Empirical Legal Studies
A.Y. 2020/2021
Learning objectives
This course aims at developing a rigorous understanding of the following concepts:
1. Why Empirical Research in Law matters
2. The concept of causality
2. Designing Research
3. Collecting & Coding Data
4. Statistical Software
5. Statistical Inference: linear and logistic regressions
1. Why Empirical Research in Law matters
2. The concept of causality
2. Designing Research
3. Collecting & Coding Data
4. Statistical Software
5. Statistical Inference: linear and logistic regressions
Expected learning outcomes
After successful completion of the course students are expected to be able to:
1. read and understand at a high-level empirical research in legal studies
2. detect empirical flaws in third-party statements or data presentations
3. identify questions that can be answered quantitatively
4. design and execute simple but rigorous empirical tests.
1. read and understand at a high-level empirical research in legal studies
2. detect empirical flaws in third-party statements or data presentations
3. identify questions that can be answered quantitatively
4. design and execute simple but rigorous empirical tests.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Second semester
The course will be enitrely online on ZOOM
Course syllabus
Syllabus
1. Preliminaries
a. Why ELS ?
b. Examples
c. Does it matter?
d. Software
2. Research design
a. RQ
b. Theories and testable implications
c. Rival HPs
3. Data collection
a. The "right" data
b. Locating the data
c. Population vs. sample
4. Summarizing the data
a. Mean, median, standard deviation and empirical rules
b. Graphical representation
5. Inference
a. Confidence intervals
b. Hypothesis testing
c. Univariate linear regression
d. Multivariate linear regression
e. Logistic regression
f. Visualizing results
1. Preliminaries
a. Why ELS ?
b. Examples
c. Does it matter?
d. Software
2. Research design
a. RQ
b. Theories and testable implications
c. Rival HPs
3. Data collection
a. The "right" data
b. Locating the data
c. Population vs. sample
4. Summarizing the data
a. Mean, median, standard deviation and empirical rules
b. Graphical representation
5. Inference
a. Confidence intervals
b. Hypothesis testing
c. Univariate linear regression
d. Multivariate linear regression
e. Logistic regression
f. Visualizing results
Prerequisites for admission
N/A
Teaching methods
Lectures, discussion of research papers, individual and group assignments
Teaching Resources
An Introduction to Empirical Legal Research
Lee Epstein and Andrew D. Martin
Papers provided by the instructor
Studnets are required to have w roking license of STATA (any version 12 and up)
Lee Epstein and Andrew D. Martin
Papers provided by the instructor
Studnets are required to have w roking license of STATA (any version 12 and up)
Assessment methods and Criteria
Individual and group assignments, in-class quizzes and a final written exam. Attendance and active participation are strongly encouraged
SECS-P/11 - FINANCIAL MARKETS AND INSTITUTIONS - University credits: 6
Lessons: 42 hours
Professor:
Bonini Stefano