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369 Prim. Res. Edu.
Table 3: Confirmatory factor analysis
Factors and item First-order
Second-order
description
Loading t-value Loading t-value
Reliability .62 10.83
(1) .74 … a
(2) .57 8.98
(3) .55 8.72
(4) .77 11.63
(5) .80 11.64
Responsiveness .84 11.85
(1) .70 … a
(2) .75 12.14
(3) .80 11.64
(4) .79 13.00
Assurance .84 12.97
(1) .79 … a
(2) .75 14.20
(3) .87 16.42
(4) .77 13.45
Empathy .57 10.99
(1) .77 … a
(2) .91 8.68
(3) .59 9.09
(4) .48 6.47
(5) .57 8.24
Tangible .70 9.32
(1) .64 … a
(2) .48 6.67
(3) .57 6.47
(4) .77 8.24
Note: a Fixed parameter.
Source: Author's computation
LISREL 8.50, show good fitness of our model (χ 2 =243.62,
df=104; P-value=0.00000; Goodness of Fit Index
[GFI]=0.92; Adjusted Goodness of Fit Index [AGFI]=0.90;
Root Mean Square Error of Approximation
[RMSEA]=0.066). The results of confirmatory Factor
Analysis have been shown in Table 3.
As it can be seen in table 3, in first level factor analysis
the factor value will be significant if the t- value is greater
than 1.96 and it can be said that it has a positive and
significant relationship with the factors. In addition, in the
second level factor analysis the 5 dimensions measuring
service quality have significant factor value (their t-value
is larger than 1.96). Therefore it can be concluded that
the dimensions assurance, tangibles, empathy, reliability
and responsiveness have positive significant relationship
with service quality. Finally, in light of first level and
second level factor analysis results, factor analysis is
reliable and the dimensions discussed are verified.
RESEARCH RESULTS
Seek of service quality distribution
One of the preconditions for testing the research
hypothesis and determination of research components
distribution is conducting Kolmogrov-Smirinov. In order to
analyse the hypothesis in the present research we use
per comparison test as long as the dimensions of
SERVQUAL have normal distribution and we use pared
sample t test which is a nonparametric test as long as the
distribution of the dimensions is not normal. The results
of test show that the 5 dimensions of SERVQUAL
measurement have normal distribution. Therefore we
used pared comparison test to test the hypothesis.
Research hypothesis analysis
Pared comparison test is a method used for pretest and
posttest, which we use dependent samples observation.
The test statistic is t and assuming unknown dσ is:
,
We use this test in empirical research and usually to
show the influence of a particular kind of interventions.
We use the following statistical assumption in order to
test the research hypothesis.
Mean difference is equal zero in both situations.
H0: = 0