 = 71, SD = 30, t = -1.871, df = 58, ns).
As in the previous analysis, writing at age 14 was entered into the
model to control for previous written language performance, followed by
vocabulary. In this case, entering the TOWRE as the third step resulted
in a significant increase in [R.sup.2] (significant F Change, p = .003).
The full model [R.sup.2] is shown in Table 5 and was significant, F(3,
57) = 21.624, p < .0005, adj [R.sup.2] = .52, with all variables
having significant effects. Thus, the impact on writing of both reading
fluency and single word decoding reading measures was similar.
Both the differential correlations between spelling and the other
variables and analyses highlighting the importance of writing fluency
indicated that their relative roles in writing performance should be
considered. Thus, we employed a third sequential multiple regression
analysis to examine the impact of these variables. As in the previous
models, WOLD writing (Wechsler, 1996) at age 14 was entered into the
model followed by vocabulary and reading. On the fourth step, we added
writing fluency but this did not significantly change the model
[R.sup.2] (F Change, p = .24). On the fifth and final step, we entered
spelling, which resulted in significant increase in [R.sup.2]
(significant F Change, p = .001). The full model [R.sup.2] was
significant, F(5, 58) = 18.891, p < .0005, adj [R.sup.2] = .61. As
Table 6 shows, reading efficiency and writing fluency did not have a
significant partial effect in the full model, but previous WOLD score,
vocabulary, and spelling did have significant partial effects.
The regression analyses clarified the relationships between
language, literacy, and writing. We tested two path analysis models in
order to examine the relative importance of oral language and literacy
on the participants' writing scores. The regression analyses had
indicated that large effect sizes were to be predicted and that, with
appropriate parameter estimates and tests of alternative models, the
small sample could be used to build an exploratory path model using
maximum likelihood estimation (Ullman, 1996). The stronger the
correlations, the more power there is to detect an incorrect model, and
this would reduce Type 1 errors in the models.
[FIGURE 2 OMITTED]
We used Amos 7.0 to test the models. Model 1 examined concurrently
the relative contribution of language, literacy, and writing speed at
age 16. Model 2 examined the longitudinal effects of language and
literacy from age 8 to age 16. A variety of fit indices are available
with Amos. The overall fit of the final model was assessed by [chi
square] and by root mean square error of approximation (RMSEA).
Following Hu and Bentler (1999), who recommend joint criteria to retain
a model, we only considered models a good fit if the [chi square] was
not significant, RMSEA < .06 and CFI > .96 (RMSEA and CFI being a
more sensitive fit index with small sample sizes; Fan, Thompson, &
For the concurrent model we predicted that spelling, vocabulary,
and speed of writing would have direct effects on writing at age 16,
with reading revealing an indirect effect at this point through
spelling. We also predicted that both vocabulary and reading would be
associated. As predicted, the path analysis in Figure 2 indicates a
direct relationship between vocabulary and writing ([beta] = .32), speed
of writing ([beta] = .21) and spelling ([beta] = .47). Reading fluency
revealed an indirect effect on writing through both spelling ([beta] =
.58) and speed of writing ([beta] = .57). The goodness of fit measures
indicated a good fit: [chi square] (4) = 3.602, p = .46, RMSEA = .00,
CF1 = 1.000. We also tested models, including nonverbal ability and
reading comprehension, to eliminate potentially relevant factors. These
models failed to provide a fit with the data.
[FIGURE 3 OMITTED]
For the exploratory predictive path analysis, we considered
measures assessed at ages 8, 11, and 14 and writing at age 16. We
predicted that vocabulary and reading at age 8 would be significant
factors in supporting writing at age 11