students (or same school at 2 time points) and supppose that underlying proportion FSM does not change but random fluctuations will produce actual proportions that differ (with binomial variation).

A natural segregation measure is But

where is the underlying proportion, N is number in each school.

For example, we have following values for the mean value of this index for a range of values of , N.

1 2| | .p p

1 2average value of | |~ 1.12 (1 ) /p p p p N

p

p

N 100 150 200

0.15 0.040 0.033 0.028

0.20 0.045 0.037 0.032

0.25 0.049 0.040 0.034

|| 21 ppE

p

N 100 150 200

0.15 0.068 0.054 0.047

0.20 0.057 0.047 0.040

0.25 0.048 0.040 0.035

E(S) (Gorard)

p

A multilevel model for measuring segregation

• A basic model consists of the proportion FSM as the response, with predictors such as school type or ‘year’ and an ‘effect’ for each school that is treated as a Normally distributed random variable.

• Thus, we can have a model where each year intake cohort is allowed to have its own (Normal) variance and the changes in these variances then are interpreted as changes in segregation. If the model ‘fits’ well the variance is an appropriate measure of variation and can then be interpreted as a measure of segregation

• The next slide shows that the (logit) proportions are close to a Normal distribution

Distribution of observed logit (proportion) for 1997:

We see that this distribution is close to Normal and this is confirmed by an analysis of residuals.Results follow. Note that we do not have intake year data, only for whole school, so successive years overlap.

Table 1. Variance estimates (standard errors) for each year

Year Between school Between LEA

Total

1994 0.625 (0.016) 0.491 (0.066) 1.116

1995 0.636 (0.016) 0.522 (0.072) 1.158

1996 0.650 (0.016) 0.503 (0.064) 1.153

1997 0.660 (0.017) 0.498 (0.069) 1.158

1998 0.685 (0.017) 0.506 (0.068) 1.191

1999 0.691 (0.017) 0.506 (0.068) 1.197

Note 11% increase for between-school from 1994 to 1999

We can compare 1994 and 1999 formally since different pupils:

Variances from joint analysis of 1994 and 1999 only

schools LEAs

1994 0.636 (0.016) 0.490 (0.066)

1999 0.707 (0.017) 0.508 (0.069)

correlation 0.95 0.98

Test 1994 - 1999 P<0.001 P>0.10

We can characterise LEAs by whether or not they have selective secondary schools:

Variance between schools

No Selective

1994 0.505 (0.016)

1999 0.547 (0.017)

correlation 0.94

Selective

1994 0.636 (0.016)

1999 0.707 (0.017)

correlation 0.95

{Test: 1999 – 1994 for selective (0.042) vs 1999-1994 for non-selective (0.071)}

P<0.001

% of FSM in Selective LEAs vs non-selective in 1994 and 1999: 13.2% vs 19.8%

Analysis of % of schools within LEA having control over

admissions. Shows no overall effect on proportion with FSM but increasing between-school variance with increasing % control over admissions.

Figure 2. Between-school variance for selective and non-selective LEAs in 1994 and 1999 by percentage admission controlling schools.

1994 not selective

1999 not selective

1999 selective

1994 selective

Conclusions

• A modelling approach is the only appropriate one if we wish to make social science generalisations

• The model has to be multilevel to take account of the structure of the educational system

• We can incorporate cross classifications and any number of predictors if needed.

References

• Allen, R. and Vignoles, A. (2006). What should an index of school segregation measure? London, Institute of Education.

• Hutchens, R. (2004). One measure of segregation. International Economic Review 45: 555-578.

• Goldstein, H. and Noden, P. (2003). Modelling social segregation. Oxford Review of Education 29: 225-237

• Gorard, S. (2000). Education and Social Justice. Cardiff, University of Wales Press.