Review of Factor Analysis of Data Matrices.

Q. McN.

doi:10.1037/007953

A generalization of extendedQ-mode factor analysis to data matrices with variable row sums

Journal of the International Association for Mathematical Geology ◽

10.1007/bf01029799 ◽

1981 ◽

Vol 13 (3) ◽

pp. 217-224 ◽

Cited By ~ 5

Author(s):

J. E. Klovan

Keyword(s):

Factor Analysis ◽

Data Matrices

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Standardized versus unstandardized data matrices: Which type is more appropriate for factor analysis

Quality & Quantity ◽

10.1007/bf00145803 ◽

1980 ◽

Vol 14 (6) ◽

pp. 699-710 ◽

Cited By ~ 1

Author(s):

John C. Woelfel ◽

Joseph D. Woelfel ◽

Mary Lou Woelfel

Keyword(s):

Factor Analysis ◽

Data Matrices

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Target factor analysis of bond dissociation energies

Canadian Journal of Chemistry ◽

10.1139/v87-233 ◽

1987 ◽

Vol 65 (6) ◽

pp. 1380-1383 ◽

Cited By ~ 1

Author(s):

Darryl G. Howery ◽

Matthew Rubenstein

Keyword(s):

Factor Analysis ◽

Hydroxyl Radicals ◽

Experimental Error ◽

Bond Dissociation Energies ◽

Bond Dissociation ◽

Dissociation Energies ◽

Error Range ◽

Number Of Factors ◽

Target Factor Analysis ◽

Data Matrices

Six data matrices of bond dissociation energies ranging from a 6 × 6 matrix involving only hydrocarbon radicals to a 14 × 14 matrix involving several types of radicals are target factor analyzed. The number of factors to reproduce the data near experimental error range from two to four for the six matrices. The hydrogen and fluorine radicals are shown to be the most unique radicals. Combination sets of vectors from the data matrices reproduce the data within experimental error, with the hydrogen, fluorine, and hydroxyl radicals being the most important ones. Using target testing, a few of the properties of the radicals are identified as basic factors.

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