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Recovery of Structure in Incomplete Data by Alscal

Published online by Cambridge University Press:  01 January 2025

Robert C. MacCallum
Robert C. MacCallum*
Affiliation:
The Ohio State University
*
Requests for reprints should be sent to Robert C. MacCallum, Department of Psychology, 404C West 17th Avenue, The Ohio State University, Columbus, Ohio 43210.
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Abstract

A Monte Carlo study was carried out in order to investigate the ability of ALSCAL to recover true structure inherent in simulated proximity measures when portions of the data are missing. All sets of simulated proximity measures were based on 30 stimuli and three dimensions, and selection of missing elements was done randomly. Properties of the simulated data varied according to (a) the number of individuals, (b) the level of random error, (c) the proportion of missing data, and (d) whether the same entries or different entries were deleted for each individual. Results showed that very accurate recovery of true distances, stimulus coordinates, and weight vectors could be achieved with as much as 60% missing data as long as sample size was sufficiently large and the level of random error was low.

Keywords

multidimensional scalingindividual differences modelsmissing data
Type
Original Paper
Information
Psychometrika , Volume 44 , Issue 1 , March 1979 , pp. 69 - 74
Copyright
Copyright © 1979 The Psychometric Society

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References

Reference Notes

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Harshman, R. A. PARAFAC2: Mathematical and technical notes, 1972, California: U. C. L. A..Google Scholar

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