Hi.
I am a sport psychology researcher and I usually analyze structural equation model (SEM) using AMOS program.
But this time, my data reported unusual results.
It revealed that variables A and B had a negative correlation (in SPSS, Pearson's r), while A had a positive effect on B in SEM.
I wonder why this happened and what is the solution in this case (There is no issue of multicollinearity).
There could be several reasons for this:
1) Differences in the sample size due to missing data and/or differences in how missing values are being handled (e.g., listwise or pairwise deletion vs. full information maximum likelihood)
2) SEM model misspecification resulting in parameter estimate bias
3) Other variables in the (latent regression or path) model that lead to an effect of suppression.
Without seeing your model(s) and output, it is impossible to give a more specific answer.
Prezado Dr. Junsu, saudações!
É interessante observar essa disparidade entre a correlação de Pearson e os efeitos no modelo de equações estruturais (SEM). Uma possível explicação pode ser a presença de variáveis de mediação ou moderação no seu modelo, o que pode influenciar as relações entre A e B.
Para explorar essa disparidade, recomendo verificar a presença de variáveis de mediação, moderação ou qualquer outra variável que possa estar influenciando a relação entre A e B no contexto do seu modelo SEM. Além disso, certifique-se de que as variáveis estejam sendo especificadas corretamente no modelo.
Revisitar a teoria por trás do seu modelo e verificar se há algum aspecto não considerado pode fornecer insights adicionais. Se persistirem dúvidas, ajustar o modelo SEM, incorporando possíveis variáveis moderadoras ou explorar modelos alternativos pode ser útil para entender melhor a dinâmica das relações em seus dados.
With pearson's correlation, you are only looking at a bivariate association (i.e., the relationship between A and B only - not considering any other variables). With SEM, I assume that you have other variables in the model that alter the association between A and B (i.e., when other variables are include / controlled for, the relationship between A and B is different than what you see via a pearson's correlation). I suspect that is the issue you are having, and it is something I've seen when comparing correlations to SEM results in the past. Hope this information is helpful.
Hi Dr. Junsu,
From memory, I think the Hayes book (Introduction to Mediation, Moderation, and Conditional Process Analysis) mentions this issue. If you are dealing with a reviewer's comment, I would just explain the difference between a simple bivariate correlation versus a complex model, and how relationships can differ because of the inclusion of multiple other variables in a complex model.
Hope this information helps.
Kind Regards,
Lorcan.
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