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.