Model-Based Direct Adjustment

Abstract

Direct adjustment or standardization applies population weights to subclass means in an effort to estimate population quantities from a sample that is not representative of the population. Direct adjustment has several attractive features, but when there are many subclasses it can attach large weights to small quantities of data, often in a fairly erratic manner. In the extreme, direct adjustment can attach infinite weight to nonexistent data, a noticeable inconvenience in practice. This article proposes a method of model-based direct adjustment that preserves the attractive features of conventional direct adjustment while stabilizing the weights attached to small subclasses. The sample mean and conventional direct adjustment are both special cases of model-based direct adjustment under two different extreme models for the subclass-specific selection probabilities. The discussion of this method provides some insights into the behavior of true and estimated propensity scores: the estimated scores are better than the true ones for almost the same reason that direct adjustment can outperform the sample mean in a simple random sample. The method is applied to a nonrandom sample in an effort to estimate a discrete distribution of essay scores in the College Board's Advanced Placement Examination in Biology.

Key Words:

• Propensity scores
• Poststratification
• Standardization
• Conditional inference