Manual for the Benzodiazepine Dependence Questionnaire (BDEPQ)

VALIDITY OF THE BDEPQ 39
was a better t for the obtained matrix (GOF =0:972�AGOF =0:903�RMSR =
2 0:031� 6 =14:94�p = :021). While the chi square value indicates a signi cant difference
between the model and the matrix the GOF, AGOF, and RMSR indicate
an acceptable t. The di erence in chi squares between the one and two factor
2
models ( 3 =44:95�p < :05) indicated that the two factor model is a signi -
cantly better t. The largest residual was ;0:102, between BWSQ and EPQ-N
scores indicating a relationship between these variables not su ciently accounted
for by the model being tested. These variables both load on the `questionnaire'
method factor. An improvement in the t might be obtained by adding additional
factors to the model but this would be at the cost of parsimony. Asthetwo
factor model had an adequate t, it was accepted as the best balance of t and
parsimony.
If LISREL (Joreskog&Sorbom, 1989a) had been used to analyse the above
model the correlation between the two factors could have been set to zero as recommended
by (Cole, 1987). This would be akin to an orthogonal rotation in an
exploratory factor analysis and would allow some statement about the proportion
of variance in BDEPQ scores uniquely associated with a latent dependence
variable. However, SIMPLIS has no mechanism for setting the relationship between
latent variables, instead it gives estimates of their strength. In the model
described above the correlation between the `dependence' and `questionnaire' factors
was 0:464. This tends to reduce the strength of the conclusions from the
above analysis, because it suggests that had this relationship been set to zero the
t of the model would not have been as good. The relationship between the `questionnaire'
and `dependence' factors suggests that a large proportion of variance
in BDEPQ scores is common with the BDI, BAI, and EPQ-N as can be seen by
inspecting the MTMM shown in Table 9. In other words the BDEPQ appears to
have a reasonable degree of convergent validity but has only moderate divergent
validity.
A subset of the wavetwo MTMM matrix shown in Table 10 was analysed using
a similar approach. Scores on the BDEPQ, BWSQA, BWSQB, BAI, BDI, and
PSQI and respondent self-ratings of dependence (SRD) were rstly transformed
using PRELIS. A matrix of polyserial, polychloric, and product moment correlations
was calculated by PRELIS from transformed data. Out of the 248 responses
to the second wave of Baillie (1992) only half (51:2%�n= 127) gave complete responses
to all of the above questionnaires. SIMPLIS was used to conduct a CFA
of the matrix. Initially a single factor model was tested and tted the data moder-
2 ately well (GOF =0:894� AGOF =0:789�RMSR =0:074� 14 =48:14�p < :05).
The largest residual was 0.215 between BDEPQ and BWSQB scores indicating
that a single factor model did not account for all of their covariance.
Atwo factor model with one trait factor (dependence) and one method factor
(questionnaire), similar to that identi ed for the rst wave, was tested. There was
some improvement in the t of this two factor model over the above one factor