values close to 1.00 being indicative of good fit. According to Hu & Bentler (1999), the
Root Mean Square Error of Approximation (RMSEA) has only recently been recognized
as one of the most informative criteria in covariance structure modeling, and it takes into
account the error of approximation in the population and asks the question, "How well
would the model fit the population covariance matrix if it were available?" (Browne &
Cudeck, 1993, pp. 137-138). This discrepancy measured by the RMSEA indicated that
values of less than .05 indicate good fit; values as high as .08 represent reasonable errors
of approximation in the population; values from .08 to .10 indicate mediocre fit; and
those greater than .10 indicate poor fit (MacCallum, Browne, & Sugawara, 1996). The fit
of the original, hypothesized model (Figure 3-1) was adequate because the chi-square
was statistically significant (X2 (45) = 78.160), and the fit indices showed that the model
has a moderate fit (NFI= .954, NNFI= .970 CFI= .980, SRMR= .050, RMSEA= .057).
Table 4-11. Results of Goodness of Fit
Goodness of Fit Indices SEM Model
X2 78.160
df 45
NFI .954
NNFI .970
CFI .980
SRMR .050
RMSEA .057
90% Confidence Interval of RMSEA (.033- ,079)
This SEM model, therefore, can be accepted due to a good model fit. As a
consequence, these results on goodness of fit can examine the following hypothesis:
H1 Phonological awareness, rapid-naming, and visual skills are not significantly
associated with reading fluency.