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We present the results of logistic regression and multinomial regression models as predicted probabilities to avoid the complexity in interpretations of interaction term in the regression models.
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They are central to understanding the environment and avoid over-interpretation of interaction (Knoblauch 2009).
We propose several graphical presentations and corresponding statistical tests alleviating the interpretation of interaction effects.
Continuous predictors were mean-centered to reduce multicollinearity and to ease interpretation of interaction terms.
The interpretation of interaction in contingency table.
Continuous predictors were mean centred to facilitate interpretation of interaction effects, and analyses were gender separated.
Graphics were used to help in the interpretation of interaction terms in the final model.
Expertise in biology provides a foundation to contextualize causal effects and guide identification and interpretation of interaction signals from noise.
This is in line with results of a review where only 3 out of 225 published epidemiological studies mentioned the additive scale for the interpretation of interaction [ 27].
Interpretation of interaction terms is scale dependent, although for public health purposes, epidemiologists are in consensus that positive departure from additivity constitutes synergism (Rothman et al. 2008).
To aid in the interpretation of interaction effects, statistically significant interactions of country by parenting style were linearlized and a one-way ANOVA, with post-hoc tests was conducted.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com