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# Contemporary Data Analysis: Methodology and Methods of Interdisciplinary Research

2023/2024
ENG
Instruction in English
6
ECTS credits
Course type:
Compulsory course
When:
1 year, 1, 2 module

### Course Syllabus

#### Abstract

This course is a required foundational course for masters’ students in “Applied Statistics with Network Analysis” program, designed to familiarize them with the most recent developments in interdisciplinary statistical methods. The students will get an overview of data and approaches to analyzing them (remember, “data” is always plural!), including complex models. The course will also emphasize problem formulation at the intersection of mathematics and social sciences, integrate the most important concepts from probability theory, and overall, is designed as a "gateway" to graduate work in statistics, where the mathematical concepts are bridged with applied concepts and research design, depending on the discipline.

#### Learning Objectives

• 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

• Be able to apply data analysis tools to real-life problems.
• Be able to build and estimate formalized mathematical models, describing real-life situations.
• Be able to criticize constructively and determine existing issues with the use of statistical methods in published work .
• Be able to estimate the data, find appropriate functions describing the data, visualize data.
• Have a working knowledge of different ways of using software programs for data analysis.
• Have a working knowledge of mathematics of data analysis.
• Know contemporary software programs used to analyze data.
• Know the four major areas that contemporary field of statistics is based on: data management, statistical inference, statistical prediction, and statistical reporting.
• Know the most recent advances in network science and applied statistical methods, complex statistical modeling, analysis, and forecasting.

#### Course Contents

• Introduction
• Social data
• Summated rating scale overview
• Scaling procedures: issues and applications
• Spacial data analysis
• Prediction
• Predictive modeling
• Conclusion: overview of the field

#### Assessment Elements

• Final In-Class or Take-home exam (at the discretion of the instructor)
• Homework Assignments
• In-Class Labs
• Quizzes

#### Interim Assessment

• 2023/2024 2nd module
0.5 * Final In-Class or Take-home exam (at the discretion of the instructor) + 0.2 * Homework Assignments + 0.2 * In-Class Labs + 0.1 * Quizzes

#### Recommended Core Bibliography

• Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research, Second Edition (Vol. Second edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=831411
• Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881
• Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Vol. 2nd ed). Mahwah, NJ: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=188193
• Statistics and Causality : Methods for Applied Empirical Research, edited by Wolfgang Wiedermann, and Eye, Alexander von, John Wiley & Sons, Incorporated, 2016. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4530803.