I have a sample composed of two categories, say males and females, when I test a certain path it gives insignificant results in both categories, surprisingly, when I test the same path for the whole sample it gives significant result!!! This doesn't appear when I use Amos (in Amos the path is insignificant for the whole sample too). If testing structural model is not yet reliable in SmartPls cb-sem, can I use it for CFA only and then test hypotheses using Amos? Given that the two softwares give very close results regarding CFA, but SmartPls is just more user-friendly
Yes, the P-value in Smaart Pis CB-SEM is reliable. It is calculated using the same statistical methods as other structural equation modeling (SEM) software.
The P-value is a measure of the statistical significance of a test. It is calculated by assuming that the null hypothesis is true and then computing the probability of obtaining a test statistic as extreme or more extreme than the one observed if the null hypothesis were true. A P-value less than 0.05 is generally considered to be statistically significant, meaning that there is a less than 5% chance of obtaining the observed results if the null hypothesis were true.
There are a few things to keep in mind when interpreting P-values in SEM:
- The P-value is only one measure of the strength of evidence against the null hypothesis. It is also important to consider the effect size and the confidence interval.
- The P-value is affected by the sample size. A larger sample size will generally produce smaller P-values.
- The P-value can be inflated by multiple testing. If you are testing multiple hypotheses, you should use a correction procedure such as the Bonferroni correction to avoid false positives.
Overall, the P-value in Smaart Pis CB-SEM is a reliable measure of the statistical significance of a test. However, it is important to keep the limitations of P-values in mind when interpreting them.
Here are some additional tips for interpreting P-values in SEM:
- Consider the effect size. The effect size is a measure of the magnitude of the effect. A statistically significant effect may not be practically significant if the effect size is small.
- Consider the confidence interval. The confidence interval tells you the range of values that is likely to contain the true population parameter. If the confidence interval includes zero, then the effect is not statistically significant.
- Consider the sample size. A larger sample size will generally produce smaller P-values. If you have a small sample size, you may want to use a more conservative significance level, such as 0.01.
- Consider the multiple testing problem. If you are testing multiple hypotheses, you should use a correction procedure such as the Bonferroni correction to avoid false positives.
If you have any questions about interpreting P-values in SEM, you should consult with a statistician or other expert.
- Badges
- Science topic