The SAGE Dictionary of Quantitative Management Research 2011
DOI: 10.4135/9781446251119.n35
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Factor Analysis

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Cited by 34 publications
(43 citation statements)
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“…Items that correlated poorly with other items were deleted. To ensure sampling adequacy, items with poor Kaiser‐Meyer‐Olkin scores (less than 0.7) were deleted until an overall score of 0.7 was obtained, thereby exceeding the minimum value of 0.6 recommended by Hutcheson and Sofroniou (1999). As a result, 15 items for identification and 18 items for management were included in the factor analysis.…”
Section: Resultssupporting
confidence: 87%
See 1 more Smart Citation
“…Items that correlated poorly with other items were deleted. To ensure sampling adequacy, items with poor Kaiser‐Meyer‐Olkin scores (less than 0.7) were deleted until an overall score of 0.7 was obtained, thereby exceeding the minimum value of 0.6 recommended by Hutcheson and Sofroniou (1999). As a result, 15 items for identification and 18 items for management were included in the factor analysis.…”
Section: Resultssupporting
confidence: 87%
“…Factor analyses were conducted on the 21 items in the questionnaire for identification and the 25 items for management of a developmental disability. As a first step, assumptions were tested to ensure the data were suitable for factor analysis (Hutcheson & Sofroniou, 1999). Correlation matrices were inspected to ensure correlation among items.…”
Section: Resultsmentioning
confidence: 99%
“…In addition, this method provided a better comparison with Tyler et al (2009), Perreau et al (2014b, and the Potvin et al (2011) study, which suggested 3-and 4-factor solutions. Because the different dimensions explored by the SHQ are very likely to be correlated with each other, an oblique factor rotation method (direct oblimin) was chosen (Hutcheson & Sofroniou 1999), similar to Akeroyd et al (2014) in their factor analysis of the SSQ. The percentages of variance explained by the different factors will be given before rotation, for easier comparison with literature data, and after rotation.…”
Section: Cluster and Factor Analysis (Conceptual And Functionalmentioning
confidence: 99%
“…Bartlett's test of sphericity (χ 2 = 2.19, p > .10) revealed that the correlation matrix departed significantly from an identity matrix (thus, the correlations were significantly different from zero). The Kaiser-Meyer-Olkin test (KMO = .72) revealed diffusion in the pattern of correlations among the items, resulting in being labeled worse than -mediocre‖ (Hutcheson & Sofroniou, 1999). Although extreme multicollinearity (the variables being very highly correlated, r > .8) was found (Haitovsky‗s χ 2 = 1.78, p > .10; Haitovsky, 1969;Rockwell, 1975), which could be one cause of the problem in separate factors being identified, it is not clear that this would result in the failure to derive a solution as principal component analysis is less concerned with multicollinearity issues than other methods (Field, 2009).…”
Section: Preliminary Analysesmentioning
confidence: 99%