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Event Details

Missing Data

Time: October 31, 2014 at 9am to November 1, 2014 at 4pm
Location: Courtyard Washington Embassy Row
Street: 1600 Rhode Island Avenue, NW
City/Town: Washington, D.C.
Website or Map: http://www.statisticalhorizon…
Phone: 610-642-1941
Event Type: training, seminar
Organized By: Statistical Horizons LLC
Latest Activity: Jul 14, 2014

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Event Description

Taught by Paul D. Allison, Ph.D. 

If you’re using conventional methods for handling missing data, you may be missing out. Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems:

  • Inefficient use of the available information, leading to low power and Type II errors.
  • Biased estimates of standard errors, leading to incorrect p-values.
  • Biased parameter estimates, due to failure to adjust for selectivity in missing data.

More accurate and reliable results can be obtained with maximum likelihood or multiple imputation.

These new methods for handling missing data have been around for at least a decade, but have only become practical in the last few years with the introduction of widely available and user friendly software. Maximum likelihood and multiple imputation have very similar statistical properties. If the assumptions are met, they are approximately unbiased and efficient–that is, they have minimum sampling variance. 

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