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# Longitudinal Data Analysis

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
1-й курс, 3 модуль

#### Преподаватель

Кускова Валентина Викторовна

### Course 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

• 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 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

• Introduction to the Framework of longitudinal data analysis
• Basics of Time Series I
• Basics of Time Series II
• ARIMA

#### Assessment Elements

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

#### Interim Assessment

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

#### 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