Open andkov opened 7 years ago
While p-values are probably not the best way to compare the results, they are generally what people rely on in drawing conclusions about their data. With this in mind:
Of, for example, the 18 measures in one study:
2 have identical p-values both for FEV and FEV100, and based on covariance and correlation. These also have identical (Mplus estimated) correlations, but neither the SEs nor the covariances are exactly the same.
1 has n.s. association according to FEV, but "statistically significant" for FEV100 (for cov and corr). Identical estimated correlations, but much smaller SE (.53 vs .14)
3 have n.s. association according to FEV and FEV100cov, but "statistically significant" for FEV100corr. Again, with identical estimated correlations, but much smaller SE (~.8 vs .2)
the rest (12) have slightly smaller SE and p-value for FEV100, but are essentially unchanged between FEV and FEV100.
An additional issue is that the computed correlation CIs (I don't see the SEs in the table) are a lot smaller than the Mplus esimated CIs (e.g., -.94, .96 vs -.05, .07)
Highlighted in the attached file (green, yellow, red, grey/white, and orange, respectively): pulmonary-meta-fev-vs-fev100-MAP-2017-10-03 - AMP.xlsx
@ampiccinin A few notes.
Fev vs Fev100
Question: will a bivariate model yield the same inference regarding bivariate linear correlation if one of the processes is linearly transformed?
Observations so far by @ampiccinin
Based on the covariances, the fev100 had the desired effect - we can now see slope covariance values larger than 0.00, and the p values become consistently lower - but they vary by how much