Experiment Design and Result Analysis for Customer Expectation Study on Online Gaming

This paper discusses experiment/research design and analysis for customer expectation study on online gaming. It covers data collection, experiment design, and result analysis. The expected and actual results are also discussed.

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Assignment 4 – Experiment Design and Result Analysis
Student’s Name
Course
Professor’s Name
Institution’s Name
Institution’s Location
Date

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Table of Contents
1.0 Customer expectation study on online gaming................................................................3
1.1 The objective................................................................................................................3
1.2 Data collection.............................................................................................................3
1.2.1 The available data sources....................................................................................4
1.2.2 Recording of the collected data.............................................................................4
1.2.3 Storage of the collected data.................................................................................5
1.3 Experiment design and implementation.......................................................................6
1.3.1 Data pre-processing...............................................................................................6
1.3.2 Feature selection/dimension reduction..................................................................7
1.3.3 Designing of the experiment.................................................................................8
1.3.4 Implementation of the research...........................................................................10
1.4 Result analysis and summary.....................................................................................12
1.4.1 The expected results............................................................................................12
1.4.2 A summary of the expected results and how they link to the research questions
...............................................................................................................................................13
1.5 The experiment and analysis chapter.........................................................................14
References........................................................................................................................15

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1.0 Customer expectation study on online gaming
1.1 The objective
The main objective of this paper is to equip the learners with the knowledge and the skills
required to conduct experiments of researches successfully. Having good knowledge and skills
on the experimental or research process is very important since we find ourselves in many
situations where we are supposed to conduct different types of researches to come up with
different results which will help us to make some relevant conclusion. This paper will discuss
experiment/research design and analysis in details where it will be based on the previous
assignment which discussed ‘customer expectation study on online gaming.’ This paper will
discuss how this research can be done effectively to get the desired results which can help the
researchers to draw meaningful conclusions.
1.2 Data collection
For any research or experiment to be successful, the appropriate data must be collected
and recorded to be used in the research (Cleary, Horsfall, and Hayter, 2014, pp.473-475). The
process of data collection is usually the first stage of any research or experiment. In this stage,
the researchers identify the most appropriate sources of data, collect and record the relevant data
to be used, and finally store the collected data for it to be accessed in the future or by other
researchers who may require the data (Harwood and Gross, 2016). Therefore, the stage of data

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collection involves three major activities which are identifying the best and the most appropriate
data sources, collecting and recording the collected data, and storing the data well for future use.
1.2.1 The available data sources
Our paper aims to study the customer expectation on online gaming, and so we must
identify the most relevant sources or places where we have many people who engage in online
games. The most appropriate sources of our data will be colleges where we have many students
who play different online games, people’s parks where we have many people who gather there
and some of them engage in different online games when relaxing in the parks, and some
companies where we have many employees who engage in different online games during their
free time. Therefore, our main sources of data will be colleges, people’s parks, and companies.
1.2.2 Recording of the collected data
After visiting the sources/places discussed above, we shall use some tables to record the
data we’ll get. Recording of data in tables is very important as it helps to enhance the
presentation and display of the data and make it essay to visualize and interpret the data
(Verdinelli and Scagnoli, 2013, pp.359-381). A sample data recording table which can be used is
shown below:
Data collection/recording table

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Data
source
name
Source
organization
Data description Data
file
format
URL (if
available
online)
Charge
fee
Target
data
source
Data 1 Colleges The data of use of
online gaming in
colleges
txt --- Free No
Data 2 People’s
parks
The data of use of
online gaming in
the people’s parks
txt --- Free No
Data 3 Companies The data of use of
online gaming in
companies
txt --- Free No
1.2.3 Storage of the collected data
After the collection and recording the required raw data, it’s very important and
recommended to store it well and in a safe place since these data may be required by other
researchers in the future. Good and safe storage of the data ensures that the data won’t be
accessed by some unauthorized people who may end up interfering with the data (Ristov,
Mrvica, and Miskovic, 2014, pp.1586-1591). The data is recorded in the relevant data storage
tables, and then the tables are safely stored in the relevant electronic or non-electronic devices.
An example of a data storage table is shown below:
Data storage table
Data source Date of Saved file Saved file Saved file Number of

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name collection location name format data records
Data 1 26/8/2018 //raw data/ Data1.txt txt 2000
Data 2 28/8/2018 //raw data/ Data2.txt txt 2200
Data 3 29/8/2018 //raw data/ Data3.txt txt 1750
1.3 Experiment design and implementation
The stage of experiment/research design and implementation gives the details of how the
entire research process will be conducted after getting the required data. This stage will discuss
all the major modifications done to the data before it can be used in the research/experiment. We
shall also identify the main methodology which will be used in the analysis and how the analysis
will be done effectively to obtain the desired results.
1.3.1 Data pre-processing
Data pre-processing is done using different techniques which help to transform the
collected raw data into other forms which can be understood and processed with much ease by
the software used by the researchers in their experiments or researches (García, Luengo, and
Herrera, 2015, pp.195-243). There are different types of techniques used in data preprocessing
where the main techniques include data cleaning which is done to remove inconsistency in data
and fill some of the missing value in the collected raw data, data reduction which is done to
remove redundancy or unwanted data, data integration which is done to combine related data for
the data to be used more easily and effectively in the analysis, and data transformation which is

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done to transform the collected into the formats required by the software or tools to be used in
analyzing and processing the data (Zhao, Wang, and Sheng, 2018, pp.13-52). Data preprocessing
is very important as it enhances the data analysis processes which come later in the experiment
or research.
1.3.2 Feature selection/dimension reduction
Feature selection is also a preprocessing technique which entails reducing some of the
unwanted features of the collected raw data to make sure you are left with only the relevant the
useful features of the data which will be required in the analysis of the data (Chandrashekar and
Sahin, 2014, pp.16-18). The raw data collected from the field may contain very many features or
details some of which are not required and just make the collected data very bulky and complex
to be analyzed and process. Through the process of feature selection, the data is sorted as
required to make sure all the irrelevant features or details of the raw data are removed from the
data and to make sure that only the relevant features of the data which are required in the
analysis of the data are required. The process of feature selection may sometimes involve
reduction of the data dimensionality, and that’s why some of the researchers call it the dimension
reduction process (Ma and Zhu, 2013, pp.134-150). For the research process to be effective and
to be done with the least time possible, it’s very important to make sure only the relevant and the
required data is used in the analysis, and this helps to save time and resources which could have
been used to analyze or process the unwanted data and its features.
A table which can be used to record the data after pre-processing and feature selection is shown
below:

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Feature selection/dimension reduction table
Date Data
sourc
e
name
Purpose of
pre-
processing
Pre-
processin
g method
Numbe
r of
original
data
records
Number
of
resultin
g data
records
Numbe
r of
original
features
Number
of
resultin
g
features
The new
data file
name
01/9/201
8
Data 1 Filling in
the missing
values
Data
cleaning
2000 2110 100 100 Data11.tx
t
02/9/201
8
Data 2 Removing
the
unnecessar
y features
from the
data
Feature
selection
2200 1995 120 108 Data22.tx
t
03/9/201
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Data 3 Combining
related data
to be
analyzed
together
Data
integration
1750 1850 100 97 Data33.tx
t
1.3.3 Designing of the experiment
After collecting and doing all the necessary modifications to the collected data, we can
now proceed with the designing of the experiment. We shall use the hybrid methodology which
is also referred to as the mixed methods design methodology. The main reason why we opt to use

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the hybrid methodology in the research is the nature of our research which will use both non-
numerical and numerical types of data (Caruth, 2013, pp.112-122). We shall come up with some
survey questions which will address the use of online gaming by our respondents, and these
survey questions will help us to get the required responses which we shall analyze to understand
the users/customers’ expectations of online gaming. The main survey questions to be used in the
research can be seen as sub-researches or sub-experiments since each of the main questions will
give different responses which will be analyzed together to get the general expectations of the
customers from online gaming. A table showing the research design or how the research will be
conducted using the major survey questions is shown below:
Date Experimen
t
Purpose of the
experiment
Description of
procedure
Input
data
Expected
output
The
resulting file
name
06/9/2018 Experiment
1
To understand the
activity of online
gaming by different
people in the
places/sources of
our interest
Using of
questionnaire
method to
understand the
people who
(respondents)
play online
games
--- There are very
many people
who play online
games
Output111.txt
06/9/2018 Experiment
2
To understand the
people’s expectation
of online gaming
Using of
questionnaire
method to
understand the
people’s
(customers’)
--- Many people
want more
online games to
be developed for
them to enjoy
Output222.txt

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expectations on
online gaming
more
1.3.4 Implementation of the research
Having formulated the main questions to be investigated in the research, we can go ahead
to carry out the actual research/experiment to get the required responses, which will then help us
to do our analysis to get the desired results, which will then help us to make some meaningful
conclusions. The results obtained are normally represented using some tables and some graphs to
enhance the presentation and the visualization of the results (Evergreen, 2017). In our case, 100
respondents took part in the survey, and the responses we got from the survey can be shown in
the table below:
The area addressed by the survey
questions
No. of people % of the respondents
compared to the total
People who engage in online
gaming
74 74%
People who engage in online
gaming and are satisfied with the
performance of the online games
33 44.6%
People who engage in online
gaming and expect online games to
be improved and more games to be
introduced
41 55.4%

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The results shown in the table above can be represented by the pie charts below:
74%
26%
People who engage in online gaming
People who don’t engage in online gaming

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44.60%
55.40%
People who engage in online gaming and are satisfied with the
performance of the online games
People who engage in online gaming and expect the online games
to be improved and more games to be introduced
1.4 Result analysis and summary
This section will discuss the expected results of the research/experiment and give the
summary of the actual results which were obtained after doing the research. Before conducting
any research or experiment, it’s highly advisable to have a rough idea of the expected results
(Neuman and Robson, 2014). This rough idea helps the researchers to conduct the
research/experiment process well and know where they get out of track for them to take the
necessary corrective measures before it’s too late. The expected results can be obtained from the
available literature or analyzing the results of similar research which was done in the past by
other researchers (Patten and Newhart, 2017). In our case, we also had some results which we

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expected to find when conducting the research, and these expected results guided us through the
entire research process.
1.4.1 The expected results
Before conduction the research, we had done gathered some information about online
gaming and what people expect from online gaming. Therefore, when conducting the actual
research, we expected to find that more than half of the people who use smartphones, laptops,
and other devices which support different online games engage in different types of online
games. We also expected that about 75% or more of the young people enjoy at least one of the
many types of online games in their smartphones, laptops, desktops, or other devices which
support the games. We also expected that more than half of the people are not fully satisfied with
the performance of the online games and would like them to be improved further and more
online games to be developed.
1.4.2 A summary of the expected results and how they link to the research questions
To summarize the major expectations of the research, we expected to find that more than
half of the people who have different devices which support online games engage in at least one
of the online games offered by their devices. We also expected that about half or more of the
people who engage in different online games are not satisfied with their performance and want
and expect them to be improved. These expectations are in line with our main research question
which aimed to study the customers’ expectations for online gaming.

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1.5 The experiment and analysis chapter
1.0 Customer expectation study on online gaming
1.1 The objective
1.2 Data collection
1.2.1 The available data sources
1.2.2 Recording of the collected data
1.2.3 Storage of the collected data

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1.3 Experiment design and implementation
1.3.1 Data pre-processing
1.3.2 Feature selection/dimension reduction
1.3.3 Designing of the experiment
1.3.4 Implementation of the research
1.4 Result analysis and summary
1.4.1 The expected results
1.4.2 A summary of the expected results and how they link to the research questions
References
Caruth, G.D., 2013. Demystifying Mixed Methods Research Design: A Review of the
Literature. Online Submission, 3(2), pp.112-122.
Chandrashekar, G. and Sahin, F., 2014. A survey on feature selection methods. Computers &
Electrical Engineering, 40(1), pp.16-28.
Cleary, M., Horsfall, J., and Hayter, M., 2014. Data collection and sampling in qualitative
research: does size matter? Journal of advanced nursing, 70(3), pp.473-475.

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Evergreen, S.D., 2017. Presenting data effectively: Communicating your findings for maximum
impact. Sage Publications.
García, S., Luengo, J. and Herrera, F., 2015. Data preprocessing in data mining (pp. 195-243).
Switzerland: Springer International Publishing.
Harwood, W.T. and Gross, R.A., 2016. Secure data storage. U.S. Patent 9,521,132.
Ma, Y. and Zhu, L., 2013. A review of dimension reduction. International Statistical
Review, 81(1), pp.134-150.
Neuman, W.L. and Robson, K., 2014. Basics of social research. Pearson Canada.
Patten, M.L. and Newhart, M., 2017. Understanding research methods: An overview of the
essentials. Taylor & Francis.
Ristov, P., Mrvica, A. and Miskovic, T., 2014, May. Secure data storage. In Information and
Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th
International Convention on (pp. 1586-1591). IEEE.
Verdinelli, S. and Scagnoli, N.I., 2013. Data display in qualitative research. International
Journal of Qualitative Methods, 12(1), pp.359-381.
Zhao, J., Wang, W., and Sheng, C., 2018. Data Preprocessing Techniques. In Data-Driven
Prediction for Industrial Processes and Their Applications (pp. 13-52). Springer, Cham.

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