Multidimensional Data Analysis

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Международная лаборатория прикладного сетевого анализа

Статус:

Курс по выбору

Когда читается:

1-й курс, 4 модуль

Course Syllabus

Abstract

This course takes a modern, data-analytic approach to the multivariate data. Multivariate data analysis (MVA) encompasses statistical techniques that are used to analyze several variables at once. The course covers some basic notions of statistics with the development into several domains: cluster analysis, principle component analysis, factor analysis, canonical corelation analysis, discriminant analysis. All the topic of the course are supplemented by the examples of MVA application to different types of data. This course serves as an important prerequisite for the course in structural equation modeling.

Learning Objectives

• The goal of the course is to ensure that students understand topics and principles of applied linear models on an advanced level.
• The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.

Expected Learning Outcomes

• Have the skill to work with statistical software, required to analyze the data.
• Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
• Have the skill to meaningfully develop an appropriate model for the research question.
• Be able to criticize constructively and determine existing issues with applied linear models in published work.
• Be able to explore the advantages and disadvantages of various linear modeling instruments, and demonstrate how they relate to other methods of analysis.
• Be able to work with major linear modeling programs, especially R and SAS, so that they can use them and interpret their output.
• Have an understanding of advanced methods of linear models and related multivariate extensions.
• Know complex methods of aggregating data and dimensionality reduction.
• Know innovative, effective methods for presenting the results from statistical investigations of empirical data.
• Know new insights into the regression analysis.
• Know various modern extensions to the traditional linear model.

Course Contents

• Introduction to multivariate data analysis
• Basic statistics
• Some basic notations
• Graphical representation of multivariate data
• Cluster analysis
• Principal component analysis
• Factor analysis
• Canonical correlations
• Discriminant analysis

Assessment Elements

• Project 1
• Project 2
• Project 3

Interim Assessment

• 2024/2025 4th module
  0.33 * Project 1 + 0.33 * Project 2 + 0.34 * Project 3

Bibliography

Recommended Core Bibliography

• Brown, B. (2012). Multivariate Analysis for the Biobehavioral and Social Sciences : A Graphical Approach. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=405437
• Chatterjee, S., Hadi, A. S., & Ebooks Corporation. (2012). Regression Analysis by Example (Vol. Fifth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=959808
• Rencher, A. C., & Christensen, W. F. (2012). Methods of Multivariate Analysis (Vol. Third Edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=472234

Recommended Additional Bibliography

• Berry, W. D., & Sanders, M. S. (2000). Understanding Multivariate Research : A Primer For Beginning Social Scientists. Boulder, Colo: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=421170
• Izenman, A. J. (2008). Modern Multivariate Statistical Techniques : Regression, Classification, and Manifold Learning. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=275789

Authors

• Pavlova Irina Anatolevna