Timeline for Regression with sample split
Current License: CC BY-SA 4.0
3 events
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| May 4, 2023 at 14:18 | comment | added | Gregg H | Others may disagree with me, but I would start with a model-building approach. First, run the two models (with the interactions and without...with all of your variables). Run a $\Delta R^2$ ANOVA to see if the models are different. If you have a significant difference in the models, then look at the interaction terms p-values individually. Drop those that are not significant. Once you have flagged all the potential variables moderated by the grouping variable, check the model again for overall improvement. | |
| May 4, 2023 at 14:01 | comment | added | derhard | Thank you. That's totally correct. I have roughly 30 predictors in the model. Including the interaction effect for each predictor would lead to significantly more coefficients (that I also would need to report, making it even more ugly). From my point of knowledge, the sample split is equivalent to your approach. Correct me if I am wrong. Of course, the interaction terms would directly show if there is a significant difference between the groups. However, I am more interested in which coefficients are really significant for which subsample. Given this, do you think my approach is ok? | |
| May 4, 2023 at 12:55 | history | answered | Gregg H | CC BY-SA 4.0 |