Message/Author 

Estee posted on Wednesday, October 16, 2013  6:22 am



Hi, 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? Thanks very much. 


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



Hi, I was wondering something similar. I have a nonsignificant 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 nonsignificant? Thank you very much. JÁ 


If the CI covers zero, the coefficient is deemed insignificant. Your lower limit is negative and your upper limit positive, so your coefficient is nonsignificant. 

Joao Garcez posted on Tuesday, March 17, 2015  2:34 am



Thank you for the clarification. Best, JA 


Hi there, I too have a similar query  I have a nonsignificant indirect effect (B = 0.153, p = .104), but then the 95% BCa CI is [0.032 , 0.417]. I'm a but confused as to whether this is a significant mediation or not  the CI would suggest yes, but the p value no? Would appreciate any advice  many thanks! RE 


If the confidence interval and pvalue do not agree, you must not have a symmetric confidence interval. 


Thank you for the very quick response! I would agree that the confidence interval is asymmetric  looking at my data, the point estimate doesn't sit in the centre of the BCa confidence interval. Does this mean that it is not meaningful to interpret this in the normal way, i.e. 0 not being between the lower and upper bounds of the confidence interval as a sign of significant mediation? Many thanks for your helpful advice. RE 


The nonsymmetric CI is interpreted the same way: If 0 is not within the interval then the effect is significant. 


Hello, Related to the above: I run a singlelevel mediation model as specified below.The indirect effect (a1d1b2) based on ztest 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: NEW(a1b1 a2b2 a1d1b2 TOTALIND TOTAL); a1b1 = a1*b1; a2b2 = a2*b2; a1d1b2 = a1*d1*b2; TOTALIND = a1*b1 + a2*b2 + a1*d1*b2; TOTAL = a1*b1 + a2*b2 + a1*d1*b2 + dash; OUTPUT: STAND CINT(bcbootstrap); 


Perhaps you made a typo when you said (.008.394) because this includes zero. In any case, a good approach is to use bootstrapping and the nonsymmetric CI you get from that. 


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 multilevel 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. 


multilevel bootstrap is not available in Mplus. You can use twolevel Bayes and get the same kinds of nonsymmetric CIs. 


Great, thanks! 


Hello, I have a bootstrapped multilevel mixed effect model where the CI's contain 0 [.0013, .04162] but the p < .05? Are asymmetrical CI's the only explanation for this anomaly? Do you know of a reference I could cite to justify this? 


Q1: Yes. 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 


Use the CI only  it is more informative and takes into account possible nonsymmetry in the distribution of the estimate. 


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 biascorrected bootstrap which is in line with Biesanz et al., 2010 in Multiv Behav Res. The biascorrected bootstrap CIs will most likely not be substantially different, however. You can also compare with what you get using Bayes which automatically gives nonsymmetric 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 nonbias 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 nonbias corrected CIs may be too conservative? 


The regular bootstrap method with percentiles creating the CI is what Mplus does (in addition to the biascorrected version). Biesanz et al showed that it did better than the biascorrected and they also showed Bayes to be good  as we have found. 


When the CI and p value do not match for DIRECT effects in a mediation model (e.g., p value is larger than .05, but CI does not include 0), which one should I rely on to make a conclusion? 


The CI and pvalues should agree unless the CI is obtained by bootstrapping. If this doesn't help, send your output along with your license number and mark where you see a disagreement. 


Thank you for your response. The CI is obtained by bootstrapping. In this case, which estimate should I rely on? p value versus CI for the DIRECT effects? 


The bootstrap CI is the better choice because it takes into account any nonnormality in the sampling distribution for the direct effect. 


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!! 


This general question is suitable for SEMNET. 


Hi, quick question: I'm using bootstrapped CIs, is there any way that I can also request for bootstrapped p values in my output command? Many thanks. 


No, but bootstrapped SEs are used in computing the pvalues. 


I see. Thank you for your quick response! 


Hi Dr. Muthen, I have a question regarding mediation analysis in mplus: I am looking at whether the relationship between overtime work (OVERHRS) and depressive symptoms (BSISD) is mediated by worktofamily conflict (WFC). I found the following results: STDYX Standardization Total indirect: Lower .5% (0.042) Lower 2.5% (0.001) Lower 5% (0.018) Estimate (0.111) Upper 5% (0.326) Upper 2.5% (0.416) Upper .5% (0.743) Is it safe to interpret the lower & upper 2.5% and say that my results do show that worktofamily conflict mediates the relationship between overtime work and depression? 


That's what the 95% CI says, yes. I assume you use bootstrapped CIs. To get more information, you could use more bootstraps and you can also use Bayes and see what it says. 


Hi Dr. Muthen, Thank you for your quick reply and confirmation of my interpretation. Yes, I did use bootstrapped CIs. I will look at Bayesian analysis. 


Hello, Dr.Muthen, I am running a crosslagged model with two time points, and I want to test two mediation paths. 1) Predicting T2Y from T1X, mediated by M. 2) Predicting T2Y from M, mediated by T1X. M was a variable assessed at T1. First, is there a better way to test mediation paths given my time point constraints? That is, the predictor and mediator were both at T1 which is not ideal. I do have information of T2X, although I am not sure that's a better solution. Second, I used Bootstrap and Model indirect command, however, the estimates were all zeros in the "confidence interval of total, total indirect, specific indirect, and direct effects" section as well as the sections below (STDYX, STDY, etc). I suspect there is a problem with those estimates because I dont think they should all be zeros...Fit indices, descriptives, as well as parameter estimates of path models all seemed ok. Do you have any suggestions for troubleshooting? e.g., CONFIDENCE INTERVALS OF TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Effects from T2Y to T1X via M Sum of indirect 0.000 0.000 0.000 0.000 0.000 0.000 0.000 


Your first question is more suited for SEMNET. Regarding your second question  send your output to Support along with your license number. 


Hi Dr. Muthen, I recently ran a mediation analysis, and I am having a hard time interpreting my findings. I have been trying to look for examples, but have not been successful. Lower (.5%) = 0.045 Lower (2.5%) = 0.001 Lower (5%) = 0.016 Estimate = 0.110 Upper (.5%) = 0.318 Upper (2.5%) = 0.410 Upper (5%) = 0.781 From this finding, is there an indication of a significant indirect effect? 

Sara Namazi posted on Wednesday, July 25, 2018  7:45 am



Hi Dr. Muthen, I reran my, and I am having a hard time interpreting my findings. I have been trying to look for examples, but have not been successful. Lower (.5%) = 0.042 Lower (2.5%) = 0.001 Lower (5%) = 0.018 Estimate = 0.111 Upper (.5%) = 0.326 Upper (2.5%) = 0.416 Upper (5%) = 0.743 From this finding, is there an indication of a significant indirect effect? I am interpreting it as there being an indirect effect at the 95% confidence interval beta= 0.11[0.001, 0.42]. Thanks, Sara 


The first example has an insignificant effect and the second a significant one. You can increase the number of bootstrap draws to better estimate the lower end of the CI. 

Sara Namazi posted on Wednesday, July 25, 2018  4:45 pm



Thank you, Dr. Muthen. What is your recommendation in term of increasing the number? I am currently drawing 10,000 random bootstrapped samples. 


That should be enough. You can also try Bayes which also gives nonsymmetric CIs. 

Sara Namazi posted on Wednesday, July 25, 2018  5:31 pm



Thank you, Dr. Muthen. I am not familiar with Bayes. Do you have guidance on this topic and Mplus code? Thanks Sara 


See our Short Course videos and handouts for Topic 9 and the shorter Bayes version within Topic 11. See also my intro under Papers, Bayesian Analysis: Muthén, B. (2010). Bayesian analysis in Mplus: A brief introduction. Technical Report. Version 3. Click here to view Mplus inputs, data, and outputs used in this paper. download paper contact author show abstract 

Lu posted on Wednesday, September 26, 2018  1:49 pm



Hi Dr. Muthen, I ran 18 models testing 6 mediators and 3 outcomes in Mplus using SEM. The mediators are correlated, so are outcomes. And I reported 95% bootstrap CIs. Is there a way to correct for multiple testing in this case (a reviewer asked for this), esp given that mplus does give bootstrap pvalues? Thanks! 


You can be more stringent in your testing by using a 98% instead of a 95% interval in the bootstrap output. 

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