4. There was no significant difference between
writing scores at age 16 for participants with missing data (M = -2.66)
and those with data for all four time points (M = -2.14, t = 1.159, df =
56, ns). As Figure 1 shows, there was a significant decrease in relative
performance as measured by z scores, F(3, 150) = 23.888, p < .0005,
[eta][p.sup.2] = .32. Post hoc comparisons of z scores adjusting for
multiple comparisons indicated that the participants' writing
performance at age 11 was significantly better than their performance at
age 12 (p = .002), 14 (p = .002), and 16 (p < .0005). Performance at
age 12 and age 14 did not differ whereas there was a significant decline
in performance again at age 16 (p < .0005 for both ages 12 and 14).
[FIGURE 1 OMITTED]
Although the mean drop between ages 11 and 16 was one SD (M = 1.0),
patterns of change varied across participants. A change score was
computed where the z score at age 11 was subtracted from the z score at
age 16, thereby providing a pattern of change across 5 years, where
positive scores would indicate a relative increase in writing
performance. Table 3 presents the relationships between literacy,
language, and nonverbal abilities and change scores. Using a Bonferroni
correction of .004 for multiple correlations there were significant
relationships between WOLD change score handwriting fluency (r = .54, p
< .0005; Wechsler, 1996) and spelling (r = .38, p = .006). There were
no significant relationships with nonverbal ability, vocabulary, reading
decoding, and reading comprehension. We examined this pattern of
relationships using multiple regression. In all cases residuals were
normally distributed. The extent of the relationship between gain score
and handwriting fluency was confirmed with a multiple regression
controlling for nonverbal ability which revealed a significant model,
F(2, 49) = 9.689, p < .0005) accounting for 29% of the variance.
Fluency was the only significant predictor in the model ([beta] = .479);
the less fluent students were in producing the 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