Filter
Top Products
283
Example
18
More about Missing Data
Introduction
This example demonstrates the analysis of data in which some values are missing by
design and then explores the benefits of intentionally collecting incomplete data.
Missing Data
Researchers do not ordinarily like missing data. They typically take great care to avoid
these gaps whenever possible. But sometimes it is actually better not to observe every
variable on every occasion. Matthai (1951) and Lord (1955) described designs where
certain data values are intentionally not observed.
The basic principle employed in such designs is that, when it is impossible or too
costly to obtain sufficient observations on a variable, estimates with improved
accuracy can be obtained by taking additional observations on other correlated
variables.
Such designs can be highly useful, but because of computational difficulties, they
have not previously been employed except in very simple situations. This example
describes only one of many possible designs where some data are intentionally not
collected. The method of analysis is the same as in Example 17.