I would like to know whether it's possible in MPlus to determine for a significant interaction for which regions in the range of the moderator variable, the effects of the focal predictor on the outcome variable are statistically significant, according to the Johnson-Neyman technique (Hayes & Matthes, 2009; Bauer & Curran, 2009). I found some examples of MPlus input to plot simple slopes, but in those cases you choose the values yourself (often 1 sd below and above the mean) whereas I would like to identify for which values of the moderator variable the effects of the predictor on the outcome are significant. Do you have an example of an MPlus input to do this? Many thanks in advance for your response! Jolien
great - i've been able to adapt that code without a problem. i have a quick follow up question. When generating the plots, is mplus assuming a value of zero for the included covariates? I plotted the interaction by hand (using the mean of each covariate), and the shape of the plots is identical to the plots generated by Mplus, but the values of Y are shifted. Thanks!
I have a regression model with a predictor (paredmarg), moderator (schpress), interaction (int), and two covariates (female, w1grade). I'm curious how (or if) the generated plots take into account the value of the covariates. Here is the syntax i'm using (adapted from 3.18):
MODEL: !main model; lonely2 on paredmarg (b1) schpress (b2) int (b3) female w1grade;
it looks like you are computing predicted values for the lonely2 outcome. Because you don't include values for the female and w1grade covariates in those expressions, you are computing the predicted outcome at zero for those two covariates.
Iím trying to determine what is significant within an interaction I have tested foróI was looking into using the Johnson-Neyman technique and found online that one could use the LOOP plot function to do this like such:
where b1 and b3 refer to the regression of the DV on the IV and the regression of the DV on the product term IVxModerator [using the xwith command], respectively.
Is this correct to do/can this still be used if my DV has a negative binomial distribution (which Iíve labeled a count variable in MPlus)? I assume so, and that the interpretation would be that the IV was significantly predictive of the DV on the log scale within the moderator range where the plot (from upper to lower CI) didnít fall on the x-axis, but confirmation would be great.
That said, perhaps itís not necessary to specify that the DV is in the log scale since even if you exponentiated the values along the y-axis the moderator values shouldnít change, right? (Would it be the same case regardless?)
Hello, I'm piggy-backing off of aprile and Carillon's posts here...
I'm using the PLOT and LOOP functions to probe a continuous (x1) by categorical (x2) interaction (x1x2) with a count DV (count is y). Are there adjustments necessary to the model constrain or PLOT/LOOP commands to adjust the plots for the count distribution of the outcome? Below are the relevant commands.
I think so - both in Chapter 1 and Chapter 2. The book doesn't cover multilevel versions, however.
Lisha Dai posted on Sunday, July 07, 2019 - 7:56 am
Dear Professors,I'm running a two-level moderation model (B2 proposed in Preacher er al. 2016) using Bayes according to the Mplus Web Notes 23 (2019). Now I want to plot simple slopes for this interaction and read ex 3.18 for reference. But the result is wrong cause the interaction are not significant in data but significant in plot.
MODEL: %WITHIN% x*.7; y*.7; y ON x*.2; %BETWEEN% xb BY x@1; xb*.7; x@.01; z*.7; y ; xzb | xb XWITH z; xb WITH z*.1; y ON xb (a); y on z*.2; y on xzb (b); [x@0 z*0 y xb*0];
model constraint: plot(HA); loop(CQ,0,60,1); HA=a+b*CQ; plot: type is plot2;