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I am conducting a survey to analyze the effect of a binary variable X on a latent variable Y. The effect is assumed to be mediated by another latent variable M. Moreover, the effect of X on M is assumed to be moderated by a latent variable W.

My research on how to statistically examine this relationship led me to two different approaches:

  1. Conditional Process Analysis with the SPSS macro PROCESS
  2. A Structural Equation Model

Which one should I prefer and for what reasons?

I have read the following paper comparing the two approaches, but am still not able to make a decision: https://www.sciencedirect.com/science/article/abs/pii/S1441358217300265

Thank you very much for your help!

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  • $\begingroup$ If you have latents, use SEM. $\endgroup$ Jun 20 '19 at 17:16
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Well, the PROCESS macro can't handle latent variables, and you have a latent variable, so I'm not sure why you'd even consider it when there is another clear option available. See Muthen & Asparouhov (2015) for an introduction to (causal) mediation with latent variables along with Mplus code. The sem function in Stata is also good for performing mediation analysis with latent variables.

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    $\begingroup$ Thank you for your answer! I was considering it because someone who used the same survey before used this macro. Conducting my research I realized that it might be wrong but wanted to get a second opinion. $\endgroup$
    – Chris
    Jun 21 '19 at 7:13

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