Module Directory

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 aims of this module are:

• To develop students’ understanding of quantitative methods in general and how to build statistical models representing sociological and criminological processes and behaviours.


• To teach students the practical skills necessary for carrying out advanced statistical analysis of sociological and criminological data using modern statistical software and programming.

By the end of this module, students will be expected to be able to:

1. Perform, critically interpret, and communicate results from analysis using OLS regression, including models with categorical predictors, interactions, and non-linear terms.


2. Perform, critically interpret, and communicate results from analysis using logistic and multinomial logit models.


3. Freely and flexibly use computational tools —R— to perform reproducible data analysis and communicate your results.


4. Identify and deal with issues of p-hacking, reverse causality, omitted variable bias, measurement error.


5. Evaluate the internal, external, and ecological validity of a research project.


6. Use OSF to pre-register your work and collaborate with others.


7. Critically analyse different criminological and sociological issues using relevant models for causal inference.

If you wish to take this module but have not taken the second year module 'Analysing Social Life' (SC202-5-AU), please contact the Module Convenor 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 https://moodle.essex.ac.uk/mod/page/view.php?id=668576

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 data science concepts, techniques, and the skills necessary to analyse a variety of criminological and sociological problems that will give students an insight into how modern social scientific work is conducted and give them the skills to gain an edge in a competitive job market. Students will engage in hands-on reproducible data analysis workflows using open-source and open-access tools, such as R, G*Power, and the Open Science Framework (OSF). No prior knowledge of programming is required.

Students will learn how to conduct rigorous and reproducible research using observational and experimental data. We will consider several forms of data collection (e.g., lab experiments, field experiments, surveys, web scraping), discuss the advantages and disadvantages of each approach, and learn how to design a project employing such methods. The content is organized around three fundamental topics crucial in social science: reproducibility, causal inference, and big data. Case studies from criminology and sociology will be used throughout the course, illustrating to students how to apply different data science techniques to practical cases in areas in which they are interested.

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.

The teaching consists of online and face to face lectures, a weekly discussion class where students read and comment on a paper on a criminological or sociological case study that uses quantitative methods, and a computer lab session where students will learn how to build statistical models and analyse the results, using social and criminological real-world datasets.

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. Lectures, classes, labs and seminars will be taught face-to-face. 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. 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.