The final section discusses some issues concerning classifications of language attrition situations – L1 vs. L2, formally vs. informally acquired L2 – and their impact on the selection of a research design.
"That's how language attrition takes place.
The first line in Table 3 and the first line in Table 4 show the situation where attrition was totally random (MCAR), and results were thus not biased due to selective attrition.
Schuler acknowledges that attrition is a "frothy situation" at AOL, but is also a concern throughout Silicon Valley and the high-tech world.
But today, due to the reality on the ground and military balances, and the resulting attrition strategy and the situation in the Yemeni arena, sticking to it to the letter is neither practical nor realistic.
Accordingly, the attrition results reflect the situation faced by the average facility.
The most biased estimate (bpred = .13) was obtained from the situation where attrition was directly dependent only on baseline variables (MAR).
The first line in Table 1 shows the situation where attrition was totally random (MCAR), and the estimate of the association between predictor and change in health was thus unbiased (bpred = .10, and coverage of the 95% C.I. = 95).
The first line in Table 2 shows the situation when attrition was totally random (MCAR), and the estimate of the association between predictor and change in health was thus unbiased.
Analyses assumed a compound symmetric structure, in line with Fairclough's recommendation that the covariance structure be restricted in situations where attrition is high [ 35].
Analyses assumed a compound symmetric structure, in line with Fairclough's recommendation that the covariance structure be restricted in situations where attrition is high [ 26].
