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We centre age around its overall mean (essentially age 11) and then we fit the model (Equation 16.3) using the Iterative Generalized Least Squares (IGLS) algorithm built into the MLwiN package (Rasbash et al., 2004). A number of other packages are available although they do not all have such an extensive range of modelling capabilities. The estimates are given in Table 16.2. The estimates of the fixed effects b00 and b10 in Table 16.2 are defined by the scale we have adopted for reading. The estimates of the effects of sex on mean attainment and growth (b01 and b11) tell us that at the mean age (age 11) girls are 0.11 units ahead of boys in reading but that this gap narrows over the age range under study such that girls are about a quarter of a standard deviation ahead at age 7 [0.11 (4 0.033) 0.24] but slightly behind at age 16 [0.11+(5 0.033) 0.06]. Turning to the random effects, we see that after allowing for differences between boys and girls there is still considerable variation around the mean growth rate of 1, as evidenced by the estimate of 2 : we would expect about 95 % of the sample s growth u1 rates to be in the range 1 2H(0.028) to 1 + 2H(0.028), or 0.67 to 1.34. The estimate of the correlation between the intercept at mean age and the growth rate is 0.996. This is improbably close to 1 and indicates that the specification of the model could be improved. Because we only have three occasions of measurement, it is not advisable to estimate a polynomial model in age (although, as we have seen, nonlinear functions would be more appropriate with this scaling if there were more than three measurement occasions or if free barcode addin for excel 2013 [SOLVED] Excel 2003 - Barcode numbers - Spiceworks Community
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