Hi everyone! I need to examine interactions between categorical and continuos predictors in a path analysis model. What strategy would be more accurate: 1) including the categorical variable, the continous one and the interaction as separate terms, 2) run a multigroup analysis?
I have the same problem with several models. For instance, examining potential differences of executive function (continuos predictor) effects on reading comprehension (outcome variable) among children from different grades (categorical predictor).
Thank you so much for your help!
Hello Angel,
proceed as in any other regression-based dummy-variable model (that is, incorporate k-1 dummy variable for each categorial variable with k levels.
The distributional assumptions underlying SEM/path analysiis concern the endogeneous variables--not the exogeneous.
Best,
Holger
Both approaches (interaction terms vs. multigroup analysis) would be appropriate and should lead to very similar if not the same results/conclusions.
Dear Angel,
For this model " potential differences of executive function (continuous predictor) effects on reading comprehension (outcome variable) among children from different grades (categorical predictor)" I prefer to apply multigroup analysis.
Best,
Hi Angel,
When examining interactions between categorical and continuous predictors in a path analysis model, there are two main strategies that you can use: including the categorical variable, the continuous one, and their interaction as separate terms, or running a multigroup analysis. Both strategies have their advantages and disadvantages, and the best approach will depend on the specific research question and data.
This approach involves including the categorical variable, the continuous one, and their interaction term as separate paths in the path analysis model. This allows for the examination of the main effects of each predictor variable, as well as their interaction effect. The advantage of this approach is that it is more flexible, as it can account for different types of interactions (e.g., additive, multiplicative). However, it can lead to more complex models, which may be difficult to interpret or estimate accurately, especially if there are many predictor variables in the model.
This approach involves creating separate path analysis models for each group defined by the categorical variable (e.g., different grades). This allows for the examination of group differences in the relationship between the continuous predictor and outcome variable. The advantage of this approach is that it can provide more precise estimates of the relationship between the continuous predictor and outcome variable within each group, as it controls for any group differences in other variables. However, it assumes that the relationship between the continuous predictor and outcome variable is the same across all groups, and may not be suitable for examining more complex interaction effects.
In your specific case, where you are examining the potential differences of executive function effects on reading comprehension among children from different grades, a multigroup analysis may be more appropriate. This is because you are interested in examining group differences in the relationship between the continuous predictor and outcome variable, and a multigroup analysis allows you to do this while controlling for other variables. However, if you are interested in examining the nature of the interaction effect (e.g., whether it is additive or multiplicative), including the interaction term as a separate path in the path analysis model may be more appropriate.
Good luck in your analysis,
Wadie
Thank you so much to you all, you’ve been really helpful! Is there any literature you might recommend in multigroup analysis?
Very helpful paper with references:
https://web.pdx.edu/~newsomj/semclass/ho_moderation.pdf
Best,
Wadie
- Badges
- Science topic