Statistical Data Collection and Interpretation Assignment PDF

Statistical Data Collection and Interpretation

Table of Contents
Abstract................................................................................................................................3
Introduction..........................................................................................................................3
Research Questions..............................................................................................................4
Data Collection.....................................................................................................................5
Descriptive Statistics............................................................................................................4
Inferential Statistics..............................................................................................................5
Conclusions..........................................................................................................................5
References............................................................................................................................5
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Assessment
Statistical Data Collection and Interpretation
Abstract
Statistical data analysis tools and techniques were used for the analysis of sample survey data
regarding the climate change, different environmental factors, contribution of corporate and
organizations for solving environment issues. It was observed that Most of the distributions for
the different responses are not symmetrical or identical. No significant difference is observed in
the population proportion for the first response and second response regarding the use of an
internal price on carbon. Also, no any significant difference is observed 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.
Introduction
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.
Research Questions
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?
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Literature Review
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).
Conceptual Model
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.
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Hypotheses
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.
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InternalPriceonCarbonStatisticalProportionsofresponses

Data Collection
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)
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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
Descriptive Statistics
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
Most of the participants in our sample data are from South Africa.
Now, we have to see the frequency distributions for the different variables or responses for
different survey questions. Required frequency distributions are summarised below:
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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
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
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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
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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
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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?
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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
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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
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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
Inferential Statistics
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: 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.
We will consider 5% level of significance for this test.
The test results by using excel are summarised as below:
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Z Test for Differences in Two Proportions
Data
Hypothesized Difference 0
Level of Significance 0.05
Group 1
Number of Items of Interest 51
Sample Size 173
Group 2
Number of Items of Interest 56
Sample Size 173
Intermediate Calculations
Group 1 Proportion
0.29479768
8
Group 2 Proportion
0.32369942
2
Difference in Two Proportions
-
0.02890173
Average Proportion 0.3092
Z Test Statistic -0.5816
Two-Tail Test
Lower Critical Value -1.9600
Upper Critical Value 1.9600
p-Value 0.5608
Do not reject the null
hypothesis
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
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Z Value
-
1.960
0
Std. Error of the Diff. between two Proportions
0.049
7
Interval Half Width
0.097
4
Confidence Interval
Interval Lower Limit
-
0.126
3
Interval Upper Limit
0.068
4
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: 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.
We will consider 5% level of significance for this test.
The test results by using excel are summarised as below:
Z Test for Differences in Two Proportions
Data
Hypothesized Difference 0
Level of Significance 0.05
Group 1
Number of Items of Interest 51
Sample Size 173
Group 2
Number of Items of Interest 65
Sample Size 173
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Intermediate Calculations
Group 1 Proportion
0.29479768
8
Group 2 Proportion
0.37572254
3
Difference in Two Proportions
-
0.08092486
Average Proportion 0.3353
Z Test Statistic -1.5943
Two-Tail Test
Lower Critical Value -1.9600
Upper Critical Value 1.9600
p-Value 0.1109
Do not reject the null
hypothesis
The p-value for this test is given as 0.1109 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 third 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
-
1.960
0
Std. Error of the Diff. between two Proportions
0.050
6
Interval Half Width
0.099
1
Confidence Interval
Interval Lower Limit -
0.180
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0
Interval Upper Limit
0.018
2
Reliability
Now, we have to check the reliability of the data used for this research study. The results for
checking reliability of data are summarized as below:
Case Processing Summary
N %
Cases Valid 173 100.0
Excludeda 0 .0
Total 173 100.0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha N of Items
.445 17
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:
1. Most of the distributions for the different responses are not symmetrical or identical.
2. 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.
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3. 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.
4. 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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