Proceedings

ICD_Index
Equal
variances
assumed
Equal
variances not
assumed
Table 8. Results of T test: Independent samples test
Levene's Test for
Equality of Variances
3.2.2. Testing hypothesis H2
F Sig. t df
~ 411 ~
Sig. (2tailed)
t-test for Equality of Means
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
2.288 0.147 1.344 19 0.195 0.07576 0.05638 -0.04224 0.19376
1.317 15.347 0.207 0.07576 0.05751 -0.04658 0.19809
H2: There is no relation between industry type and IC disclosure
In the case of H2 hypothesis both independent and dependent variables are scale so it
is a basic two variable associational hypothesis. Accordingly to test the hypothesis we
chose Pearson correlation.
For Pearson correlation we first must check whether the two variables have a linear
relationship. For this we are going to generate a scatterplot. We considered both a
curve and a linear line.
ICD_Index
0.50
0.40
0.30
0.20
0.10
0.00
Graph 3. Correlation between ICD_Index and Company_size
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Company_size
R Sq Quadratic =0.152
R Sq Linear = 0.139
From the graph above we can observe that there is a positive correlation between the
variables, but that the correlation is relatively week (r squared is only 0.139 or 0. 152).
In addition, it seams that the linear line fits the point better than the curve so we can
consider that the variables have a linear relationship and we can continue out testing
with Pearson correlation. The Table 9 below presents the results obtained for Pearson
correlation (r). The Table 9 shows that the two variable are not significantly correlated
r (21) = 0.373, p > 0.05. Because the p value of 0.096 > 0.05 it means that r in not
statistically significant and thus it suggests that hypothesis H2 is not being rejected
and thus we can state that company size does not influence the IC disclosure.
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