Evaluation of Carbon Disclosure Scores for Different Company Sectors and Countries

Running Head: MGT723 RESEARCH PROJECT
MGT723 Research Project
Name of the Strudent
Name of the University
Author Note

1VOLUNTARY DISCLOSURE
Acknowledgement:
I certify that I have carefully reviewed the university’s academic misconduct policy. I understand
that the source of ideas must be referenced and that quotation marks and a reference are required
when directly quoting anyone else’s words.

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Data Analysis – Inferential
The main initiative of this research is to evaluate the differences in the carbon disclosure
scores between different company sectors and different countries. Further the relationship
between the company factors on carbon emission on the disclosure scores are also of interest to
evaluate. Thus, in order to find out the stated concerns, the following hypothesis can be framed:
Null Hypothesis (H01): There are no significant differences in the carbon disclosure scores with
respect to different sectors of companies.
Alternate Hypothesis (HA1): There are significant differences in the carbon disclosure scores
with respect to different sectors of companies.
Null Hypothesis (H02): There are no significant differences in the carbon disclosure scores with
respect to disclosure status.
Alternate Hypothesis (HA2): There are significant differences in the carbon disclosure scores
with respect to disclosure status.
Null Hypothesis (H03): There are no significant relationship between the carbon emission
conditions of the companies with their disclosure scores.
Alternate Hypothesis (HA3): There are significant relationship between the carbon emission
conditions of the companies with their disclosure scores.
Hypothesis Testing
In order to test the first and the second hypothesis, chi square test of association has been
conducted and a correlation and regression analysis has been conducted in order to test the third
stated hypothesis.

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Chi Square Test
The results of the chi square test for testing the first hypothesis are provided in table 1. It
can be seen from the table that the value of the chi square statistic with 105 degrees of freedom
has been obtained as 116.7 with a significance value of 0.205. The significance value of the chi-
square test is higher than the estimated level of significance (0.05). This indicates that the test of
association is insignificant (Seber 2015). Thus, the null hypothesis (H01) is rejected.
The results of the chi square test for testing the second hypothesis are provided in table 2.
It can be seen from the table that the value of the chi square statistic with 21 degrees of freedom
has been obtained as 60.000 with a significance value of 0.000. The significance value of the chi-
square test is lower than the estimated level of significance (0.05). This indicates that the test of
association is significant (Olive 2014). Thus, the null hypothesis (H02) is accepted.
Table 1: Chi-Square Tests for different Sectors
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 116.700a 105 .205
Likelihood Ratio 96.181 105 .719
Linear-by-Linear Association 1.436 1 .231
N of Valid Cases 60
a. 132 cells (100.0%) have expected count less than 5. The minimum expected count is .10.
Table 2: Chi-Square Tests for Disclosure Status
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 60.000a 21 .000
Likelihood Ratio 23.822 21 .302
Linear-by-Linear Association 36.604 1 .000
N of Valid Cases 60
a. 41 cells (93.2%) have expected count less than 5. The minimum expected count is .05.
Correlation Analysis