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日期:2023-04-11 07:05

UNSW Business School

ECON1203 Business Economics and Statistics


Project Overview

The project aims to enhance your career-focused learning experience by bringing real-world

scenarios and a real business problem into the classroom, creating a safe space for you to

explore, collaborate and make changes.

The assignment is intended to promote problem-based learning (PBL), in which you learn about

a subject by working in teams to solve real-life problems. It is also intended to develop your skills

in research, critical thinking and problem-solving, your data analysis and Excel skills, and your

ability to present your ideas and solutions concisely and coherently.

Solving real-life problems is an inherently complex and messy process, but such a process also

offers plenty of learning opportunities. You will learn about working through problems

persistently, seeking creative solutions, and being comfortable changing solution paths where

necessary.

In this sandboxed assignment (see Sandbox Education Program), you will have an opportunity

to solve a real-world problem and receive feedback from the problem owner (i.e. our project

partner). Your experience in this project will be helpful in your transition into the professional

environment – you will be prepared to leverage your existing knowledge and skills while at the

same time identifying and acting on knowledge and skill gaps, responding to new challenges

and seizing emerging opportunities coming your way.

Project Brief

Business Problem:

How can the Career Accelerator team increase Business School student

participation rates in the Microsoft Excel Certification?


Industry Partner/Problem Owner: Career Accelerator @ UNSW Business School


1. Background: Career Accelerator @ UNSW Business School

The Career Accelerator @ UNSW Business School is a specialised team that provides a suite

of opportunities and experiences designed to help UNSW Business School students build


their professional skills and improve student employability and career readiness across all

undergraduate, postgraduate and MBA programs.

Career Accelerator provides students with a diverse range of curricular, co-curricular and

extra-curricular offerings ranging from internships, global opportunities, mentoring programs,

industry events and networking opportunities, PASS classes, learning consults, and a suite of

technology-driven tools and resources.

2. What is the Excel Certification Program?

The Microsoft Office Excel Certification Program is a free and exclusive offering to UNSW

Business School students. Since launching in 2018 as a co-curricular opportunity, the

program has expanded. It is now embedded in key UNSW Business School programs (e.g.

Bachelor of Commerce and its combined degrees), providing students with the opportunity

to build their technical skills for data analysis, interpretation and presentation and gain an

industry-recognised digital credential that can be shared on their LinkedIn profile.

There are two parts to the program:

1. The Excel Training Program (ETP): This provides students access to online skills

modules, resources and practice exams that are self-paced and allow students to develop

their skills. For COMM1110 and ECON1203 courses, the training program is mandatory

(e.g. in COMM1110, the Excel Training Program is your Assessment 1), and students are

required to complete the practice exams at two levels - Associate level and Expert level.

Students are graded on their Practice Exam results for their course assessment.

2. The Excel Certification Test (ECT): on completion of the course assessment, students

are encouraged (but not required) to take the optional next step to complete the official

certification test, to receive their digital credential and be considered fully qualified.

By completing the course assignment, students have undertaken the majority of the work

required. The official certification step involves booking into a 50-minute online

invigilated test and achieving a pass rate of 70% to obtain the certification.

The key difference between the official ECT and the Practice Exams (i.e. Assessment 1a

and 1b students completed in their ETP in the course) is that the ECT is invigilated by a

test proctor. Apart from this, the ECT has the same exam length (50 minutes), question

types and difficulty levels as the Practice Exams students completed in the ETP as part

of their assessment. This means that students who completed their Assessment 1a and

1b and achieved a score over 700 (out of 1,000) would pass the ECT and receive the

official industry-recognised Excel Certificate if they choose to sit in the ECT. However, the

student participation rates in the ECT have been very low (see section 4 below).

Students must take the ECT within the calendar year that they start the ETP in the course

as their access code (i.e. the one received in Week 1 to access GMetrix for your

Assessment 1) expires at the end of that year.


The ECT is available to any enrolled student who signs up for the ETP as part of their

course (e.g. all students in ECON1203) or as a co-curricular activity. The ECTs are

scheduled regularly throughout each term as well as during term breaks, and students

are able to select their preferred timeslot through a dedicated Excel Certification Moodle

site: https://moodle.telt.unsw.edu.au/course/view.php?id=58401 (sign in using self-

enrolment key: excel_student).

3. Benefits of the Excel Certification Program

Increasingly, employers are looking for graduates with strong technical skills, and the

Microsoft Excel Certification Program provides students with the opportunity to develop

these essential skills and demonstrate their competencies to future employers. On

successful completion (i.e. completion of the ECT), it is a verified skill that students can add

to their CV. They also receive an industry-recognised Microsoft digital credential that can be

shared on their LinkedIn profile to demonstrate their Excel competency to potential

employers.

An added benefit is the Excel Certification test earns students experience points towards the

COMM1999/COMM3999 requirements of their program. Points are converted to BCoins,

which can be redeemed for UNSW Business School merchandise.

The Microsoft Excel Certification Program is offered to UNSW Business School students free

of charge, saving them the current fee of approx. $140 if they were to enrol in the program

independently.

4. The Business Problem

There is a significant drop off rate between students completing the Excel Training Program

(ETP) as part of their course assessment or co-curricular activity and those who take the

optional next step to complete the Excel Certification Test (ECT) to receive the official

certification. In 2021, almost 3,000 students completed the training component of the

program (i.e. the ETP), but only 20% took the next step to complete the ECT to get officially

certified.

UNSW Business School recognises the value in the certification that not only allows students

to develop key technical skills, but also enables them to demonstrate proven competency at

an industry level, ultimately enhancing their employability skills and employer demand for

their qualifications. In support of this initiative, approximately $50,000 is allocated to the

program every year, but with current participation rates, this is a low return on investment.

The licences that are issued to students are valid only for one calendar year, so students must

complete both the training (i.e. ETP, which students already completed in their courses, like

ECON1203, as part of their assessment) and the certification test (i.e. ECT, which they need

to self enrol via the Excel Moodle site1) within that year. The low participation rate results in

unused licences that cannot be carried over to the following year. It also means that if


students fail to certify within the calendar year but choose to do so later, they will be liable for

the cost to undertake certification independently.

5. The Task

We would like you to help the Career Accelerator team understand student behaviours with

the Excel Program, identify and evaluate the potential barriers to students taking the optional

next step of official certification (i.e. take the ECT - Excel Certification Test) and provide any

recommendations on how the Career Accelerator team can improve the ECT participation

rates – ideally increasing from 20% to 80%.


Note about the data

These data are real-world data provided by Career Accelerator. The data has been anonymised

to remove any personal identifiers. Please do NOT share this data with anyone without written

approval from the course authority.

Access Data:

Your Assessment data can be accessed via the R-Shiny App at the following site:

>>> https://unswteaching.shinyapps.io/ECON1203_ShinyApplets/ <<<

Click the link above and follow the steps below to obtain and download your personalised

Assessment dataset:


(1) Click on “Assessment data”.

(2) Enter your student ID without the "z" to load your assessment data. Click “Load

Assessment Data” to access your assessment data.

(3) To download your data, click "Download Data".

*You can use the R-Shiny App to perform preliminary analysis to explore the key

features of the data. However, you are required to use Excel to analyse the full data

(see Note below).

Video Guide: R-Shiny App Overview Video This is a general video guide introducing you to the

R-Shiny App (NOT the assessment data or your Assessment this term). The dataset used in

this example video is different from the one that you are required to use for your Assessment

(so please ignore any references to data or assessment requirements in the video, and focus

on the use of the R-Shiny App itself). Some of the topics discussed in the video (e.g.

hypothesis test) will be covered in the course in later weeks.

Note: While the R-Shiny App allows you to perform some quick analysis to understand the

data, it is restricted to 50 observations. For Assessment 2, your dataset contains 100

observations, which means that you are required to download the data from the R-Shiny App

and perform your final analysis in Excel.

IMPORTANT: the R-Shiny app selects a unique sample of 100 observations for each student.

This means that the results of identical analyses will vary across students depending on their

zID numbers. While this provides markers with a plagiarism check, this is not the primary reason


for providing the data in this form. Instead, it is an opportunity for different students to discuss

common modelling issues without necessarily coming to the same conclusion.

The data set contains 100 observations that were collected over a one-year period (over three

teaching terms) during 2021. Each observation refers to a different student in 2021. The

variables that have been selected for your use are:

Variable Name Description

Term The teaching term that the student

undertook the course (2021_T1 = 2021 Term

1, 2021_T2 = 2021 Term 2, 2021_T3 = 2021

Term 3)

Term_code The teaching term that the student undertook

the course (2021_T1 = 1, 2021_T2 = 2,

2021_T3 = 3)

Num_of_Attempts_Associate_Test Excel Training Program (ETP) - Associate

level - Number of Practice Exam 2 attempts a

student completed before the Assessment 1a

deadline

Score_1st_Attempt_Associate_Test


Excel Training Program (ETP) - Associate

level - Practice Exam 2 test score of the first

attempt (out of 1,000)

Score_Best_Attempt_Associate_Test


Excel Training Program (ETP) - Associate

level - Practice Exam 2 test score of the best

attempt (out of 1,000), i.e. the highest test

score obtained

1st_Attempt_Before_Deadline_Associate_Test Excel Training Program (ETP) - Associate

level - Practice Exam 2 first attempt

completion time before the assessment

deadline, measured in hours (e.g. if this

variable = 48, it means that the student

completed the first Associate Level Practice

Exam 2 attempt 48 hours, i.e. 2 days, before

the Assessment 1a deadline)*

last_Attempt_Before_Deadline_Associate_Test


Excel Training Program (ETP) - Associate

level - Practice Exam 2 last attempt

completion time before the assessment

deadline, measured in hours (e.g. if this

variable = 2, it means that the student

completed the last Associate Level Practice

Exam 2 attempt 2 hours before the

Assessment 1a deadline)*

Num_of_Attempts_Expert_Test


Excel Training Program (ETP) - Expert level -

Number of Practice Exam 2 attempts a


student completed before the Assessment 1b

deadline

Score_1st_Attempt_Expert_Test


Excel Training Program (ETP) - Expert level -

Practice Exam 2 test score of the first

attempt (out of 1,000)

Score_Best_Attempt_Expert_Test Excel Training Program (ETP) - Assessment

1b (Expert level) - Practice Exam 2 test score

of the best attempt (out of 1,000) – i.e. the

highest test score obtained

1st_Attempt_Before_Deadline_Expert_Test


Excel Training Program (ETP) - Expert level -

Practice Exam 2 first attempt completion

time before the assessment deadline,

measured in hours (e.g. if this variable = 24,

it means that the student completed the first

Expert Level Practice Exam 2 attempt 24

hours, i.e. 1 day, before the Assessment 1b

deadline)*

last_Attempt_Before_Deadline_Expert_Test


Excel Training Program (ETP) - Assessment

1b (Expert level) - Practice Exam 2 last

attempt completion time before the

assessment deadline, measured in hours

(e.g. if the variable = 1, it means that the

student completed the last Expert Level

Practice Exam 2 attempt 1 hour before the

Assessment 1b deadline)*

Male_Dummy Student gender (0=Female, 1=Male)

Age Student age (in 2021)

Local_Student_Dummy Domestic (=1) or international student(=0)

Participate_Cert_Exam A binary variable (i.e. a variable that takes

only the value 1 or 0) that indicates whether

a student participated in the Excel

Certification Test (ECT) (1=participated,

0=not participated)

*If this variable = 0, it means that the student completed his/her attempt just before (i.e., less

than 30 mins) the assessment deadline.

If a student only had one attempt, the first and last attempt data would be the same: e.g., if

Num_of_Attempts_Associate_Test = 1, Score_1st_Attempt_Associate_Test will have the same

value as Score_Best_Attempt_Associate_Test, and

1st_Attempt_Before_Deadline_Associate_Test will have the same value as

last_Attempt_Before_Deadline_Associate_Test


UNSW Business School

ECON1203 Business Economics and Statistics

Final report

The purpose of the final report is for you to synthesize your findings from the short answer

question and to produce report which attempts to address the needs of the client. In this

case this is to improve participation rates for the excel certification test (i.e. variable

Participate_Cert_Exam).

To break this analysis into digestible parts first they are interested identifying behvioural

traits which could affect these probabilities. As a starting point, these include (for both

tests):

Number of attempts (A1a_Num_of_Attempts, A1b_Num_of_Attempts);

Score of the attempts(A1a_Score_1st_Attempt, A1a_Score_Best_Attempt,

A1b_Score_1st_Attempt, A1b_Score_Best_Attempt); and

Completion of attempts hours before deadline (A1a_1st_Attempt_Before_Deadline,

A1a_last_Attempt_Before_Deadline, A1b_1st_Attempt_Before_Deadline,

A1b_last_Attempt_Before_Deadline);

Further details of these variables can be found in “Note about the data”.

To understand whether these factors play a important role, broadly speaking, you will need

to (i) formulate a multiple linear regression model (refer to week explaining your choice of

variables) and (ii) explain whether the relationship is statistically and/or economically

significant. Aforementioned, the use of a multiple linear regression, confidence intervals and

hypothesis testing would help you address this and thus provide evidence for your

arguments.

More specifically, you need to consider relevant factors to understand the relationship

between Participate_Cert_Exam and the behavioural variables incorporating any control

variables which are relevant.

In addition to interpreting the results of your analysis you will also need to draw to attention

issues of causality and confoundment which can impact the conclusions from the analysis.

As a part of this assessment, assumptions and limitations need to be explicitly identified.

e.g., What variable would you want to have in an ideal situation to measure different

variables in this analysis? Do you have this variable in the dataset? If not (which is often the

case in practice: we often don’t have all the ideal data/variables that we need to perform an

analysis, and have to rely on the data available to us), what variable in the dataset do you

have to use as an performance/ability measure? What are the assumptions and limitations

of using this variable?]


Once, you have considered the above issues and analysis, you will need to understand its

implications and consider the appropriate recommendations for the career accelerator team

as they try to improve participation in the certification exam. (as above) As this is the purpose

of this report.

Report structure

This report will consist of three parts:

Introduction – Which highlights the purpose of the report and how the report is

structured.

Body – Which describes the data and the analysis which is undertaken. As part of

this you will need to state the assumptions you are making around the sample and

the model. You will need to justify you model and acknowledge the limitations of your

model. Naturally, you are expected to run your model, report and interpret the results

of your model.

Conclusion – Here you summarise your results and its implications. What are the

limitations of your analysis and how they may be overcome? What additional data

you would like? What is your recommendation?

Below we will highlight some of the technical requirements in relation of the descriptive

statistics and modelling aspects in the body of your report.

Descriptive statistics

Like with your few short answer questions the first part of any analysis is to describe the

data you have. This could include a mix of graphs and summary statistics. Note that there

are a few variables which you need to consider, details of these variables can be found in

the data notes section above. Remember that the dependent variable of interest here is

“Participate_Cert_Exam”. Thus, in preparation of the modeling section you may want

to read about linear probability models in chapter 7 and chapter 8 as this will be

extremely useful for the modelling section.

Remember that you will be assessed on the presentation of the summary statistics as well

as any charts that your produce. As part of the reporting requirements, you are expected

to summarise the key features of the data including any interesting relationships.

Modelling participation

Next, you will need to run a multiple linear regression. In many ways, this will complement

the descriptive statistics that you have found above and to identify whether there are any

behavioural traits (see above) and other variables that can influence participation. As part

of this exercise, you need to:

Explain why a multiple linear regression is beneficial i.e., justify the need for multiple

linear regression and the issues associated with running a simple linear regression.

Contextualise it in the context of the current problems are there confounding factors

which motivate you to do this?

Associated with this think about the what type of model you’d like to use e.g. level-

level, log-level, log-log or level-log model for each of the considered variables. You

need to justify this. Given that this is a longitudinal dataset you may want to also

consider interaction variables and other non-linear aspects.

Choose your independent variables and justify. Remember that the dependent

variable is already chosen for you which is Participate_Cert_Exam.

Run the model and interpret.

As part of the reporting requirements for the multiple linear regression:

Interpret the coefficients.

Define and comment whether each of the coefficients are statistically significant.

Remember to state your assumptions.

Define and comment whether each of the coefficients are economically significant.

Remember to state your assumptions. You’ll also need to define what is economically

significant and use a benchmark to determine this.

Limitations and issues with your model. If you decide to use technical terms e.g.,

multicollinearity, homoskedasticity, bias, consistency etc. you need to explain what

these terminologies are and place them in the context of your problem and how

it will affect your results.

We will be paying attention to the presentation of the data and excel output as

well as whether you have used the full sample to conduct the analysis.

Recommendations

The regression above will allow you to better control the impact of an independent variable

on the dependent variable and provide correlations on these relationships. However, it

may not allow you to understand the mechanism which will improve participation which is

the core of the client’s problem.

As a first step you will need to summarise your results, its implications, and the limitations

of the analysis. Following this, you will need to provide some recommendations using

the results above and any additional literature which can improve participations.

Some exploration of the academic literature on how to improve participation may be worth

exploring.

Common reporting mistakes made by students (avoid these ?)

Regarding the descriptive statistics component it is important that you follow best reporting

practice. Many students tend to neglect things like decimal places, labels and font size at an

elementary level. For graphs, think about what is appropriate should you use a bar chart or

pie chart. How should I construct my histogram? What is the best practice here? We will be

paying attention to the presentation of the data, excel output as well as whether you

have used the full sample to conduct the analysis.


Regarding multiple linear regression. The common mistakes of made by your peers in the

past is the lack of justification of the functional form and the reason why a multiple linear

regression model is needed. Remember the ability to control for other factors is one thing

but it is very important for you to contextualise it in the context of your problem.

In the same vein, many of your peers in the past really did not justify why they used a pure

level-level model. Think about what a linear model means, do you expect the relationship

to be linear. You should construct an interaction variable; you will need to justify it (often

missed by the student) and interpret it (often misinterpreted). In fact, many students

tended to also mis-interpret the marginal impacts given the presence of the interaction

variables.

Interpretation is also a common issue especially when it comes to log models. Your peers

in the past have got this confused. So please pay attention to this. Finally, a very common

mistake was the discussion of economic significance. Often your peers say it without much

justification, you’ll need a benchmark to make your case more compelling e.g., using the

average of that variable as a benchmark may be a good starting point but we also know

what the issues are with averages. Another common mistake is that students often forget

what statistical significance means, it means that it is not zero, and often do not state this

nor do they recognize its implications. Remember that you can also perform other tests

beyond testing it as zero.

I urge you to please take note of all of this when you are writing up your report. If this is still

unfamiliar you should immediately see your demonstrator or lecture


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