INTERNATIONAL-SURVEYING-RESEARCH-JOURNAL-ISrj-ISrJ-Vol-11-Year-2022

requires it". Metacognitive CQ, cognitive CQ, motivational CQ and behavioural CQ will be added up into sub-
total and finally added up as an overall CQ score. A higher score indicated a higher CQ.
Reliability and Validity Analysis
A reliability test was carried out to compute Cronbach's alpha to assess twenty (20) constructs' internal
consistency. Table 2 showed that the alpha for metacognitive formed by four (4) items was .931, which
indicated that the construct was excellent in internal consistency reliability. Similarly, the alpha for the cognitive
scale (.918), motivational scale (.945) and behavioural scale (.926) also indicated excellent internal
consistency. The alpha included all twenty (20) items showed excellent internal consistency (.957),
representing that all items deserved to be retained, leading to a decrease in the alpha if deleted.
Table 2. Reliability Statistics on CQ
CQ Cronbach's Alpha N of items
Metacognitive 0.931 4
Cognitive 0.918 6
Motivational 0.945 5
Behavioral 0.926 5
Total CQ items 0.957 20
Confirmatory Factor Analysis (CFA) was conducted to investigate the construct validity of the CQS as
shown in Figure 1. In CFA, chi-square statistics are used as one of the indicators for goodness-of-fit. The
biggest issue with the use of chi-square statistics is that with large samples, even with a slight variance of the
data from the model being evaluated, there would be a substantial change in the chi-square value. In this
study, the chi-square, χ2(164, N=173) = 358.955, p= .000 suggested that the model was not consistent with
the observed data. However, since chi-square is sensitive to sample size and non-normality data, it was more
suitable to conclude other indices of fit. Hence, the root-mean-squared error of approximation (RMSEA) value
was used to measure fit. If the RMSEA value is less than .05 considered a close-fit, .05-.08 is a good model
.08 to .10 considered an acceptable range. If the RMSEA value is more than 0.10 it would be a poor fit model.
Comparative fit index (CFI) is an index that falls between 0 and 1, with values greater than 0.90 considered to
be indicators of a good fitting model (Kline, 2015). Goodness-of-fit related to this 20-items model of the CQS
(CFI = .941; RMSEA = .083) exhibited an acceptable good fit to the data. CFA model of the 20-items CQS was
shown in Figure 1. The loading factors were all more than .70 except COG2, indicating that all four factors had
a strong correlation and worked well together.
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