Marking criteria

Participation will be assessed according to whether student participate in class through 

(1) asking questions of the lecturer and other students,  (2) demonstrating that you have done readings,  (3) listening and responding to comments and questions from the lecturer and other students,  (4) undertaking in-class exercises, and  (5) doing so in a way that is respectful of other participants in class.

Example grades and students' behaviour that meets marking criteria:

• 55% (Pass) - attends 70%-90% of classes, but does so with little participation beyond completing exercises
• 65% (Credit) - attends 80%-90% of classes, and participates in discussion occassionally.
• 75% (Distinction) - attends 90%-100% of classes, and participates actively and fully
• 85% (High Distinction) - attends 90%-100% of classes, and shows deep engagement with teaching material and with other students. Shows serious preparation before class. Completes exercises at a very high standard, perhaps extending analysis beyond those immediately taught.

Instructions:

1. Replicate a published study with public dataset: Students are to:
   1. Find an article: Find a social science (or closely related discipline) study that has been published as a peer-reviewed academic article, and that uses a publically accessable dataset, and 
   2. Replicate in R: Replicate the analysis presented in the paper using R.
2. R code should not already exist: Article and dataset should NOT already have publicly available R code (this would make the exercise pointless).
3. By Week 7: By Week 7 students should have identified the article, downloaded the dataset, and conducted preliminary analysis (i.e. univariate and bivariate analysis).
4. Presentation: In class in Week 7 students will present for a maximum of 12 minutes, and provide:
   • a brief introduction to the article and the dataset
   • their preliminary analysis, including tables and figures.
5. Report: In class in Week 7 students will submit their written report (printed out, and also submitted through ilearn), which shall consist of:
   • A copy of the article to be replicated
   • A link to the dataset
   • A copy of their R code (script file) with brief annotations to explain what you have done
   • A short report of approximately 600-1000 words with no more than five tables and figures, which present a preliminary analysis of the dataset.
6. Consultation: Students are expected to consult with the lecturer (Nick) before class (4pm - 6pm), in class (6pm - 9pm), and outside class (Facebook messenger, WhatsApp) to
   1. confirm their choice of article, and
   2. discuss any issues and problems they are having with the analysis. 

Marking criteria:

1. Motivates interest of audience: Presentation and report should motivate the interest of the audience by identifying both what is **intellectually curious** about the topic, and why it is **substantively important** for the public, policy makers, or other non-academic audiences.
2. Clear writing style: Straight-forward, clear, easy to read writing. This means generally using short sentences, having a single coherent and easy to understand argument, and using paragraphs with topic sentences.
3. Professional Tables and Figures: Tables and figures should be presented like they would appear in an academic article, which means, at the least, (1) that tables are not just cut and paste from R output, (2) that tables and figures include only the necessary information, (3) that tables and figures include all appropriate information, and (4) they should be able to be interpreted on their own (without the text), and (5) all tables and figures should be referred to by number in the text. 
4. Analysis: Analysis should be a high quality replication of the analysis in the academic article which it comes from. Analysis may briefly extend on the analysis in the article, if space and time permits. 
5. Explanation: Explanation of the analysis should be simple, clear, and correctly use terminology. It should also point out the substantive significance of the results in a way that another person with a Masters Degree, but not in this area of research, could understand.
6. R code: R code should be as simple and tidy as possible, with brief annotated comments (after # symbols), which explain the purpose of each section of code. The R code should be in a form which the lecturer can run on their computer and replicate the analysis.

Instructions:

1. 30 minutes: This will be a short (30 minute) online exam, with multiple choice, fill-in the blank, and short answer questions.
2. Laptops; Open book: Exam will be completed on student’s laptops and be open book.
3. Concepts from Weeks 1 to 6: Exam will test material from Week 1 to 6, with a strong focus on the “Concepts” identified at the beginning of each week’s class.
4. Example questions: Example questions will be provided in advance of the class for students to familiarise themselves with the format.

Instructions and marking criteria:

1. Same as Week 7 Presentation & Report, but full analysis: Instructions and marking criteria are the same as for Week 7 Presentation and Report, except that
   1. Full analysis: the full analysis should be presented, and
   2. Multivariate analysis: should include some form of multivariate analysis (some form of regression, factor analysis, ANOVA, or similar).

Instructions:

1. Same as Week 8 Midterm Exam, but 2 hours: Instructions are the same as for Week 8 Midsemseter exam, except that exam is 2 hours, and will test all material from weeks 1 through 12.