Models and Measurement in Quantitative Sociology
Undergraduate: Level 6
Thursday 06 October 2022
Friday 30 June 2023
08 October 2021
Requisites for this module
SC203 or GV207 or SC208
BSC L315 Sociology (Applied Quantitative Research),
BSC L316 Sociology (Applied Quantitative Research) (Including Year Abroad),
BSC L317 Sociology (Applied Quantitative Research) (Including Placement Year),
BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (including foundation Year)
This module will develop students' understanding of quantitative analysis and impart the practical skills necessary for carrying out advanced statistical analysis of social data using modern statistical software and programming.
The first term of the module is focused on statistical models and begins with simple OLS regression and provides a framework for modelling strategy and variable selection. Students are then taken through extensions to the basic OLS model, with categorical predictors, interactions and non-linear terms. Next, we introduce models for categorical outcomes: binary logistic and multinomial logit. The term concludes with a discussion of practical topics in survey data analysis – how to deal with complex sample designs, weighting and non-response adjustments. The modelling framework outlined in this term builds the foundations for advanced quantitative social science methods.
The second term of the module introduces students to the data science concepts, techniques, and skills necessary to perform reproducible data analysis of variety of quantitative social data. Students will engage in hands-on reproducible data analysis workflow using open source computational tools, including the Python programming language, JupyterLab (and Jupyter Notebook), Markdown, GitHub, and the Open Science Framework (OSF). Prior knowledge of programming is not required and students that experience difficulties in installing software will have the opportunity to access it online from their laptops, tablets, or smartphones via JupyterHub. The students will learn, in an accessible way, basic models for machine learning, causal inference, and network analysis as well as practical data science skills, including data wrangling and visualization of various big data sources. The content is organized around three fundamental data science tasks--description (and exploratory data analysis), prediction, and causal inference (which includes experimental design). Attention is given to model evaluation and problems of selection bias, measurement error, confounding, and overfitting. Throughout the course are discussed issues of ethics, privacy, and fairness of quantitative models in social sciences.
Case studies from social sciences (e.g., decision making in criminal justice, censorship and collective action, social networks and public health) will be used throughout the course to provide synergy between sociological issues, statistical techniques, and data analysis. The students will engage with data-driven exercises, which they will consolidate in research portfolios demonstrating their data science accomplishments in the domain of sociology as well as employability skills.
This module will develop students' understanding of quantitative analysis and impart the practical skills necessary for carrying out advanced statistical analysis of social data using modern statistical software.
Transferable skills and learning outcomes
By the end of the module, you will be able to:
Perform, critically interpret, and communicate results from analysis using OLS regression, including models with categorical predictors, interactions, and non-linear terms.
• Perform, critically interpret, and communicate results from analysis using logistic and multinomial logit models.
• Deal with practical issues of data analysis, including complex sample designs, weighting and non-response adjustments.
• Freely and flexibly use computational tools—Python, Jupyter, Markdown—to perform reproducible data analysis and communicate your results.
• Wrangle, explore, visualize, and model your dataset using various Python libraries.
• Build an open and reproducible research workflow ranging from raw data to research report.
• Perform, critically interpret, and communicate results from analysis using basic models for machine learning, causal inference, and network analysis.
• Identify and deal with issues of selection bias, measurement error, confounding, and overfitting.
• Articulate and address issues of ethics, privacy, and fairness of quantitative models in the social domain.
• Write a clean, reusable code in Python.
• Use GitHub and OSF to share your work and collaborate on research projects with others.
If you wish to take this module but have not taken the second year module 'Researching Social Life II' (SC203-5-FY), please contact the module supervisor to see if you have the appropriate background in statistics.
Please click on the link below to view the Introduction video to SC385 Models and Measurement in Quantitative Sociology
This module is part of the Q-Step pathway. Q-Step is an award which you can gain simply by enrolling on specific modules and will signal to employers your capability in quantitative research. Learn more about the Q-Step pathway and enhance your degree now.
As there are still restrictions related to COVID-19 in place, some of the teaching on most modules will take place online. Most modules in Sociology are divided into lectures of around 50 minutes and a class of around 50 minutes. Some are taught as a 2hr seminar, and others via a 50-minute lecture and 2-hr lab. For the majority of modules the lecture-type content will be delivered online – either timetabled as a live online session or available on Moodle in the form of pre-recorded videos. You will be expected to watch this material and engage with any suggested activities before your class each week. Most classes labs and seminars will be taught face-to-face (assuming social distancing allows this).
Please note that you should be spending up to eight hours per week undertaking your own private study (reading, preparing for classes or assignments, etc.) on each of your modules (e.g. 32 hours in total for four 30-credit modules).
The lectures provide an overview of the substantive debates around the topic of the week, while the classes will give you the opportunity to reflect on your learning and actively engage with your peers to develop your understanding further. The weekly classes will take place face-to-face (unless there is a change in the current COVID safety measures). You are strongly encouraged to attend the classes as they provide an opportunity to talk with your class teacher and other students. The classes will be captured and available via Listen Again. However, if you want to gain the most you can from these classes it is very important that you attend and engage. Please note that the recording of classes is at the discretion of the teacher.
The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list
Assessment items, weightings and deadlines
|Coursework / exam
||Data Analysis Report
||Data Analysis Exercise 1
||Data Analysis Report
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.
Module supervisor and teaching staff
Prof Nick Allum, email: email@example.com.
Dr Valentin Danchev, email: firstname.lastname@example.org.
Professor Nick Allum, Dr Valentin Danchev
Jane Harper, Undergraduate Administrator, Telephone: 01206 873052
Dr Jennifer Fleetwood
Goldsmiths, University of London
Senior Lecturer in Criminology
Available via Moodle
Of 54 hours, 36 (66.7%) hours available to students:
1 hours not recorded due to service coverage or fault;
17 hours not recorded due to opt-out by lecturer(s), module, or event type.
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