Hi, I am conducting analysis using one independent variable, 3 intervening variables, 1 dependent variable, and 2 control variables for a sample size of 233. I have tested mediation for the intervening variables (considering them in the same model) using path analysis and bootstrapping in Amos SEM. While path analysis is indicating a full mediation of all the three intervening variables, bootstrapping is indicating full mediation of only 2 of them. For the 3rd variable, it is showing an insignificant direct relation with the dependent variable. How do I interpret these conflicting results obtained using the same tool? Thanks for the help in advance.
Conflicting results in your analysis can be challenging to interpret, especially when using different methods. Here are a few considerations to help you understand the conflicting results between path analysis and bootstrapping in Amos SEM.
1. Methodological Differences: Path analysis and bootstrapping are two different analytical techniques, and they may produce slightly different results due to their underlying assumptions and estimation procedures. Path analysis estimates direct and indirect effects, while bootstrapping estimates the significance and confidence intervals of these effects. Therefore, it is not uncommon to observe some discrepancies between the two methods.
2. Sample Size: The sample size of 233 is relatively small, which might influence the statistical power and precision of your estimates. In smaller samples, it can be more challenging to detect significant effects accurately, leading to inconsistent results. Consider whether increasing the sample size is feasible to obtain more robust and reliable findings.
3. Statistical Significance: It's important to note that statistical significance does not necessarily imply practical or substantive significance. An insignificant direct relation between an intervening variable and the dependent variable might still be theoretically meaningful or may require further investigation. Look beyond statistical significance and consider the effect sizes, theoretical relevance, and the broader context of your research question.
4. Theoretical Considerations: Evaluate the theoretical underpinnings of your model and the intervening variables. Examine whether the two bootstrapping results align with existing theory or prior research. Additionally, consider any unique characteristics or specific dynamics related to the third intervening variable that could explain its distinct behavior in the model.
5. Alternative Models: Consider exploring alternative models or additional analyses to gain a more comprehensive understanding of the relationships between your variables. You could test different mediation models, explore potential moderators or mediators, or conduct robustness checks to verify the stability of your results.
6. Expert Consultation: If you are still uncertain about interpreting the conflicting results, it can be helpful to consult with a subject matter expert or a statistician who has expertise in your specific research domain. They can provide valuable insights and guidance tailored to your study.
Remember that conflicting results are not uncommon in research, and they can lead to valuable opportunities for further investigation and refinement of your hypotheses.
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