Analysis Of Academic Misconduct Policy Review Report

Theoretical argument leading to the conceptual model

This report is used to conduct the data analysis, where the companies for the analysis are first selected to precede the analysis. To begin the research, CDP Survey 2015 is utilized, which helps in gathering the data. From 1000 companies, 60 companies are filtered depending on some specific parameters.  Only after the filtration of companies the analysis conducted. The objective is to complete the data analysis for the selected companies by using the statistical tests will be conducted to determine the effective results which don’t seem to be unusual.

Then, implications of outliers will be considered. Regression will be carried out, followed by t-tests, paired t-test, descriptive analysis and correlation. To understand the environmental effects by various research papers will be reviewed. The t-tests will be performed separately for every single industry type. The hypothesis will be tested. The limitations of the analysis will be identified and the future possible research will be forecasted. The results will be determined and evaluated. Finally, the relationships of the variables will be discussed and concluded.

1 Data Analysis

The data was collected from the CDP Survey 2015 which initially had more than 1000 companies and it cut down to 60 companies. After getting the data, it is found that there are 30 companies which are integrated to the business strategy with climate change and 23 companies are not (Hays, 2008). As, it is not equally distributed on the basis of control variables and it more into the favor of companies which are integrated. Further, for making the comparison the analysis is done on all companies together. The data analysis is done by using the SPSS analysis.

1. First Open the IBM SPSS.
2. Then Load the data set. 

1.1 Descriptive Analysis

Here we will use the descriptive analysis is to analysis the data from the data. Descriptive analysis for 2011 Disclosure score, 2012 Disclosure Score, 2013 Disclosure Score and 2014 Disclosure Score is shown below (Witte, 2017).

2 Hypothesis Testing

The hypothesis testing in SPSS included the following test like,

1. T-Test

This analyzed and discussed in below.

2.1 T-Test

The T-test analysis is shown below.

T-Test is used to test the variable from the data set. Here, we will use the two test variable like 2015 Disclosure score and 2014 Disclosure Score. It is shown below. 

1Discussion

Almost from 1000 companies, 60 companies were selected to complete the analysis. CDP Survey 2015 is utilized for gathering the data.  It is observed that these companies were selected based on some specific parameters. In this report, several research papers are reviewed to get knowledge on the effects of environment. The SPSS analysis is performed. The hypothesis discussed and tested (Bhowal and Barua, 2008). They are,

1. H0: There is no significant relationship between Monitoring compliances with climate change policy and regulation of Austra and climate changes
2. H1: There is a significant relationship between Monitoring compliances with climate change policy and regulation of Austra and climate changes

Gathering and filtering of data from CDP Survey 2015

The appropriate statistical tests are completed such as, t-test, regression, Levine’s test, descriptive statistics, ANOVA, Paired T-Test, one-sample test and correlation. The data analysis for the selected companies is completed and the practical implications shown below.

The descriptive statistics show that the N is 60 for 2015’s Disclosure score is public, GICS sector and 2015’s Disclosure score for the Valid N is 60. The minimum is 1 and the maximum is 6, for the GICS sector whereas, its mean is 3.40 and its standard deviation is 1.942. However, for 2015’s Disclosure score is public, the minimum value is 1 and the maximum value is 2. Then, the mean is 1.02 and the standard deviation is .129.For 2015’s Disclosure score, the minimum value is 0 and the maximum value is 100. Then, the mean is 93.32 and the standard deviation is 14.836.

The theory of ACC620 is applied. From one-sample statistics, the mean is 1.02, the standard deviation is .129 and the standard error mean is 0.17 for 2015 disclosure score is public and the mean is 93.32, the standard  deviation is 14.836 and the standard error mean is 1.915 for 2015 disclosure score. The test value is equal to zero, for the one-sample test.

Followed by, the value of “t” is 61.00, the “df” is 59, the sig. (2-tailed) is .000 and the mean difference is 1.017 for 2015 disclosure score is public. When the lower and upper value of “95% Confidence Interval of the Difference” is checked it shows .98 and 1.05, respectively. And, the value of “t” is 48.72, the “df” is 59, the sig. (2-tailed) is .000 and the mean difference is 93.317 for 2015 disclosure score. When, lower and upper value of “95% Confidence Interval of the Difference” is checked it shows 89.48 and 97.15 respectively (Ibm.com, 2018).

In one-way ANOVA statistics, the 2015 disclosure core has the sum of squares is 9158778.082, df is 5053 and Mean square is 1812.543. Then, the 2014 disclosure score has the sum of squares is 869910.391, df is 1849 and Mean square is 470.476.

In paired samples statistics, the pair 1 account number has the mean is 18849.39, standard deviation is 12331.4522 and standard error mean is 173.459 and pair 1 2015 disclosure score has the mean is 29.24, standard deviation is 42.54 and standard error mean is .599. The Pair 2 account number has the mean is 1334.15, standard deviation is 9239.536 and standard error mean is214.815 and pair 2 2014 disclosure score has the mean is 76.80, standard deviation is 21.690 and standard error mean is .504 (Rainn.org, 2018). 

Descriptive analysis and appropriate statistical tests

From Paired sample tests, the mean is 18820.144, the standard deviation 12343.57 and the standard error mean is 173.629 for Pair 1 (Account Number and 2015 disclosure score) and the mean is 13267.356, the standard  deviation is 9242.033 and the standard error mean is 24.873 for pair 2 (Accoun Number and 2014 disclosure score). Then, the test value is equal to zero. Followed by, the value of “t” is 108.393, the “df” is 5053, the sig. (2-tailed) is .000 for Pair 1. When the lower and upper value of “95% Confidence Interval of the Difference” is checked it shows 18479.755 and 19160.533 respectively. And, the value of “t” is 61.74, the “df” is 1849, the sig. (2-tailed) is .000 for Pair 2. When, lower and upper value of “95% Confidence Interval of the Difference” is checked and it shows the values 13688.775 and 61.745 respectively (Integrating R Methods into SPSS, 2012).

Conclusion

This report is used to conduct the data analysis, where the companies for the analysis are first selected to precede the analysis. To begin the research, CDP Survey 2015 is utilized, which helps in gathering the data. From 1000 companies, 60 companies are filtered depending on some specific parameters.  Only after the filtration of companies the analysis conducted. Appropriate statistical tests are conducted to find effective results which don’t seem to be unusual. The implications of outliers are considered.

Regression is performed along with t-tests, where the t-tests are performed separately for every single industry type. The hypothesis is set for the research. Various research papers are reviewed to understand the environmental effects. Thus, even the hypothesis are tested and analyzed. The limitations of the analysis are identified. The future possible researches are mentioned. The results are determined and evaluated. At last, the relationships of the variables are discussed and concluded (Developing of SPSS Teaching Micro-Videos, 2018).

Limitations

The identified limitations are listed below.

• Automatic Scaling
• Not intuitive to use.
• The selected companies are should be integrated to the business strategy to provide the effective data analysis for an organization. This analysis is used to identify the climate changes based on 2014 and 2015 disclosure score. The most of the companies are disclosing the 100% score (SPSS survival manual: a step by step guide to data analysis using IBM SPSS, 2013).
• Future Research

The Future research, the must be focus on data because it is refers to data transforms into a useful format for analysis. Generally, it involves the storage, structure; source, accessibility and quality of the data and it can be queried and analyzed by other analysis. It ensures the system will be securely integrated with business strategy. It also focuses on the exploratory data analysis and predictive models and it used to reveal the trends and patterns in data from existing data sources (Hilbe, 2003).

References

Bhowal, M. and Barua, P. (2008). Statistics. New Delhi: Asian Books.

Developing of SPSS Teaching Micro-Videos. (2018). Advances in Social Sciences, 07(01), pp.96-100.

Graham, A. (2011). Statistics. London: Hodder Education.

Hays, W. (2008). Statistics. Belmont, Calif.: Wadsworth/Thomson Learning.

Hilbe, J. (2003). A Review of Current SPSS Products. The American Statistician, 57(4), pp.310-315.

Ibm.com. (2018). IBM SPSS Software | IBM Analytics. 

Integrating R Methods into SPSS. (2012). Statistical and Application, 01(02), pp.26-30.

Rainn.org. (2018). Statistics | RAINN. 

SPSS survival manual: a step by step guide to data analysis using IBM SPSS. (2013). Australian and New Zealand Journal of Public Health, 37(6), pp.597-598.

Witte, R. (2017). Statistics. New York: Wiley.

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