SCS: Seminars 2008-09

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Table of contents

Week 1: Gelman & Hill Chapters 1 and 2

  • Friday, September 19, 2008 at 2:30 pm

Data sets

  • coop (http://www.stat.columbia.edu/~gelman/arm/examples/coop): tallies from an election for the board of a cooperative housing organization, used in Chapter 2 for testing the hypothesis of underdispersion in count data
  • death.polls (http://www.stat.columbia.edu/~gelman/arm/examples/death.polls): time series of Gallup polls on the death penalty, used in Chapter 2 to show simple confidence intervals

Week 2: Gelman & Hill Chapters 3, 4, 5

  • Friday, October 3, 2008 at 2:30 pm

Data sets

  • beauty (http://www.stat.columbia.edu/~gelman/arm/examples/beauty): Daniel Hamermesh's data on beauty and teaching evaluations, used in regression exercises in Chapters 3 and 4
  • child.iq (http://www.stat.columbia.edu/~gelman/arm/examples/child.iq): study of children's IQ's used in Chapter 3 to illustrate linear regression
  • earnings (http://www.stat.columbia.edu/~gelman/arm/examples/earnings): data on height, weight, earnings, and other variables from a national survey (Chapters 4 and 13)
  • girls (http://www.stat.columbia.edu/~gelman/arm/examples/girls): the proportion of girl births in Vienna for 24 consecutive months, from Mises (1953), used in an exercise in Chapter 2 for testing the binomial model
  • pyth (http://www.stat.columbia.edu/~gelman/arm/examples/pyth): simple dataset for a linear regression exercise in Chapter 3
  • arsenic (http://www.stat.columbia.edu/~gelman/arm/examples/arsenic): data on switching drinking water wells in Bangladesh, used in Chapter 5 to illustrate logistic regression
  • nes (http://www.stat.columbia.edu/~gelman/arm/examples/nes): data from the National Election Study, used in Chapters 4 and 5 to show a time series of linear and logistic regressions and in an exercise in Chapter 5 to illustrate nonidentifiability in logistic regression
  • pollution (http://www.stat.columbia.edu/~gelman/arm/examples/pollution): old dataset of mortality and pollution in 60 U.S. cities, used in an exercise in Chapter 4 to illustrate transformations in linear regression

Week 3: Gelman & Hill Chapters 5,6

  • Friday, October 17, 2008 at 2:30 pm

Week 4: Gelman & Hill Chapters 7,8

  • Friday, October 31, 2008 at 2:30 pm

Week 5: Gelman & Hill Chapters 8, 9

  • Friday, February 13, 2009 at 2:30 pm
R Script playing with ideas related to the propensity score
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Week 6:Gelman & Hill Chapters 9 and 10

  • Friday, February 27, 2009 at 2:30 pm

Hugh McCague to give a presentation on circular statistics

Week 7:Gelman & Hill Chapters 10 and 11

  • Friday, March 13

Week 8 Lord's Paradox: Gain scores or ANCOVA

  • Friday, April 3 (MOVED -- no longer March 27)

Readings on Lord's paradox previously circulated via email:

Some references

  • Rogosa, D. R., & Willett, J. B. (1983). Demonstrating the reliability of the difference score in the measurement of change. Journal of Educational Measurement, 20, 335-343.
  • Rogosa, D. (1988). Myths about longitudinal research. In K. W. Schaie, R. T. Campbell, W. M. Meredith, & S. C. Rawlings (Eds.), Methodological issues in aging research (pp. 171-209). New York, NY: Springer Publishing Company.
  • Maris, E. (1998). Covariance adjustment versus gain scores--revisited. Psychological Methods, 3, 309-327.
  • Maxwell, S. E., & Delaney, H. D. (1990). Designing experiments and analyzing data: A model comparison approach. Belmont, CA: Wadsworth.

Links

Multilevel models

Week 9: Gelman & Hill Chapters 11,12

  • Friday, April 24, 2008 at 2:30 pm