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Bayesian Data Analysis: Introduction
No full textIn this video, Andrei introduces a series of videos looking at Bayesian data analysis. These will offer a brief definition of Bayesian data analysis, while the likelihood function and the Bayes rule will be discussed. They will also provide an opportunity to carry out some manual calculations and some simple Bayesian data analysis in R
Cross Classified Models Part 4: Practical
No full textIn this final video, Professor William Browne offers a practical using MLwiN that goes with the learning materials on cross classified models
Case Studies in Research Methods Pedagogy - Teaching quantitative design methods through exposition
Get PDFThere are different pedagogical traditions surrounding the teaching of quantitative methods. This case study comprises a detailed account of pedagogy for science-based quantitative methods from an intensive international seasonal school. The case study draws upon one teacher interview, eight hours of classroom observation, written field-notes, teaching materials, two short student interviews and informal research conversations with students across the sessions. In this case, the teacher, who I call Larry, is lecturing on quantitative design within a wider context of hands-on and computational sessions. Here, Larry’s pedagogic approach is one of exposition. However, this nominally didactic teaching – the lecture – is shown to convey rich pedagogical strategies and tactics, particularly in terms of pedagogical rhetoric. The pedagogic hook (and locus) of ‘teaching through data’, a preeminent facet of quantitative teaching [1], is clearly visible, even at this conceptual and discursive content stage as theory is constantly drawn back to data
Advanced Bayesian Methods: Introduction
No full textIn this video, Dr Gabriel Katz introduces this online resource which will explore fundamental aspects of modern Bayesian computation. He explains that he will focus on two Bayesian algorithms, the Gibbs Sampler and the Metropolis-Hastings, he will then review some criteria to address model convergence and he will finally present methods to speed up conversion or execution time
Multinominal logistic regression, Part 1: Introduction
No full textIn this video, Dr Heini Väisänen introduces multinomial logistic regression and discusses about when it might be used. She shows a simple example with one explanatory variable to illustrate how the method works and how the results can be interpreted using either odds ratios or predicted probabilities.
Moreover, she talks about categorical response variables, namely what kind of outcome variables might be used with this type of regression models
Studying Fake News
No full textIn this video, Professor Richard Rogers discusses studying fake news, particularly referring to the example of the run-up to the 2020 US Presidential elections. He provides an insight into how to check sources for misinformation and fake news
Time in Agent-based models
No full textIn this video, Oliver Reinhardt introduces two different ways to advance time in an agent-based simulation. He identifies some of the inherent problems of time-stepped simulations and explains how to avoid them with discrete event simulation
An Introduction to Factorial Survey Experiments- Part 2: Setting Up the Experiment and the Survey
No full textIn this second video, Dr Tamara Gutfleisch explores the most important steps in setting up and conducting Factorial Survey Experiments. She also discusses methodological choices related to FSEs and how to analyse the data obtained using this method
Decolonial Research Methods: Resisting Coloniality in Academic Knowledge Production (Webinar 1)
Get PDFThis is the first webinar in a six-part series from NCRM called Decolonial Research Methods: Resisting Coloniality in Academic Knowledge Production. The speaker is Professor Vineeta Sinha, from the National University of Singapore. Her presentation is titled Annihilating the "savage slot" from anthropology: Materializing reflexive practices. The webinar took place on 26 October 2021
Binary logistic regression: Interactions (video 3 of 3)
No full textIn this last video of the series, Dr Heini Väisänen discusses how to interpret interaction effects in binary logistic regression models. Using a simple regression model example, she presents two options: interpretation using predicted probabilities and interpretation using odds ratios