Panel Data: Analysis and Applications for the Social Sciences

2022/2023

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Кафедра высшей математики (Общеуниверситетские кафедры)

Статус:

Курс по выбору

Когда читается:

1-й курс, 3 модуль

Преподаватель

Сальникова Дарья Вячеславовна

Course Syllabus

Abstract

The course “Panel data: Analysis and Applications for the Social Sciences” aims to provide students with the theoretical background and practical skills in conducting panel data analysis. The first part of the course gives an overview of multiple regression models. The second part of the course focuses on the methodological tools necessary to succeed in handling panel data, namely, regression models with interaction terms and exploratory longitudinal data analysis. The third part covers fixed-effects and random-effects models. Lectures provide students with the theoretical foundations of panel data analysis. Practical sessions develop data analysis and data visualization skills. Students use Python for statistical analysis. At the practical sessions, students discuss the key approaches to handling panel data and illustrate them with different examples from social science research, in particular, economic sociology. Students are given datasets from original studies to replicate the findings and change the model specifications if needed.

Learning Objectives

• The course aims to provide students with the theoretical background and practical skills in conducting panel data analysis. Specifically, the learning objectives are as follows: to enable students to choose appropriate models for panel data analysis; to develop data manipulation and visualization skills; to enable students to implement linear panel models in Python
• The course aims to provide students with the theoretical background and practical skills in conducting panel data analysis. Specifically, the learning objectives are as follows:  to enable students to choose appropriate models for panel data analysis  to develop data manipulation and visualization skills  to enable students to implement linear panel models in Python

Expected Learning Outcomes

• By the end of the course students are expected to apply fixed- and random- effects models to analyze panel data, to interpret the results, to have data visualization skills and skills in implementing the afore-mentioned methods by using RStudio in the context of panel data analysis. Students will learn the advantages and limitations of different approaches to panel data analysis. This knowledge will help students choose a set of appropriate statistical tools to test their research hypotheses.

Course Contents

• Introduction. Linear regression analysis
• Data manipulation. Supplementary tools for panel data analysis
• Interaction terms in regression analysis
• Fixed-effects models
• Random-effects models VS Fixed-effects models

Assessment Elements

• Quiz 1
• Quiz 2
• Home Assignment 1
• Home Assignment 2
• Seminar Activity
• Essay

Interim Assessment

• 2022/2023 3rd module
  0.15 * Quiz 2 + 0.15 * Home Assignment 2 + 0.25 * Essay + 0.15 * Seminar Activity + 0.15 * Quiz 1 + 0.15 * Home Assignment 1

Bibliography

Recommended Core Bibliography

• Charles N. Halaby. (2003). Running Head: Panel Models Panel Models in Sociological Research: Theory into Practice. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.72A0015F

Authors

• SALNIKOVA DARIA VYACHESLAVOVNA