Data-Driven Education Insights

Book Review 1 Rosenbaum, P. R. (2023). Causal Inference (2nd ed.) . The MIT Press.  Buy the book here: https://mitpress.mit.edu/9780262545198/causal-inference/    In the second edition of "Causal Inference," Paul R. Rosenbaum presents a multidimensional examination of determining causation amidst confounded observational studies. Published by The MIT Press, the 203-page volume expounds on philosophical and technical intricacies in nine meticulously crafted chapters. Through an expert navigation between statistical concepts and applied contexts, Rosenbaum makes an indelible case for precise methodology in cementing credible causal claims from non-experimental data. The initial chapters outline the foundational function of randomized controlled trials (RCTs), where calculated randomization in treatment assignment balances unobserved biases. This sets up the central argument – that observational analyses, despite lacking controlled randomization, can accurately estimate treatmen

  AUTHOR AFFILIATION Nirmal Ghimire, Ph.D.   K-16 Literacy Center at University of Texas at Tyler PUBLISHED May 19, 2023 Introduction Multiple Correspondence Analysis (MCA) is a multivariate statistical technique that is used to analyze the relationships between categorical variables. It is a generalization of correspondence analysis (CA), which is used to analyze the relationships between two categorical variables. MCA can be used to explore the associations between multiple categorical variables simultaneously. MCA works by creating a map of the categorical variables. The map is created by calculating the distances between the different categories of the variables. The closer two categories are on the map, the more similar they are. The further apart two categories are on the map, the less similar they are. MCA can be used to explore a variety of research questions. For example, MCA can be used to: Explore the relationships between different demographic variables, such as age, gender