Computer Science Usability Engineering : PDF

Running head: COMPUTER SCIENCE – USABILITY ENGINEERING
Computer Science – Usability Engineering
Name of Student:
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1COMPUTER SCIENCE – USABILITY ENGINEERING
Table of Contents
Introduction:....................................................................................................................................2
Data Analysis:..................................................................................................................................2
1. Data cleaning, data encoding and missing data:......................................................................2
2. Descriptive Statistics:..............................................................................................................2
3. Quantitative Analysis of Galvanic Skin Response:.................................................................4
4. Quantitative Analysis of positive emotional valence response:..............................................5
5. Quantitative analysis of 3 research questions:.........................................................................6
5.1. Research Question: Does perceived positive feet localization vary with different audio
frequencies?.............................................................................................................................6
5.2. Research Question: Does perceived positive vividness response change with various
audio frequencies?...................................................................................................................7
5.3. Research Question: Do the Z-scores of the three variables “Heel Pressure”, “Toe
Pressure” and “Foot Acceleration” equally respond according to the three types of audio
frequency that are High, Low and Control?............................................................................8
Critical Evaluation of the Research:................................................................................................9
Research Limitations:......................................................................................................................9
Reference:......................................................................................................................................10

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Introduction:
The undertaken data analyses the experimental outcomes with varying audio interface of
the footwear and observing the changes in the behaviour of users. In this assignment, the
researcher analyses the data set gathered from a footwear user study considering the physical
aspects of gender, weight, shoe size and height. The participants received the high frequency,
low frequency and controlled audio feedback from walking at the time of wearing the prototype
shoes. For each cases of this experiment, the researcher captured the perceptions of the
participants about their body weight, mood, emotions and changes in behaviour involving three
dimensions. The commonly used models that are valence, arousal and dominance.
The software MS-Excel is used for analysing the data set.
Data Analysis:
1. Data cleaning, data encoding and missing data:
The data cleaning process is very crucial and necessary aspect to data analysis. Before,
using the data for analysis purpose, an analyst should focus on data cleaning for having correct
information. As per data sheet, the data set has many variables such as Gender, Age,
“ShoeSizeUK”, “Weight_Kg”, “Height_cm”, “GSR_Z-scores”,
“BodyVisualization_LOGscore”, “HeelPressure_Zscore”, “ToePressure_Zscore”,
“FootAcceleration_Zscore”, “Valence”score, “Arousal” score, “Dominance” score and
questionnaires regarding several variables alike speed, weight, strength, straightness, agency,
vividness, surprise and “FeetLocalization”. It is observed that-
 Sample 4 has lots of missing values starting from L5 to AC5.
 Sample 2 and 8 has missing values in the range AD3 to AF2.
 Besides, sample 16, 11 and 10 also have some missing values.
The missing data appeared as measurements of replication was carried out for some of the
samples completely. 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 omit the whole sample from the data set. For example, sample
4 is deleted from the entire data set but the missing value of “GSR_Zscore” with high frequency
of sample 2 is replaced by the average value of the entire AD column (Hand 2007). The
outcomes with the presence of missing value may be misleading. The quantitative data is
therefore removed.

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2. Descriptive Statistics:
To have descriptive statistics, the study consists with 21 sample that have 17 females and
4 males. The response of these people is collected as per survey questionnaires. The survey has
different factors such as Age, Size of shoes of the responders, Weights of responders in Kg and
Heights of responders in cm.
The average age of the responders is found to be 24.36 years (SD = 4.86 years). The
middle most value of age is 22.5. The age ranges from 18 to 35 years. It is 95% evident that
average Age lies in the interval of 26.52 years to 22.21 years. The mean shoesize of the sampled
responders is 6.023 unit (SD = 1.829 unit). The 50% of the values lie above the shoe size 5.75
units. The shoe size ranges in the interval of 3 units to 10 units. The estimated average shoe size
of all the samples lie in the interval of 6.83 units to 5.21 units. The weight of the sampled people
is 59.25 Kgs (SD = 10.546 Kgs). The middle most value of the 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 mean height of all the sampled persons lie in the
interval of 54.574 Kgs and 63.926 Kgs. The heights of the sampled responders have average
164.818 cm (SD = 6.905 cm). The median and modal value of the heights of the responders are
both 165 cm. The estimated mean with 95% probability lies in the interval of 161.757 cm and
167.880 cm. The “GSR_Zscore” is calculated with the average of “GSR_Zscore” with high
frequency, low frequency and control. The average GSR or Galvanic skin response is (-0.01251)
(SD = 0.327119). The lower and upper control limit of estimated in the range (-0.16141 and
0.136393) with 95% probability.

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3. Quantitative Analysis of Galvanic Skin Response:
The data analysis table as per “Galvanic Skin Response” discovers that z-score of Galvanic
Skin Response is greater in case of High Frequency and lowest in Low Frequency. The margin
of error is highest for control group and least for high frequency group. More of it is discovered
that-
 The average z-score of the high frequency Galvanic Skin Response lies in the interval of
0.21 to 0.04 with 95% probability (Morey 2008).
 The average z-score of the low frequency Galvanic Skin Response lies in the interval of
0.017 to (-0.29) with 95% probability.
 The average z-score of the Galvanic Skin Response of control lies in the interval of 0.26
to (-0.31) with 95% probability.
GSR_Zscore_HighFrequency GSR_Zscore_LowFrequency GSR_Zscore_Control
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
Galvanic Skin Response
GSR
Scores

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The bar chart of Galvanic Skin response is overall highest in case of high frequency and
lowest in case of low frequency. However, the range of z-score is highest in case of Galvanic
Skin Response in control state.
4. Quantitative Analysis of positive emotional valence response:
The positive Valence response is higher in case of the responses that have response rate
greater than 5. Out of 21 samples, 17 samples have higher valence rate, 11 samples have low
valence rate and 13 samples have positive rate of response. The proportions of Valence response
in high frequency, low frequency and controlled frequency are 0.81, 0.52 and 0.62 respectively.
 With 95% possibility, the proportion of positive response ranges in the interval of 0.64 to
0.98 with high frequency.
 With 95% possibility, the proportion of positive response ranges in the interval of 0.74 to
0.31 with low frequency.
 With 95% possibility, the proportion of positive response ranges in the interval of 0.83 to
0.41 with controlled frequency.
Valence_High Valence_Low Valence_Control
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Valence
Proportions

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5. Quantitative analysis of 3 research questions:
5.1. Research Question: Does perceived positive feet localization vary with different audio
frequencies?
The positive perceived feet localization are the samples that have feet localization
measure more than 4 from two replications. The proportion of positive feet localization is greater
for High audio frequencies and lower for Controlled audio frequencies. The estimated feet
localization with high audio frequency varies from the proportion 0.78 and 1. The estimated feet
localization with control audio frequency ranges within the proportion 0.52 to 0.91.
FeetLocalization_High FeetLocalization_Low FeetLocalization_Control
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
FeetLocalization
Proportions

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The bar chart clearly indicates that the proportion of positive feet localization is greater
for high audio frequency followed by low audio frequency. The proportion of positive feet
localization is lowest for controlled audio frequency.
Conclusion:
It could be concluded that perceived positive feet localization varies with different audio
frequencies significantly.
5.2. Research Question: Does perceived positive vividness response change with various
audio frequencies?
The positive perceived Vividness are the samples that have Vividness measure more than
4. The proportion of positive surprise feeling is greater for Low audio frequencies and lower for
both High and Controlled audio frequencies. The estimated Vividness feeling with low audio
frequency varies from the proportion 0.53 and 0.13. The estimated Vividness response with both
high and control audio frequency that ranges within the proportion 0.48 to 0.09.

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Vividness_High Vividness_Low Vividness_Control
0.26
0.27
0.28
0.29
0.3
0.31
0.32
0.33
0.34
Vividness
Proportions
The bar chart firmly refers that the proportion of positive surprise feeling is greater for
high audio frequency followed by low audio frequency. The proportion of positive surprise
feeling is lowest for controlled audio frequency.
Conclusion:
Therefore, perceived positive vividness response change with various audio frequencies
insignificantly.
5.3. Research Question: Do the Z-scores of the three variables “Heel Pressure”, “Toe
Pressure” and “Foot Acceleration” equally respond according to the three types of audio
frequency that are High, Low and Control?
The averages of Z-score of two replications for Heel pressure refers that-
 The average Z-score is higher for Low audio frequency followed by Controlled audio
frequency. The average Z-score is least for high frequency.
The averages of Z-score of two replications for Toe pressure indicates that-
 The average Z-score is higher for Low audio frequency followed by High audio
frequency. The average Z-score is least for controlled audio frequency.
The averages of Z-score of two replications for Foot acceleration refers that-
 The average Z-score is greater for high audio frequency followed by controlled audio
frequency. The average Z-score is therefore least for Low audio frequency.

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HeelPressure_High
HeelPressure_Low
HeelPressure_Control
ToePressue_High
ToePressue_Low
ToePressure_Control
FootAcceleration_High
FootAcceleration_Low
FootAcceleration_ontrol
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Average Z-scores of HeelPressue, ToePressure and
FootAcceleration
Variables according to the levels
Z-score
The bar plot depicts the same findings that the data table produces.
Conclusion:
Hence, it can be concluded that the Z-scores of the three considered variables “Heel
Pressure”, “Toe Pressure” and “Foot Acceleration” do not equally respond according to the three
types of audio frequency that are High, Low and Control (Wiederstein and Sippl 2007).
Critical Evaluation of the Research:
The research indicates that various types of replicated measures and its analytical
interpretation are crucial for decision-making. The 95% confidence intervals created the upper
and lower bound of the averages and proportions of variables. No statistical inferential test such
as ANOVA is applied here. Only by visual observation of the statistic (average or proportion)
and its confidence intervals the variability and grouped wise differences are measured. In future
analysis, the researcher would like to use statistical inferences and tests for better understanding.
Research Limitations:
All the experimental variables are not included in the data set. Hence, lack of
understanding about footwear interface and its effect on preferability may be created. The sample
size of this experiment is not also high. Hence, sampling error may be present in this
experimental data (Martin and Sayette 1993).

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Reference:
Hand, D.J., 2007. Principles of data mining. Drug safety, 30(7), pp.621-622.
Martin, C.S. and Sayette, M.A., 1993. Experimental design in alcohol administration research:
limitations and alternatives in the manipulation of dosage-set. Journal of Studies on
Alcohol, 54(6), pp.750-761.
Morey, R.D., 2008. Confidence intervals from normalized data: A correction to Cousineau
(2005). reason, 4(2), pp.61-64.
Wiederstein, M. and Sippl, M.J., 2007. ProSA-web: interactive web service for the recognition of
errors in three-dimensional structures of proteins. Nucleic acids research, 35(suppl_2),
pp.W407-W410.