COMM1100 Assessment 2B
Case Business Report
T18B Instructor: Sheirly Rianto
Mo Ding z5450531
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Statistical Toolbox
Statistical Model
We use multiple linear regressions to understand what factors correlate to the
Excel Certification Test (ECT) score.
Assumptions
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Samples are randomly selected, and our sample is representative.
A large enough sample size leads to a normal sample distribution.
Transfer qualitative data into quantitative data to run regressions.
e.g., Associate = 0, Expert = 1 (ECT level);
Local = 0, International = 1 (students’ nation);
Man = 0, Female = 1 (students’ gender).
Data Preprocessing
Since the sample of the ECT score has abnormal values like 0, we winsorise the
original data by replacing the extreme values with specific percentiles (the top and
bottom 1% of the ECT score). That means we handle the outliers to reduce the
deviation effect and get more reliable data.
Variables
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Dependent variable: ECT score.
Independent variables: ECT level, student’s nationality, student’s gender,
maximum practice score.
These variables come from Regression Model 8 (Table 1), which has the most
parsimonious regression equation for the ECT score and best describes how it
relates to these factors.
We use stepwise regression to find the best subsets of variables. This most efficient
model is generated when we run the regression, delete the variable with the highest
p-value, and rerun the regression until the p-value of every variable in the model is
statistically significant (less than 0.05). This process eliminates the effects of
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irrelevant variables and improves the accuracy of our regression model.
Furthermore, while rerunning the regression, the adjusted R square increases,
indicating a higher valuation percentage and a stronger argument for why these
variables can explain the ECT score.
Table1 Stepwise Regression Analysis
The Process of Multiple Linear Regression
Take Model 8 as an example to interpret how multiple linear regression works.
We use our sample of 150 observations to assess two mutually exclusive hypotheses
for a population, considering sampling error.
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Null Hypothesis (H0): No relationship exists between the ECT score and the
independent variables. ( β1= β2 = β3 = β4 = 0)
Alternative Hypothesis (H1): At least one variable correlates with the ECT score.
(βi ≠ 0)
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Regression Table Analysis
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R Square
The independent variables explain 42.6% of the ECT score’s variation in this
model. The R2 is low since confoundment exists and human behaviour remains
hard to forecast. However, we regard it as irrelevant because we mainly target
understanding the variables’ association—which is defined by coefficients—
rather than predicting the value of the ECT score.
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Adjusted R square
41.1% of the data fit the regression model, which means that ECT level,
maximum practice score, student’s nationality, and gender explain 41.1% of the
variance.
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Significance F
We receive a great F here, representing the extremely low probability of
accepting H0 (all coefficients appear at zero).
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Coefficients
The ECT level, student’s gender, and nationality are economically significant
and negatively related to ECT scores. When they increase, the ECT score
decreases. This means students are likelier to fail the exam if they satisfy the
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following situations: exam level at expert, international students, or female
character.
The maximum practice score is not economically significant but positively
relates to the ECT score. It implies: improving your highest practice score has
low applicability while you aim to get excellent results from ECT.
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P-value
At the 95% confidence level, the significance level is 0.05. These four variables
are all statistically significant because their p-values are less than 0.05. The
statistically significant result suggests that the sample effect or association
exists. Therefore, we reject the null hypothesis.
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t Stat
The t-values of the four variables all fall in the rejection region (higher than
+1.96 or less than -1.96); this
proves the statistical significance
of the variables to reject H0.
Among them, the ECT level has a
high absolute t-value of 7.715, so
we have tremendous confidence
in its coefficient as a predictor.
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Lower 95%, Upper 95%
Take the ECT level as an example. We are 95% confident of the true population
slope that β1 will locate among (-233.905, -138.500); 95% of the chance falls in
this range, and 5% of the opportunity falls outside. 0 is not part of the
confidence interval, so the ECT level is statistically significant. The other
variables have the same results.
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Equation
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Limitations
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Lack of internal validity
Without controlled experiments, we fail to declare causality because of the
confoundment, like students’ WAM or discussion completion rate. Moreover,
these omitted variables generate biased estimates concerning population
parameters of interest.
Unavoidable uncertainty occurs since we only have a sample, not a population.
Hypothesis testing can quantify the uncertainty but not eliminate it. Therefore,
type 1 and type 2 errors may appear because it remains unclear whether the
sample coefficients and mean fall in the confidence interval or the margin of
error.
In addition, our sample focuses on students who participated in the ECT and
ignores those who did not. This could lead to survivorship bias and availability
bias.
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Scarce external validity
This toolbox does not list similar study findings in different but related
populations. The real world still needs to examine and demonstrate the
applicability or generalisability of the results.
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Ethical Toolbox
Step 1
An ethical dilemma arises when the ECT results become a component of the course
score. This dilemma refers to the conflicts between some stakeholders' impaired
rights and the action's high utility outcome for all stakeholders.
Stakeholders in this dilemma include the UNSW teaching team, the career
accelerator, and students. They have different views towards this action. The
teaching and CA teams, who aim to improve the completion and passing rates,
support it, but some students with high stress, scarce learning abilities, or spare
time oppose the overwhelming task.
This action contradicts care ethics, distributive justice, and the non-maleficence
principle from deontology. It harms students’ rights by increasing pressure,
especially on those who cannot pass the exam or remain uninterested in this
certification.
Step 2
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Assumptions
Suppose UNSW has students with low learning ability or interest in this
certification.
Suppose all stakeholders have the rights, power, and freedom to make choices
based on their subjective willingness and benefits.
Suppose the stakeholders have natural duties towards each other.
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Biases
We may have cognitive biases. While we think about this issue, we seek
information supporting our deontological worldview. Therefore, confirmation
bias occurs.
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Moreover, the community has an unconscious bias: all students should aspire
to be the most competent in their future job-hunting careers. Hence, this
unconscious virtue bias may pressure the student and the relevant teaching
team.
Step 3
Students have both positive rights to seek satisfaction and negative rights to be free
of excessive mandatory burdens. The UNSW career accelerator and teaching team
have duties to protect those rights. However, they also need to promote a career
future for students. Therefore, they face duty conflicts regarding whether to
calculate ECT results as a compulsory part of course scores.
Step 4
If we implement this project, we will achieve a cost-effective outcome, but it will
hurt some students’ rights. The CA and teaching team will benefit from a high
completion rate that satisfies them. However, the students will suffer from
excessive stress.
If we don’t implement this project, the CA and teaching team will still have the
issue, and students cannot benefit from the certification. In this situation, we must
consider alternative solutions to recommend to the CA team.
Step 5
We apply three virtues—fairness, empathy, and respect—to this situation. UNSW
(2015), as a "virtuous" organisation, emphasises equality and justice surrounded by a
supportive culture in its vision for 2025. UNSW (2015) also aims to promote an ethos
of inclusion and diversity in a performance-based education and, accordingly,
nurture a respectful community for all students, staff, and stakeholders to stimulate
development.
Steps 6 & 7
We don’t implement this project due to the requirements for respect or empathy
with students in different learning conditions to foster fairness. Assume we carry
out the project even though the practical benefits outweigh the costs from a
utilitarian lens; in that case, we will harm some students’ rights through a
deontological lens and accordingly fail to balance this issue's economic and ethical
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concerns. Hence, we cannot put this project into action, considering it will sacrifice
the interests of some stakeholders.
Nevertheless, in this stance, if we stop here, students cannot benefit from the
certification, and the CA team cannot solve the ECT completion issue to enhance
students’ career futures. Therefore, we provide three resolutions for this issue in
the information toolbox.
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Information Toolbox
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Situation & Observation
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Resolution
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Provide more assistance.
Offer more consulting activities targeting female and international students,
and set up workshops or PASS classes interpreting expert-level Excel skills.
(Evidence is shown in Observation 1.)
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Adjust the learning environment.
Improve interactivity in Excel’s non-mandatory online training. This also helps
students learn knowledge instead of memorising answers to the mock test.
(Evidence is shown in Observation 2.)
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Economically enhance students' motivation.
Market the utility of the ECT and give incentives (e.g., a $40 gift card) for
students who pass it.
According to the ten principles of economics, people respond to incentives
(Mankiw 2021). By marketing the utility of the certification and providing
financial incentives, students perceive a higher benefit than the cost.
Therefore, they have more motivation to participate in the exam while facing
the tradeoff.
(This is linked to Observation 3.)
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Utilise the scarcity effect.
Provide a limited number of places to register for the ECT exams
intermittently.
The scarcity effect is a cognitive bias that causes individuals to place a higher
value on rare products and a lower value on readily available products.
(Convertize 2022). Low vacancies in ECT will arouse students' interest and
make ECT more desirable than a readily available event. With a few minor
changes to the marketing format and pages of the ECT, we can improve the
participation rate of the exam.
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Limitation
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Representative bias
We analyse the problem only based on a small sample of 150
students and extrapolate the results to infer the entire population with a
degree of subjective confidence.
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Availability bias
The statistical analysis uses limited information on four variables about
students who participated in the ECT to understand the issue and its
contributing components. It fails to describe data about those who did not
register for the exam but are in the Excel Training Program.
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Validity
This problem-solving process has reliability but not validity. Our analysis yields
consistent results across observers but lacks sufficient internal and external
validity. We fail to achieve internal validity because we cannot establish
causality for the ECT score without controlling confoundment. Furthermore,
representative bias contributes to the absence of external validity.
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References
Carlisle, J., Bhanugopan, R. and D'Netto, B. 2019, ‘Enhancing task performance through effective
training: The mediating role of work environment and moderating effect of non-mandatory
training’, Journal of Business Research, 104, pp.340-349.
Convertize UK 2022, What is the Scarcity Effect?, Convertize. Available at: <https://
www.convertize.com/glossary/scarcity-effect/:text=Whatisthesacrcity20%> [Accessed:
November 21, 2022].
Gonski, D. 2015, UNSW 2025 Strategic Plan, UNSW 2025 Strategic Plan–UNSW Sydney. UNSW
Marketing Services. Available at: https://www.2025.unsw.edu.au/ (Accessed: November 22,
2022).
Keyser, R. 2019, ‘A Correlation Between Non-Mandatory Attendance and Course Grades in a
Fourth-Year Hybrid Industrial Engineering Course’, Journal of Higher Education Theory and
Practice, 19(8).
Mankiw, N.G. 2021, Principles of Economics. Boston (Mass.) etc., Published: Cengage Learning.
MosaicLab 2017, #MONTHLYMYTH: LOW PARTICIPATION IS CAUSED BY LOW INTEREST —
MosaicLab. [online] Available at: <https://www.mosaiclab.com.au/news-all-posts/2017/3/20/
monthlymyth> [Accessed 9 October 2022].
UNSW CA Team 2022, Assessment 2A: Assessment Guide and Marking Rubric. 1st ed. [ebook]
Sydney: UNSW Business School, p.11. Available at: <https://moodle.telt.unsw.edu.au/course/
view.php?id=70116#section-8> [Accessed 6 October 2022].
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