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ERIC Number: ED431794
Record Type: Non-Journal
Publication Date: 1998-Apr
Pages: 12
Abstractor: N/A
Reference Count: N/A
ISBN: N/A
ISSN: N/A
Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data.
Tirri, Henry; Silander, Tomi
A new information-theoretically justified approach to missing data estimation for multivariate categorical data was studied. The approach is a model-based imputation procedure relative to a model class (i.e., a functional form for the probability distribution of the complete data matrix), which in this case is the set of multinomial models with some independence assumptions. Based on the given model class assumption, an information-theoretic criterion can be derived to select between the different complete data matrices. Intuitively this general criterion, called stochastic complexity, represents the shortest code length needed for coding the complete data matrix relative to the model class chosen. Using these information-theoretic criteria, the missing data problem is reduced to a search problem, that of finding the data completion with minimal stochastic complexity. The results of two empirical studies of the approach, using educational data sets of 478 elementary school students ("Popular kids" - POPKIDS in Michigan) and 500 Irish schoolchildren ("Irish educational transitions data - Irish), are presented and compared to those achieved with commonly used techniques such as case deletion and imputation of sample averages. (Contains 3 figures, 6 tables, and 36 references.) (Author/SLD)
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A