PO92Q-20 Advanced Quantitative Research
Introductory description
QS903-20
Module aims
This module introduces students to a selected set of advanced statistical methods that are commonly used in quantitative social research. A further aim is to familiarise students with the key issues in the craft of applied work so that they become careful, considered and thoughtful researchers in quantitative social sciences.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Week 1: Introduction to the module, data and software
Weeks 2-3: Topic 1
Weeks 4-5: Topic 2
Week 6: Reading Week
Weeks 7-8: Topic 3
Weeks 9-10: Revision and Data Analysis Workshops
The three topics offered might vary from year to year, depending on staff availability and student demand. The methods covered will be announced in advance, and they are likely to be drawn from the following list:
Categorical data analysis (e.g. loglinear analysis)
Causal inferences with observational, experimental and quasi-experimental data
Event history analysis
Qualitative Comparative Analysis
Correspondence Analysis
Latent Class Analysis
Multilevel analysis
Panel data analysis
Social network analysis
Bayesian Analysis
Agent Based Simulation
Spatial Analysis
Learning outcomes
By the end of the module, students should be able to:
- understand and appreciate forms of statistical analysis and advanced quantitative approaches
- have an awareness of the value of, and practical experiecience of applying advanced quantitative analysis techniques
- have a heightened awareness of both the technical and theoretical/conceptual dimensions of quantitative data analysis
- carry out a range of advanced statistical analyses using statistical computing software
- manipulate and to analyse secondary survey data using statistical computing software, and to present and interpret the results of these analyses appropriately at an advanced level
- carry out advanced statistical analyses using statistical software
Indicative reading list
Agresti, A. (2007) An Introduction to Categorical Data Analysis. 2nd ed. Wiley.
Bartholomew D. (2011) Analysis of multivariate social science data. CRC
Crawley M. J. (2013) The R book. Wiley
Firebaugh, G. (2008) Seven Rules for Social Research. Princeton University Press.
Fox, J. and Weisberg S. (2019) An R companion to applied regression. Sage
Fox J. (2016) Applied regression analysis and generalized linear models. Sage
Hardy M. A. and Bryman A. (2009) Handbook of data analysis. Sage
Powers, D.A. and Xie, Y. (2008) Statistical Methods for Categorical Data Analysis. 2nd ed. Howard House.
Singer, J.D. and Willett, J.B. (2003) Applied Longitudinal Data Analysis. Oxford University Press.
Wasserman, S. and Faust, K. (1995) Social Network Analysis. Cambridge University Press.
View reading list on Talis Aspire
Subject specific skills
Key Skills; Subject-Specific/Professional Skills
- carry out a range of advanced statistical analyses using statistical computing software
- manipulate and to analyse secondary survey data using statistical computing software, and to present and interpret the results of these analyses appropriately at an advanced level
- carry out advanced statistical analyses using statistical software
- provide in-depth interpretation about advanced quantitative analysis
critique and critically engage published quantitative research
Transferable skills
Cognitive Skills
- have a heightened awareness of both the technical and theoretical/conceptual dimensions of quantitative data analysis
Study time
| Type | Required |
|---|---|
| Lectures | 9 sessions of 1 hour (4%) |
| Practical classes | 9 sessions of 2 hours (9%) |
| Private study | 173 hours (86%) |
| Total | 200 hours |
Private study description
tbc
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
|||
| Technical Report | 100% | Yes (extension) | |
|
One 4,000-word technical report. |
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Reassessment component is the same |
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Feedback on assessment
Detailed and regular feedback will be provided throughout the module.
FORMATIVE
Verbal feedback on lab work will be provided at relevant points in the lectures and workshops throughout all sessions. This will be provided by module teachers at each session. In addition, student participation is encouraged and this includes students giving each other peer feedback during classes on their own work as well.
SUMMATIVE
Detailed written feedback will be provided on summative assessments. This will be provided by tutors marking individual student work and commenting on issues and errors throughout their assignments.
Courses
This module is Core for:
-
TIMS-L990 Postgraduate Big Data and Digital Futures
- Year 1 of L990 Big Data and Digital Futures
- Year 2 of L990 Big Data and Digital Futures
- Year 1 of TPOS-M9Q1 Postgraduate Politics, Big Data and Quantitative Methods
This module is Core optional for:
- Year 1 of TPOS-M9PV Double MA in Journalism, Politics and International Studies (with Monash University)
This module is Optional for:
- Year 1 of TPOS-M9PT MA in International Development
- Year 1 of TPOS-M1PA MA in International Politics and Europe
- Year 1 of TIMA-L99A Postgraduate Taught Digital Media and Culture
- Year 1 of TPOS-M1P3 Postgraduate Taught International Political Economy
- Year 1 of TPOS-M1P8 Postgraduate Taught International Politics and East Asia
- Year 1 of TPOS-M9P9 Postgraduate Taught International Relations
- Year 1 of TPOS-M9PC Postgraduate Taught International Security
- Year 1 of TPOS-M9PS Postgraduate Taught Political and Legal Theory
- Year 1 of TPOS-M9PF Postgraduate Taught Public Policy
- Year 1 of TPOS-M9PQ Postgraduate Taught United States Foreign Policy
- Year 1 of TIMA-L99D Postgraduate Taught Urban Analytics and Visualisation