Quantitative Analysis of Galvanic Skin Response

Running head: COMPUTER SCIENCE – USABILITY ENGINEERING
Usability Engineering
Name of Student:
Name of University:
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COMPUTER SCIENCE – USABILITY ENGINEERING1
Table of Contents
Introduction:....................................................................................................................................2
Data Analysis:..................................................................................................................................2
1. Data cleaning, data shaping and Removal of missing data:....................................................2
2. Descriptive Statistics:..............................................................................................................3
3. Quantitative Analysis of Galvanic Skin Response:.................................................................4
4. Quantitative Analysis of positive emotional valence response:..............................................5
5. Quantitative analysis of three Research questions:.................................................................6
5.1. Research Question: Does the proportion of perceived positive Straightness changes
with different audio frequencies?............................................................................................6
5.2. Research Question: Does the proportion of perceived positive surprise response change
with various audio frequencies?..............................................................................................7
5.3. Research Question: Do the positive response of the three variables “Speed”, “Weight”
and “Strength” are equal with respect to the three types of audio frequency that are High,
Low and Control?....................................................................................................................8
Reference List:...............................................................................................................................10

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Introduction:
The researcher examines the data set collected from an experimental study about footwear as per
the physical aspects of the individuals such as gender, weight, shoe size and height. The
considered data variables are – 1) “BodyVisualization_LOG” (it is the perceived body weight
after each exposure captured by the user changing an image of a body until it compared the body
weights for the individuals), 2) “GSR_Zscore” (galvanic skin response), 3) “Valence” (valence),
4) “Questionnaire_Speed” (speed perception), 5) “Questionnaire_Weight” (weight perception),
6) “Questionnnaire_Strength” (strength perception), 7) “Questionnaire_Straightness” (body
straightness perception), 8) “Questionnaire_Vividness” (vividness of body feelings) and 9)
“Questionnnaire_Surprise” (unexpected body feelings). The data analysis is based on the MS-
Excel operation and measures extracted from the experiments with changing frequency and
audio interface of the footwear at the time of witnessing the variations in the reaction of users.
The participants acknowledged the high frequency, low frequency and controlled audio feedback
from walking while wearing the prototype shoes. The researcher captured the perceptions of the
participants about their body weight, mood, emotions and changes in behaviour involving three
dimensions for each case of this experiment.
Data Analysis:
1. Data cleaning, data shaping and Removal of missing data:
The data cleaning helps to decrease wastage and consolidate the dirty or inaccurate data.
A data analyst should focus on data cleaning for having correct information before using the data
for analysis purpose. The analysis without cleaning the data may lead to a range of problems,
linking problems, errors in parameter estimation, model mis-specification and linking biases. It
may lead to draw a false conclusion. Data set in data sheet, the data set has a total of 21
variables. It is observed that- Sample no. 2 and 8 has missing values in the range “AD3” to
“AF2”. Besides, sample no. 16, 11 and 10 also have some missing values. Sample no. 4 has lots
of missing values starting from “L5” to “AC5”. The attempt of removing the missing values and
eliminating empty cells is therefore carried out.
For single empty response of any sample, the analyst manually put the mean value of the
column in the blank space. For the sample whose number of missing values are more than 3, the
analyst has decided to neglect the whole sample from the data set. For example, the missing
value of “GSR_Zscore” with high frequency of sample no. 2 is replaced by the average value of
the entire “AD” column. On the other hand, sample no. 4 is removed from the whole data set.

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2. Descriptive Statistics:
The response of these people is gathered as per questionnaires of the survey. To have
descriptive statistics, the study depicts that out of 21 samples, 17 are females and 4 are males.
 The average age of the samples is found to be 24.36 years with the standard deviation
4.86 years [2]. It is 95% evident that average Age lies in the interval of 26.52 years to
22.21 years. The median of age is 22.5. The age of the samples varies from 18 to 35
years.
 The mean height of the sampled responders is 164.818 cm with standard deviation 6.905
cm. The median of the heights of the responders are both 165 cm. Most of the responders
have heights 165 cm. The estimated average of the heights lies in the interval of 161.757
cm and 167.880 cm.
 The average weight of the samples is 59.25 Kgs and standard deviation 10.546 Kgs. The
95% estimated average weight of the samples are 54.574 Kgs. And 63.926 Kgs. The
median weight of the responders is 57 Kgs. The maximum weight of any responder was
found to be 89 Kgs and minimum weight of any responder was found to be 47 Kgs.
 The average shoesize of the samples is 6.023 unit and standard deviation 1.829 unit. The
estimated average shoe size of all the samples lie in the interval of 6.83 units to 5.21
units. The median of the shoe size 5.75 units. The shoe size ranges in the interval of 3
units to 10 units.