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Incomplete longitudinal data

  • Nicholas T. Longford
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

In most statistical applications, we expect that a set of relevant variables is available for each subject. However, human subjects are notorious for imperfect cooperation with surveys, especially when they have little or no stake in the outcome of the data collection exercise. Pervasive examples of imperfect cooperation are failure to answer an item from the background questionnaire and, more generally, failure to adhere to the protocol of the survey. For instance, examinees may lose motivation half-way through the test and abandon the test without attending to a segment of items, or they may mark the responses to these items arbitrarily. More radical forms of incomplete cooperation are not turning up for the appointment and rejecting the approach of the data collector. Such instances are not uncommon because educational surveys typically demand a substantial commitment, in terms of time and mental effort, from the examinees. Our concern in this chapter is not with alleviating these problems, such as providing incentives to examinees and improving the presentation of the survey instruments, but rather with facing the problem of missing data as a fact of life and devising methods of analysis that make full use of data from all subjects, however incomplete their records may be.

Keywords

Reading Comprehension Conditional Expectation Variance Matrix Effective Sample Size Variance Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1995

Authors and Affiliations

  • Nicholas T. Longford
    • 1
  1. 1.Research DivisionEducational Testing ServicePrincetonUSA

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