BS3397 Microeconometrics Coursework Assignment Brief 2026 | AU

BS3397 Coursework Assignment Brief

Coursework Title:  72-hour Take-home Mid-term Assessment

Due: 12 noon, Tuesday 4^th November 2025 

Task Details/Description: 

Complete all of the tasks in this assessment (starting page 4). You should use R for all analysis and/or data manipulation. Guidelines for submission/presentation are featured on the final pages.

In addition to the take-home component, there will be a short (10-minute) confirmatory task in-class in week 8:

• This take-home component is worth 2/3 of the grade you will receive for this assessment
• The linked in-class confirmatory task is worth 1/3 of the grade you will receive for this assessment

BS3397 Assessment Brief and Assessment Task continue overleaf

Assessment Brief  

Purpose of an Assessment Brief 

(How should I use this brief to help with my assessment?)

This brief gives you key information and guidance about your assessment. It’s the starting point for your work, and your module tutors will refer to it during assessment discussions. Read the brief carefully, along with any other materials your tutors provide, and use them to help you plan and complete your assessment. 1.0 Essential Information  

2.0 Assessment Overview  

Assessment Mode (What do I need to produce at the end?)

A well-formatted set of answers to the questions you have been set

Task Description (What do I have to do for this assessment?) 

Complete the technical task you have been set, answering all questions.

Submission Instructions (When and Where do I submit?)

Submit to submission link on module Blackboard page

3.0 Purpose of Assessment

Link to Learning Outcomes (How does the assessment help you demonstrate the outcomes of this module?) 

This assessment links to all of the LOs for the module (available on Blackboard under module information). It assesses the extent to which you have met the standards expected to pass the module.

Purpose of Assessment (Why am I doing this? How does this link to my future role as an professional in the discipline? What knowledge, concepts or skills should I demonstrate? How might this links to (name of specific PSRB?) )

The skills acquired on this module align with identified skills shortages in the UK workforce and are widely applicable acorss work or further study. Specifically, this assessment tests your ability to autonomously conduct and communicate statistical/quantitative analysis.

4.0 Feedback Expectations

When and where can I find my grades and feedback? 

Staff will aim to return provisional marks for coursework assessments to students within 4 weeks of the submission deadline during term time.

 For this module (Formative/Summative) Feedback and/or provisional grade will be released/provided on [Date and time ] via [location in Blackboard]

Any grades that you see outside of My Aston Portal (MAP) should be thought of as provisional. Only final Exam Board approved grades will appear in MAP. Feedback and provisional grades can be accessed in Blackboard for both Turnitin and Blackboard Assignments. [Select one of the following depending on your submission]  – For Turnitin assignments, see our guide to accessing your Turnitin feedback. For all other types of assignment, use this Grades guide.

5.0 Guidance on AI Usage 

6.0 Assessment Top Tips 

What further information and advice might help me to complete this task?

All the skills and knowledge you need to excel in this assignment can be found in the lectures or homework activities.

7.0 Assessment Criteria and Rubrics

What will my work be marked against? 

Your work will be marked against the Marking Criteria for BS3397, available in the Assessment and Feedback area on Blackboard.

Task Details/Description: 

On BSc Economics programmes, students generally study a range of mathematical or technical modules, which generally build in complexity throughout the degree.

You have been provided with a set of data (in CSV format) concerning student characteristics and performance in the module Econometrics 2, a second-year module which takes place in the Spring term (full data key below).

Some specifics of interest:

• The module has a pre-requisite (Econometrics 1), which must be studied in the Autumn.
• Assessment for the module takes the form of a mid-term technical essay-based assessment (worth 30% of the marks for module) and a final in-person exam, worth the remaining 70%.

You have been asked to conduct some analysis of the data to better understand the determinants of students’ academic performance.

Data Key

‘econometrics grades.csv’

N = 323                                                               

Average number of minutes spent on social media social_media_minutes per day.

Student’s preferred study location (Library, Home, study_location or Cafe).

ANSWER ALL THE QUESTIONS BELOW

1) Produce appropriate, well-formatted, visualisations which show:

a. The distribution of students’ overall marks for the module (which may require some manipulation of variables)

b. A comparison of girls versus boys performance, for each of the individual assessments

In each case, you should briefly comment on the results (50 words or less)

(10 marks each, 20 marks in total)

Marks will be awarded/lost based upon the standard of presentation of visualisation, and the clarity and conciseness of the explanation.

2) You are asked to estimate two regression models, with the assignment marks as dependent variables (so, one model will feature the mid-term mark as dependent variable, the other will feature the exam mark). You should include all the variables that have been provided to you (but ensure that all variables are correctly coded before estimating your regression models):

a. Using an appropriate equation editor, properly specify the two models you intend to estimate (paying particular attention to subscripts, accents, error terms, etc.)

b. Use R to estimate the two models, ensuring that all variables are included in an appropriate form, and present your results in two neatly formatted tables (copying and pasting raw R output is not appropriate). Briefly highlight any results of interest.

c. For each of your regressions, conduct an appropriate set of robustness checks and, if necessary, re-estimate your models following any necessary remedial steps. Explain your steps/reasoning and, again, present your results in well-formatted tables.

d. To what extent do you think your results show a causal relationship?

(300 total words or less (not including tables/formulae), 50 marks in total)

3) There is concern that the amount of time spent on social media (TikTok, Instagram, etc.) is negatively affecting student academic performance. To what extent do your results support/refute this claim, and to what extent would you rely on your results from 2b), above?

(15 marks, under 150 words)

4) Universities are very keen to ensure that students receive sufficient academic support during their studies, including the provision of dedicated ‘office hours’ when students can receive one-to-one support from their lecturers. To what extent do your results demonstrate the purported benefits of one-to-one support, and to what extent would you rely on your results from 2b), above?

(15 marks, under 150 words)

The key to success in this assignment is to demonstrate informed judgement, that you can justify your choices, and that you can demonstrate that you understand the econometrics you are executing and the results that you elicit. Presentation is important, so ensure that your formulae, tables, and diagrams are properly presented, and that you follow the rest of the submission guidelines .

Module Learning Outcomes Assessed:

This assessment covers both technical and theoretical material from the first part of the course. It partially assesses all learning outcomes associated with the module:

1. Manage and describe economic data and datasets from a range of sources
2. Demonstrate and communicate a conceptual understanding of a range of econometric issues and approaches relevant to microeconomic data
3. Demonstrate the ability to appropriately select, and use computer software to implement, a range of econometric approaches dependent on context and suitability
4. Critically interpret microeconometric results, to demonstrate the ability to understand their implications, and to relate them to broader real-world settings

Presentation Requirements: 

Submission guidelines:

Your work should be submitted electronically and should consist of a single document in three parts:

• Your written responses to the questions, including numbered and properly formatted tables and any figures/graphs you produce
• A short statement (200 words or less) which summarises any way in which you made use of AI in the preparation of your work.
• The R script used to generate your answers. Ensure this is complete, tidy, and free of superfluous commands or draft experimentation with commands. The key to a good R script is replicability, therefore it should be in such a format that (barring changing source directory) I can run it myself. Incomplete or non-replicable R scripts will be penalised.

General guidelines:

This is a final-year assessment and, as such, it is expected that you uphold appropriate standards of presentation:

• Graphs, figures, and tables should be produced using computer software (using R where specified by the question)
• Raw R output should not be included as part of your final written work
• Formulae and equations should be produced using an appropriate equation editor with correct subscripts and accents
• Language, descriptions, and explanations should be formal and should not resort to simplistic or childlike exposition—students will be penalised for misuse of statistical terms
• Short written sections should be concise and should not exceed the specified word limits
• Text should be written in a non-serif font (e.g. Aptos, Calibri, Arial, Helvetica), size 11, 1.5 line spacing.
• It is expected that your work is legible, written in formal academic language, and spelling and grammar checked.
• Any citations are properly referenced using an established referencing style (e.g. Harvard or APA)

Academic integrity

It is expected that you uphold the expected standards of academic integrity:

• You produce your own work.
• You do not plagiarise and that any sources you use in your answer are properly acknowledged in a properly formatted bibliography (a list of sources relied upon, not just a list of sources specifically cited).
• This is individual assessed work: conversations and collaboration with colleagues is kept at a general level and you do not share or generate specific answers to questions with one another.
• It is an academic offence to pass off unadulterated AI-generated answers as your own.

Failure to adhere to guidelines or standards of academic integrity will result in a lower mark.

Submission Date & Time:   

Assessment available 12 noon, Friday 31^st October 2025

Assessment due 12 noon, Tuesday 4^th November 2025

Assessment Weighting for the Module: 

20%

BS3397 Assessment Criteria 

This module is assessed via:

Coursework (20%), of which:

• 2/3 mark comes from this take-home assessment
• 1/3 mark comes from linked confirmatory in-class activity

Final take-home exam (80%)

Both assessment tasks will require a combination of theoretical and conceptual knowledge on the topics we have covered during the course (up to that point), and on the technical requirements to analyse data using the software package R.

This coursework therefore accounts for 20% of the final grade for the module. You will receive feedback on your work which will help you when it comes to the final take-home assessment.

Ethical Requirements

N/A

Essential Reading for Coursework Task 

You have been provided with links to two academic articles which provide context for the assessment task. Though not essential, you are encouraged to consult further sources which might add support to your answers.

(if in addition to reading provided in the module outline):

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