Statistical Data Collection And Interpretation

Research Questions

Discuss about the Statistical Data Collection and Interpretation.

We know that the statistical data analysis and interpretation is very important after data collection for making more efficient decisions. Here we want to use different tools and techniques of statistical data analysis for analysis of the collected data. We have to use descriptive statistics and inferential statistics for the analysis purpose. We want to check whether the distributions for the responses are same or not. Also, we will check whether the proportions for the different types of responses are same or not. For checking different types of claims, we will use some random data. Here, we have to analyse the survey data collected for the large sample survey regarding global issues of climate change, CO₂ emissions, pollution, etc. Let us see this statistical analysis of the collected data in detail.

It is important to establish the research questions for any type of research study. For this research study, the research questions are summarised as below:

Whether the distribution for the responses regarding the climate change and different environmental issues is same or not?

Whether the proportion for the first response and second response for use of an internal price on carbon is same or not?

Whether the proportion for the first response and third response for use of an internal price on carbon is same or not?

Companies are facing a huge financial burden like costs of monitoring to meet the carbon emission target which also affects decreased profit. Agency theory clearly implies that the slack in the targets of agents is created by the asymmetry in their incentives (Timo &Volker, 2011). As per the agency theory, the expenses on environmental responsibilities increase the profit of the business and this environmental activity has the negative impact on a company’s profitability. They also indicated that there exists a good relationship between environmental and company performances because carbon emission disclosure results in building a good reputation of the company in the market which attracts shareholders to invest in the company deriving financial benefits as well as non-financial benefits (Freeman, 2010).

Moreover, companies may imply CSR policy for the positive relations with the legal bodies and increase their reputation and goodwill. Companies which are involved with CSR activities may charge higher cost on their products but on the other hand the relationship between supplier and customers being stronger as customers who are environmental friendly always contribute to the efforts which are more towards the pollution free environment (Saleh, Zulkifli and Muhamad, 2010).

Literature Review

For this research study, the concept of statistical analysis by using testing hypothesis is used. Data for the collected responses would be analyzed by using descriptive analysis and hypothesis testing or inferential statistics. In descriptive statistics, we will see the descriptive summaries and frequency distributions for the different responses. While in the testing of hypothesis, we will test different claims or hypotheses established for this research study.

For this research study, we have to use inferential statistics or testing of hypothesis of checking the following null and alternative hypothesis:

Hypothesis 1

Null hypothesis: H0: There is no any significant difference in the population proportion for the first response and second response regarding the use of an internal price on carbon.

Alternative hypothesis: Ha: There is a significant difference in the population proportion for the first response and second response regarding the use of an internal price on carbon.

Hypothesis 2

Null hypothesis: H0: There is no any significant difference in the population proportion for the first response and third response regarding the use of an internal price on carbon.

Alternative hypothesis: Ha: There is a significant difference in the population proportion for the first response and third response regarding the use of an internal price on carbon.

For this research study, we collect the data from secondary sources. Primary data sets are very large in sample sizes and use of these large sample sizes is not possible for this research study. So, we select the random sample of size 173from the given data. Also, we do not select all variables given in the primary data set. There are more than 50 variables for comparison and study, but we select only 17 variables for study purpose. Data is mainly collected for the different types of responses from the organizations. The list of variables used for this research study is provided below:

No.	Variable
1	CC1.1 – Where is the highest level of direct responsibility for climate change within your organization?
2	CC1.2 – Do you provide incentives for the management of climate change issues, including the attainment of targets?
3	CC2.1 – Please select the option that best describes your risk management procedures with regard to climate change risks and opportunities
4	CC2.2 – Is climate change integrated into your business strategy?
5	CC2.2c – Does your company use an internal price on carbon
6	CCNo.3 – Do you engage in activities that could either directly or indirectly influence public policy on climate change through any of the following? (tick all that apply)
7	Your organization supports international agreement between govt. regarding global climate change?
8	CC2.1a C1 – Please provide further details on your risk management procedures with regard to climate change risks and opportunities – Frequency of monitoring
9	CC2.1a C2 – Please provide further details on your risk management procedures with regard to climate change risks and opportunities – To whom are results reported
10	CC2.1a C4 – Please provide further details on your risk management procedures with regard to climate change risks and opportunities – How far into the future are risks considered?
11	CC2.1d C1 – Please explain why you do not have a process in place for assessing and managing risks and opportunities from climate change, and whether you plan to introduce such a process in future – Main reason for not having a process
12	CC2.1d C2 – Please explain why you do not have a process in place for assessing and managing risks and opportunities from climate change, and whether you plan to introduce such a process in future – Do you plan to introduce a process
13	CC3.1. Did you have an emissions reduction or renewable energy consumption or production target that was active (ongoing or reached completion) in the reporting year?
14	CC3.2. Do you classify any of your existing goods and/or services as low carbon products or do they enable a third party to avoid GHG emissions?
15	CC3.3. Did you have emissions reduction initiatives that were active within the reporting year (this can include those in the planning and/or implementation phases)
16	CC5.1 – Have you identified any inherent climate change risks that have the potential to generate a substantive change in your business operations, revenue or expenditure? Tick all that apply
17	CC6.1 – Have you identified any inherent climate change opportunities that have the potential to generate a substantive change in your business operations, revenue or expenditure? Tick all that apply

We know that the descriptive statistics for any type of data gives us the general idea about the nature of data or variables involved in the study. For the given research study, data is collected from different corporate in the world and responses were recorded. Here, we have to classify these responses by using frequency distributions.

First of all we have to see the frequency distribution for the variable country which is given as below:

country
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	India	44	25.4	25.4	25.4
South Africa	71	41.0	41.0	66.5
South Korea	58	33.5	33.5	100.0
Total	173	100.0	100.0

Now, we have to see the frequency distributions for the different variables or responses for different survey questions. Required frequency distributions are summarised below:

CC1.1 – Where is the highest level of direct responsibility for climate change within your organization?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	-1.00	1	.6	.6	.6
1.00	2	1.2	1.2	1.7
2.00	4	2.3	2.3	4.0
3.00	166	96.0	96.0	100.0
Total	173	100.0	100.0

Conceptual Model

CC1.2 – Do you provide incentives for the management of climate change issues, including the attainment of targets?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	151	87.3	87.3	87.3
2.00	21	12.1	12.1	99.4
999.00	1	.6	.6	100.0
Total	173	100.0	100.0

CC2.1 – Please select the option that best describes your risk management procedures with regard to climate change risks and opportunities
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	6	3.5	3.5	3.5
2.00	161	93.1	93.1	96.5
3.00	5	2.9	2.9	99.4
999.00	1	.6	.6	100.0
Total	173	100.0	100.0

CC2.2 – Is climate change integrated into your business strategy?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	167	96.5	96.5	96.5
2.00	6	3.5	3.5	100.0
Total	173	100.0	100.0

CC2.2c – Does your company use an internal price on carbon
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	51	29.5	29.5	29.5
2.00	56	32.4	32.4	61.8
3.00	65	37.6	37.6	99.4
999.00	1	.6	.6	100.0
Total	173	100.0	100.0

CCNo.3 – Do you engage in activities that could either directly or indirectly influence public policy on climate change through any of the following? (tick all that apply)
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	2.00	14	8.1	8.1	8.1
3.00	131	75.7	75.7	83.8
4.00	27	15.6	15.6	99.4
999.00	1	.6	.6	100.0
Total	173	100.0	100.0

CC2.1a C1 – Please provide further details on your risk management procedures with regard to climate change risks and opportunities – Frequency of monitoring
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	2.00	33	19.1	19.1	19.1
3.00	134	77.5	77.5	96.5
999.00	6	3.5	3.5	100.0
Total	173	100.0	100.0

CC2.1a C2 – Please provide further details on your risk management procedures with regard to climate change risks and opportunities – To whom are results reported
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	2.00	5	2.9	2.9	2.9
3.00	3	1.7	1.7	4.6
4.00	158	91.3	91.3	96.0
999.00	7	4.0	4.0	100.0
Total	173	100.0	100.0

CC2.1a C4 – Please provide further details on your risk management procedures with regard to climate change risks and opportunities – How far into the future are risks considered?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	1	.6	.6	.6
2.00	25	14.5	14.5	15.0
3.00	47	27.2	27.2	42.2
4.00	92	53.2	53.2	95.4
5.00	2	1.2	1.2	96.5
999.00	6	3.5	3.5	100.0
Total	173	100.0	100.0

CC2.1d C1 – Please explain why you do not have a process in place for assessing and managing risks and opportunities from climate change, and whether you plan to introduce such a process in future – Main reason for not having a process
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	3.00	1	.6	.6	.6
4.00	2	1.2	1.2	1.7
5.00	1	.6	.6	2.3
8.00	1	.6	.6	2.9
999.00	168	97.1	97.1	100.0
Total	173	100.0	100.0

CC2.1d C2 – Please explain why you do not have a process in place for assessing and managing risks and opportunities from climate change, and whether you plan to introduce such a process in future – Do you plan to introduce a process
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	2	1.2	1.2	1.2
2.00	3	1.7	1.7	2.9
999.00	168	97.1	97.1	100.0
Total	173	100.0	100.0

CC3.1. Did you have an emissions reduction or renewable energy consumption or production target that was active (ongoing or reached completion) in the reporting year?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	2.00	24	13.9	13.9	13.9
10.00	43	24.9	24.9	38.7
11.00	19	11.0	11.0	49.7
12.00	11	6.4	6.4	56.1
14.00	7	4.0	4.0	60.1
20.00	51	29.5	29.5	89.6
24.00	15	8.7	8.7	98.3
32.00	2	1.2	1.2	99.4
999.00	1	.6	.6	100.0
Total	173	100.0	100.0

CC3.2. Do you classify any of your existing goods and/or services as low carbon products or do they enable a third party to avoid GHG emissions?
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	108	62.4	62.4	62.4
2.00	61	35.3	35.3	97.7
3.00	3	1.7	1.7	99.4
999.00	1	.6	.6	100.0
Total	173	100.0	100.0

CC3.3. Did you have emissions reduction initiatives that were active within the reporting year (this can include those in the planning and/or implementation phases)
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	1.00	161	93.1	93.1	93.1
2.00	12	6.9	6.9	100.0
Total	173	100.0	100.0

CC5.1 – Have you identified any inherent climate change risks that have the potential to generate a substantive change in your business operations, revenue or expenditure? Tick all that apply
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	30.00	8	4.6	4.6	4.6
31.00	1	.6	.6	5.2
32.00	7	4.0	4.0	9.2
33.00	155	89.6	89.6	98.8
999.00	2	1.2	1.2	100.0
Total	173	100.0	100.0

CC6.1 – Have you identified any inherent climate change opportunities that have the potential to generate a substantive change in your business operations, revenue or expenditure? Tick all that apply
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	10.00	1	.6	.6	.6
20.00	1	.6	.6	1.2
21.00	2	1.2	1.2	2.3
30.00	9	5.2	5.2	7.5
31.00	13	7.5	7.5	15.0
32.00	5	2.9	2.9	17.9
33.00	140	80.9	80.9	98.8
999.00	2	1.2	1.2	100.0
Total	173	100.0	100.0

In this section, we have to see some inferential statistics techniques or testing of hypothesis for checking different claims regarding the variables involved in the study. First of all we have to check the hypothesis whether the proportion for the first response and second response for use of an internal price on carbon is same or not? For checking this hypothesis we have to use z test for difference in two proportions. The null and alternative hypothesis for this test are summarised as below:

Null hypothesis: H₀: There is no any significant difference in the population proportion for the first response and second response regarding the use of an internal price on carbon.

Alternative hypothesis: H_a: There is a significant difference in the population proportion for the first response and second response regarding the use of an internal price on carbon.

We will consider 5% level of significance for this test.

The p-value for this test is given as 0.5608 which is greater than the given level of significance so we do not reject the null hypothesis that there is no any significant difference in the population proportion for the first response and second response regarding the use of an internal price on carbon.

Confidence interval for difference between these two proportions is given as below:

Confidence Interval Estimate
of the Difference Between Two Proportions
Data
Confidence Level	95%
Intermediate Calculations
Z Value	-1.9600
Std. Error of the Diff. between two Proportions	0.0497
Interval Half Width	0.0974
Confidence Interval
Interval Lower Limit	-0.1263
Interval Upper Limit	0.0684

Now, we have to check another hypothesis whether the proportion for the first response and third response for use of an internal price on carbon is same or not. We have to use same tests for this hypothesis. The null and alternative hypothesis for this test is given as below:

Null hypothesis: H₀: There is no any significant difference in the population proportion for the first response and third response regarding the use of an internal price on carbon.

Alternative hypothesis: H_a: There is a significant difference in the population proportion for the first response and third response regarding the use of an internal price on carbon.

We will consider 5% level of significance for this test.

The value for Cronbach’s Alpha is given as 0.445 which is very low. We know that if the value of alpha is greater than 0.7, then we can say that the internal consistency of the data is acceptable. For the alpha values more than 0.9, we consider excellent internal consistency of the data. Here, value for alpha is given as 0.445, so we conclude that there is a very poor internal consistency exists in the data for different variables.

Conclusions

Conclusions for this research study are summarised as below:

Most of the distributions for the different responses are not symmetrical or identical.
There is no any significant difference in the population proportion for the first response and second response regarding the use of an internal price on carbon.
There is no any significant difference in the population proportion for the first response and third response regarding the use of an internal price on carbon.
We conclude that there is a very poor internal consistency exists in the data for different variables.

References

Degroot, M. and Schervish, M. (2002). Probability and Statistics. Addison – Wesley.

Dobson, A. J. (2001). An introduction to generalized linear models. Chapman and Hall Ltd.

Evans, M. (2004). Probability and Statistics: The Science of Uncertainty. Freeman and Company.

Freeman, R.E., 2010, “Strategic management: A stakeholder approach”, Cambridge University Press

Hogg, R., Craig, A., and McKean, J. (2004). An Introduction to Mathematical Statistics. Prentice Hall.

Ross, S. (2014). Introduction to Probability and Statistics for Engineers and Scientists. London: Academic Press.

Saleh, M., Zulkifli, N., and Muhamad, R. (2010). Corporate social responsibility disclosure and its relation to institutional ownership. Managerial Auditing Journal, 25(6), 591-613

Timo B., and Volker H., 2011, “How hot is your bottom line? Linking carbon and financial performance”, Business & Society.

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