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Course 2019-2020 a.y.

20517 - QUANTITATIVE METHODS FOR SOCIAL SCIENCES

GIO
Department of Decision Sciences

Course taught in English

Go to class group/s: 14

GIO (6 credits - I sem. - OB  |  SECS-S/01)
Course Director:
SIMONE PADOAN

Classes: 14 (I sem.)
Instructors:
Class 14: SIMONE PADOAN


Mission & Content Summary
MISSION

This course aims to provide a high level of understanding of quantitative methods so that, after its attendance, students are able to perform data analysis to support management decision-making. The course is delivered with an emphasis on introductory and advanced concepts of statistics for data analysis, where statistical techniques are taught in order to give students confidence in preparing accurate and informative data summaries, experiments, surveys and interpretation of research management reports. The practical activities are implemented using the specific statistical software STATA. It is essential that students develop skills for data processing as well as the interpretation of results.

CONTENT SUMMARY

Topic Reference:

  1. Course presentation & simple linear regression (Chapter 9 of the textbook A and notes).
  2. Multivariate regression I (Chapter 11 of the textbook A, paragraphs 11.1-11.4 and notes)
  3. Multivariate regression II (Chapter 11 and 14 of the textbook A, paragraphs 11.5-11.7, 14.4 and notes).
  4. Examples & exercises (Notes).
  5. Analysis of the variance I (Chapter 12 of the textbook A, paragraphs 12.1-12.3 and notes).
  6. Analysis of the variance II (Chapter 12 of the textbook A, paragraphs 12.4-12.5 and notes).
  7. Combining ANOVA and Regression (Chapter 13 of the textbook A, paragraphs 13.1-13.4 and notes).
  8. Examples & exercises (Notes).
  9. Longitudinal Data I (Chapter 12 of the textbook A, paragraphs 12.6-12.7 and notes).
  10. Longitudinal Data II (Chapter 2 of the textbook B, paragraphs 2.1-2.3 and notes).
  11. Longitudinal Data III (Chapters 3, 4 of the textbook B, paragraphs 3.1-3-5, 4.1-4.3 and notes).
  12. Examples & exercise (Notes).
  13. Categorical data analysis I (Chapter 8 of the textbook A, paragraphs 8.1-8.3).
  14. Categorical data analysis II (Chapter 8 of the textbook A, paragraphs 8.4 and Notes).
  15. Categorical data analysis III (Chapter 8 of the textbook A, paragraphs 8.5-8.6 and Notes).
  16. Examples & exercises (Notes).
  17. Logistic regression I (Chapter 15 of the textbook A, paragraphs 15.1, 15.3 and notes).
  18. Logistic regression II (Chapter 15 of the textbook A, paragraphs 15.2, 15.3 and notes).
  19. Logistic regression II (Chapter 15 of the textbook A, paragraphs 15.4, 15.5 and notes).
  20. Examples & exercises (Notes).

Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Understand the theoretical background of the main statistical techniques.
  • Learn how to organize and analyze a dataset.
  • Learn how to apply suitable methods to estimate the impact of public interventions.
  • Learn the use of the software STATA for data analysis. 
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
  • Become confident with the main statistical techniques for data analysis and select the most appropriate technique to respond to the research questions.
  • Independently organize a dataset and define an appropriate strategy for the data analysis process.
  • Apply suitable methods to estimate the impact of public interventions.
  • Become an independent user of the software STATA for data analysis.

Teaching methods
  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
DETAILS
  • Exercises (Exercises, database, software etc.): the learning phase includes workshops where students practice with statistical analysis of real data sets in order to learn how to perform and interpret real research management reports. Students have the opportunity to learn how to use the software STATA for data analysis on real databases provided by the instructor. Finally theoretical exercises are solved together with the instructor during  tutorial lessons. 
  • Case studies/Incidents (traditional, online): the instructor provides real datasets and case studies that the students can solve on their own or in small groups. Case studies consist of a brief presentation of the case (generally a research study conducted at national or international level for government or international organizations), a questionnaire or codebook, a dataset. Students are asked to correctly identify the research questions of the case study, define the research strategy, identify the most appropriate quantitative technique, apply the technique identified on STATA, write a research or policy evalutation report. Case studies are meant to simulate concrete situations of providing evidence through statistical methods to support decision making. Further material is provided for students such as papers or policy evaluation studies with the aim to show how quantiative methods are used to respond to research or policy evaluation questions of governmental or international organizations. 

Assessment methods
  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  •   x x
  • Group assignment (report, exercise, presentation, project work etc.)
  •   x x
    ATTENDING AND NOT ATTENDING STUDENTS

    Attending and non-attending students. The assessment methods have been designed to stimulate the active involvement in the course. The grade breakdown is as follows:

    • Group assignment 30%
    • First and second partial or final written exam 70%

     

    • At the end of the course there is an exam to test the knowledge acquired. There is a written exam with questions and exercises on the topics taught in class (see the detailed description). The maximum grade for the final exam is 22 points. To consider the final written exam successfully passed students need to obtain a grade of 12 points. Alternatively, students can complete two mid-term exams during the course. The maximum grade for the mid-term exams is still 22 points for both. Students can attend the second partial exam if they have obtained a minimum grade of 11 points in the first partial exam. If the second partial exam is not handed in, the student must take the final exam. To consider the the two written partial exams successfully passed students need to obtain an average grade of 12 points between the two exams.
    • During the course there is also an assignment (empirical analysis) that students should perform on their own or in groups and hand in before the end of the course. This assignment is worth a maximum of 9 points. The grade of the assignment is valid also for the following academic years; hence you do not need to repeat the assignment. To consider the final successfully passed students need to obtain a grade of 18 points out of 31. The examination procedures are the same for students who attend and do not attend the classes.

    Teaching materials
    ATTENDING AND NOT ATTENDING STUDENTS
    • Textbook A: A. AGRESTI, B. FINLAY, Statistical Methods for the Social Sciences, Prentice Hall, 2009, 4th edition.
    • Textbook B: E. W. FREES, Longitudinal and Panel Data, Cambridge University Press, 2004, 1st edition. 
    • A selection of notes and other materials, available in the course reserve (e-learning).
    • U. KOHLER, F. KREUTER, Data Analysis Using Stata, Stata Press, 2012, 3rd edition. 
    Last change 10/06/2019 18:24