L. Turner-Stokes et al.
full dataset, and the Rasch sub-sample of n = 320 were
similar on all parameters.
The results of EFA are summarized in Table II. All
items loaded strongly onto a single first component
(all loadings > 0.58) with Cronbach’s alpha 0.976,
accounting for 60% of the variance. Four factors had
eigenvalues > 1.0, but the fourth contained only one item
(”Community mobility”), which is rarely assessed and
so rated 1 (”not assessed”) by scoring convention. The
remaining 3 factors (Motor (16 items), Psychosocial (9
items) and Communication (5 items)) together accoun-
ted for 73% of the variance. As suggested by a previous
factor analysis in TBI patients, a 2-factor model was
also explored, which accounted for 70% of the variance.
CFA for a 3-factor model showed a marginal fit as
shown in Figure 2. This model included correlated
of error terms for nine pairs of items with the largest
modification indices. The fit indices did not meet the
criteria for an excellent fit, but were all in the border-
line acceptable range (RMSEA 0.094, CFI = 0.926,
TLI = 0.918, NFI = 0.922) While the χ 2 /df ratio was
large (18.103), giving a highly significant p-value
(0.000), this is more likely to be a reflection of the
large sample size than the model fit (30). The fit was
only marginally improved by further correlation of
error terms and thus we proceeded to Rasch analysis.
Rasch analysis
The suitability of the Partial Credit Model for Rasch
analysis was confirmed by the significant likelihood-
ratio test (χ 2 (df86) = 7,325.0, p < 0.001) Table III
includes fit statistics for individual items together
with response frequencies for each category within
the main dataset (n = 1,956) and for the domain scores
presented for both the full dataset and the sub-sample
(n = 320). A preponderance of scores at the ends of
the range is expected for a dataset that includes both
admission and discharge data, and indeed there was
a greater preponderance of response category scores
at the lowest end of the scale for the admission
sample, and upper end in the discharge sample.
Table IV summarizes the overall fit statistics from
the main stages of the Rasch analysis.
First analytical pathway (all 30 items). The initial
analysis of the full 30-item scale is marked by
satisfactory reliability (PSI = 0.94). However, the
overall model fit was poor with significant item-
trait interaction. At the individual item level, 14
out of 30 items showed significant misfit to the
Rasch model on the n = 320 sub-sample (Table III).
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Fig. 2. Summary of findings from confirmatory factor analysis. CFA confirmed
a 3-factor solution. Correlations between the factors ranged from 0.74 to 0.86.
www.medicaljournals.se/jrm
Second analytical pathway (3 super-items). The
second analytical pathway was conducted using 3
super-items created by combining the items within
the Motor, Communication and Psychosocial do-
mains identified from the factor analyses. Prior to
combining domain items into super-items residual
correlation matrix was examined. Residual cor-
relations exceeding the cut-off point of 0.20 above
the mean of all residual correlations were found
for 59 pairs of items in Motor, 5 pairs in Com-
munication, and 26 pairs in Psychosocial domain.
Rasch analysis of 3 super-items resulted in accep-
table overall model fit (χ 2 (df24) = 36.72, p = 0.05),
strict uni-dimensionality and no local dependency
when tested on the sub-sample (n = 320) (Table IV,
Pathway 2). The proportion of common error-free
variance A=0.88 was marginally below 0.90 sug-
gesting that use of transformation table based on
this 3 super-item solution may be preferable to
the ordinal scale for calculation of change scores.
This analysis was replicated with the full dataset
(n = 1,956) showing good reliability PSI of 0.81,