This module will develop students’ understanding of quantitative analysis and causal inference. In doing so, it will impart the practical skills necessary for carrying out advanced statistical analysis of social data using modern statistical software and programming.
Module Information:
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 concepts, techniques, and skills necessary to analyse a variety of criminological and sociological problems. Students will engage in hands-on reproducible data analysis workflows using R. 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 and assess a project employing such methods. The content is organized around two fundamental topics in social science: reproducibility and causal inference. Case studies from criminology and sociology will be used throughout the course, illustrating to students how to apply different statistical techniques to practical cases.
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.
Module Aims:
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 develop students’ understanding of causal inference and how to properly design and evaluate research projects assessing causal relationships.
· To teach students the practical skills necessary for carrying out advanced statistical analysis of sociological and criminological data using modern statistical software and programming.
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.
· Freely and flexibly use computational tools —R— to perform reproducible data analysis and communicate your results.
· Identify and assess issues of p-hacking, reverse causality, omitted variable bias, measurement error.
· Evaluate the reliability, internal validity, external validity, and construct validity of a research project.
· Critically analyse different criminological and sociological issues using relevant models for causal inference.
- Module Supervisor: Nick Allum
- Module Supervisor: Magda Borkowska
- Module Supervisor: Sergio Lo Iacono