Dear Colleagues,
I have a question regarding the relationship between sample size and the likelihood of obtaining significant results in SEM. If it is generally true that larger sample sizes can increase statistical power and the chances of detecting significant effects, then what can researchers do to make sure that the hypothesis-testing results are reliable and meaningful?
Let's say my SEM study has a sample size of 5000, does this mean that the p values in the hypotheses I will be testing are very likely to be significant due to the large sample size? Are there any effective measures that we may take to deal with this problem?
I am thinking about the following three steps but am unsure if they are useful in effect:
1) reporting precise effect size,
2) lowering the significance threshold from p < 0.05 to p < 0.001,
3) testing the robustness of the structural model across different subgroups.
Do you have any thoughts or recommendations? Feel free to recommend any literature that you may find useful!
Thank you!
Best,
Leon
In Structural Equation Modeling (SEM) analysis, a large sample size can increase the likelihood of obtaining significant results, but its impact is more nuanced than in traditional statistical analyses. Here's why:
Advantages of large sample sizes in SEM:
However, beware of:
In conclusion:
A large sample size can increase the likelihood of obtaining significant results in SEM analysis, but it's crucial to be mindful of the potential pitfalls. Researchers should:
- Use appropriate model specification and validation techniques
- Consider the theoretical significance of the findings, not just statistical significance
- Be cautious when interpreting results, especially with complex models
By doing so, you can harness the benefits of large sample sizes in SEM while avoiding common pitfalls.
Let's move beyond AI answers. First, the constructs must be qualitatively connected and theoretically appropriate. Secondly, p values are inversely connected to sample size. The larger the sample size, the more likely even trivial differences will meet the predefined p value threshold. One must move beyond statistically significant to practically significant. Third, the more tests you run, such as with subgroups, increases the likelihood of error. Finally, sample sufficiency must be determined related to what variables are included or excluded. Consider p hacking and HARKing.
See here https://stats4edu.com/services/structural-equation-modeling/
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