MKTG2510- Assignment Marking Guide
Weight: 40%, Due Date: Monday 20 May, 14:00 AEST
Your client is a new private university looking to enter the tertiary education market
(You can call yourself whatever you like). Your client is considering the best
delivery methods for courses (e.g. external vs. flexible or both). The client is also
very interested in the attitudes students have towards studying and wish to know
their preferences for choice of university.
In this assignment you will analyse the survey data about students’ attitudes,
preferences and demographics in the context of their experience at university when
studying online. Students were found via a convenience sample, taken in classes at
UQ and QUT.
You should consider yourself/your team to be acting as a research consultant
providing advice and insights about current student experiences to decision makers
interested in the tertiary education market. This assignment should be an
absolute maximum of 5,000 words (excluding title page, table of contents, tables,
graphs and references).
Introduction (300 words- 2.5 marks)
The introduction section introduces the market research report, placing emphasis on
the broad context and highlighting the specific domain of interest. In this case it is the
study of student experiences and preferences in tertiary education).
In this section you should discuss
• the overall importance of understanding dynamics of student experiences
(1 mark) and
• preferences in order to motivate the need to conduct research. (1 marks)
• Consider this section the “selling” part of the report. (0.5 marks)
Sample Description (700 words- 6 marks)
This should include descriptive statistics about the student sample and consider how
representative these are of the general “population of interest”.
The population of interest is for you to determine based on your positioning of the
research report. For example, with a focus on attitudinal data and/or preferences
changes during and after COVID19, perhaps university study preferences have
changed. Alternatively, their attitude towards learning or being social (or limited
opportunities during COVID) may hampered some- but may be of interest to others.
You may consider conducting some further simple desk research on what the
broader characteristics of students in the Australian market are. Here, perhaps a
focus on demographic data (e.g. mature age students, studying fulltime vs parttime)
can be considered.
It is expected that you do two Word Clouds (one for universities, one for online
learning) with R Studio constructed from the qualitative text data.
• This should include descriptive statistics about the student sample (e.g. chi-
square test) and consider how representative these are of a general
“population of interest”. (1 mark- value of population of interest)
• The population of interest is for you to determine based on your positioning of
the research report (1 mark- identify population of interest).
o For example, it may be new high school graduates in the Southeast
Queensland region, current students (i.e. tending towards developing
retention strategies), new prospective international students, mature
age students, etc.
• Information about the characteristics of the “sampling frame” have been
provided (i.e. the characteristics of the MKTG2510 cohort), but you may
consider conducting some further simple desk research on what the broader
characteristics of students in the Australian market are (1 mark - sample
frame/characteristics)
• Further demographic information is available in the data which you are free to
choose to include or not dependent on your judgement of what is important
depending on your own positioning of the research report- A/B test can be
used here (1 mark- basic demographics). Two word clouds constructed from
the qualitative text data. (1 mark - basic demographics) and (1 mark for
each word cloud).
Thoughts to consider in this section:
• Who is the population of interest? How is it related to your client's target
market?
• How was your survey distributed? Sample frame? Sample size?
• What are the implications of these word clouds? It is insufficient to just
include them but not provide a discussion.
Segmentation (900 words- 5 marks)
• You should split the sample into subsets for comparative analysis based
on some schema of market segmentation. As said in class, generally 3 or
4 clusters are better. Consider the methods we applied in the tutorial for
starting points. The schema options available should be psychographic
segmentation based on using a K-means cluster analysis of the attitudinal
rating scale data. (2 marks for K-Means cluster analysis)
• The minimum expectation in this section is that you make a comparison
using the statistical tests outlined on the differences between three
potential groups on at least three attitude variables of interest. You are
also expected to do at least 1 iterative process (1 mark for analysis)
• It is up to you/your team to decide how to segment the data, and which
variables/specific methods you use to compare between groups. (1.5
mark for process documentation).
Thoughts to consider in this section:
• Why did you use certain items for your cluster?
• Benefit to the client? And/or
• Larger amount of variation/std dev? AND/OR
• Hypothesized segments based on certain characteristics?
• Why did you think they were suitable for the client?
• What are the characteristics of the segments that you identified?
• What are the K-Means results and what are the implications for the segments?
• Look at the average scores from the K-Means output.
• How can they better target these segments based on your initial questions and
the results, etc.
• This goes in the recommendation section!
• Look at the wording of the questions, and then propose a marking strategy
that would improve the measure.
• The segmentation section could have been improved by including your
process documentation as well as your interpretation of the results. It is
unclear what analyses were applied to yield this result.
Market Structure Analysis (900 words- 6 marks)
• You should analyse the best-worst data using arithmetic methods.
• This includes calculating the scale of best score (1 mark) implied
market shares (1 mark) and producing a means-variance preference
map (lot) (1 marks) (3 marks total).
• In your interpretations of this analysis, you should report what the differences in
choice probabilities are for one focal university relative to its competitors at the
aggregate level, as well discuss which (if any) universities are close direct
competitors (i.e. where brand switching is a possible threat). (1 mark for each
analysis interpretation) (3 marks total)
• It is not necessary to conduct this analysis at the segment level, although
you may extend this analysis in that direction if you wish.
Learning Design Preference Modelling Analysis (900 words- 5 marks)
• The discrete choice experiment included in the student survey generates a
choice model of students’ preferences for the different features/attributes of
university offerings which affect students (your) experience.
• The results of a conditional logistic regression model will be provided to you,
and the process by which this model is produced will be discussed in-class. You
should provide your interpretation of the model in your research report, noting
which features of the university offerings are statistically significant and how
these might align (or misalign) with current offerings available in the market.
• 2 Marks for correct table with rank ordering
• 1 Mark for correct interpretation of 'Neither' option
• 1 Marks for correct interpretation of +/- magnitude
• 1 Mark for correct interpretation of strength.
Information to consider:
This "Neither" option is telling you that there are other attributes that exist outside of
the model that were more important to the respondent than what was included within
the model. For example, "Social activities, number of bars on-campus, number of
study locations, male/female ratio" etc were not captured attributes but could lead to
a decision by students to choose a particular university offering over another.
Recommendations (800 words- 9 marks)
The recommendations section should provide:
• A concise list of the key insights you have drawn from the data.
• Try to generate a list of at least 3 key insights which inform at least 3 key
recommendations.
• Think about some of the potential costs associated with your recommendations
and whether or not they may be feasible for the client in the short, medium or
long term. Ideally, at least one of your recommendations should be something
that is immediately actionable and some should take on a more of a long-term
outlook.
• For each Recommendation (3 marks for each recommendation). Consider for
each recommendation.
• linking to basic descriptive regarding the sample
• linking to your k-means segment/s
• linking to each k-mean segment/s and providing an actionable
recommendation
• Industry changes- what you think might be appropriate.
Conclusion (300 words)
Provide a conclusion to the report which:
• re-states what the original motivations were in conducting this research to re-
affirm to your client why this research was necessary to conduct. (1.5 marks for
restating motivations and conclusion).
• As current students who were part of the sampling frame from which the data you
analysed was drawn, you are encouraged to reflect in this section upon whether
the results of your analysis reflect your/your team’s experience(s). (1 mark for
reflection).
Miscellaneous
Language (2 marks) and referencing (2 Marks) will be marked as per the rubric.
Note: it is expected that approximately 5-10 references will be used. It is expected that
most of these will come from industry research such as a search of government
documents and websites. Please ensure that you do not copy directly from websites.
Statistical analysis from government websites is encouraged.
It is expected that you will use APA Referencing (6th or 7th edition).
You can use the format example I have provided- I would prefer Times New Roman or
Arial. Please consider a font size of 12. I would suggest line spacing of 1.5cm. You
can include spacing after a paragraph of about 11.25cms. It is also encouraged to use
headings. Please include tables and/or figures within the document not at the end.
Notes
You can download the working files from previous tutorials (e.g. k-means cluster
analysis) and add them to your Excel spreadsheet (as an additional tab).
You will also need to submit your data Excel file to prove how you came to your
solutions.