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Course Syllabus

2021/2022

Longitudinal Data Analysis

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Optional course (faculty)
Delivered by: International laboratory for Applied Network Research
Where: Faculty of Social Sciences
When: 3 module
Open to: students of all HSE University campuses
Instructors: Valentina Kuskova
Language: English
ECTS credits: 4
Contact hours: 40

Course Syllabus

Full Syllabus

Abstract

This course is about quantitative methods, namely statistics, applied to social sciences. Specifically, we will focus on certain statistical competencies that help evaluate processes over time. I expect you to understand the basics of statistics you’ve learned previously in this course; everything else we will learn in this class. As you will see, we will use a lot of real-world datasets, and I am concerned more with your understanding on how statistic works as opposed to memorizing the formulas. This class will be unique in a sense that I will bring a lot of non-statistical material to help you understand the world of decision sciences.
Learning Objectives

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

Expected Learning Outcomes

  • Be able to present and/or interpret data in tables and charts.
  • Be able to understand and apply descriptive statistical measures to real-life situations.
  • Be able to understand and apply probability distributions to model different types of social processes.
  • Be able to understand the meaning and use of longitudinal models.
  • Have an ability to forecast future numbers based on historical data.
  • Have an ability to resolve problems and recognize the most common decision errors and make tough decisions in a competent way.
  • Have an ability to use computer software to perform statistical analysis on data (specifically, STATA).
  • Know modern applications of longitudinal analysis.
  • Know the theoretical foundation of longitudinal analysis.
  • Know the variety of time-series models that are available to analyze real-life problems, starting with the simple OLS regression and ending with highly advanced models.
Course Contents

Course Contents

  • Introduction to the Framework of longitudinal data analysis
  • Basics of Time Series I
  • Basics of Time Series II
  • ARIMA
  • Advanced time-series models I
  • Advanced time-series models II
  • Advanced time-series models III
  • Advanced time-series models IV
Assessment Elements

Assessment Elements

  • non-blocking Final In-Class or Take-home exam (at the discretion of the instructor)
  • non-blocking Homework Assignments (5 x Varied points)
  • non-blocking In-Class Labs (9-10 x Varied points)
  • non-blocking Quizzes (Best 9 of 10, Varied points)
Interim Assessment

Interim Assessment

  • 2021/2022 3rd module
    0.5 * Final In-Class or Take-home exam (at the discretion of the instructor) + 0.1 * Quizzes (Best 9 of 10, Varied points) + 0.2 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points)
Bibliography

Bibliography

Recommended Core Bibliography

  • Analysis of financial time series, Tsay, R. S., 2005
  • Derryberry, D. R. (2014). Basic Data Analysis for Time Series with R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=817454
  • Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (Vol. Second edition). Hoboken, New Jersey: Wiley-Interscience. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=985114
  • Taris, T. (2000). A Primer in Longitudinal Data Analysis. London: SAGE Publications Ltd. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=251795

Recommended Additional Bibliography

  • Beran, J. (2017). Mathematical Foundations of Time Series Analysis : A Concise Introduction. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1741935
  • Franses, P. H., & Paap, R. (2004). Periodic Time Series Models. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780199242030
  • Palma, W. (2016). Time Series Analysis. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1229817