Insegnamento a.a. 2024-2025

Part 1. Causal inference, principles of surveys and modern literature review:

Economics, Social Sciences and Big Data

Survey methods

The potential counterfactual outcome model

Randomization and its internal and external validity

Overview of quasi-experimental methods

Systematic reviews and meta-analysis

Part 2. Foundations of social science research in the digital era and big data analysis:

Social Network Analysis

Introduction to machine learning

Big data analytics

Data retrieval

Finding patterns in big data

Data reduction

Model fit and validity

Text as data

Large language models

Define the role of quantitative research in the social sciences

Recognize the strenghts and limitations of randomized experiment

List the main quasi-sperimental research designs

Recognize the variety of data available on the web

Explain some computational methods

Reproduce simple computational applications

Design and draw up a survey

Demonstrate the absence of internal biases in randomized experiments

Develop a systematic review

Apply STATA to simple research questions

Analyze data through machine learning

Use web sources

Apply textual analysis to simple datasets

Prepare a presentation and draw up scientific paper

• Lectures
• Guest speaker's talks (in class or in distance)
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments

The practical exercises will involve utilizing a dataset for analysis using STATA and employing user-friendly applications for tasks such as machine learning and web scraping (using Python, Stata or R). Instructors will assign students to groups of four, tasking them with formulating a research question and employing suitable data analysis techniques to address it (refer to the Assessment section for more details).

In the preparation of the paper and the class presentations, students will have the opportunity to develop their critical thinking skills, enhance team-building abilities, and gain exposure to a diverse range of research designs and methods within the social sciences. Collaborating with peers will not only foster creativity but also provide a platform for learning from one another and gaining insights into the criteria used to evaluate research proposals in international scientific settings.

Groups will have the flexibility to choose from three options for their final group project: 1) conducting a systematic review (with the option to include a meta-analysis), 2) designing, implementing, and analyzing data from an online survey experiment, or 3) undertaking a big data study (utilizing machine learning applications, textual analysis, or other relevant techniques).

If you are looking to learn Python, or to refresh the basics, make sure to take Bocconi course “20683- Python preparatory course”. The official Python website also lists a number of excellent online tutorials and textbooks. The course will also assume some basic familiarity with Python. For STATA there is an in-person course offered by Bocconi at the end of August. 

Students' assessment in this course will encompass three key elements to gauge their overall understanding of the content:

1. Comprehensive grasp of essential research methodologies.
2. Proficiency in conducting basic data analysis using software (Stata and Python).
3. Development of an original research paper for presentation in class.

Each of these components evaluates distinct competencies and equips students with the foundational skills needed to excel in research design within the social sciences.

The grading structure will consist of the following components:

1. Two problem sets (5% each, individual assessment).
2. Group Project Presentation (40%): Collaborative creation and presentation of an original research paper by a group of four students during the final four sessions of the course. For projects involving an online survey experiment or big data study (option 2 and 3), students must also submit a paper formatted as a PNAS Brief Report, adhering to specific guidelines outlined by the PNAS website (PNAS Brief Reports are limited to 3 pages, which is approximately 1,600 words including the manuscript text, title page, abstract, and figure legends, and 15 references).(https://www.pnas.org/author-center/submitting-your-manuscript)  Alternatively, groups opting for a Systematic Review should adhere to PRISMA guidelines and submit a paper not exceeding 3,000 words, excluding tables, graphs, and references.
3. Final written exam: A one-hour examination comprising six multiple-choice questions and one open-ended question, accounting for 50% of the total grade.
2. Proficiency in conducting basic data analysis using software (Stata).
3. Development of an original research paper for presentation in class.

Each of these components evaluates distinct competencies and equips students with the foundational skills needed to excel in research design within the social sciences.

The grading structure will consist of the following components:

1. Two problem sets (5% each, individual assessment).
2. Group Project Presentation (40%): Collaborative creation and presentation of an original research paper by a group of four students during the final four sessions of the course. For projects involving an online survey experiment or big data study, students must also submit a paper formatted as a PNAS Brief Report, adhering to specific guidelines outlined by the PNAS website. Alternatively, groups opting for a Systematic Review should adhere to PRISMA guidelines and submit a paper not exceeding 3,000 words, excluding tables, graphs, and references.
3. Final written exam: A one-hour examination comprising six multiple-choice questions and one open-ended question, accounting for 50% of the total grade.

Students' assessment will be based on three elements to assess the overall comprehension of the content of the course: a) testing that they fully understand the essential approaches of research methods, b) doing simple data analysis with a software (Stata); c) conceptualizing an original research paper to be presented in class. Each of these elements tests different competences and provides students with the essential approaches to master research design in the social sciences

The grading will be composed of three elements:

Two problem sets (5% each, individual):

Group Project Presentation (40%): Develop and original research paper presented by 4 students in the last four sessions of the course. For option 2) (online survey experiment) and 3) (big data study) students are also required to submit the paper in the style of a PNAS Brief Report that is limited to 3 pages, which is approximately 1,600 words including the manuscript text, title page, abstract, and figure legends, and 15 references (https://www.pnas.org/author-center/submitting-your-manuscript). Groups opting for a Systematic Review should follow PRISMA guidelines (https://www.prisma-statement.org/) and are expected to deliver a paper no longer than 3,000 words, excluded tables, graphs and references.

Final written exam. One-hour exam with 6 multiple choice and one opened-ended question (50%).

1. Final written exam: A one-hour examination comprising six multiple-choice questions and one open-ended question, accounting for 50% of the total grade. 

2. Individual research paper (50%), in the style of a PNAS Brief Report, using one or more of the methods and techniques covered in class. PNAS Brief Reports are limited to 3 pages, which is approximately 1,600 words including the manuscript text, title page, abstract, and figure legends, and 15 references (https://www.pnas.org/author-center/submitting-your-manuscript. The systematic review is not an option for non attending students.

Unfortunately, there is not an up-dated and adequate book for this course. We will use books' chapters, articles and web sources. The list of readings will be available in the syllabus. They will be downloadable from Blackboard or through links in the syllabus