An analytical positive manifold algorithm for use with latent class analysis

Robert E. Tarjan; Curtis R. Miller

doi:10.1207/s15327906mbr0603_6

An analytical positive manifold algorithm for use with latent class analysis

Robert E. Tarjan
, Curtis R. Miller

Research output: Contribution to journal › Article › peer-review

Abstract

A simple computational method for latent class analysis is that of Gibson (1951, 1962), For the method to be adapted to computers, however, it is necessary to utilize an algorithm which will rotate the intermediate solution to a positive manifold result. Such algorithms have not appeared in the literature. A positive manifold algorithm which has proven successful, is developed and presented, with comments on its use.

Original language	English (US)
Pages (from-to)	363-371
Number of pages	9
Journal	Multivariate Behavioral Research
Volume	6
Issue number	3
DOIs	https://doi.org/10.1207/s15327906mbr0603_6
State	Published - Jul 1 1971
Externally published	Yes

All Science Journal Classification (ASJC) codes

Statistics and Probability
Experimental and Cognitive Psychology
Arts and Humanities (miscellaneous)

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10.1207/s15327906mbr0603_6

Cite this

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title = "An analytical positive manifold algorithm for use with latent class analysis",

abstract = "A simple computational method for latent class analysis is that of Gibson (1951, 1962), For the method to be adapted to computers, however, it is necessary to utilize an algorithm which will rotate the intermediate solution to a positive manifold result. Such algorithms have not appeared in the literature. A positive manifold algorithm which has proven successful, is developed and presented, with comments on its use.",

author = "Tarjan, \{Robert E.\} and Miller, \{Curtis R.\}",

note = "Funding Information: 1. This investigation was supported by Public Health Service Research Grant No. MH-08667 from the Nakional Institute of Men'tal Health, Department of Health, Education and Welfare, and General Research Support Grant FtR-06632 from the General Research Support Branch, Division of Research Facilities and Resources, National Institutes of Health. Computing assistance was obtained from the Healkh Sciences Compnting Facility, UCLA, sponsored by NIH Grant FR-8. Appreciation is extended tu Dr. W. A. Gibson for critical commenta .o.n an earlier version of this dicla",

year = "1971",

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doi = "10.1207/s15327906mbr0603\_6",

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AU - Miller, Curtis R.

N1 - Funding Information: 1. This investigation was supported by Public Health Service Research Grant No. MH-08667 from the Nakional Institute of Men'tal Health, Department of Health, Education and Welfare, and General Research Support Grant FtR-06632 from the General Research Support Branch, Division of Research Facilities and Resources, National Institutes of Health. Computing assistance was obtained from the Healkh Sciences Compnting Facility, UCLA, sponsored by NIH Grant FR-8. Appreciation is extended tu Dr. W. A. Gibson for critical commenta .o.n an earlier version of this dicla

PY - 1971/7/1

Y1 - 1971/7/1

N2 - A simple computational method for latent class analysis is that of Gibson (1951, 1962), For the method to be adapted to computers, however, it is necessary to utilize an algorithm which will rotate the intermediate solution to a positive manifold result. Such algorithms have not appeared in the literature. A positive manifold algorithm which has proven successful, is developed and presented, with comments on its use.

AB - A simple computational method for latent class analysis is that of Gibson (1951, 1962), For the method to be adapted to computers, however, it is necessary to utilize an algorithm which will rotate the intermediate solution to a positive manifold result. Such algorithms have not appeared in the literature. A positive manifold algorithm which has proven successful, is developed and presented, with comments on its use.

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JO - Multivariate Behavioral Research

JF - Multivariate Behavioral Research

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ER -

An analytical positive manifold algorithm for use with latent class analysis

Abstract

All Science Journal Classification (ASJC) codes

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