Dear all,
I am sharing the model below that illustrates the connection between attitudes, intentions, and behavior, moderated by prior knowledge and personal impact perceptions. I am seeking your input on the preferred testing approach, as I've come across information suggesting one may be more favorable than the other in specific scenarios.
Version 1 - Step-by-Step Testing
Step 1: Test the relationship between attitudes and intentions, moderated by prior knowledge and personal impact perceptions.
Step 2: Test the relationship between intentions and behavior, moderated by prior knowledge and personal impact perceptions.
Step 3: Examine the regression between intentions and behavior.
Version 2 - Structural Equation Modeling (SEM)
Conduct SEM with all variables considered together.
I appreciate your insights on which version might be more suitable and under what circumstances. Your help is invaluable!
Regards,
Ilia
Version 2 (path analysis/SEM) is generally more practical/efficient because it allows you test everything, including indirect (mediated) effect(s), in a single step/model. It's also more effective because many SEM software programs have appropriate methods for testing indirect (mediated) effects (e.g., asymmetric bootstrap confidence intervals) built into their routines for estimating mediation models.
Christian Geiser thanks a lot! Have you seen this ever being done the first way, step-by-step approach? Would this be incorrect to do?
Version 1 is not incorrect for a saturated path model. It just does not seem very practical compared to a single-step, all-in-one path analysis model. That being said, you may be able to "automate" some of the steps in Version 1, e.g., by using the PROCESS macro (https://processmacro.org/index.html).
Hi Ilia Gugenishvili .
I think SEM is good. Because it calculates the relationship between the variables at the same time and all in one graph. You can do it with STATA software easily.
But I have a question. In the graph, you sent, why are the arrows such as H1.2 and H1.3 connected to another arrow? Because as far as I know, arrows are usually connected to variables
Ilia, some thoughts on your model. According to your path diagram you have 4 moderator effects. For such a large model, you need a large sample size to detect all moderator effects simultaneously. Do you have a justification for all of these nonlinear relationships?
Some relationships in the path diagram are missing. First, prior knowledge, personal impact, and attitude should be correlated - these are the predictor variables. Second, prior knowledge and personal impact should have direct effects on the dependent variables behavioral intentions and behavior (this is necessary).
As this model is quite complex, I would suggest to start with analyzing the linear model. If this model fits the data well, then I would include the interaction effects one by one. Keep in mind that you need to use a robust estimation method for parameter estimation because of the interaction effects. If these effects exist in the population, then behavioral intentions and behavior should be non-normally distributed.
Kind regards, Karin
Thank you all! The sample size is 460 so SEM would work just fine. Thanks for your help and advice!
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