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Tests for group differences, based on comparing the above described GLMMs, revealed a significant main effect in face recognition, i.e. a difference in the average performance, with CPs performing worse (LR-test of main effect against nullmodel, D = 11.94, df = 1, p<0.001; βCP = −1.04, HPDI95% = [−1.69, −0.62]).
Non-parametric tests for group differences between categories of IR and glucose metabolism disorders were performed.
In Table 1 the post-hoc tests for group differences are presented.
Non-parametric tests for group differences between categories of glucose tolerance were performed.
Tests for group differences were performed using chi-square or Kruskal-Wallis tests, as appropriate.
Cytokines were evaluated by standard statistical nonparametric tests for group differences, and correlations were performed using the software package SPSS 13 for MacOSX.
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To describe and provide estimates of the distribution of waist circumference (WC) according to percentiles in African-, Europeand, and Mexican-American children, and to test for group differences at different percentiles.
Results: Analysis of covariance was used to test for group differences in mean change after adjusting for initial status.
We applied region of interest analysis to each individual's right fusiform face area and tested for group differences.
Tract-based spatial statistics (TBSS) was used to test for group differences in fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and trace.
Repeated serial measurements were tested for group differences over time by univariate repeated measures using analysis of variance [ANOVA].
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