Insegnamento a.a. 2019-2020

30320 - QUANTITATIVE METHODS FOR SOCIAL SCIENCES (MODULE II - STATISTICS)

Department of Decision Sciences

Course taught in English
Go to class group/s: 23
BIG (6 credits - II sem. - OB  |  SECS-S/01)
Course Director:
REBECCA GRAZIANI

Classes: 23 (II sem.)
Instructors:
Class 23: REBECCA GRAZIANI


Suggested background knowledge

PREREQUISITES

The exam code 30320 ‘Quantitative methods for social sciences - module 2 (Statistics)’ is a prerequisite of the exam code 30337 Policy evaluation

Mission & Content Summary

MISSION

This course provides an introduction to both the logic and mathematics of statistics, with an emphasis on political science applications. The course introduces students to the practice of data analysis, covering univariate and bivariate descriptive statistics and inference. Emphasis is put on the investigation and statistical assessment of relationships between two or more variables. The lectures switch between frontal lecturing, small group discussions and simulations. Students should bring their laptop to each session. Each session is indeed designed with the aim of fostering independent work by students, who are strongly encouraged to autonomously explore and apply the techniques shown and discussed by the instructor. The analyses are run using STATA with an emphasis on an effective use of the software and interpretation of the results. In sum, this course aims to cultivate students’ nascent analytical abilities and develop their statistical reasoning and literacy; equip students with a computational competence in STATA software in a supportive learning environment for all students.

CONTENT SUMMARY

  • Describing data through tables, charts and synthetic measures.
  • Describing association between two variables.
  • Inference on the mean and the proportion: point estimation, confidence interval estimation and significance tests.
  • Inference on bivariate associations: two-samples t test and chi-square test of independence.
  • Correlation analysis.
  • Regression analysis: simple linear regression and multiple linear regression.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Read reports and scientific articles that make use of basic statistical techniques.
  • Learn more advanced statistical techniques.
  • Set up and run empirical analyses, that require the use of basic statistical techniques to study relevant problems in Economics, Sociology and Political and other Social Sciences.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Use a statistical software (STATA) for data management and to implement basic statistical techniques for the empirical part of social sciences study.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments

DETAILS

  • Exercises are delivered through Bboard platform for E-Learning. They are multiple choice questions, with solutions provided as feedbacks
  • Group assignments are run as marked in-class activities. Students are asked to analyse a provided dataset and write a report with the interpretation of the analyses, to be posted through Bboard platform for E-Learning. An evaluation grid is provided

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
   x x
  • Individual assignment (report, exercise, presentation, project work etc.)
 x    
  • Group assignment (report, exercise, presentation, project work etc.)
   x x

ATTENDING STUDENTS

Four tests or a general written exam, all held in a IT room.

  • The four tests are delivered through Bboard platform for E-Learning and graded out of 31. Students are asked to answer multiple choice questions both on the theory and on the basis of the results of a data analysis with STATA. The arithmetic average of the four tests grade contributes 80% to the final mark.
  • The general exam has open-ended questions to be answered on the basis of the theory or of the results of a data analysis with STATA. The general exam is graded out of 31 and contributes 80% to the final mark.
  • In-class marked activities graded out of 31. Students are asked to run a group project consisting in an analysis of a provided dataset with STATA. A report with the results of the analysis is submitted through Bboard platform for E-Learning at the end of the activities. The in-class activities mark contribute 20% to the final mark.

NOT ATTENDING STUDENTS

One general written exam marked out of 31 on all topics introduced in the course. The exam is takenin an IT room at the exam dates. Students are asked to answer to open ended questions on a separate worksheet. The questions are both on the theory and on the results of analyses run with STATA on a provided dataset.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • P. POLLOCK, The Essentials of Political Analysis, Sage, 2016, 5th edition (selected chapters).
  • A. AGRESTI, C.FRANKLIN, B. KLINGENBERG, Statistics: The Art and Science of Learning from Data, Pearson 2017, Global Edition (selected chapters).
  • Online exercises delivered through Bboard platform.
  • Lectures Notes.
Last change 02/07/2019 10:24