Data-Driven Education Insights

Welcome to the inaugural article of “Education Matters”, a thought-provoking series that sheds light on the pressing issues within Nepal’s educational system while fostering a spirit of positive transformation. In this first installment, I provide an overview of the current state of education in Nepal, examining the general problems faced by the system. I also delve into the experiences of individuals who have pursued education abroad, exploring their struggles in utilizing their Nepalese education and credentials effectively. As I embark on this enlightening journey, I invite you to join me in uncovering the concerns within Nepal’s education system and inspiring meaningful change. Together, we can ensure that education truly matters, empowering every Nepalese child with the quality education they deserve.   Education as a Fundamental Right The right to education, including quality education, is enshrined in both international and national frameworks. Article 26 of the Universal Declar

  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