Estee posted on Wednesday, October 16, 2013 - 6:22 am
I tested a mediation model. The indirect effect was bootstrapped. The 95% confidence limit was [.000, .038]. According to literature, an indirect effect is considered sig. if the CI does not include/cross zero. Is the .000 showed in the results really equal to zero or it may be for example.0001 ? so, how to confirm whether the 95% confidence limit [.000, .038] is sig or not?
I would count that as non significant even if the value was .0001. With so many tests being performed, I would be conservative.
Joao Garcez posted on Friday, March 13, 2015 - 10:27 am
I was wondering something similar. I have a non-significant indirect effect (b = -.019, p>.05) with the following bc bootstrap 95% CI: ( -.047 - .002 ).
Is a value of .002 considered to be equal to zero?
Also, when values in the lower and upper bound have opposite signs like the one above, does it mean something? The path above is negative with a positive value in the CI. Does it mean is non-significant?
Related to the above: I run a single-level mediation model as specified below.The indirect effect (a1d1b2) based on z-test does not become sign (p = .112), but the 95% CI does not include 0 (.008-.394). If I run the respective process model in SPSS, the CI does also not include 0, showing significance. Bootstrapping is a more powerful test, but any idea how I should report these findings? My n is 146 and the CIs asymmetric.
Many thanks, Kristin
USEVARIABLES = WFBS Health ALQ FSSB sexI1 ageI1 child; ANALYSIS: TYPE = GENERAL; ESTIMATOR = ML; BOOTSTRAP = 5000; MODEL: Health ON FSSB (b1); Health ON WFBS (b2); Health ON ALQ (cdash); ! direct effect of X on Y FSSB ON ALQ (a1); WFBS ON ALQ (a2); WFBS ON FSSB (d1); Health on sexI1 ageI1 child; MODEL CONSTRAINT:
Thank you for this Bengt and sorry for the delay in responding. Yes, I meant (.008,.394) and just used the "-" to indicate the boundaries of the CI (I am aware that this is not normally done so, so sorry). Would you mind advising me on the best way to do bootstrapping in multi-level analysis as I think MPlus does not (yet) offer this feature and hint me towards a relevant paper/article etc. where I can read up on this? I heard various approaches but unsure which to pursue.
Q2: You can cite our new RMA book if you like but it is a basic fact.
Ceren Gunsoy posted on Friday, September 29, 2017 - 10:41 am
Hi, When the CI and p value do not match for an indirect effect; for example, when the p value is > .05 but the CI does not include zero, do I need to report both values or is the CI more informative than the p value? Thank you
Hello, You recently commented that you no longer recommend using the bias corrected CI (BCBOOTSTRAP)to test indirect effects. Could you provide a brief explanation of why you are no longer recommending this? Any information would be very much appreciated as we have consistently used this method in the past to test indirect effects. Thanks very much
We discuss this in connection with simulations studies in our 2016 book Regression and Mediation Analysis using Mplus. The Type I error is found to be somewhat high for bias-corrected bootstrap which is in line with Biesanz et al., 2010 in Multiv Behav Res. The bias-corrected bootstrap CIs will most likely not be substantially different, however. You can also compare with what you get using Bayes which automatically gives non-symmetric CIs.
Thanks very much for your response. I'll take a look at the references to learn more. I believe that the impetus for using the bias corrected CIs was because of concern that the non-bias corrected CIs for an indirect effect were too conservative. Is there something about the new bootstrap method in mplus that addresses this concern? Or do you recommend just using the Bayes to address this concern as it is still likely that the non-bias corrected CIs may be too conservative?
The regular bootstrap method with percentiles creating the CI is what Mplus does (in addition to the bias-corrected version). Biesanz et al showed that it did better than the bias-corrected and they also showed Bayes to be good - as we have found.
Hi. We have in our article a relationship between A and B, b = 0.009 [.001, .016], and between C and D, b = 0.0002 [.00004, .0003]. In a review we have been asked to use the same number of decimals for both values within the last CI. How we should do this?? b = 0.0002 [.000, .000]?? Thank you very much!!