Multiple regression statistical analysis was performed to interpret interaction effects of the variables.
To interpret interaction coefficients in the model, the STATA command lincom was utilized to calculate the odds ratios between specific groups and their associated confidence intervals.
Developed using the ACT-R cognitive architecture, the model simulates the cognitive and perceptual operations involved in interpreting interaction graphs and provides a detailed characterisation of the information extracted from the diagram, the prior knowledge required to interpret interaction graphs, and the knowledge generated during the comprehension process.
Furthermore, the findings show that the most used implicit methods are interpreting interaction traces and translating learner's actions (used by six games, namely Prime Climb, Talking Island, Vectors in Physics and Mathematics, Balance game and ELEKTRA).
To test the disease interaction concept, 11 (28%) studies used some variation of a product term, with less than half of these (5/11 [45%]) providing sufficient information to interpret interaction both on an additive and on a multiplicative scale.
Since the logit coefficient in the non-linear model cannot be used for interpreting interaction terms, we computed cross-partial derivatives to interpret the interaction terms [ 33, 34].
The covariance C 3 that was used in the simulations had an easy to interpret interaction parameter γ ∈ 0[ 1].
To interpret interaction terms in a non-linear model, a marginal effect is computed with the average marginal effect.
Because we are trained to interpret interactions as qualifications of main effects, this alternative, and frequently more parsimonious, interpretation may be ignored.
The discussion also presents two scenarios for better and rapid understanding of how to interpret interactions in industrial experiments.
