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Fig. 5 Overall average correlation and correlation with confidence interval for each study correlating Chinese and English morphological awareness.
Fig. 4 Overall average correlation and correlation with confidence interval for each study correlating Chinese and English decoding.
Fig. 2 Overall average correlation and correlation with confidence interval for each study correlating Chinese and English phonological awareness.
Fig. 3 Overall average correlation and correlation with confidence interval for each study correlating Chinese and English vocabulary.
Both overall correlation and correlation across sample pairs were calculated.
Factor analysis is very sensitive to the sizes of correlation, and correlation coefficients tend to be less reliable when estimated from small sample sizes [ 33].
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The equivalent firing rate dependent increase in spike correlations and correlation coefficients for low correlation strengths has been reported for the level-crossing model, see [3] and Fig. 3(A) (right) and Fig. 2(A) (top) in [10].
Specifically, we used common principal components analysis (CPCA) and two simpler approaches, pair-wise correlations and correlation circles.
We tested this assumption using CPCA, and then employed pair-wise correlations and correlation circles to delve deeper into the consistency and inconsistency of these relationships among groups.
P-values < 0.05 were considered as significant correlations, and correlation coefficients between 0.0 - 0.4, 0.4 - 0.7, and 0.7 - 1.0 were considered as weak, moderate, and strong correlations, respectively.
A matrix was computed with item-scale correlations and correlations were thereafter compared across scales.
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