Sentences with phrase «eigenvalues greater»

Individual items were retained if they had a loading near or over 0.35 and the number of factors was based upon those with eigenvalues greater than one.10 A two - factor solution was the clearest at both ages and accounted for over 95 % of the total variance in the observed variables (Table 2).

An initial component extraction showed that there were five dimensions with eigenvalues greater than one.

The three items measuring modelling of healthy eating all loaded onto one unique factor with an eigenvalue greater than one, explaining 63 % of the variance.

A cutoff of 0.40 was used for factor loading with an eigenvalue greater than 1, which allows the extracted factor to explain a reasonable proportion of the total variance.

Initial examination of the items using principal component analysis with varimax rotation to maximize variance, revealed three factors having an eigenvalue greater than one.

Not exact matches

The first factor covered more than 64 % of the total variance of the readability measures with an eigenvalue of 32.3, which is more than 23 units greater than the next factor's eigenvalue.

Decisions on the number of components to extract were based on parallel analysis, Kaiser's eigenvalue - greater - than - one rule, total proportion of variance explained and Cattell's scree plot.

Two component solutions were examined: (1) component extraction based on a parallel analysis, proportion of variance explained, Kaiser's eigenvalue - greater - than - one rule and on the examination of Cattell's scree plot and (2) a three - component solution as originally conceptualised in the VAWI.

The number of factors was determined by a minimum eigenvalue of 1.00 or greater, followed by a minimum loading of.40 for the items in each factor.