CMI Short CoursesCMI Short CoursesBeyond the Mean: Quantile Regression for Distributional Analysis in Social Science ResearchDescriptionThe Cathie Marsh Institute are proud to introduce their 2026 short courses. All participants will receive state of the art teaching and a lunch voucher as part of the course.
An Introduction to Quantitative Text Analysis in Social Sciences Taught by: Tatjana Kecojevic Location: Ellen Wilkinson Building A3.6
Description, Learning Objectives, and Prerequisites Many research questions in the social sciences focus not only on average outcomes but also on how relationships differ across the distribution of an outcome. For example, the effects of education on wages, socio-economic status on health, or housing characteristics on prices may vary substantially between lower and higher parts of the outcome distribution. Standard regression methods such as Ordinary Least Squares (OLS) focus on conditional means and may therefore obscure important forms of heterogeneity. This short course introduces quantile regression, a statistical method that allows researchers to estimate and interpret relationships across different points of an outcome distribution. The course will combine conceptual explanations with practical implementation using R. Participants will learn when quantile regression is appropriate, how it differs from standard regression approaches, and how to interpret distributional effects in applied research. The course will be divided into two parts. The morning session will introduce the conceptual foundations of quantile regression, including the motivation for modelling conditional quantiles, interpretation of coefficients across quantiles, and comparisons with standard regression models. The afternoon session will consist of guided hands-on exercises in R, where participants will estimate quantile regression models using real-world data, compare results to OLS models, and visualise how effects vary across the outcome distribution. Learning Objectives By the end of the course participants will be able to: • Understand the difference between modelling conditional means and conditional quantiles. • Identify research questions where quantile regression is appropriate. • Estimate quantile regression models using R. • Interpret coefficients across different quantiles of the outcome distribution. • Visualise and interpret heterogeneous effects across the outcome distribution. Prerequisites Participants should have: • Basic familiarity with regression analysis, such as OLS. • Some experience using R for statistical analysis. • Rand RStudio installed on their laptop prior to the workshop. No prior experience with quantile regression is required. using R with real-world datasets.
CMI Short Course: Record Linkage and Statistical MatchingDescriptionThe Cathie Marsh Institute are proud to introduce their 2026 short courses. All participants will receive state of the art teaching and a lunch voucher as part of the course.
Record Linkage and Statistical Matching Taught by: Natalie Shlomo Location- Ellen Wilkinson Building A3.7 Abstract: The aim is to provide theory and practical applications of deterministic and probabilistic approaches to data (record) linkage. The course provides a review of best practices and covers theory with respect to the pre-processing requirements, the methods used to calculate matching weights, types of errors in the classification, evaluation procedures and introduction to the E-M algorithm and analysis of linked data. By the end of the course, students should have an understanding of data linkage techniques and be able to implement and evaluate data linkage procedures. Pre-requisite: No pre-requisite is required but students should have an understanding of basic statistical concepts.
CMI Short Course: Semantic Network AnalysisDescriptionThe Cathie Marsh Institute are proud to introduce their 2026 short courses. All participants will receive state of the art teaching and a lunch voucher as part of the course. Semantic Network Analysis Taught by: Nikita Basov Location: Ellen Wilkinson Building A3.7 Abstract: Semantic network induction allows for computational mapping of discoursive and cultural landscapes using a variety of verbal expressions, would these be originally written texts or transcripts of oral speech. This method relies on the idea that meaning of a word is shaped by its relations with other words in a particular social and temporal context (semantic/pragmatic interface). Semantic network mapping allows for capturing such relations, representing them as a semantic network in order to explore the corresponding discoursive/cultural landscape and identify the particular elements of this network to inspect closer. Mixed-method semantic network analysis offered by this workshop includes: (1) manually enhanced computational word collocation approach rooted in corpus linguistics to produce semantic networks of associations between words; (2) basic network analysis—to locate key nodes and links in these networks; and (3) interpretive analysis of texts—to understand these key elements in their textual contexts. Apart from capturing and understanding what is expressed directly, this approach enables revealing the latent contextual surroundings that mold the meaning of focal elements. After completing the workshop, participants will be able to use point-and-click software tools Automap and ORA to preprocess texts for semantic mapping and produce semantic networks, visualise the networks, and use them to inspect discoursive/cultural landscapes represented in texts, using basic network analysis techniques. Prerequisites and Software Requirements:
Training | Cathie Marsh Institute for Social Research (CMI) | The University of Manchester
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