Page 119 - Second language development of newly arrived migrant kindergarteners - Frederike Groothoff

Development of narrative ability 119 wil hebbe gepakt voor voet geit. en de geit wil wegracen. ja maar huilen. de geit niet huilen. huilen niet. de geit is moe. in de die twee een grote nog een kleine is ook moe. en allemaal geiten nu is blij. en nu is een vogel met neus wat kan pijn doen. in die wat heb neus grote weg. An animal is in the water. In \[?\] one \[?\] wanted \[?\] eat \[?\] and \[?\] another \[?\] wanted \[?\] eat \[?\]. And another two is in the water. And are \[?\] to the flowers. Where is that what can hurt \[?\]. the this the tongue \[?\] outside in the big nose \[snout?\]. and the fox wants that to eat that what can hurt it with that. He want to eat the goat. But is two that knows it. the that what has \[?\] with that little one what can it. The little goat is want away for the what has big nose. Wants away for the fox eats with his teeth. In that little has like this the goat that what has a big nose want to have grabbed the goat’s foot. And the goat want to race away. Yes but crying. The goat not crying. Crying not. The goat is tired. In the those two a big one also a little one is also tired. And all goats are happy now. And now is a bird with nose \[beak\] what can hurt. In that what has snout big away. From the comparison between the consecutive models for the SS score (Table 5.15) it is apparent that a model with a fixed linear component – allowing for differences in Age – fit the data better than a model with only an intercept (ΔΧ2 (SS1) = 101.86; df = 1; p < .001). The variance within individuals depends on the age of participants (ΔΧ2 (SS2) = 9.25; df = 2; p = .01). The variance between individuals appeared not to depend on Age (SS3). However, adding Age2 to the model improved the fit significantly (ΔΧ2 (SS4) = 16.94; df = 1; p < .001, and the variance between individuals was dependent on age after all. Hence, in the final model (SS4) a fixed effect of Age1 and Age2, as well as variance within and between pupils components which depends on Age needs to be included, and with this model we continued the analysis. Table 5.15: Fit of Different Models (-2LL) for Changes in Story Structure (168 cases) as well as the Comparison of Consecutive Models. Comparison -2LL Models ΔΧ2 Δdf p Model SS0: β0ijcons a 886.11 SS1: SS0 + β1Age1ij SS2: SS1 + e1ijAge1ij SS3: SS2 + u10jAge1ij SS4: SS3 + β2Age2ij SS5: SS4 + e2ijAge2ij SS6: SS5 + u20jAge2ijc a SS 0: In addition to the intercept, variance components for differences within and between individuals are estimated b Only the covariance-coefficient between the intercept- and the age-residuals was estimated 784.25 SS0 vs SS1 775.00 SS1 vs SS2 774.66 SS2 vs SS3 757.71 SS3 vs SS4 754.02 SS4 vs SS5 753.02 SS5 vs SS6 101.86 1 9.25 2 0.35 1b 16.94 1 3.68 3 1.01 3 <.001 .01 .56ns <.001 .30ns .80ns