1974
DOI: 10.1037/h0036316
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When is a correlation matrix appropriate for factor analysis? Some decision rules.

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Cited by 1,106 publications
(684 citation statements)
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“…To avoid this, we performed a main component factor analysis for each category of variables. The Kaiser -Meyer -Olkin (KMO) measure of sampling adequacy is considered a good criterion for testing when data sets are suited to factor analysis ( Stewart, 1981 ;Dzubian and Shirkley, 1974 ). The results proved that all our data sets fulfi ll the minimum requirement ( Cureton and D ' Agostino, 1983 ) of a KMO larger than 0.5.…”
Section: Empirical Results Of the Discriminant Analysismentioning
confidence: 52%
“…To avoid this, we performed a main component factor analysis for each category of variables. The Kaiser -Meyer -Olkin (KMO) measure of sampling adequacy is considered a good criterion for testing when data sets are suited to factor analysis ( Stewart, 1981 ;Dzubian and Shirkley, 1974 ). The results proved that all our data sets fulfi ll the minimum requirement ( Cureton and D ' Agostino, 1983 ) of a KMO larger than 0.5.…”
Section: Empirical Results Of the Discriminant Analysismentioning
confidence: 52%
“…The second EFA demonstrated meritorious sampling adequacy (Kaiser-Meyer-Olkin KMO = 0.88) (Dziuban and Shirkey 1974;Kaiser 1974) and Bartlett's test of sphericity (χ 2 = 4508.65, p < 0.001) suggested the items may be efficiently analyzed. Item loadings are displayed in Table 1.…”
Section: Exploratory Factor Analysesmentioning
confidence: 99%
“…The corrélation matrix was tested for its appropriateness for factor analysis. The overall measure of sampling adequacy using the Kaiser-Meyer-Olkin index was found to be .989 (4). This indicated a very high degree of appropriateness for factor analysis.…”
Section: Resultsmentioning
confidence: 86%