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Qi posted on Wednesday, October 08, 2008  4:06 pm



I want to use MacKinnon & Lockwood (2001) proposed asymmetric distribution of products method to test the significance of the mediation effect, do I just specify "bootstrap=1000;" under analysis, and then specify "CINTERVAL (BCBOOTSTRAP);" in output? There are several options for CINTERVAL, I am not sure about which one I should use, BOOTSTRAP or BCbootstrap? I shouldn't use the default symmetric, should I? And when interpreting the output, I just check whether 0 is in the 95% CI in " CONFIDENCE INTERVALS OF STANDARDIZED TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS for STDYX Standardization", right? Thanks for your guidance! 


BCBOOTSTRAP is what MacKinnon and Lockwood do. This does not require the BOOTSTRAP option unless you want to use bootstrapped standard errors with the bootstrapped confidence interval. 

Qi posted on Wednesday, October 08, 2008  8:01 pm



Hi Linda, thanks for your prompt reply. Did you mean that I don't need to specify "bootstrap=1000" under analysis? I tried with only "CINTERVAL (BCBOOTSTRAP)" in the output command, however, I got the following warning and didn't get any CI for indirect effect, am I missing sth? Thanks again! *** WARNING in OUTPUT command BOOTSTRAP and BCBOOTSTRAP confidence intervals require the specification of BOOTSTRAP in the ANALYSIS command. Request for CINTERVAL is ignored. 

Qi posted on Wednesday, October 08, 2008  8:09 pm



I am sorry, I guess misunderstood your previous instruction. I think you were talking about the difference between CI(BCBOOTSTRAP) and CI(BOOTSTRAP). What's the difference between CI(BCBOOTSTRAP) and CI(BOOTSTRAP)? Is CI(BCBOOTSRRAP) not using bootstrapped SE with bootstrapped CI? What's the purpose of each option? Thanks! 


You are correct. You do need the BOOTSTRAP option of the ANALYSIS command for both CINTERVAL (BOOTSTRAP) and CINTERVAL (BCBOOTSTRAP). Both of these options are described in the MacKinnon and Lockwood paper. CINTERVAL (BOOTSTRAP) uses 2.5% and 97.5% percentiles of the sample distribution while CINTERVAL (BCBOOTSTRAP) these percentiles values are estimated from the sample distribution. The biascorrected confidence intervals are more accurate. 

Brianna H posted on Sunday, August 18, 2013  4:39 pm



Hello, The Mplus v.6 manual on p.647 indicates that "with frequentist estimation, only SYMMETRIC confidence intervals are available for standardized parameter estimates." I am using BCBOOTSTRAP to obtain biascorrect bootstrap confidence intervals. Does this mean I have to use the unstandardized output? I.e. I cannot use the confidence intervals of standardized indirect effects, if those are available only for SYMMTERIC? Also, in the CINTERVAL output, the fourth column labeled Estimate contains the parameter estimates. If the standardized estimate of an indirect effect is 0.255, is it correct to say that 25.5% of the variance in DV is explained by the indirect effect of the IV on the DV? Thank you. 


If you want nonsymmetric confidence intervals, you must use unstandardized estimates. Your indirect effect is interpreted as an increase of one standard deviation is x results in an increase of 25.5% of a standard deviation increase in y. 


Hello, I'm trying to calculate indirect effects in my model. There are two mediators (m1m2), linking independent variables(IV, x1x4) and dependent variables (DV, y1y2). My model is specified as below. Since the dependent variables are nested within a group, I used multilevel modeling to allow for the grouplevel random effect. Q1. It says that bootstrapping is not allowed for multilevel modeling. How should I test indirect effects? can i just skip bootstrapping? (Mplus tested indirect effects although i didn't type bootstrapping) Q2. I want to test main effects from IV to DV in this model. The thing is that from IV to DV, there are direct paths, as well as indirect paths. Can I sum both direct paths and indirect paths and test total effects (not just sum of two indirect paths)? Thank you very much! data: file is data.txt; variable: names are lid y1y2 x1x4 m1m2; cluster is lid; within= x1x4 m1m2; analysis: type=twolevel; model: %within% m1 on x1x4; m2 on x1x4; y1 on m1m2; y2 on m1m2; y1 on x1x4; y2 on x1x4; model indirect: y1 ind m1 x1; y2 ind m2 x2; 


I also tried another model drawing on the Mplus syntax for testing indirect effects in a multilevel model as recommended by Preacher, Zyphur, Zhang (2010) & Preacher, Zhang, Zyphur (2011), data: file is data.txt; variable: names are lid y1y2 x1x4 m1m2 c1c5; usevariables are lid y1 x1 m1; cluster is lid; within= x1 m1; analysis: type=twolevel; model: %within% m1 on x1(a); y1 on m1(b); model constraint: new(indm); indm=a*b; output: tech1 tech8 cinterval; Q1. Do you think this modeling is correct? Q2. in the model results, there is New/Additional Parameters INDM Estimate S.E. Est./S.E. Pvalue 0.055 0.022 2.457 0.014 can i refer to estimate and pvalue to judge significance of indirect effect? (I can't understand which part of tech1 and tech8 output I should look for...) Thank you! 


Please don't post in more than one window  this is our rule. Answers to your first message: Q1 Indirect effect estimation and testing can be done without doing bootstrapping. In my experience it is the exception rather than the rule that you need bootstrapping  you may need it with very small samples or when considering ratio estimates. Q2. Yes. Answers to your second message: Q1. Yes and this should give the same results as in your Model Indirect run. Q2. Yes, the pvalue says that INDM is significant on the 5% level. You don't need to look at tech1 or tech8. You may want to listen to our Topic 1 teachings on mediational modeling. 


Thank you very much for your prompt and kind reply! And sorry for multipleposting. i'll keep that in mind. Have a lovely week! 


Dear Sir and Madame, I did a bootstrapping  but I don't know how I could interpret the results of the confindence intervals. Could you help me? Thank you very much, Steffi M. 


Please send your output and license number to support@statmodel.com so I can see which confidence intervals you are using. 

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