Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Social Science School
Skills in applied quantitative data analysis are highly sought after by employers and are widely used by researchers in academia, government agencies, private companies and community organisations. This course teaches some of the most important quantitative data analysis techniques to equip students to undertake their own research and to assess the research of others. The course is ideal for those who are planning to undertake a quantitative honours thesis or work in an area that requires evaluating or conducting quantitative analyses. Topics covered include: a revision of descriptive and inferential statistics; bivariate and multiple linear regression; binary, ordered and multinominal logistic regression; and the use of statistical software for data analysis (Stata). A defining characteristic of this course is its focus on the practical application of the methods covered, rather than on their mathematical or statistical properties.
Quantitative research methods are a powerful way to empirically explore substantive research questions in the Social Sciences and to rigorously examine sociological and other scientific theories. Skills in quantitative research and data analysis are also highly sought after by employers in both the public, private and not-for-profit sectors. This course follows on from 'SOCY2339/7339 Introducing Quantitative Methods' and aims toᅠfurther enhance your skills in applying quantitative analysisᅠin the Social Sciences. The course begins with a brief revision of the key materials covered in SOCY2339/7339, followed by a discussion of techniques used to measure bivariate relationships. The course will then concentrate on powerful and useful regression-based techniques for examining relationships among both continuous and categorical data. The methods that we shall cover in this course are most often used in Social Science to analyse data generated by social surveys but they can also be applied to numeric data in general (e.g., experimental or administrative data).
Course requirements
Assumed background
Students enrolling in this advanced course should have completed 'SOCY2339/7339 Introducing Quantitative Methods', or an equivalent course on statistics or quantitative research methods for the Social Sciences at UQ or elsewhere. Students enrolling in this course are therefore assumed to have acquired foundational knowledge in univariate measures of central tendency and dispersion (e.g., mean, median, mode, standard deviation, variance), bivariate measures of association (e.g., contingency tables, correlations), and basic notions of probability theory and inferential statistics (e.g., sampling distributions, hypothesis testing, p values, t statistics, confidence intervals…). Some familiarity with Stata software is desirable, but not essential.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
SOCY2339
Incompatible
You can't enrol in this course if you've already completed the following:
SO320 or SOCY3019, SO323 or 423 or SOCY7039
Course contact
School enquiries
Level 3, Michie Building (09), St Lucia campus, The University of Queensland.
Monday-Friday, 9:00am-12:00pm, 1:00pm-4:00pm.
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Participation in this course entails:
- Attendingᅠtwo-hour weekly lectures,
- Attending one-hour weekly tutorial sessions.
For students to progress appropriately through the course, it is essential that they attend each lectureᅠand tutorial sessions. Tutorial sessions will be essential for students to acquire hands-on data analysis skills that will be used in assessment.ᅠ
Please refer to MyTimetable for the most up-to-date timetable information.
Aims and outcomes
This course aims at enhancing students' skills on the following areas of Applied Quantitative Research.
1. Foundational knowledge and application of key statistical concepts.
The course introduces and discusses important quantitative analytic techniques applied in contemporary Social Science research, and revises key ideas about introductory statistics dealt with in 'SOCY2339/7339 Introducing Quantitative Research'. This includes basic descriptive statistics for univariate and bivariate relationships, and foundational notions about how to draw inferences about a population of interest using a sample of it.
2. Reseach design for quantitative social science.ᅠ
Good quantitative research is based in theory and subject matter expertise - it is not a purely statistical endeavour. The course will introduce foundational ideas in research design that underpin quantitative social science, including an understanding of different research goals, study types, and tools to understand causality (experimentation, potential outcomes, and directed acyclic graphs). These conceptual tools will provide students with the ability to think clearly about a variety of research problems, critique quantitative research, and translate real-world understanding into compelling research design.ᅠ
3. Bivariate and multivariate linear regression models.
Regression is the most common technique for statistical analysis in Social Science research, and the 'bread and butter' for academic and professional researchers who rely on these methods. Regression models are formal mathematical representations of Social Science theories that can be used to empirically examine those theories. This section of the course focuses on how to use regression techniques for quantitative social research. It begins by discussing simple bivariate models used to ascertain relationships between only two variables, and then moves onto discus
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Articulate the logic and benefits of advanced quantitative social science research
LO2.
Interpret advanced quantitative analyses in published academic and non-academic research outputs
LO3.
Review and critique advanced quantitative analyses done by others
LO4.
Identify the appropriate regression model for analysing continuous or categorical data
LO5.
Appreciate conceptual differences between confounding, moderation and mediation
LO6.
Use statistical software to access, manage and manipulate survey data in preparation for quantitative analyses
LO7.
Use statistical software to undertake bivariate and multiple linear and logistic regression models
LO8.
Access and manage data according to the principles of responsible data use
LO9.
Understand core concepts and tools for causal inference
LO10.
Gain familiarity with advanced study designs for multilevel, longitudinal, and experimental/quasi-experimental research.
Assessment
Assessment summary
Category | Assessment task | Weight | Due date |
---|---|---|---|
Computer Code, Paper/ Report/ Annotation | Assignment 1 | 30% |
16/08/2024 2:00 pm
Submit via Course Blackboard (TurnItIn). |
Computer Code, Paper/ Report/ Annotation | Assignment 2 | 35% |
20/09/2024 2:00 pm
Submit via Course Blackboard (TurnItIn). |
Computer Code, Paper/ Report/ Annotation | Assignment 3 | 35% |
21/10/2024
Submit via Course Blackboard (TurnItIn). |
Assessment details
Assignment 1
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 30%
- Due date
16/08/2024 2:00 pm
Submit via Course Blackboard (TurnItIn).
- Learning outcomes
- L01, L02, L06, L08
Task description
A computer and statistical assignment worth 30% of the overall course grade. This assignment will be placed on Blackboard in Week 2 and will be completed in a three-week period (weeks 2, 3, and 4). The assignment will pose a series of separate questions that with cover the material covered in the lectures and lab sessions. There is no strict word limit for this assignment, but it is expected that students' combined responses will be no longer than 1,000 words (excluding Stata output, tables, graphs, and Stata log files).
Marking criteria and/or marking rubrics are available in the ‘Assessment’ folder in Blackboard for this course.
AI use: This assessment task evaluates student’s abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
You must submit your assignment electronically by the due time, on the due date.
Your assignment must be submitted via Turnitin on BlackBoard and no emailed submissions will be accepted.
By uploading your assignment via Turnitin, you are certifying that the work you submit is your own work except where correctly attributed to another source. Do not submit your assignment if it contains any work that is not your own. Please note that on the preview page, your assignment will be shown without formatting. Your assignment will retain formatting and your coordinator/tutor will be able to see formatted assignments. Once you have submitted your assignment you are able to go back and view your submission with the correct formatting.
You are required to retain proof of submission of your assessment. Your Digital Receipt is available for download from your Assignment Dashboard. If you cannot see your submission and download your digital receipt, your assessment has not been successfully submitted, please submit again. If you are unable to submit your assignment by the due date and time, you need to apply for an extension as set out in section 5.3.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
An extension request without penalty will only be considered under exceptional circumstances as outlined on my.UQ. You must submit the extension request as soon as it becomes evident that an extension is needed, but no later than the assessment item submission due date.
A request for an extension to an assessment due date must be accompanied by supporting documentation corroborating the reason for the request. The student submitting the request is fully responsible for all supporting documentation that is provided with the request and should ensure all documents are authentic.
Extensions on the basis of an approved Student Access Plan (SAP) or an Extension Verification Letter (EVL) can be approved for a maximum period of 7 calendar days. Extensions exceeding this duration or subsequent extensions for a piece of assessment will require additional supporting documentation (e.g., a medical certificate or other supporting evidence listed on my.UQ) and Course Coordinator approval.
When you submit an extension request in the student portal, it is received, read, and actioned by the Social Science Student Administration Team. It does not go to the course coordinator.
Late applications (requests received after the assessment item submission due date) must include evidence of the reasons for the late request, detailing why you were unable to apply for an extension by the due date.
In considering applications for extensions, students may be asked to supply the work they have completed to date on the assessment piece. This is to establish what efforts have already been made to complete the assessment, and whether the proposed work plan is feasible.
Late submissions of extension requests in your final semester of study could delay your graduation by up to one semester.
Work can NOT be accepted if it is more than one week (7 calendar days) late without prior approval.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 2
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 35%
- Due date
20/09/2024 2:00 pm
Submit via Course Blackboard (TurnItIn).
- Learning outcomes
- L02, L03, L04, L05, L06, L08
Task description
A computer and statistical assignment worth 35% of the overall course grade. This assignment will be placed on Blackboard in Week 7 and will be completed in a three-week period (weeks 7, 8, and 9). The assignment will pose a series of separate questions that with cover the material covered in the lectures and lab sessions. There is no strict word limit for this assignment, but it is expected that students' combined responses will be no longer than 1,000 words (excluding Stata output, tables, graphs, and Stata log files).
Marking criteria and/or marking rubrics are available in the ‘Assessment’ folder in Blackboard for this course.
AI use: This assessment task evaluates student’s abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Your assignment must be submitted via Turnitin on blackboard. To submit your assignment electronically log in to http://learn.uq.edu.au/ with your UQ username and password, then click on Course Code>>Assessment>>Assignments, and use the appropriate assignment submission link for each piece of assessment. No e-mailed submissions of assessments will be accepted.
Turnitin links will be configured to permit early submission of assessment items. Students will have the opportunity to submit draft assignments to Turnitin prior to submission of the final assignment in order to review similarity index content and to improve academic writing practice in accordance with UQ Academic Integrity policies.
By uploading your assignment via Turnitin, you are certifying that the work you submit is your own work except where correctly attributed to another source. Do not submit your assignment if it contains any work that is not your own. Please note that on the preview page, your assignment will be shown without formatting. Your assignment will retain formatting and your coordinator/tutor will be able to see formatted assignments. Once you have submitted your assignment you are able to go back and view your submission with the correct formatting.
You are required to retain proof of submission of your assessment. Your Digital Receipt is available for download from your Assignment Dashboard. If you cannot see your submission and download your digital receipt, your assessment has not been successfully submitted, please submit again. If you are unable to submit your assignment by the due date, you need to apply for an extension as set out in section 5.3.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
An extension request without penalty will only be considered under exceptional circumstances as outlined on my.UQ. You must submit the extension request as soon as it becomes evident that an extension is needed, but no later than the assessment item submission due date.
A request for an extension to an assessment due date must be accompanied by supporting documentation corroborating the reason for the request. The student submitting the request is fully responsible for all supporting documentation that is provided with the request and should ensure all documents are authentic.
Extensions on the basis of an approved Student Access Plan (SAP) or an Extension Verification Letter (EVL) can be approved for a maximum period of 7 calendar days. Extensions exceeding this duration or subsequent extensions for a piece of assessment will require additional supporting documentation (e.g., a medical certificate or other supporting evidence listed on my.UQ) and Course Coordinator approval.
When you submit an extension request in the student portal, it is received, read, and actioned by the Social Science Student Administration Team. It does not go to the course coordinator.
Late applications (requests received after the assessment item submission due date) must include evidence of the reasons for the late request, detailing why you were unable to apply for an extension by the due date.
In considering applications for extensions, students may be asked to supply the work they have completed to date on the assessment piece. This is to establish what efforts have already been made to complete the assessment, and whether the proposed work plan is feasible.
Late submissions of extension requests in your final semester of study could delay your graduation by up to one semester.
Work can NOT be accepted if it is more than one week (7 calendar days) late without prior approval.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 3
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 35%
- Due date
21/10/2024
Submit via Course Blackboard (TurnItIn).
- Learning outcomes
- L01, L03, L06, L07, L08, L09
Task description
A computer and statistical assignment worth 35% of the overall course grade. This assignment will be placed on Blackboard in Week 10 and will be completed in a three-week period (weeks 10, 11, and 12). The assignment will pose a series of separate questions that with cover the material covered in the lectures and lab sessions. There is no strict word limit for this assignment, but it is expected that students' combined responses will be no longer than 1,000 words (excluding Stata output, tables, graphs, and Stata log files).
Marking criteria and/or marking rubrics are available in the ‘Assessment’ folder in Blackboard for this course.
AI use: This assessment task evaluates student’s abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Your assignment must be submitted via Turnitin on blackboard. To submit your assignment electronically log in to http://learn.uq.edu.au/ with your UQ username and password, then click on Course Code>>Assessment>>Assignments, and use the appropriate assignment submission link for each piece of assessment. No e-mailed submissions of assessments will be accepted.
Turnitin links will be configured to permit early submission of assessment items. Students will have the opportunity to submit draft assignments to Turnitin prior to submission of the final assignment in order to review similarity index content and to improve academic writing practice in accordance with UQ Academic Integrity policies.
By uploading your assignment via Turnitin, you are certifying that the work you submit is your own work except where correctly attributed to another source. Do not submit your assignment if it contains any work that is not your own. Please note that on the preview page, your assignment will be shown without formatting. Your assignment will retain formatting and your coordinator/tutor will be able to see formatted assignments. Once you have submitted your assignment you are able to go back and view your submission with the correct formatting.
You are required to retain proof of submission of your assessment. Your Digital Receipt is available for download from your Assignment Dashboard. If you cannot see your submission and download your digital receipt, your assessment has not been successfully submitted, please submit again. If you are unable to submit your assignment by the due date, you need to apply for an extension as set out in section 5.3.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
An extension request without penalty will only be considered under exceptional circumstances as outlined on my.UQ. You must submit the extension request as soon as it becomes evident that an extension is needed, but no later than the assessment item submission due date.
A request for an extension to an assessment due date must be accompanied by supporting documentation corroborating the reason for the request. The student submitting the request is fully responsible for all supporting documentation that is provided with the request and should ensure all documents are authentic.
Extensions on the basis of an approved Student Access Plan (SAP) or an Extension Verification Letter (EVL) can be approved for a maximum period of 7 calendar days. Extensions exceeding this duration or subsequent extensions for a piece of assessment will require additional supporting documentation (e.g., a medical certificate or other supporting evidence listed on my.UQ) and Course Coordinator approval.
When you submit an extension request in the student portal, it is received, read, and actioned by the Social Science Student Administration Team. It does not go to the course coordinator.
Late applications (requests received after the assessment item submission due date) must include evidence of the reasons for the late request, detailing why you were unable to apply for an extension by the due date.
In considering applications for extensions, students may be asked to supply the work they have completed to date on the assessment piece. This is to establish what efforts have already been made to complete the assessment, and whether the proposed work plan is feasible.
Late submissions of extension requests in your final semester of study could delay your graduation by up to one semester.
Work can NOT be accepted if it is more than one week (7 calendar days) late without prior approval.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
Grade | Cut off Percent | Description |
---|---|---|
1 (Low Fail) | 1 - 29 |
Absence of evidence of achievement of course learning outcomes. |
2 (Fail) | 30 - 44 |
Minimal evidence of achievement of course learning outcomes. |
3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Academic Integrity: All students must complete the Academic Integrity Modules https://www.uq.edu.au/integrity/
UQ Assignment Writing Guide: Steps for writing assignments - my.UQ - University of Queensland
Release of Marks: The marks and feedback for assessments will be released to students in a timely manner, prior to the due date of the next assessment piece for the course. This is with the exception of the final piece of assessment. The marks and feedback for the final assessment item will only be made available to the student on the Finalisation of Grades date at the end of semester.
Assessment Re-mark: For information on requesting an assessment re-mark, please view the following page on my.UQ: https://my.uq.edu.au/querying-result
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Additional learning resources information
Further recommended readings may be added to Blackboard over the course of the semester. These recommended readings will be used to provide alternative explanations of the techniques covered throughout the course and to illustrate their practical applications.
For computer lab sessions, students are advised to bring the following materials:- The computer lab workbook (available on Blackboard).
- The lecture notes.
- A USB flash drive tp back up their work.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
Learning period | Activity type | Topic |
---|---|---|
Week 1 (22 Jul - 28 Jul) |
Lecture |
Course introduction (Week 1) This lecture will introduce the course. Among others, it will cover the course structure, the objectives, the recommended readings, and the assessment. Learning outcomes: L01 |
Tutorial |
Lab session (Week 1) This lab session will introduce the datasets used in the course and provide a refresher of statistical concepts and Stata software syntax. Learning outcomes: L02, L06, L08 |
|
Week 2 (29 Jul - 04 Aug) |
Lecture |
Revision of statistical concepts (Week 2) This lecture will provide a revision of key ideas concerning the logic of quantitative research, the properties of quantitative data, bivariate data analyses, and foundational notions in statistical inference. This is a refresher on/summary of material covered in 'SOCY2339 Introducing Quantitative Research'. Learning outcomes: L01, L02, L03 |
Tutorial |
Lab session (Week 2) This lab session will cover Stata commands to implement statistical inference for bivariate associations (correlation, chi-squared tests, and t-tests). Learning outcomes: L04, L08 |
|
Week 3 (05 Aug - 11 Aug) |
Lecture |
Index and scale construction (Week 3) Learning outcomes: L01, L02, L03 |
Tutorial |
Lab session (Week 3) This session will introduce techniques for index construction using Stata Learning outcomes: L06, L07, L08 |
|
Week 4 (12 Aug - 18 Aug) |
Lecture |
Introduction to regression (Week 4) Learning outcomes: L04 |
Tutorial |
Lab session (Week 4) This lab session will provide hands-on practice using multiple linear regression. Students will use Stata software to plot bivariate scatterplots. |
|
Week 5 (19 Aug - 25 Aug) |
Lecture |
Fundamentals of bivariate and multiple linear regression (Week 5) Learning outcomes: L05 |
Tutorial |
Lab session (Week 5) Students will continue using Stata to estimate bivariate linear regression. Learning outcomes: L06, L07, L08, L10 |
|
Week 6 (26 Aug - 01 Sep) |
Lecture |
Incorporating categorical predictors in linear regression (Week 6) Learning outcomes: L04, L05 |
Tutorial |
Lab session (Week 6) Students will fit and interpret multiple regression models in Stata Learning outcomes: L06, L07, L08 |
|
Week 7 (02 Sep - 08 Sep) |
Lecture |
Statistical moderation (Week 7) Learning outcomes: L04, L05, L10 |
Tutorial |
Lab session (Week 7) This lab session will consist of Stata-based exercises using multiple linear regression. Learning outcomes: L06, L07, L08 |
|
Week 8 (09 Sep - 15 Sep) |
Lecture |
Statistical mediation (Week 8) Learning outcomes: L02, L03, L04, L05 |
Tutorial |
Lab session (Week 8) This lab session will involve Stata-based exercises illustrating the logics of moderation and using it in regression models. Learning outcomes: L06, L07, L08, L10 |
|
Week 9 (16 Sep - 22 Sep) |
Lecture |
NO LECTURE |
Tutorial |
Lab session (Week 9) This lab session will involve Stata-based exercises using mediation Learning outcomes: L06, L07, L08, L10 |
|
Mid Sem break (23 Sep - 29 Sep) |
Lecture |
Mid-Semester Break There will be no lecture this week. |
Tutorial |
Mid-Semester Break There will be no tutorial this week. |
|
Week 10 (30 Sep - 06 Oct) |
Lecture |
Modelling non-linear relationships (Week 10) Learning outcomes: L02, L03, L04, L05, L10 |
Tutorial |
Lab session (Week 10) This lab session will involve Stata-based exercises in which students learn how to fit non-linear regression models. Learning outcomes: L03, L04, L05, L10 |
|
Week 11 (07 Oct - 13 Oct) |
Lecture |
Binary logistic regression models (Week 11). Learning outcomes: L06, L07, L08, L09 |
Tutorial |
Lab session (Week 11) This lab session will enable students to fit binary logistic regression models. Learning outcomes: L06, L07, L08, L09 |
|
Week 12 (14 Oct - 20 Oct) |
Lecture |
Advanced techniques and external reviews (Week 12) Learning outcomes: L03, L04, L05, L10 |
Tutorial |
Lab session (Week 12) In this lab session, students will practice fitting models with ordered and multinomial outcome variables. Learning outcomes: L06, L07, L08, L09 |
|
Week 13 (21 Oct - 27 Oct) |
Lecture |
Quantitative research showcase (Week 13) This session will offer students the opportunity to learn about quantitative research projects undertaken by students of Social Science at UQ. Several current and former post-graduate students and research fellows will present findings from their own research and share their experiences with using quantitative research techniques for the analysis of Social Science data. Learning outcomes: L01, L02, L03, L10 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.