The Role and Possibilities of Wind Energy

THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS 2013. THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS 5 5 The Role and Possibilities of Wind Energy in Various Production Sectors Name of Student Name of University Course ID Abstract: The research report focuses on the research topic – “The role and possibilities of wind energy in various production sector of Australia”.

Running head: THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION
SECTORS
The Role and Possibilities of Wind Energy in Various Production Sectors
Name of Student
Name of University
Course ID

1THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Abstract:
The research report focuses on the research topic – “The role and possibilities of wind energy in
various production sector of Australia”. In many places of Australia, the role and possibilities of
wind energy in production sectors are getting highly efficient. Wind energy is never-ending,
renewable, sufficient and eco-friendly. The wind-mills, wind-blades and wind machines are the
mediums of wind energy. The enhancement of wind energy is very much essential these days as
natural calamities like global warming and environment pollution are increasing rapidly. For
clean environment and proper sustainability of the environment, renewable energy resources like
wind energy is essential. Governments of different countries are following the new trend of
transportation in different segments of Australian provinces. The increment of production of
wind energy directly or indirectly influencing mainly electric supply sector. Both the primary
and secondary research sources follow that assertion. However, it is also proved by primary data
analysis (executed by questionnaire process) that Australian government neither had taken
milestone steps nor the economical investment for wind energy production. The non-renewable
energy sources are decreasing day by day in this world. Sampled people are concerned about
wind energy sources decreasing the usage of enough amount of thermos-electricity received from
coal and petroleum. It is also found that people are not fully confirmed about the fact that in near
future, wind energy sources would absolutely replace the conventional energy sources reducing
the national gross and effective expense.

2THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Table of Contents
Introduction:....................................................................................................................................3
Discussion about wind energy in Australia:....................................................................................3
Population Selection and Sample Drawing:....................................................................................4
Survey Questionnaire:.....................................................................................................................5
Data Description and Parameters:...................................................................................................5
Sampling Errors:..............................................................................................................................7
Data Validity and Reliability:..........................................................................................................8
Data Analysis:..................................................................................................................................8
Secondary Analysis:....................................................................................................................8
Primary Analysis:........................................................................................................................9
Categorical Variables:.............................................................................................................9
Numerical Variables:.............................................................................................................21
Conclusion:....................................................................................................................................24
References:....................................................................................................................................25

3THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Introduction:
Wind power is the least costly resource of wide-spread renewable energy that includes
producing electricity from the naturally generating power of the wind. In wind machines, wind
turbines seize wind energy within the location sweeping their blades. The wind machines
generate a power output that is proportional to the density of air and cubical power of speed of
wind. The spinning blades of air machines initiate an electrical generator that reproduces
electricity for supplying in various segments of Australia that are agriculture, industry,
household, mining, electric supply system, fishing, forestry and water supply system. In 2016,
wind farms of Australia produced 30.8% of the clean and renewable energy of Australia (Saidur
et al. 2010). The amount of energy is 4-5% of the overall electricity of the year of Australia.
A number of states and territories of Australia have recognized the investment regionally
that increases the scopes that wind energy offers. The government of Australia accomplished its
complete reverse wind auction in 2016 that would help to make most promising renewable
energy objective of 100% by 2020. Five wind farms became active in 2016 along with 44
turbines and almost 140 MW of electricity producing capacity (Wüstenhagen, Wolsink and
Bürer 2007). The recently launched additional projects on wind energy totally 79 wind farms
with a joined capacity of 4327 MW produced with the help of 2106 turbines (Ackermann and
Söder 2000). The statistics refer that Australia has taken 17th place in the world of wind power.
The research report discusses about recent scenario of the wind energy in Australia
according to the primary and secondary data analysis. SPSS-20 software has been utilised for
analysing the secondary data set. On the other hand, Ms-Excel has been used to analyse the
primary data set.
Discussion about wind energy:
A study of 2016 on the economic advantages of wind industry in Australia perceived that
for every 50 MW in capacity, a wind farm supplied the undertaken advantages:
 Up to $250000 annually for farmers in rental income of land and $80000 on community
reflects each year (Hall, Ashworth and Devine-Wright 2013).
 Indirect employment and job offers the construction phase of almost 160 people locally,
795 nation wise jobs and 504 state jobs.
 Direct employment in wind energy employment has invested a total of $125000 in local
economy.
 Straight employment of almost 48 construction workers in a province with each
employee spending almost $250000 in the local region of restaurants, shops, hotels and
other service sectors totally up to $1.2 million (Gross 2007).
To investigate the validity of secondary statistics, data analysis on the basis of both
primary and secondary data are investigated. Data analysis planning could be subdivided into
two parts that are primary and secondary data analysis. To highlight the key points of wind
energy in production sectors the data analysis has considered the quantitative and qualitative data
analysis both. Quantitative or numerical data variables depend on numerical data and qualitative
data variables are the categorical data.

4THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
In this research, the researcher used the primary data gathered from target population. In
survey method with the help of questionnaire, the responses are collected from the target
population. Primary data collection technique is not costly but greater time taking. The
questionnaire consists close-ended questions only. The researcher also used the secondary data
analysis gathered from target population. The data about wind energy is collected from
“Aus.gov” website. The data source is authentic; however, the researcher is unable to produce
full assurance of the secondary data set.
Population Selection and Sample Drawing:
Most of the Australians expect the prosperity of wind power. It is therefore very much
vital for wind farm organizations for engaging in stable loyalty and satisfaction with people
dwelling in the neighbourhood of wind energy projects. The aiming population signifies defined
segment within larger population that are placed to serve as a primary data resource for the
research purpose. The population selected for data analysis is the sample of randomly chosen 50
samples. With the help of questionnaire method, 35 samples were selected for analysis as the
data should be free from missing values. The population is hance the community of Australia and
the sample has the individuals of each community. The samples are primary data in nature. The
data is created with the help of sample-drawing process. The assigned data is the representative
of sampling scheme for determining the number of physical samples according to the sampling
scheme (Tongco 2007).
Figure 1: Population and Sample
Sampling could be described as the defined norm utilised to choose individuals of the
population involved in the research. It should be precisely distinguished that how many
populations of interest are working directly. The procedures of statistical computing have
planned the samples gathered from larger populations. The larger size of the target population
has no preference but to observe a number of cases of elements within the sample for
representation of the conclusions about the population. The population frame undertaken in the
current analysis explains lots of people within target population who can take participation in the
research (Quinn and Keough 2002). Although sampling size is small as the number of
individuals from the sampling frame who would participate in the primary data gathering
method. Here, simple random sampling is undertaken that consists sample group members of
Population
Sample
Case or
Element

5THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
random variables. The advantage of it is very effective if all aspects take part in the data
collection. However, sampling error at high level is present in this case.
Sampling
Probability sampling Non-probability sampling
Simple Systematic Cluster Systematic Quota Purposive Haphazard Volunteer
Figure 2: Sampling methods (Choi, Park and Zhang 2002)
Survey Questionnaire:
The survey questionnaire for primary data analysis is given below:
1. What is your age?
2. Are you aware about advantages of renewable energies?
3. Are renewable energies better than conventional energies according to you?
4. Is wind energy very popular in current situation in Australia?
5. Which sector would get most benefitted due to high production of wind energy in
Australia?
6. Is wind energy resource enough available in Australia?
7. Is technology for producing wind energy enough available in Australia?
8. Is government enough eager towards the wind energy development?
9. Is the invested amount enough for wind energy in Australia according to you?
10. Do you believe that high production of wind energy would keep environment clean?
11. Do you believe that in near future wind energy would be mostly acceptable?
12. What percentage of produced energy is wind energy recently in Australia according to
you?
13. What percentage of estimated producible energy would be proper in Australia according
to you?
Data Description and Parameters:
Primary data is very specifically crucial in market research. Primary data is the
information that gather the purpose of the research project. An advantage of primary data is
specifically maintained in the needs of research. The data observed or gathered directly from
first-hand experience. The data is generally unbiased. However, sampling error may be present in
such types of data. The data includes primarily 50 samples and after cleaning the data for making
it free of missing values, 35 samples were selected for primary data analysis.
The data set have only three numerical variables. These are age, predicted producible
wind energy in Australia and estimated wind energy that would be proper for environment. The
numerical variables indicate – awareness about the advantages of wind energy, comparison
perception of renewable and conventional energy, popularity of wind energy and the sector that

6THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
would be most beneficial from high production of wind energy. Besides, several facts such as
validity of enough energy resource in Australia, technology of producing energy in Australia,
eagerness of government towards the upliftment of wind energy production, sufficiency of wind
energy, cleanliness of renewable energies such as wind energy and consideration of people about
the popularity of wind energy in near future.
Some of the qualitative variables are measured with the help of “Likert” scale that
indicates, 1 = “Strongly disagree”, 2 = “Disagree”, 3 = “Neutral”, 4 = “Agree” and 5 = “Strongly
agree” (Sullivan and Artino Jr 2013). Rest of the qualitative variables are measured with the help
of nominal scale, such as “yes” or “no”.
Variable name Variable description Type of
variables
Response scale
Gender Gender of the responder Nominal
variable
Male (M) and
Female (F)
Age Age of the responder Scale
variable
Numerical values
Adv_renew Are you aware about
advantages of renewable
energies?
Nominal
variable
“Yes” and “No”
Renew_conven Are renewable energies
better than conventional
energies according to
you?
Nominal
variable
“Yes” and “No”
Wind_popular Is wind energy very
popular in current
situation?
Nominal
variable
“Yes” and “No”
Energy_produced What percentage of
produced energy is wind
energy recently in
Australia according to
you?
Numerical
(Scale)
Numerical values
Esti_energ_prod What percentage of
estimated energy would
be proper in Australia
according to you?
Numerical
(Scale)
Numerical values
Bene_high_prod Which sector would get
most benefitted due to
high production of wind
energy?
Nominal
variable
Seven levels of the
variable

7THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Enou_wind_Aus Is wind energy enough
available in Australia?
Ordinal
variable
Five point “Likert”
scale
Enou_tech_wind_avail Is technology for
producing wind energy
enough available in
Australia?
Ordinal
variable
Five point “Likert”
scale
Govn_eager_wind_ener Is government enough
eager towards the wind
energy development?
Ordinal
variable
Five point “Likert”
scale
Enou_invested_amount Is the invested amount
enough for wind energy?
Ordinal
variable
Five point “Likert”
scale
High_wind_cle_env Do you believe that high
production of wind
energy would keep
environment clean?
Ordinal
variable
Five point “Likert”
scale
High_wind_accptab_future Do you believe that in
near future wind energy
would be mostly
acceptable?
Ordinal
variable
Five point “Likert”
scale
(Armstrong 1987)
Table 1: Table of data description
Sampling Errors:
Sampling error in a statistical analysis is arising from the unrepresentativeness of the
undertaken samples. The common five types of sampling error that could be identified in the data
analysis are-
1) Population Specification Error, 2) Sampling Frame Error, 3) Error due to Selection, 4) Errors
due to sampling by wrong questionnaire, 5) Errors due to non-response (Bendat and Piersol
2011).
The sampling errors arise due to the variability in the frequency or representativeness of
the responding samples. Mainly in three ways sampling errors could be controlled with the help
of large samples, careful sample designs and multiple contracts to assure representative response
(Silverman 2016). The greater sampling size of the sampling frame can reduce sampling error.
The larger sample size in survey-based studies prosper experimental studies. The most essential
factor is the cost effective, cost investment and cost factors.
Data Validity and Reliability:

8THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
The two terms data validity and reliability are often used interchangeably when these are
not related to the research statistics. Critics of statistics use the terms that indicate the various
properties of experimental or statistical process (Noble and Smith 2015). Reliability is the
measure of consistency that takes the equal personality test claimed. The validity is the measure
of authenticity of data used for analysis. Reliability and validity are independent of each other. It
is notable fact that a measurement may be valid but not reliable.
The concept behind reliability of any significant outcomes must be more than findings of
inherently repetition. The data set has replicability and universal approach (Drost 2011). The
statistical tools perform time critical experiment. The data set is neither manipulated nor
transformed deliberately for the purpose of data analysis. The social experiment concerns about
reliability and robustness of the data set.
Data Analysis:
Descriptive data analysis are short descriptive coefficients that condense the given data
set that can either be a representation of the whole population or a sample of the population.
Quantitative or numerical variables are summarized by descriptive or summary statistics process.
Categorical or qualitative variables are summarized by frequency tables and respective diagrams.
The descriptive analysis as well as correlation analysis are executed here.
Secondary Analysis:
Electricity supply
Gas supply
Water and waste services
Construction
Road
Rail
Water
Air
Other transport, storage and services
Wholesale and retail trade
Accommodation
Communication
Other commercial and services
Total production; residuals
Of which direct extraction by households
Imports
Flows from the environment
TOTAL SUPPLY
0
20000
40000
60000
Energy expense in 2016
Natural inputs .. .. .. .. .. .. .. .. Production of energy products - - - - - - - -
Energy residuals - 277 90 - 132 86 2 - Total supply (gross) 74 22429 199 28 1207 198 13 -
Total supply (net) .. .. .. .. .. .. .. ..
Figure 3: Wind Energy expense in 2016 with respect to other expenses

9THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
The wind energy expense in 2016 with respect to other expenses displays that expense of wind
energy is significantly smaller than total supply. The amount of supply of wind energy in 2016
with respect to other expenses displays that expense of wind energy is significantly very
insignificant than total supply amount. The produced wind energy is highlighted in electricity
supply.
Agriculture, forestry and fishing
Mining
Food, beverages, textiles
Wood, paper, printing
Petroleum and chemical products
Iron and steel
Non-ferrous metals
Other manufacturing
Electricity supply
Gas supply
Water and waste services
Construction
Road
Rail
Water
Air
Other transport, storage and services
Wholesale and retail trade
Accommodation
Communication
Other commercial and services
Total use by industry
Consumption by households
Consumption by government
Changes in inventories
Exports
Flows to the environment
Total use
0
10
20
30
40
50
Expense of wind energy in 2016
Energy from natural inputs Transformation of energy products End use of energy products
Figure 4: Expense of wind energy in 2016
The bar chart of expense of wind energy in 2016 shows that no other sectors than Electric supply
system takes part in the expense of wind energy in 2016. The expense allocated for wind energy
in 2016 is $44 million (Chambers 2017).
Primary Analysis:
Categorical Variables:
Frequency tables
(Bryman and Cramer 2012)
Table 2: Frequency table of gender

10THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 5: Frequency distribution of gender
The frequencies of females is greater than males.
Table 3: Frequency table of Adv_renew
Out of 35 sampled people, 82.9% people are aware of the advantage of renewable energies. Rest
of 17.1% people are not aware about the advantages of renewable energies.
Figure 6: Frequency distribution of Adv_renew

11THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
The pie chart indicates the frequency distribution of assertion and negation of the people about
the consciousness of advantages of renewable energies.
Table 4: Frequency table of renew_conven
Among 35 people, 34.3% people considered that renewable energies are not better than
conventional energies, 65.7% people regarded that renewable energies are better than
conventional energies.
Figure 7: Frequency distribution of renew_conven
The pie chart shows that the number of people who consider that renewable energies are better
than conventional energies are greater in number than who deny.
Table 5: The frequency table of wind_popular

12THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Only 42.9% people think that wind energy is very popular in current situation. On the other
hand, 57.1% people consider that wind energy is not a very popular energy source in current
situation.
Figure 8: The frequency distribution of wind_popular
The frequency distribution graph displays that the responders who consider that wind energy is
very popular in current situation.
Table 6: Frequency table of bene_high_prod
Most of the “Electric supply” sector get most benefitted because of the high production of wind
energy. Agricultural sector, Industrial sector and Mining sector would also be highly benefitted
because of high production of wind energy as per response. Responders informed that
comparatively lesser beneficial would be Fishing, Household and Forestry sector because of high
production of wind energy.

13THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 9: Frequency distribution of bene_high_prod
The hight of bar is highest for Eleectric supply followed by Agriculture and Industry.
Table 7: Frequency table of enou_wind_Aus
Only 28.6% people regarded that wind energy resource is enough available in Australia. A
significant percentage of responder are nutral or agreed abuout it.

14THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 10: Frequency distribution of enou_wind_Aus
The bar chart indicates that people with highest frequency agreed that wind energy resources are
enough available in Australia.
Table 8: Frequency table of enou_tech_wind_avail
A significant amount of 34.3% responders are neutral about the fact that technology for
producing wind energy is enough available in Australia. 8 people rejected the validity of the fact
that technology for producing wind energy is enough available in Australia. 15 people accepted
the fact that technology for producing wind energy is enough available in Australia.

15THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 11: Frequency distribution of enou_tech_wind_avail
Least frequency of responders strongly disagreed that technology for producing wind energy is
enough available in Australia.
Table 9: Frequency table of govn_eager_wind_Aus
74.3% people informed that government is not eager towards the prosperity of wind energy
development. Only 8.6% people state that Australian government had not taken enough step
towards the development of wind energy.

16THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 12: Frequency distribution of govn_eager_wind_Aus
The frequency distribution bar graph refers that the people who denied the fact that Australian
government is enough eager towards the development of wind energy are greater in number than
the people who agreed that proposition.
Table 10: Frequency table of enou_invested_amount
57.1% responders conveyed that invested amount is not at all enough for wind energy. Lesser
amount of 14.3% people informed that invested amount in case of wind energy is enough.

17THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 13: Frequency distribution of enou_invested_amount
The heights of bar chart indicate that most of the responder denied that invested amount for wind
energy is enough.
Table 11: Frequency table of high_wind_cle_env
Only 8 people either disagreed or strongly disagreed that high production of wind energy would
keep the environment clean. On the other hand, 18 people either agreed or strongly agreed to the
proposition.

18THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 14: Frequency distribution of high_wind_cle_env
Most of the people according to the bar chart inferred that they agree with the fact that high
energy of wind energy would keep environment clean.
Table 12: Frequency table of high_wind_accptab_future
A certainity in the responses was found in case of the undertaken question. 13 people either
disagreed or strongly disagreed to the fact that wind energy would be mostly acceptable in near
future. Conversely, 12 people either agreed or strongly agreed to this assertion. A significant
number of 10 people were indifferent about it.

19THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 15: Frequency distribution of high_wind_accptab_future
The highest number of people as per the bar chart arises in the class of neutral response.
Correlation Coefficients of qualitative variables:

20THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Table 13: Table of Pearson’s correlation coefficients between qualitative variables
“In near future wind energy would be mostly accepted” has negative and significant correlation
with two variables-
 “Technology for producing wind energy enough available in Australia” (correlation
coefficient = -0.168) (Nahler 2009).
 “Government enough eager towards the wind energy development” (correlation
coefficient = -0.144).
Numerical Variables:
Descriptive statistics:
Table 14: Table of descriptive statistics of numerical variables
The numerical variable indicates that-
 Average amount of produced wind energy in Australia with respect to all the energy
sources is estimated as 1.436%. The percentage is calculated as the estimated perception
of the responders. The percentage share for wind energy varies from 0.05% to 6% having
a range of 5.95%.
 Average amount of estimated energy in Australia that should be producible is 13.743%.
The percentage is tabulated as per the recommendation of the responders. The percentage
share of estimated producible energy varies from 3% to 40% with a range of 37%.
Graphs:

21THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Figure 16: Box-plots of numerical variables
The box plots indicate the distribution of both the numerical variables: percentage share of
produced wind energy with respect to all energy sources and percentage share of estimated
producible wind energy in Australia. The location measures (three quartiles along with median)
is lesser for the second variable than the first variable.
Correlation coefficients:

22THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Table 15: Table of Pearson’s correlation coefficients between quantitative variables
The Pearson’s correlation coefficient between produced wind energy and estimate of predicable
wind energy is -0.204. That indicates a negative weak significant correlation between these two
variables.
Scatter plot:
Figure 17: Scatter plot of two numerical variables
The association of two scale variables “percentage of energy production in Australia” and
“percentage of producible estimates of energy production in Australia” could be predicted from
scatter plot. It indicates the absence of significant association between the variables.
Conclusion:
The data analysis about primary and secondary research showed that wind energy is not
very much familiar in the field of Australian energy resources. The government and common
people are aware about it. However, government is not pretty much enthusiastic about the
enhancement of wind energy production enhancement. On the other hand, technology and
resource of wind energy in Australia is not insufficient. People and government of Australia
should develop the structure to fully utilise the wind energy. Further, higher production of wind
energy would help to keep the environment clean, green and unpolluted. The exploitation of non-
renewable energies would reduce. The wind energy is cheap. Only electric supply sector utilises

23THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
the wind energy. Higher production would reduce its price and more other sectors would use it.
Common people are optimistic about the prospected future of the wind energy in Australia. The
Australian government would be able to control their expense in electricity and energy.

24THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
References:
Ackermann, T. and Söder, L., 2000. Wind energy technology and current status: a
review. Renewable and sustainable energy reviews, 4(4), pp.315-374.
Armstrong, R.L., 1987. The midpoint on a five-point Likert-type scale. Perceptual and Motor
Skills, 64(2), pp.359-362.
Bendat, J. S., & Piersol, A. G. (2011). Random data: analysis and measurement
procedures (Vol. 729). John Wiley & Sons.
Bryman, A. and Cramer, D., 2012. Quantitative data analysis with IBM SPSS 17, 18 & 19: A
guide for social scientists. Routledge.
Chambers, J.M., 2017. Graphical Methods for Data Analysis: 0. Chapman and Hall/CRC.
Choi, B.Y., Park, J. and Zhang, Z.L., 2002, June. Adaptive random sampling for load change
detection. In ACM SIGMETRICS Performance Evaluation Review (Vol. 30, No. 1, pp. 272-273).
ACM.
Drost, E.A., 2011. Validity and reliability in social science research. Education Research and
perspectives, 38(1), p.105.
Gross, C., 2007. Community perspectives of wind energy in Australia: The application of a
justice and community fairness framework to increase social acceptance. Energy policy, 35(5),
pp.2727-2736.
Hall, N., Ashworth, P. and Devine-Wright, P., 2013. Societal acceptance of wind farms: Analysis
of four common themes across Australian case studies. Energy Policy, 58, pp.200-208.
Nahler, G., 2009. Pearson correlation coefficient. In Dictionary of Pharmaceutical Medicine (pp.
132-132). Springer, Vienna.
Noble, H. and Smith, J., 2015. Issues of validity and reliability in qualitative research. Evidence-
Based Nursing, pp.ebnurs-2015.
Quinn, G. P., & Keough, M. J. (2002). Experimental design and data analysis for biologists.
Cambridge University Press.
Saidur, R., Islam, M.R., Rahim, N.A. and Solangi, K.H., 2010. A review on global wind energy
policy. Renewable and Sustainable Energy Reviews, 14(7), pp.1744-1762.
Silverman, D. ed., 2016. Qualitative research. Sage.

25THE ROLE AND POSSIBILITIES OF WIND ENERGY IN PRODUCTION SECTORS
Sullivan, G.M. and Artino Jr, A.R., 2013. Analyzing and interpreting data from Likert-type
scales. Journal of graduate medical education, 5(4), pp.541-542.
Tongco, M. D. C. (2007). Purposive sampling as a tool for informant selection. Ethnobotany
Research and applications, 5, 147-158.
Wüstenhagen, R., Wolsink, M. and Bürer, M.J., 2007. Social acceptance of renewable energy
innovation: An introduction to the concept. Energy policy, 35(5), pp.2683-2691.