Introduction to Research: Collection, Design, and Implementation of Experiment

The experiment was conducted to analyze the data collected from public and private hospitals in Australia. The data was collected from electronic medical records and a sample of 150 patients was analyzed. The demographic information of the patients was recorded, including their age, gender, occupation, education, and income. The data was analyzed to determine the average number of patients admitted in a week, the number of patients admitted for different diseases, and the number of insured patients. The results showed that the highest frequency of patients admitted to hospitals was in the Urology department, and the highest number of insured patients were also in the Urology department. The experiment provided valuable insights into the healthcare system and can be used to improve the quality of healthcare services.

Running head: INTRODUCTION TO RESEARCH
Introduction to Research
Name of the Student
Name of the University
Course ID

1INTRODUCTION TO RESEARCH
Table of Contents
1. Collection of data.........................................................................................................................2
1.1Data source.............................................................................................................................2
1.2 Data collection.......................................................................................................................2
1.3 Data storage...........................................................................................................................4
2. Design and implementation.........................................................................................................5
2.1 Pre – Processing of data.........................................................................................................5
2.2 Feature selection or dimension reduction..............................................................................6
2.3 Experiment Design................................................................................................................7
2.4 Implementation of designed experiment method................................................................10
3. Analysis of Result......................................................................................................................17
3.1 Estimation of obtained results.............................................................................................17
3.2 Summary of the obtained results.........................................................................................18
4. Brief Outline of Experiment and Result Analysis.....................................................................19
5. Reference list.............................................................................................................................20

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1. Collection of data
Proper design of method of experiment and result analysis from the experiment should
be done before performing experiment and obtaining result. The first task in conducting
experiment process is to collect data that would be used in the experiment. There are different
sources for gathering data (Tang and Zhang 2013). The detailed information regarding selected
data source is given in section 1.1. The most appropriate data source is then selected and is used
for analysis. All the data are then put into the table to prepare a record (Deutsch et al. 2013). The
data file is created in the subsection 1.1.3. The collected data are stored in the created file. The
data file is saved is to be saved in the computer.
1.1 Data source
Before starting the experiment process, research needs to be conducted regarding research
problem, design of the experiment and selection in data sources (Montgomery 2017). The
particular study targets common people of different age. Data collected from public and private
hospital.
 Public hospital
 Private hospital
1.2 Data collection
The following table is prepared to collect data and prepare record for the experiment. The
details regarding data source, name of the organization and data description, type of data, format
for collected data, fees and appropriateness of the data is given in the table (Creswell and
Creswell 2017).

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Table 1: Collection of data
Data Source
Name
Source
Organization
(Public =
hospitals)
Data
Description
Data file
format
Charge fee Target data
source
Data 1 Public How many
patients are
admitted on
an average in
a week?
CSV Free Yes
Data 2 Public What type of
diseases the
patients are
suffering
from?
CSV Free Yes
Data 3 Public Which
diseases has
the highest
frequency?
CSV Free Yes
Data 4 Public How many
patients had
medical
CSV Free Yes

4INTRODUCTION TO RESEARCH
insurance
Data 5 Public Which
department
had the
highest
frequency of
insured
patients?
CSV Free Yes
1.3 Data storage
The creation and completion of the data table is followed by gathering the data and put
those data on a separate table (Pedhazur and Schmelkin 2013). The second data table has raw
data that is the data directly collected from the data source. The data is stored for safety purpose
so that data might not lose (Kratochwill et al. 2013).
Table 2: Storage of data
Data Source
name
Date of
collection
Saved file
location
Saved file
name
Saved file
format
No of
records
Survey from
private
hospitals
18/10/2018 // raw data/ Survey.csv CSV 70
Survey from 20/10/ 2018 // raw data/ Survey.csv CSV 80

Read Data
Filter data
Resampling data
Generate
required data set
Specialized
data
Raw
data
5INTRODUCTION TO RESEARCH
public
hospitals
2. Design and implementation
After collecting the data, the next step is to design and implement the method of
experiment (Johnson et al. 2013). The subsection 2.1 contains method of pre-processing the data.
In the subsection 2.2, method of reducing dimension or featured selection is given. The
subsection 2.3 and 2.4 contain phase of design and implementation procedure.
2.1 Pre – Processing of data
Figure 1: Pre-processing of data

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2.2 Feature selection or dimension reduction
After pre-processing the data, the next step is to reduce the dimension of data to use the
data for specific purposes (Williams et al. 2014). For this a new table has been created represent
the pre-processing method. The table also contain reduction in the selected random data. The
featured data is then stored in a new file (Schwartz-Shea and Yanow 2013). The final survey file
is then used for evaluation.
Table 3: Feature selection and dimension reduction of the data
Date Data
Source
Name
Purpose of
pre-
processing
the data
Method
used for
pre-
processing
Raw data
records
New data
records
Name of
new data
file
20/10/2018 Data 1 Elimination
of missing
data
Data
filtering
150 120 Final
survey.csv
20/10/2018 Data 2 Avoidance
of duplicity
Data
reduction
110 90 Final
survey.csv
20/10/2018 Data 3 Featured
selection
Data
integration
75 50 Final
survey.csv
20/10/2018 Data 4 Filter data Data
reduction
65 45 Final
survey.csv
20/10/2018 Data 5 Eliminate
missing
Data
integration
50 40 Final
survey.csv

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data
2.3 Experiment Design
2.3.1 Detailed step for designing
Experiments were conducted based on the methodology that is proposed for the research.
The hybrid methodology is regarded as the combination of the qualitative and quantitative
method (Michels et al. 2013). The data were collected from electronic record stored in private
and public hospital, based on which the questionnaire were prepared. A list of five questions
were prepared in the questionnaire that formed the main question in the survey (Merriam and
Tisdell 2015).
Prior to conducting survey, certain general features were designed that were categorized
under the heads of gender, age, education, occupation and income. These features were made it
easy to obtain the approximate idea regarding the people and their preference, which ultimately
resulted in reduction in dimension or selection of features (Dougherty and Shuker 2014). The
statistical tools were used to record the data.

Methodology Collect data
Data Analysis
Data Evaluation
8INTRODUCTION TO RESEARCH
2.3.2 Results of the experiment
Figure 2: Steps for developing Questionnaire
Table 4: Information on demographics
Sex Male
Female
Age group 15 – 25
25 – 35
35 – 45
Above 45

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Occupation Student
Housewife
Business
Service
Education High school
Graduate
Post graduate
Others
Income 10,000 – 20,000
20,000 – 30,000
30,000 – 40,000
40,000 – 50,000
Above 50,000
Table 5: Questionnaire for survey
Question no Description of the Question
Question 1 How many patients are admitted on an
average in a week?
Question 2 What type of diseases the patients are

10INTRODUCTION TO RESEARCH
suffering from?
Question 3 Which diseases has the highest frequency?
Question 4 How many patients had medical insurance?
Question 5 Which department had the highest frequency
of insured patients?
The given above a description regarding demographics and main questionnaire for the
designed research. Demographic information makes it easy to understand from the prevalence of
particular diseases among the targeted population (Ioannidis et al. 2014).
2.4 Implementation of designed experiment method
2.4.1 Software and tools used
The next importance phase of experiment design is implementation. The research design
and collection of sample data need proper implementation for analysis (Candioti et al. 2014).
The table below (Table 6) summarizes the data obtained from the selected sample.
Table 6: Recorded Demographic information
(Number of sample = 150)
Demographic features Percentage of sample (N)
Sex
 Male
 Female
90 (60%)
60 (40%)

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Age group
 15 – 25
 25 – 35
 35 – 45
 Above 45
27 (18%)
33 (22%)
42 (28%)
48 (32%)
Occupation
 Student
 Housewife
 Business
 Service
25 (16.67%)
30 (20%)
45 (30%)
50 (33.33%)
Education
 High school
 Graduate
 Post graduate
 Others
30 (20%)
60 (40%)
45 (30%)
15 (10%)
Income
10,000 – 20,000
20,000 – 30,000
30,000 – 40,000
40,000 – 50,000
20 (20%)
25 (16.67)
40 (26.67%)
30 (20%)

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Above 50,000 25 (16.67%)
60%
40%
Sex
Male
Female
18
22
28
32
Age Group
15 – 25
25 – 35
35 – 45
Above 45

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16.67
20.00
30.00
33.33
Occupation
Student
Housewife
Business
Service
20
40
30
10
Education
High school
Graduate
Post graduate
Others

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20.00
16.67
26.67
20.00
16.67
Income
10,000 – 20,000
20,000 – 30,000
30,000 – 40,000
40,000 – 50,000
Above 50,000
Figure 3: Representation of recorded demographic data
2.4.2 Result of experiment
Table 7: Average number of Patients in a week
Row Labels Average of Number of patients in OPD
3/1/2018 -
3/7/2018 11.57142857
3/8/2018 -
3/14/2018 9.857142857
Grand Total 10.71428571

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3/1/2018 - 3/7/2018 3/8/2018 - 3/14/2018
8.5
9
9.5
10
10.5
11
11.5
12
Average number of patients in a week
Figure 4: Average number of Patients in a week
Table 8: Number of patients in OPD
Row Labels
Sum of Number of patients in
OPD
Cardiology 25
gynecology 32
Neuroscience 14
Pediatric 31
Urology 48
Grand Total 150

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Cardiology gynaecology Neuro Science Paediaric Urology
0
10
20
30
40
50
60
25
32
14
31
48
Number of patients with different diseases
Total
Figure 5: Number of patients in OPD
Table 9: Number of insured patients for different diseases
Row Labels
Sum of Health
Insurance
Cardiology 18
gynecology 21
Neuroscience 10
Pediatric 20
Urology 40
Grand Total 109

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Cardiology gynaecology Neuro Science Paediaric Urology
0
5
10
15
20
25
30
35
40
45
18
21
10
20
40
Type of dieases and health insurance
Figure 6: Number of insured patients for different diseases
3. Analysis of Result
3.1 Estimation of obtained results
The Electric medical record refers to the composite system where health related data of
individual can be gathered, maintained and consulted for future (Haddi, Liu and Shi 2013). The
present experimental analysis considers EMR of public and private hospitals in Australia. A
sample data of 150 patients for two weeks are analyzed. In the recorded sample, number of male
patients exceed that of the female one. Related to the age group, the highest number of patients
admitted in different hospital belong to the age of above 45. People engaged in different service
profession have the highest frequency of admitted to the hospital. Students belonging to the age
group of 15 – 25 are the healthiest group. Most of the people in the sample have completed their
graduation degree. As far as income is concerned, there is the highest percentage of sample
people have an income ranged from 30,000 to 40,000. People are admitted in the hospitals with
various kind of diseases. Five popular category of diseases are considered for analysis. The

18INTRODUCTION TO RESEARCH
weekly average number of patients in hospital is around 11. Most of the patients have a medical
insurance. Patients having Urology related problems have a higher tendency to be medically
insured.
3.2 Summary of the obtained results
In the sample of 150 patients, 60 percent are male and 40 percent are female. Only 27
percent of patients belong to the youngest age group of 15 – 25. People having an age of 45 and
above are more likely to suffer from different diseases and admitted in hospitals. The percentage
of students got admitted in hospital with different diseases is 16.67 percent. The same for the
housewife is 20 percent. People engaged in business and service profession have respective
percentage of 30 and 33.33 of total sample survey. 40 percent of the recorded sample are
graduate. 45 percent hold a postgraduate degree while 30 percent are only high school passed.
20.67 percent of the sample have an income ranged from 30,000 to 40,000.
The weekly average number of patients in the first week is higher than that in the second
week. In the sample, there are 25 peoples suffering from cardiac problems. Number of people
suffering from urological diseases is the highest with the number being 48. The respective
number of patients for gynecology and pediatric are 32 and 31. Only 14 patients admitted in
Neuroscience department. Among the 150 patients, 109 are medically insured. Patients in
Urological diseases are mostly insured with number of people being 40. The least number of
insured patients is patients admitted in Neuroscience department.
Using the analysis, I can design suitable health insurance policy for different group of
people. The collected information through EMR provides useful insight about mostly prevalent
diseases and the particular field shared by most of the people.

19INTRODUCTION TO RESEARCH

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4. Brief Outline of Experiment and Result Analysis
1. Experiment design and analysis of result
1.1 Collection of data
1.1.1 Data source
1.1.2 Collection of data
1.1.3 Storage of data
1.2 Design and implementation procedure of experiment
1.2.1 Pre-Processing of data
1.2.2 Feature selection and reduction of data
1.2.3 Designing experiment
1.2.3.1 Detailed steps for experiment design
1.2.3.2 Description of experiment
1.2.4 Implementation
1.2.4.1 Used software and tools
1.2.4.2 Results of the experiment
1.3 Analysis of result
1.3.1 Estimation of results
1.3.2 Summary of the results

21INTRODUCTION TO RESEARCH
5. Reference list
Candioti, L.V., De Zan, M.M., Cámara, M.S. and Goicoechea, H.C., 2014. Experimental design
and multiple response optimization. Using the desirability function in analytical methods
development. Talanta, 124, pp.123-138.
Creswell, J.W. and Creswell, J.D., 2017. Research design: Qualitative, quantitative, and mixed
methods approaches. Sage publications.
Deutsch, M.B., Green, J., Keatley, J., Mayer, G., Hastings, J., Hall, A.M., Deutsch, M.B.,
Keatley, J., Green, J., Allison, R. and Blumer, O., 2013. Electronic medical records and the
transgender patient: recommendations from the World Professional Association for Transgender
Health EMR Working Group. Journal of the American Medical Informatics Association, 20(4),
pp.700-703.
Dougherty, L.R. and Shuker, D.M., 2014. The effect of experimental design on the measurement
of mate choice: a meta-analysis. Behavioral Ecology, 26(2), pp.311-319.
Haddi, E., Liu, X. and Shi, Y., 2013. The role of text pre-processing in sentiment
analysis. Procedia Computer Science, 17, pp.26-32.
Ioannidis, J.P., Greenland, S., Hlatky, M.A., Khoury, M.J., Macleod, M.R., Moher, D., Schulz,
K.F. and Tibshirani, R., 2014. Increasing value and reducing waste in research design, conduct,
and analysis. The Lancet, 383(9912), pp.166-175.
Johnson, F.R., Lancsar, E., Marshall, D., Kilambi, V., Mühlbacher, A., Regier, D.A., Bresnahan,
B.W., Kanninen, B. and Bridges, J.F., 2013. Constructing experimental designs for discrete-

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choice experiments: report of the ISPOR conjoint analysis experimental design good research
practices task force. Value in Health, 16(1), pp.3-13.
Kratochwill, T.R., Hitchcock, J.H., Horner, R.H., Levin, J.R., Odom, S.L., Rindskopf, D.M. and
Shadish, W.R., 2013. Single-case intervention research design standards. Remedial and Special
Education, 34(1), pp.26-38.
Merriam, S.B. and Tisdell, E.J., 2015. Qualitative research: A guide to design and
implementation. John Wiley & Sons.
Michels, K.B., Binder, A.M., Dedeurwaerder, S., Epstein, C.B., Greally, J.M., Gut, I.,
Houseman, E.A., Izzi, B., Kelsey, K.T., Meissner, A. and Milosavljevic, A., 2013.
Recommendations for the design and analysis of epigenome-wide association studies. Nature
methods, 10(10), p.949.
Montgomery, D.C., 2017. Design and analysis of experiments. John wiley & sons.
Pedhazur, E.J. and Schmelkin, L.P., 2013. Measurement, design, and analysis: An integrated
approach. Psychology Press.
Schwartz-Shea, P. and Yanow, D., 2013. Interpretive research design: Concepts and processes.
Routledge.
Tang, Q.Y. and Zhang, C.X., 2013. Data Processing System (DPS) software with experimental
design, statistical analysis and data mining developed for use in entomological research. Insect
Science, 20(2), pp.254-260.

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Williams, A.G., Thomas, S., Wyman, S.K. and Holloway, A.K., 2014. RNA‐seq data: challenges
in and recommendations for experimental design and analysis. Current protocols in human
genetics, 83(1), pp.11-13.