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 and that from age 11 writing,
itself, would show the strongest relationships with subsequent writing
performance. No model including reading at age 8 or reading at age 11
fit the data; however, a longitudinal model including vocabulary at age
8 having an indirect effect on writing provided a good fit. The path
analysis in Figure 3 indicates direct effects of reading ([beta] = .27),
spelling ([beta] = .34), and writing ([beta] = .32) at age 14 with
writing at age 16. Oral language skills had an indirect effect through
reading at age 14 ([beta] = .34) and writing at age 14 ([beta] = .25).
Vocabulary at age 8 revealed indirect effects on writing through
vocabulary at age 11 ([beta] = .75). Moreover, vocabulary at age 11
revealed indirect effects on writing at age 16 through oral language at
age 14 ([beta] = .51). More important, the indirect effect of writing at
age 11 was evident through spelling at age 14 ([beta] = .55) but not, as
predicted, writing at age 14 ([beta] = .16). The goodness of fit
measures indicated a good fit: [chi square] (16) = 11.350, p = .79,
RMSEA = .00, CF1 = 1.000. We also tested models examining alternative
directions of effect and models including nonverbal ability and reading
comprehension to explore a better fit. These alternative models did not
provide a fit with the data.
We examined longitudinally the writing skills of a cohort of
students with a history of SLI to age 16. We examined measures of
language and literacy assessed both longitudinally and concurrently to
establish their relative contribution to written text production. The
students in this study continued to experience specific difficulties
with language and literacy. Production of written text continued to be
an area of marked vulnerability, with writing scores being the lowest
standardized score of the receptive language and literacy measures.
Moreover, during their teenage years the students' writing skills
decreased relative to standardized norms. Thus, the current data
contrast with data in the elementary years for ch