Mgt723 Carbon Disclosure For Standard Assessment Answer

Data Analysis

Descriptive Analysis

The results obtained as a result of the descriptive statistics analysis is helpful to the researcher in understanding about the nature of the data in a meaningful manner. The most common and popular measure of descriptive statistics are mean, median, mode, standard deviation, variance (Devore 2013). Further, the skewness and the kurtosis of the data is helpful in understanding the shape of distribution of the data.

By finding out the descriptive statistics for the dependent variable disclosure scores, it can be seen that the average disclosure score 74.61 with a maximum score of 97.80 which is a very good score. It can also be seen that the median of the disclosure score has been obtained as 78.38 which is higher than the mean value. Thus, it can be said that the data is positively skewed. The standard deviation of the scores has been obtained as 14.39, which is quite less. This indicates that most of the disclosure scores of the companies are close to the average disclosure score (Blanca et al 2013).

Similarly, it can also be seen from the descriptive statistics obtained for the independent variable firm size, it can be seen that the average size of the firms is 12190 with a maximum size of 31487 which is a very large firm. It can also be seen that the median of the firm size has been obtained as 11347 which is less than the mean value. Thus, it can be said that the data is negatively skewed (Blanca et al 2013). The standard deviation of the size of the firms has been obtained as 7821, which is quite high. This indicates that most of the disclosure scores of the companies are scattered and not close to the average firm size.

Further, from the frequency table of climate change, it can be seen that out of the selected 80 companies, change in the climate have enhanced 69 companies to reduce the carbon emissions and the disclosure scores which comprises of 86.3 percent of the sample. Thus, it can be said that in order to restore the sustainability of the organizations, the companies are taking initiatives so that they can meet the demand of the stakeholders (Saka and Oshika 2014).

Correlation analysis has been conducted on the dependent and the independent variable to find out if there is any relationship between the two variables. The value of the spearman correlation coefficient gives an idea about the nature of the relationship that exists between the two variables. The results of the correlation analysis show that there is a negative relationship between the disclosure scores and the size of the firm. Thus, the higher the firm size, the lesser will be the disclosure score of the firm. The correlation coefficient is also significant at 0.01 level of significance.

Inferential Analysis

It has already been observed from the descriptive analysis that none of the independent and the dependent variables are symmetric and hence are not distributed normally. Thus, regression analysis cannot be conducted on the variables to test the relationship between the independent and the dependent variables. Further, the difference between the average disclosure scores opting and not opting carbon disclosures for their companies cannot be tested using independent sample t-test as the normality assumptions of the data is not satisfied (Park 2015).

Hence, in this case, a non-parametric test has to be conducted to test the difference in the average disclosure scores of the two categories. The Mann-Whitney U-Test is the most appropriate test that can be run in order to test this difference (Pallant 2013).

The test conducted in this case is a two tailed test having an asymptotic level of significance. From the analysis, it can be concluded whether the disclosure scores of the companies over the years are statistically significant in reducing the carbon emission for changes in the climate (Karanja, Zaveri and Ahmed 2013). The test is conducted at 95 percent confidence interval with a level of significance of 0.05. Thus, the result will be statistically significant of the p-value obtained from the test is less than 0.05 and will not be statistically significant if the p-value obtained from the test is higher than the level of significance (0.05).

It can be seen from the results of the analysis that the 2 tailed level of significance has been obtained as 0.009 which is less than the level of significance. Thus the results of the test are significant and hence, it can be concluded that the carbon emissions are reduced with the changes in the climate and thus affect the disclosure scores.

References

Blanca, M.J., Arnau, J., López-Montiel, D., Bono, R. and Bendayan, R., 2013. Skewness and kurtosis in real data samples. Methodology.

Karanja, E., Zaveri, J. and Ahmed, A., 2013. How do MIS researchers handle missing data in survey-based research: A content analysis approach. International Journal of Information Management, 33(5), pp.734-751.

Devore, J.L., 2013. Probability and Statistics for Engineering and the Sciences. Cengage learning.

Saka, C. and Oshika, T., 2014. Disclosure effects, carbon emissions and corporate value. Sustainability Accounting, Management and Policy Journal, 5(1), pp.22-45.

Park, H.M., 2015. Univariate analysis and normality test using SAS, Stata, and SPSS

Pallant, J., 2013. SPSS survival manual. McGraw-Hill Education (UK)