### Practical Quantitative Research Exercise

KIT714 ICT Research Principles – Semester 2, 2020
Practical Quantitative Research Exercise
Type: In-Semester (Individual Assignment)
Weighting: 20% of total assessment for this unit
Due Date: 11:55pm, 5 October 2020
Submission: Completed Assignments need to be uploaded to MyLO by the due
date.
Description: This practical exercise will engage students in a qualitative research
exercise that will enable them to directly deploy skills, tools and techniques covered
in the unit. Each student will be required to complete the exercise and prepare a
written report that displays their level of research understanding and competence in
qualitative data analysis and interpretation.
In this assignment, we will be conducting:
An investigation of the impact of technology on the processing
capacity of letters, small and large parcels in Australia Post facilities.
You have been presented with data across three Australia Post facilities (Tullamarine,
Mascot and Brisbane) from January 2018 to December 2019. To cope with increasing
demand, Australia Post has installed an automated sorting station for parcels in
Tullamarine and an artificial intelligence-based prioritization system for all items in
Mascot. Brisbane has not had any new technology implemented over the same time
period.
CSV
Date date A date between 01/01/2018 to 31/12/2019
Year integer Year between 2018 to 2019
Facility string Australia Post processing area
Letters integer Can include greeting cards, bills, A4 documents, personal letters
Small Parcels integer Parcels weighing less than 5kg
Large Parcels integer Parcels weighing 5kg or more
Requirements:
Your task is to select and apply statistical analysis techniques to examine the data and
derive some conclusions to the following questions:

1. Formulate a research question for this study.
2. Select the appropriate data to conduct a statistical test to determine if there is a
significant difference between 2 independent groups.
• Describe and plot relevant data selected from the full dataset using an
appropriate chart.
• Formulate the appropriate hypotheses.
• Discuss and justify whether parametric or non-parametric test would be
suitable for this data.
• Run the appropriate statistical test and discuss the results.
3. Select the appropriate data to conduct a statistical test to determine if there is a
significant difference between 2 dependent groups.
• Describe and plot relevant data selected from the full dataset using an
appropriate chart.
• Formulate the appropriate hypotheses.
• Discuss and justify whether parametric or non-parametric test would be
suitable for this data.
• Run the appropriate statistical test and discuss the results
4. Select the appropriate data to conduct a statistical test to determine if there are
significant differences between more than 2 independent groups.
• Describe and plot relevant data selected from the full dataset using an
appropriate chart.
• Formulate the appropriate hypotheses.
• Discuss and justify whether parametric or non-parametric test would be
suitable for this data.
• Run the appropriate statistical test and discuss the results.
The output of your analysis will be a brief report (including title, abstract, brief
introduction, results and discussion) in Springer’s Lecture Notes in Computer Science
(LNCS) format. The report should be no more than 2,000 words length (note that the
LNCS format has very wide margins) and should be submitted in PDF.
You may use any suitable tools for analysing the data. For instance, descriptive statistics
can be produced by Excel, Python, R, and many other packages equally well, whereas
Python and R support many non-parametric tests that Excel does not. You may need to
do some data preparation in Excel in order to organize the data appropriately for the
comparisons you wish to perform in another tool.
Submission:
Submit your completed report in PDF to [Assignments] on the unit’s MyLO site by
11:55pm, Monday 5 October 2020. The report must comply with the LNCS format for
conference papers.
Lecture Notes in Computer Science is a long-running series of edited books,
predominantly containing conference proceedings. There are templates available for
both LaTeX and MS Word. On that page look at the sections titled Templates, sample
files and useful links – LaTeX2e Proceedings Templates or Microsoft Word Proceedings
Templates, which contain zip files of the relevant templates.
Questions about approaches you are considering using or how to use the LaTeX
template can be directed to [email protected].
Criteria

Outstanding
(HD)
Very Good
(DN)
Good
(CR)
(PP)
Poor
(NN)
Description of dataset
(10%)
Provided a complete and
comprehensive description of the
relevant data selected form the
dataset with appropriate charts
Provided a mostly complete
description of the relevant data
selected from the dataset with
appropriate charts
Provided a good description of the
relevant data selected from the
dataset with appropriate charts
Provided a basic description
of the dataset without
appropriate charts
Provided no description
of the dataset
Hypotheses and
Statistical tests
(50%)
Hypotheses are all clearly stated and
are aligned with the statistical tests
being performed.
Correct use of statistical tests in
relation to the stated hypotheses
with follow-up tests where
appropriate incorporating
description and discussion of
significance.
Excellent discussion provided as to
the appropriateness of the tests for
the dataset and suggestions of
alternatives available.
Hypotheses are mostly clearly
stated and are aligned with the
statistical tests being performed.
Mostly correct use of statistical tests
in relation to the stated hypotheses
with follow-up tests where
appropriate incorporating
description and discussion of
significance.
Very good discussion provided as to
the appropriateness of the tests for
the dataset and suggestions of
alternatives available.
Hypotheses contain minor errors
but are aligned with the statistical
tests being performed.
Mostly correct use of statistical
tests in relation to the stated
hypotheses.
Good discussion provided as to
the appropriateness of the tests
for the dataset and suggestions of
alternatives available.
Hypotheses contain major
errors but are somewhat
aligned with the statistical
tests being performed.
Mostly correct use of
statistical tests.
as to the appropriateness of
the tests for the dataset.
No hypotheses stated.
Mostly correct use of
statistical tests but no
discussion provided.
Presentation of results
(25%)
Presentation is clear with appropriate
choice of chart type incorporating
error bars showing variance where
appropriate.
Axes are clearly labelled and
appropriate for making clear
comparisons.
Captions fully describe figures.
Presentation is clear with
appropriate choice of chart type
incorporating error bars showing
variance where appropriate.
Axes are clearly labelled and
appropriate for making clear
comparisons.
Captions mostly describe figures.
Presentation is clear with
appropriate choice of chart type.
Axes are clearly labelled and
appropriate for making
comparisons.
Captions fully describe figures.
Presentation is clear with
appropriate choice of chart
type.
Axes are clearly labelled and
appropriate for making
comparisons.
figures.
Presentation is unclear
with inappropriate
choice of chart type.
Captions do not describe
figures.
Overall presentation
(15%)
question and demonstrate insight
and incorporate information not
found within the given data.
Report is clear, well-structured and
contains sufficient detail following
the LNCS format.
Report is mostly free of grammar and
spelling errors.
question and demonstrate some
insight.
Report is clear, well-structured and
contains sufficient detail following
the LNCS format.
Minor spelling and grammatical
errors.
research question and
demonstrate limited insight.
Report is mostly clear and wellstructured following the LNCS
format.
Some spelling and grammatical
errors.
the research question and
demonstrated little insight.
Organization of the report
could be improved but is
generally comprehensible.