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ERIC Number: ED407418
Record Type: Non-Journal
Publication Date: 1997-Jan-24
Pages: 22
Abstractor: N/A
Reference Count: N/A
Higher-Order Factor Analysis.
Gray, B. Thomas
Higher order factor analysis is an extension of factor analysis that is little used, but which offers the potential to model the hierarchical order often seen in natural (including psychological) phenomena more accurately. The process of higher order factor analysis is reviewed briefly, and various interpretive aids, including the Schmid-Leiman solution, are discussed. An example of the use of higher-order factor analysis is provided using the Alcohol Use Inventory. The basic process of factor analysis can be conceptualized in terms of a series of matrices. A matrix of data is analyzed to produce a matrix of associations. An appropriate extraction technique is used to produce the factor matrix. An interfactor matrix of associations (factors by factors) is constructed, and factors are again extracted to yield higher order factors that can be rotated. Repeating the process will yield sequentially higher-order factors until either a single factor is extracted, or the extracted factors are uncorrelated even with rotation. Interpreting the higher order factor follows. The solution proposed by J. Schmid and J. Leiman (1975)"orthogonalizes" the factors by residualizing the variance from the primary factors and attributing it to the second-order factor alone. This approach gives another look at a data set that may provide useful information. Higher-order factor analysis is not often used, but it has the potential to aid interpretation. (Contains 1 figure, 6 tables, and 29 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