Unit convenor and teaching staff |
Unit convenor and teaching staff
Nicholas Harrigan
Contact via 0490 911 666
Hangyoung Lee
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Credit points |
Credit points
4
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Prerequisites |
Prerequisites
SOC830
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit provides training in advanced quantitative analysis with an emphasis on social science applications using existing survey data. Lectures will cover the underlying theory and laboratory sessions the application and interpretation of models. This course will cover the following topics: variance analysis, correlation and alternative correlation coefficients, linear and logistic regression, multilevel modelling, factor analysis, and path analysis.
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Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Rubrics will be provided for all three assessments.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Attendance + Weekly Exercises | 30% | No | Weekly |
First Presentation + Report | 20% | No | In class in Week 7 |
Second Presentation + Report | 50% | No | In class in Week 13 |
Due: Weekly
Weighting: 30%
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:
Due: In class in Week 7
Weighting: 20%
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
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.
Due: In class in Week 13
Weighting: 50%
NOTE: The differences between the second presentation and the first are:
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
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.
Laptops
You are required to have a laptop which you bring to class. This is needed for running statistical analysis in R (both in class demonstrations, and at home), and also for presentations and preparation of reports.
Installation of R
You will be required to install R Statistical Package on your laptop, so you will need to have administrator privileges for your computer. Instructions will be provided in advance of the class to guide you through self-installation of R.
Weekly assignment
At the end of each weeks' class you will be given an assignment which will involve using R to conduct the analysis taught that week. Students will generally conduct this analysis on a dataset of their choice, and through these weekly assignments start working on their major project. Students will share their analysis in the second half of class (workshops) each week in the form of a 2-3 minute presentation. Note that workshops will be on the previous week's topic (e.g. At the end of Week 1 students will be provided an assignment on the topic of Week 1, which will be discussed in the second half of class in Week 2).
Structure of class
Each class will be three hours in length, and will be divided into two halves each 1-2 hours in length. The first half of class will be a lecture on that week's topic. The second half of the class will be a workshop where students will present their weekly assignments and we will have discussion about any issues which arise.
Lecturers:
Week 1: Revision of fundamentals of quantitative social science (NH)
Topics covered: Puzzles; theory; causality; conceptualisation; operationalisation; variables and data structure; hypotheses; significance and confidence intervals; correlation; factors/indexes/clustering; comparison of means; crosstabs; regression (linear and logit); missing data; dummy variables; interaction effects.
Week 2: Linear Regression (NH)
Week 3: Logistic Regression + Probit (NH)
Week 4: ANOVA + Propensity matching (NH)
Week 5: Factor and cluster analysis (NH)
Week 6: Path analysis and Structural Equation Modeling (NH)
Week 7: Student presentations (NH + HYL)
Week 8: Social Network Analysis (NH)
Week 9: Visualisation (HYL)
Week 10: Other types of regression 1: Multinomial and ordinal (HYL)
Week 11: Other types of regression 2: Count models and zero inflated count models (HYL)
Week 12: Longitudinal and panel data: Fixed and random effects, multi-level models, and hierarchical linear models (HYL)
Week 13: Student presentations (NH + HYL)
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Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct
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