SC504-7-AU-CO:
Introduction to Quantitative Analysis
2024/25
Sociology and Criminology
Colchester Campus
Autumn
Postgraduate: Level 7
Current
Thursday 03 October 2024
Friday 13 December 2024
20
15 March 2024
Requisites for this module
(none)
(none)
(none)
(none)
SC968
MSC B99012 Health Research,
MSC MF9012 Organised Crime, Terrorism and Security,
MA L30112 Sociological Research Methods,
MSC L31012 Survey Methods for Social Research,
MSC L310MO Survey Methods for Social Research,
MSC L31112 Migration Studies,
MPHDB79748 Health Studies,
PHD B79748 Health Studies,
MPHDL30048 Sociology,
PHD L30048 Sociology
The module is designed for students from social science and humanities backgrounds to provide students with a foundational understanding of statistics and quantitative research methods. This course will equip students with the fundamental skills for conducting independent empirical quantitative research.
Students will learn how to use and analyse quantitative surveys with R.
The aims of this module are:
- To introduce the fundamental concepts and principles of statistics for sociological quantitative research.
- To develop critical thinking about quantitative research
- To familiarise students with different types of data available, such as panel surveys, time-use surveys, and crime statistics.
- To understand the appropriate statistical analysis methods to use in various contexts.
- To provide the foundations for causal inference.
- To write an empirical quantitative paper.
- To develop students' ability to communicate statistical findings to a general audience.
By the end of this module, students will be expected to be able to…
- Define and explain key statistical concepts and principles of quantitative social science.
- Know how to find and download quantitative data.
- Select appropriate statistical techniques for different types of data and research questions.
- Perform statistical analysis using R.
- Interpret statistical results appropriately.
- Critically evaluate quantitative results and findings.
- Communicate quantitative empirical findings accurately and effectively in writing.
- Topic 1 - week 2 Social statistics and descriptive statistics
- Topic 2 - week 3 Probability and Inference
- Topic 3 - week 4 Hypothesis testing, t-test, chi2-test, correlation
- Topic 4 - week 5 Group Comparison
- Topic 5 - week 6 Introduction to Regression
- Topic 6 - week 7 Multivariate Regression, Decomposition Methods, Mediation
- Topic 7 - week 8 Advanced Regression
- Topic 8 - week 9 Introduction to Binary Logit Models
- Topic 9 - week 10 Exploratory Data Analysis
- Topic 10 - week 11 Revision and starting the Final Assignment
This module will be delivered via:
- One 1-hour lecture each week.
- One 2-hour lab each week.
Attendance in person is expected.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
| Coursework |
Report 1 |
|
20% |
| Coursework |
Report 2 |
|
20% |
| Coursework |
Final Report |
|
60% |
Additional coursework information
Assessment details can be found on Moodle
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Giacomo Vagni, email: g.vagni@essex.ac.uk.
Dr Giacomo Vagni
socpgtad@essex.ac.uk
Yes
No
Yes
Prof Benjamin Bradford
University College London
Professor
Available via Moodle
Of 18 hours, 6 (33.3%) hours available to students:
12 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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