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However, most of the studies above focus on a link between a single factor and L2 listening proficiency.
In such a situation, calculating the correlation between a single factor and its interaction with CO2 concentration implies to correlate this factor with itself.
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To determine the statistical significance of the relationships between measured variables, a single factor ANOVA was performed.
This study is a single factor between-subjects design.
The study employed a single factor between-subjects design, using a questionnaire completed by the patients.
A between-subjects design was used with a single factor of awareness with two levels: aware and unaware.
Except for MANOVA/dCaP, the output statistics for other methods were obtained from each pairwise comparison on a single factor between all pairs of conditions.
These factors had high internal consistency reliability (Cronbach's α range 0.80 to 0.95), were not collapsible into a single factor, and had moderate between-factor correlations (Pearson correlation coefficient r = 0.24 to 0.44).
The percentage of variance explained by a single factor varied between 64% and 78% for the item sets and was equal to 77% for the whole data set.
Simple correlation analysis of weight loss and a single factor revealed a correlation between TB progression and lung expression of IL-1β, IL-11, CCL3, CXCL2, and MMP-8.
Comparisons between groups (Susceptible vs. Resistant) were performed using a single factor ANOVA test (StatView software).
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