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Relations of changes between variables were examined with Pearson correlation coefficients.
Normally distributed data were analyzed with SPSS statistical software using a one-way or two-way ANOVA, and significant changes between variables were detected using a Tukey's test.
We were able to present risk factor changes between variables measured similarly in the 2001 and 2003 CHISs, but these data are limited by low response rates that could result in selection bias.
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Where there were two classifying groups (e.g., gender) the Mann–Whitney U-test was used to compare these variables (or changes between variable values); when there were more than two groups (e.g., tumour site) the Kruskal Wallis test was used.
Therefore, users should base their explanations on theory when discussing the associated changes between latent variables and indicators.
The positive or negative sign of the value represents the difference of the change direction between variable and factor.
First, the z-score method uses student's t-test to measure the amount of change between two variables.
Reconceptualization is reflected by changes in connections and contrasts between variables, and more generally by changes in graph structure of PCA.
When communities' locations have changed, will the relationships between variables in our model evacuation population, TWET, and TSA persist?
The within-group and between-group changes in variables during the study were analysed with repeated measures analysis of variance (ANOVA).
Pearson's correlations were tested to contrast the association between changes in variables pre and post training, followed by linear regression.
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