MGT723 Research Project Assignment Sample
Here’s the best sample of MGT723 Research Project Assignment, written by the expert.
Data Analysis – Inferential (ASSESSED)
The spearman test is used as non-parametric test since the independent and dependent variables are not under normal distribution. It is used to measure the correlation between two variables. It measures the extent to which two variables influence to each other on making changes. The value of correlation coefficient obtained from this test is used to determine the strength and direction of the relationship.
This test is significant for evaluating the monotonic relationship between two continuous or ordinal variables. It is important to examine the relationship between variables by using Scatter plot. It is more reliable method to determine correlation because it can be used when the variables are not normally distributed. It is not considerable sensitive to outliers, which are the observations not following the usual pattern (Schabenberger & Gotway, 2017). It means this test is useful to obtain the valid outcomes eve in the presence of outliers in the data.
However, this test is based on two assumptions as these assumptions cannot be violated while conducting this test. First assumption is that if the two variables in not in a monotonic relationship, it is more significant to use different statistical test. Monotonic relationship shows that increase in one variable causes changes in the value of another variable. Second assumption is that two variables should be measured on an ordinal, interval or ratio scale. The data which is taken to analyse in this study fulfils both assumptions. Data is taken based on ordinal and interval scale and there is monotonic relationship between the variables. The Spearman correlation coefficient, rs, is measured between +1 to -1. The value of +1 indicates positive and strong relationship between both variables while zero value shows no relationship and -1 shows a perfect negative relationship.
The following tables presents Spearman’s correlation test for the data (disclosure score and application of emission reduction initiatives:
| Correlations | ||||
| Disclosure score_ | Application of Emission reduction initiatives | |||
| Spearman’s rho | Disclosure score | Correlation Coefficient | 1.000 | -.624** |
| Sig. (2-tailed) | . | .000 | ||
| N | 80 | 80 | ||
| Application of Emission reduction initiatives | Correlation Coefficient | -.624** | 1.000 | |
| Sig. (2-tailed) | .000 | . | ||
| N | 80 | 80 | ||
| **. Correlation is significant at the 0.01 level (2-tailed). | ||||
From the above table, it can be interpreted that for variables namely disclosure score and application of emission reduction initiatives, Spearman correlation coefficient is -0.624. It is a higher negative value means there is negative and strong relationship between both variables. It means one variable decreases when another increases, but amount is not consistent. When emission reduction initiatives are taken, the carbon disclosure scores increases. When the firms are not interested to take carbon reduction initiatives, the carbon disclosure score decreases.
The following table presents Spearman’s correlation test for the data (disclosure score and Gross global combined scope 1 and scope 2 emission in metric unit):
| Correlations | ||||
| Disclosure score_ | Gross global combined scope 1 and scope 2 emission in metric un | |||
| Spearman’s rho | Disclosure score | Correlation Coefficient | 1.000 | .261* |
| Sig. (2-tailed) | . | .020 | ||
| N | 80 | 79 | ||
| Gross global combined scope 1 and scope 2 emission in metric un | Correlation Coefficient | .261* | 1.000 | |
| Sig. (2-tailed) | .020 | . | ||
| N | 79 | 79 | ||
| *. Correlation is significant at the 0.05 level (2-tailed). | ||||
Based on the above table, it can be interpreted that for variables namely disclosure score and Gross global combined scope 1 and scope 2 emission in metric unit, Spearman correlation coefficient is 0.261. It is a low positive value means there is positive and weaker relationship between both variables. It means one variable slightly increases when another increases. When Gross global combined scope 1 and scope 2 emission in metric increases, the carbon disclosure scores also slightly increases. The firms are not considerably interested to show carbon disclosure score, when emission increases.
Hypothesis testing (ASSESSED):
H0 (Null Hypothesis): There is no relationship between industry performance in terms of carbon emission and voluntary disclosure.
H1 (Alternate Hypothesis): There is a relationship between industry performance in terms of carbon emission and voluntary disclosure.
Hypothesis testing:
Disclosure score and application of emission reduction initiatives:
Spearman correlation: -0.624
p-value: 0.000
Disclosure score and Gross global combined scope 1 and scope 2 emission in metric unit:
Spearman correlation: 0.261
p-value: 0.02
Conclusion: On the basis of the SPSS reports, the p-value for this test as being .000 and 0.02 respectively for independent variable and control and dependent variable. If the p-value is less than significance value of 0.05 means null hypothesis can be rejected. Therefore, it can be stated that there is a strong evidence to believe H1 as there is a significant relationship between industry performance in terms of carbon emission and voluntary disclosure. Both variables are monotonically correlated in the population (Rapaport, et. al. 2013). It means if the firms reduce carbon emission, they are encouraged to make voluntary disclosure. The control variable i.e. organizational initiatives also had significant effect in getting these results. The firms which take emission reduction initiatives are oriented to make more disclosure.
Discussion (ASSESSED): (30 marks)
From the above analysis, it can be determined that the spearman test is used as non-parametric test. It allows the researcher to conduct test at the time when independent and dependent variable are not found under the normal distribution. At the same time, it is also interpreted that correlation the value of p is indentified 0.000 and 0.02 for the independent and dependent variable (Taylor, et. al. 2015). Hence, according the principle of the correlation, the null hypothesis fails to prove or approve. On the other hand, the alternative hypothesis is accepted. Therefore, it can be said that there is a significant relationship between the Industry performance and carbon emission by the industries. This interpretation can be also supported by the Legitimacy theory that determines that only 301 major companies in Australia are responsible for the 182.56 million metric tonnes of carbon dioxide equivalent (CO2-e) reported under Scope 1 emissions (The Senate Economics Reference Committee, 2016) that is the equal to 40% of the overall. These companies are those who are operating and producing at the grate level. At the same time, it is also summarised that top 370 companies in Australia are liable for 47.5 million metric tonnes of carbon equivalent (CO2-e) were reported for the Scope 2 emissions (Gelman, et. al. 2014).
Limitations (ASSESSED)
Even though, this research study has conducted with very careful manner by the researcher but still some limitation are presented here. In this, it is found that this research study is only based on the one single depended variable that limits the researcher to make research more briefly. At the same time, the result of this research study is based on the correlation analysis that also involves the some limitation.
Further Research
There are various kinds of the research opportunities are presented for the further researchers. The future researcher can use the other analysis methods such Anova, regression and T test. By the help of these tests, the researcher can enhance the validity and reliability of the research. At the same time, the further researcher can also concern to include this more wide (Thomson & Emery, 2014).
References
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2014). Bayesian data analysis. Boca Raton, FL: USALCRC press.
Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., … & Betel, D. (2013). Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome biology, 14(9), 3158.
Schabenberger, O., & Gotway, C. A. (2017). Statistical methods for spatial data analysis. USA: CRC press.
Taylor, S. J., Bogdan, R., & DeVault, M. (2015). Introduction to qualitative research methods: A guidebook and resource. USA: John Wiley & Sons.
Thomson, R. E., & Emery, W. J. (2014). Data analysis methods in physical oceanography. Newnes.
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