Insegnamento a.a. 2024-2025

20684 - STATA PREPARATORY COURSE

IT Education Center

Course taught in English

Student consultation hours
Go to class group/s: 1
DES-ESS (I sem. - P) - EMIT (I sem. - P) - DSBA (I sem. - P) - PPA (I sem. - P)
Course Director:
MARIA CHIARA DEBERNARDI

Classes: 1 (I sem.)
Instructors:
Class 1: MARIA CHIARA DEBERNARDI


Suggested background knowledge

To feel comfortable in this course, students should be familiar with basic statistical concepts (i.e., frequency distribution, average, standard deviation, probability, bivariate descriptive statistics…) as taught in a first level statistical course. Basic computer knowledge is given as acquired (i.e., file manager use, basic knowledge of Excel...).

Mission & Content Summary

MISSION

Stata is a statistical software package widely adopted in scholar and research environments. The aim of this preparatory course is to help students begin their Master of Science studies with comfort and competence, since in many courses basic Stata topics will be taken for granted. The course is designed for students who have little or no experience with Stata application and intend to develop the knowledge of this useful and user-friendly software for business and economics data analysis. The course, that wants to be an introduction to the statistical software package, has these main objectives: - to present Stata's structure and how it works - to demonstrate the potentialities of the software for analyzing real datasets - to enable students to do, by their own, basic statistical analyses - to teach the best practices in files and code organization for research projects.

CONTENT SUMMARY

  • Stata IDE overview
  • Variables management
  • Data file management
  • Preparing data for analysis
  • Exploratory data analysis
  • Graphic representations
  • Hypothesis testing
  • Linear regression and its diagnostics (OLS only)
  • Coding with Stata (hints only)
  • Management of research projects (data files, do-files, documentation)

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Use different types of datasets
  • Clean and prepare data for subsequent analysis (the pre-processing step)
  • Produce basic descriptive analyses by means of simple statistical tables, measures, and graphs
  • Estimate an OLS linear regression model
  • Read and edit Stata scripts

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Understand which kind of data are needed by a specific model and how to adapt/transform data accordingly
  • Perform and read simple exploratory data analyses
  • Interpret the main outputs of regression
  • Organize research project materials

Teaching methods

  • Lectures
  • Practical Exercises
  • Interaction/Gamification

DETAILS

  • The face-to-face lectures are held in IT classrooms, so that each student has their own PC to use Stata during the lesson
  • Every lesson combines the presentation of syllabus topics using examples and in class exercises
  • After each lesson, students can download exercises from the course web page on Bboard. These exercises are meant to test the indicated learning aspects, by comparing students' own solutions with the ones provided on Bboard (Stata scripts)
  • For doubts or clarifications, students can use the dedicated online forum on the eLearning page of the course or send an email to the teacher. The answers will be published in the forum too, anonymously
  • To collect classroom feedback the teacher might use instant polls during the lessons

Assessment methods

  Continuous assessment Partial exams General exam
  • Self-assessment
 x    

ATTENDING AND NOT ATTENDING STUDENTS

In order to measure the acquisition of the learning outcomes, the self-assessment will be based on tests, carried out independently as homework.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • Slides, starting dataset and final scripts will be shared with students at the beginning of the lesson (download from Bboard)
  • After each lesson, homework with solutions will be available on Bboard
  • Additional online materials will be indicated during the course
  • Suggested optional bibliography (with increasing degrees of detail relating to the topic Statistics):

    • Felix Bittmann, Stata: A Really Short Introduction, De Gruyter Oldenbourg, 2019

    • Lisa Daniels, Nicholas W. Minot, An Introduction to Statistics and Data Analysis Using Stata: From Research Design to Final Report, SAGE Publications, 2019

    • Lawrence C. Hamilton, Statistics with STATA: Version 12, 8th Edition, Cengage Learning, 2013

Last change 15/05/2024 09:41