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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.
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.
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.
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.
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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.
For example Derry et al. (2010) formulate an overarching strategy for focusing the analysis on three central steps of indexing, macrolevel and microlevel coding/ narrative summaries/ diagrams, and transcription that help the researcher understand and interpret interactions.
These choices include: whether to use a factorial design, selection of the number and type of factors to include, how to address the compatibility of the different factors included, whether and how to avoid confounds between the type and number of interventions a participant receives, and how to interpret interactions.
Interactions were further visualized using an interaction entropy graph, which uses a color-coding scheme to interpret interactions.
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