 alphabet the more likely
were their writing scores to decrease (in relation to their peers) over
RELATIONSHIPS BETWEEN LANGUAGE, LITERACY, AND THE WOLD
There were statistically significant correlations for the WOLD z
score (Wechsler, 1996) at age 16 and all the predictor variables apart
from the TROG (Bishop, 1983). As expected, the literacy measures of
spelling, reading decoding, and reading comprehension were significantly
related to writing. In addition, the language measures of vocabulary
(BPVS; Dunn et al. 1997) and formulated sentences were statistically
significantly related and each correlated with reading decoding and
We predicted that the most significant influence on students'
writing would be previous levels of writing, but that vocabulary and
reading levels would account for additional variance (Dockrell, Lindsay,
Connelly, et al., 2007). Three sequential multiple regression analyses
examined prediction of WOLD writing (Wechsler, 1996) at age 16. In the
first analysis, we entered writing at age 14 into the model as the first
step to control for previous written language performance. On the second
step, we entered vocabulary, resulting in a significant increase in
[R.sup.2] (significant F Change, p = .001); on the third step, single
word reading again resulted in a significant increase in [R.sup.2]
(significant F Change, p = .005). The full model [R.sup.2] is shown in
Table 4 and was significant, F(3, 57) = 20.624, p < .0005, adj
[R.sup.2] = .53.
The second sequential multiple regression used word reading
efficiency (TOWRE; Torgesen et al., 1999) as the predicting literacy
variable. We reasoned that for later literacy, fluency in word reading
and phonological decoding were likely to have greater impacts on the
production of written text than untimed measures of single word reading.
There was no statistically significant difference between the
participants' performance on single word reading efficiency and
phonemic decoding efficiency (reading efficiency M = 69, SD = 27;
phonemic decoding efficiency M = 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 