Accueil > Policy analysis and policy evaluation level 1

KAFP 3170 - Policy Analysis and Policy Evaluation beginner

Type d'enseignement : Lecture and tutorials

Semester : Autumn 2018-2019

Number of hours : 36

Language of tuition : English

Pre-requisite

None.

Course Description

This is an introductory course of quantitative methods for policy analysts. It aims to prepare researchers to intelligently apply basic statistical methods for the purposes of empirical analysis. This course is thus a practical guide to statistical application for future policy analysts and makers. However, to become effective users of statistics, the students must learn elementary statistical concepts and theory, and be aware of the various assumptions of the methodology. The course will consequently combine simple exposition to statistical theory with practical use of statistical modeling. The course will alternate between lectures and practical lab sessions where students will be encouraged to apply the material while learning to program in the statistical software package R. While I do not expect students to be confident users of math, I do expect them to be keen learners. Learning quantitative methods requires continuous practice and repetition. This course steadily builds on previous material which is needed for understanding subsequent lectures. It is thus essential that you come to class, do all your readings on time, and complete all homework assignments.

Teachers

  • FRUCTUS, Iris (Analyste quantitatif)
  • JACQUETIN, Florian (.Chargé d'études macroéconomiques)
  • ROVNY, Jan (Assistant professor LIEPP, CEE – Sciences Po)
  • RUGAMBAGE, Norbert (Teaching assistant)

Pedagogical format

Lecture (12 sessions). Students are encouraged to learn together, however, all assignments (homework, exams) are individual, and must be completed by each student alone. Any cooperation on these will be considered as plagiarism.

Course validation

Homework assignments 30% (2 assignments, each worth 15%). Midterm exam 30%. Final exam 40%.

Workload

Students will be expected to prepare for class every week, which will include one or two readings. Every other week students will be expected to attend a practical session that will help them with programming in R.

Required reading

  • Meier et al. (various chapters)
  • Michael Lewis-Beck “Univari- ate Statistics” in Data Analysis: An Introduction
  • Kmenta, Jan “Introduction to Statistical Inference” in Elements of Econometrics
  • Fox “Collinearity” and “Non-constant Error Variance in Regres- sion Diagnostics”
  • Fox “Outlying and Influential data” and “Non-normally distributed errors” in Regression Diagnostics

Senior lecturers

  • FRUCTUS, Iris (Analyste quantitatif)
  • JACQUETIN, Florian (.Chargé d'études macroéconomiques)
  • RUGAMBAGE, Norbert (Teaching assistant)