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Similar to the random-effects models used for analyzing binary outcomes, this model takes two levels of variance, between-cluster variance and within-cluster variance, into account for clustered or longitudinal nominal responses [ 41, 42].
A linear mixed effects model with centre as the random effect has been used to account for a possible centre effect.The ICC (intraclass correlation coefficient) is the measure of the ratio of the between cluster variance to the total variance (between-cluster + within-cluster).
The test allowed us to identify the differences in variance between clusters in terms of PC scores (Field [2005]).
A method named as Ward, a very efficient method for analysis of variance between clusters, was selected as measurement.
We use Ward's method to generate a hierarchical clustering of stories, which proceeds by minimizing variance between clusters of books [26].
Ward's method, a very efficient method for the analysis of variance between clusters was applied, and Euclidean Distance was selected as measurement.
MDA and multivariate analysis of variance showed that all latent variables contribute to significantly differentiate clusters (p < 0.0001), and this is confirmed by the fact that the two discriminant functions arising from the MDA exert, respectively, 53.2% and 46.8% of the total variance between clusters.
The variance between clusters was then taken to be 0.01.
The ICC compares the variance within clusters with the variance between clusters.
‡Proportion of total variance between clusters, and measured within framework of ANCOVA.
The ICC is a measure of the relatedness of clustered data by comparing the variance within clusters with the variance between clusters.
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