Message/Author 

Mike Todd posted on Monday, March 12, 2007  12:27 pm



My colleague and I are attempting to examine mediation (X>M>Y) in a multilevel context where X and M are dichotomous, Y is continuous, and all 3 variables are measured 252 times for each of 55 individuals. Our interest is in testing purely withinperson associations (effect of symptoms (X) at day t on mood (Y) at day t+1 as mediated by sleep disturbance at night t). While there are certainly straightforward multilevel regression methods for estimating and testing indirect effects in allcontinuous variable models, I've yet to find an analogous approach for models where either M or Y is categorical. So, it has been recommended that we use Mplus. The major question is, can such an analysis reasonably be conducted in Mplus. Whether we choose the typical multivariate repeated measures approach (ala latent curve modeling) or the multilevel (TYPE=TWOLEVEL) approach, it seem that we would run into problems given the very large number of variables (~750) and the relatively small number of cases (55). Any advice you might have would be appreciated. Thanks 


You can do this in Mplus as a 2level model for 3 variables (X, M, Y) with repeated measures over time as level 1 and individuals as level 2. You do this using ML estimation with logit regression for the binary dependent variables. I can send you a paper by MacKinnon et al which is under review and that endorses the Mplus product approach to indirect effect estimation with a binary outcome. Although the Mplus Model Indirect features are not available for the numerical integration algorithm that is neeed, you can use Model Constraint to produce the product of slopes that is of interest. 

Mike Todd posted on Tuesday, March 20, 2007  10:28 am



Thanks so much, Bengt. Please do send the MacKinnon paper. I would like to see what Dave has come up with on this. 


Please email your email address to this email address bmuthen@ucla.edu 

Mike Todd posted on Thursday, October 11, 2007  4:20 pm



Hi Bengt: I've started working with these data again. I've successfully set up and run simple 3variable multilevel path models of the form X>M>Y (including a direct path from X to Y), where X and M are binary and Y is continuous and all variables are at the withinperson level (i.e., Level 1). I have 4 questions now: 1) In your opinion, would it make sense to obtain bootstrapped estimates and standard errors for the product of the X>M and M>Y paths to test the indirect effect of X on Y? 2) If yes to question 1, is there a way to set this up in Mplus 4.21 where TYPE=TWOLEVEL and ALGORITHM=INTEGRATION? 3) If yes to question 2, is there a relevant example online and/or in the manual? 4) Will version 5 allow for the MODEL: INDIRECT specification under the INTEGRATION algorithm? Thanks again for your help with this. 


Having a bootstrapped standard error for a and b does not help you obtain a bootstrapped standard error for a*b. Version 5 will not have MODEL INDIRECT for models that require numerical integration. 

Mike Todd posted on Friday, October 12, 2007  11:20 am



Thanks, Linda. Sorry about being unclear in my earlier note. My intention was to (1) compute a*b values using the values of a and b estimates from a set of bootstrap samples and (2) derive an empirical (bootstrap) standard error for the a*b value. Is this possible in Mplus? And if so, do you think it would be advisable? Thanks again. 


Mplus does not bootstrap parameter estimates only standard errors. 


Dear Dr. Muthen, I am attempting to examine multiple mediation analyses with repeated measurements (N = 119). The predictor X and the two mediators M1 and M2 were assessed at Time 1, Time 2, and Time 3. The criteria Y was assessed at Time 4. All variables are continuous. My intention is to test multiple mediation 'controlled' for time. Should it be conducted within a multilevel model or should I examine the mediation with time as a control variable? How could it be implemented in MPlus? Thanks a lot for help! Sabrina 


Use a singlelevel analysis with data in wide format. 

Sabrina Krys posted on Wednesday, February 15, 2017  1:29 am



Thank you Dr. Muthen, but how can I implement a multiple mediation using intercepts and slopes in mplus? Which specifications have to be done? Do you have any article or chapter where a mediation with data in wide format is conducted? I hope my understanding is not fundamental wrong. Thanks again 


Which intercepts and slopes are you referring to? 

Sabrina Krys posted on Thursday, February 16, 2017  12:15 am



I thought you were referring to growth analysis. If not, what is meant by 'single level analysis using data in wide format'? My question is, whether I can analyze mediation analyses with repeated measures (dependent samples) or not. Should I analyze each mediation individually or is it possible to integrate them in one model? My approach was to put my data into long format, build a group variable (=time), and control for it. But the problem is that the samples are dependent. Thank you again 


No growth analysis. Just a path model. For simplicity use X only at T1: M1_t2 M2_t2 ON X; M1_t3 M2_t3 on M1_t2 M2_t2 X; Y ON M1_t3 M2_t3 X; But there are many alternatives  se e.g. Cole & Maxwell (2003) in J Abnorm Psych. 

Sabrina Krys posted on Thursday, February 16, 2017  11:27 am



Okay, thanks a lot! 


For other interested readers: I found another paper that might be interesting within the context of mediation with repeated measures. Itīs Preacher (2015)  Advances in Mediation Analysis. Also could be of interest: Vancouver & Carlson (2015)  All Things in Moderation, Including Tests of Mediation. 


Sorry, but I have another question: The following input uses the mentioned data in long format with a group (=Time) variable (t1, t2, t3). Is it possible to put the three samples together and control for time? M1 ON x group; M2 ON x group; Y ON x m1 m2 group; m1 with m2; MODEL INDIRECT: Y IND x; 


I think Mplus won't complain but I am not sure you get at the indirect effect correctly this way. There is a big literature on longitudinal mediation  also outside the SEM references you give. See, e.g., this contribution from the causal inference area: VanderWeele (2011) in J of Consulting and Clinical Psych. 

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