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Monday 29 July – Friday 2 August 08:00 – 08:45
This is a free supplementary course. You must register and pay for a one-week or two-week course to qualify for attendance. To book, please check the box when registering.
This course is designed to refresh critical mathematical background in quantitative social science research. It provides an overview of the essential concepts and language required for competent analysis using quantitative approaches in social science.
Julia Koltai is an assistant professor at the Faculty of Social Sciences, Eötvös Loránd University. She is also a research fellow at the Centre for Social Sciences, Hungarian Academy of Sciences. She gained her PhD in sociology in 2013.
Julia has led several domestic research programs and has taken part in international research projects and groups, including EU FP6-funded programs.
Her main scientific focus is on statistics and social research methodology, so her research has ranged widely, from minority research through political participation to social justice and integration.
In recent years, Julia's interest has turned to computational social science, especially network analysis and big data processing.
@koltaijuliThe topics focus on dimensions of calculus, linear algebra and probability theory most commonly applied in social science research. Rather than taking a comprehensive mathematical approach, the course will focus on the most critical concepts and approaches behind widely used tools like multivariate regression, logistic regression, principle component analysis, structural equation modelling, and other statistical methods.
This is a refresher course. We won't have time for deeper elaboration of the topics or detailed computation of examples. We will focus on the concept, context and basic rules of the mathematical topics. For each topic, I will talk briefly about the application of the given mathematical concept, and suggest literature for deeper understanding.
Basic algebra (secondary school level)
| Day | Topic | Details |
|---|---|---|
| 1 | Basics |
Relations and functions Sets Limits and continuity |
| 2 | Basic concept of calculus: differential and integral |
Differential calculus Partial derivatives The theory of integration |
| 3 | Matrices |
Matrix rules Representing a system of equations in matrix form Determinants Inverses of matrices |
| 4 | Probability theory 1 |
Permutations, combinations Conditional probability and Bayes' Law Odds |
| 5 | Probability theory2 |
Probability distributions: normal, binomial, poisson Random variables |
| Day | Readings |
|---|---|
| 1 |
Timothy M. Hagle (1995) |
| 2 |
Hagle. Chapters 3–5 |
| 3 |
Hagle. Chapter 6 |
| 4 |
Hagle. Chapter 1 Tamás Rudas (2004) |
| 5 |
Rudas. Chapter 4 |
None.
None.