Students are required to attend and participate in weekly classes.

Each week students will be given exercises to complete for the next week's class.

Students are expected to complete these exercises on their own, and then present them in class during 'The Clinic' session - the second half of class where students will share their analysis of their data, application of the techniques they have learnt, and where we will workshop students analysis in preparation for the week 7 and week 13 presentations and reports.

Attendance of weekly classes and completion of these exercises is compulsory.

If students miss class, they can submit a written response (a 'make up assignment) to that week's exercises, including their R-code.  

For each week's class that is missed without a make-up assignment, will incur a 20% penalty to attendance and weekly exercise grade.

The rubric for this assessment will be simple. Each week students will be given one of four grades:

1. No attendance and not completed assignment or make up assignment - 20% penalty overall.
2. Attendance and not completed assignment - 33% marks for that week
3. Attendance and satisfactory completion of assignment - 67% of full marks for that week
4. Attendance and excellent completion of assignment - 100% of full marks for that week

Students are required to deliver a 10 minute presentation and provide a 2,000 word report (with attached R code) in which they present analysis of their chosen dataset.

Analysis should

1. address an important academic puzzle or policy problem;
2. utilise multiple methods taught in the first six weeks of the course, including (but not limited to) descriptive and bivariate statistical analysis;
3. be used to advance a substantive argument;
4. test one or more hypotheses; and
5. through testing these hypotheses provide evidence for or against one or more academic theories or competing perspectives on a policy issue. 

Students should choose their own topic.  

Students are free to use their own dataset or use one of the datasets provided by the lecturers for this class. 

Students should frame their analysis - justifying their choice of topic, competing theories, and hypotheses - with a brief literature review which makes a plausible - if brief - argument that the analysis is important, worthwhile, and a contribution to the literature. 

NOTE: The differences between the second presentation and the first are:

1. The second presentation must use multivariate analysis (multivariate analysis is optional in the first presentation and paper)
2. The second presentation must use visualisations in R, either through maps, figures, graphs, or similar. 
3. The second presentation must be a substantial improvement and/or extension of the work presented in the first presentation and paper. This can either be through extending the analysis of the same dataset, or through analysing a new dataset.

Students are required to deliver a 10 minute presentation and provide a 2,000 word report (with attached R code) in which they present analysis of their chosen dataset.

Analysis should

1. address an important academic or policy problem or puzzle,
2. utilise multiple methods taught in the 12 weeks of the course, including (but not limited to) multivariate statistical analysis; 
3. be used to advance a substantive argument;
4. test one or more hypotheses; 
5. through testing these hypotheses provide evidence for or against one or more theories and/or competing perspectives on a policy issue; and
6. utilise visualisations in R to illustrate their findings. 

Students should choose their own topic.  

Students are free to use their own dataset or use one of the datasets provided by the lecturers for this class. 

Students should frame their analysis - justifying their choice of topic, competing theories, and hypotheses - with a brief literature review which makes a plausible - if brief - argument that the analysis is important, worthwhile, and a contribution to the academic or policy literature.