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However, there was a significant interaction of trial and anger expression style [F 1,54) = 10.89, p = .02].
A repeated-measures ANOVA on mean RTs for each subject with trial type (target singleton, target nonsingleton, or target absent) and display size (3, 6, or 9) as factors revealed a main effect for trial type, F 2, 26) = 6.5, p<.01, and display size, F 2, 26) = 26.4, p<.01, and a significant interaction of trial type by display size, F 4, 52) = 3.0, p<.05.
Factors associated with IgG included DPI (p < 0.0001) and the interaction of trial by DPI (p < 0.0001).
During model simplification, the interaction of trial number with target type dropped out of the model, suggesting that learning rates were not different for different targets.
Here, adjusted phenotypes were phenotypes adjusted for estimates of fixed effects (sex, pen within trial, and the interaction of trial and parity class) within the validation population.
Sex and the interaction of trial and parity of the sow were included as fixed factors and pen within trial, animal, and litter as random effects.
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This analysis yielded one significant effect, the two-way interaction of Trials by Potential Harm, multivariate F 2, 117) = 5.803, p =.004, which explained 9% of the variance (Wilk's Lambda =.910).
This analysis focused on main or interaction effects of trial outcome.
Welk et al. [ 11] reported that 63.4% of the variation in the counts during a treadmill walking trial could be ascribed to subject variation, whereas the remaining variation was explained to a greater or lesser extent by the interactions of trials and monitors with subjects.
There was no interaction of training trials and groups (F = 0.97, P > 0.05).
A difference in escape latency was also found between groups (F = 4.97, P < 0.01), which was not influenced by interaction of training trials and groups (F = 0.83, P > 0.05).
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