Non-Gramian and singular matrices in maximum likelihood factor analysis

Dong, Hei-Ki
1985
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Non-Gramian and singular matrices in maximum likelihood factor analysis

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1985

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In some cases, a correlation matrix may be singular because of the multicollinearity in data, and it may become non-Gramian because of computational inaccuracies. In such cases, popular methods of factor extraction, such as maximum likelihood factor analysis, image factor analysis, and canonical factor analysis, cannot be used because of computational difficulties. This article provides a simple heuristic procedure for converting such a matrix into a proper matrix, so that maximum likelihood factor analysis may be performed.

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Applied Psychological Measurement, Volume 09, 1985

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Dong, Hei-ki. (1985). Non-Gramian and singular matrices in maximum likelihood factor analysis. Applied Psychological Measurement, 9, 363-366. doi:10.1177/014662168500900404

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doi:10.1177/014662168500900404

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Dong, Hei-Ki. (1985). Non-Gramian and singular matrices in maximum likelihood factor analysis. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/102191.

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