EUI Multi-Level Models Course

May 23rd, 2011 Ken Posted in Course-related, Statistics No Comments »

An Introduction to Multi-Level Models (Using Stata)

European University Institute, May 23–27, 2011
Professor Kenneth Benoit
Methodology Institute, London School of Economics

Course handout here.

Readings are available from Mark Franklin’s Dropbox account for this course. If you are not yet subscribed, then email mark at

Day 1: Introduction and Motivation for multi-level models.

  • Required Reading: Rabe-Hesketh & Skrondal (2008, Chs. 1–2); Stata manual for reshape.
  • Recommended Reading: Franzese (2005); Gelman (2006); Austin, Goel & van Walraven (2001).
  • Homework 1 and Homework 1 Answer code.

Day 2: Estimating models with multi-level data.

  • Required Reading: Continue with Rabe-Hesketh & Skrondal (2008, Chs. 1–2) and Stata [XT] manual.
  • Recommended Reading: Steenbergen & Jones (2002); Austin, Goel & van Walraven (2001); Snijders & Bosker (1999); Goldstein (2003).
  • Homework 2: Question 2.3 from p87 of Rabe-Hesketh & Skrondal. Use xtmixed for part 2.3.2. Homework 2 Answer code.

Day 3: Random-intercept models.

  • Required Reading: Rabe-Hesketh & Skrondal (2008, Ch. 3).
  • Recommended Reading: Austin, Goel & van Walraven (2001); Snijders & Bosker (1999).
  • Homework 3: Question 3.2 from pp133-4 of R-H&S, plus:
    5. Compare a “random effects”, “fixed effects” (using the “fe” option to xtreg), and “between effects” regression by running them and discussing the differences on the estimated coefficient on the “deprive” variable, including the student-level covariates as in part 3 of the question.
    Homework 3 Answer code.

Day 4: Random-coefficient models.

  • Required Reading: Rabe-Hesketh & Skrondal (2008, Ch. 4)
  • Recommended Reading: Austin, Goel & van Walraven (2001); Snijders & Bosker (1999)
  • Homework 4: No pre-assigned homework today, although we will go through an example together in class that I will post on-line on the morning of Day 5.

Day 5: Extensions of the multi-level model

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The power of simulation

August 24th, 2009 Ken Posted in Quantitative Methods, Statistics No Comments »

This is very close to what we do in statistical sciences!

Success through simulation

Success through simulation

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