--- title: "Standardized Moderation Effect in a Path Model by stdmod_lavaan()" author: "Shu Fai Cheung and David Weng Ngai Vong" date: "2026-01-04" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Standardized Moderation Effect in a Path Model by stdmod_lavaan()} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- # Purpose This document demonstrates how to use `stdmod_lavaan()` from the package `stdmod` to compute the standardized moderation effect in a path model fitted by `lavaan::sem()`. More about this package can be found in `vignette("stdmod", package = "stdmod")` or at [https://sfcheung.github.io/stdmod/](https://sfcheung.github.io/stdmod/). # Setup the Environment ``` r library(stdmod) # For computing the standardized moderation effect conveniently library(lavaan) # For doing path analysis in lavaan. ``` # Load the Dataset ``` r data(test_mod1) round(head(test_mod1, 3), 3) #> dv iv mod med cov1 cov2 #> 1 23.879 -0.133 -0.544 10.310 -0.511 -0.574 #> 2 23.096 1.456 1.539 11.384 0.094 -0.264 #> 3 23.201 0.319 1.774 9.615 -0.172 0.488 ``` This test data set has 300 cases, six variables, all continuous. # Fit the Model by `lavaan::sem()` The product term can be formed manually or by the colon operator, `:`. `stdmod_lavaan()` will work in both cases. This is the model to be tested: ``` r mod <- " med ~ iv + mod + iv:mod + cov1 dv ~ med + cov2 " fit <- sem(mod, test_mod1, fixed.x = FALSE) summary(fit) #> lavaan 0.6-21.2434 ended normally after 1 iteration #> #> Estimator ML #> Optimization method NLMINB #> Number of model parameters 23 #> #> Number of observations 300 #> #> Model Test User Model: #> #> Test statistic 1.058 #> Degrees of freedom 5 #> P-value (Chi-square) 0.958 #> #> Parameter Estimates: #> #> Standard errors Standard #> Information Expected #> Information saturated (h1) model Structured #> #> Regressions: #> Estimate Std.Err z-value P(>|z|) #> med ~ #> iv 0.221 0.030 7.264 0.000 #> mod 0.104 0.030 3.489 0.000 #> iv:mod 0.257 0.025 10.169 0.000 #> cov1 0.104 0.025 4.099 0.000 #> dv ~ #> med 0.246 0.041 5.962 0.000 #> cov2 0.191 0.023 8.324 0.000 #> #> Covariances: #> Estimate Std.Err z-value P(>|z|) #> iv ~~ #> mod 0.481 0.063 7.606 0.000 #> iv:mod -0.149 0.059 -2.501 0.012 #> cov1 -0.033 0.058 -0.575 0.565 #> cov2 -0.071 0.059 -1.216 0.224 #> mod ~~ #> iv:mod -0.180 0.062 -2.923 0.003 #> cov1 -0.060 0.059 -1.010 0.313 #> cov2 -0.107 0.061 -1.763 0.078 #> iv:mod ~~ #> cov1 -0.051 0.061 -0.837 0.403 #> cov2 0.063 0.063 1.001 0.317 #> cov1 ~~ #> cov2 0.071 0.061 1.158 0.247 #> #> Variances: #> Estimate Std.Err z-value P(>|z|) #> .med 0.201 0.016 12.247 0.000 #> .dv 0.169 0.014 12.247 0.000 #> iv 0.954 0.078 12.247 0.000 #> mod 1.017 0.083 12.247 0.000 #> iv:mod 1.088 0.089 12.247 0.000 #> cov1 1.039 0.085 12.247 0.000 #> cov2 1.076 0.088 12.247 0.000 ``` The results show that `mod` significantly moderates the effect of `iv` on `med`. # Compute the Standardized Moderation Effect As in the case of regression, the coefficient of `iv:mod` in the standardized solution is not the desired standardized coefficient because it standardizes the product term. ``` r standardizedSolution(fit)[3, ] #> lhs op rhs est.std se z pvalue ci.lower ci.upper #> 3 med ~ iv:mod 0.466 0.043 10.842 0 0.382 0.55 ``` After fitting the path model by `lavaan::lavaan()`, we can use `stdmod_lavaan()` to compute the standardized moderation effect using the standard deviations of the focal variable, the moderator, and the outcome variable [(Cheung, Cheung, Lau, Hui, & Vong, 2022)](https://doi.org/10.1037/hea0001188). The minimal arguments are: - `fit`: The output from `lavaan::lavaan()` and its wrappers, such as `lavaan::sem()`. - `x`: The focal variable, the variable with its effect on the outcome variable being moderated. - `y`: The outcome variable. - `w`: The moderator. - `x_w`: The product term. ``` r fit_iv_mod_std <- stdmod_lavaan(fit = fit, x = "iv", y = "med", w = "mod", x_w = "iv:mod") fit_iv_mod_std #> #> Call: #> stdmod_lavaan(fit = fit, x = "iv", y = "med", w = "mod", x_w = "iv:mod") #> #> Variable #> Focal Variable iv #> Moderator mod #> Outcome Variable med #> Product Term iv:mod #> #> lhs op rhs est se z pvalue ci.lower ci.upper #> Original med ~ iv:mod 0.257 0.025 10.169 0 0.208 0.307 #> Standardized med ~ iv:mod 0.440 NA NA NA NA NA ``` The standardized moderation effect of `mod` on the `iv`-`med` path is 0.440. # Form Bootstrap Confidence Interval `stdmod_lavaan()` can also be used to form nonparametric bootstrap confidence interval for the standardized moderation effect. There are two approaches to do this. First, if bootstrap confidence intervals was requested when fitting the model, the stored bootstrap estimates will be used. This is efficient because there is no need to do bootstrapping again. We fit the model again, with bootstrapping: ``` r fit <- sem(mod, test_mod1, fixed.x = FALSE, se = "boot", bootstrap = 2000, iseed = 987543) ``` If bootstrapping has been done when fitting the model, just adding `boot_ci = TRUE` is enough to request nonparametric percentile bootstrap confidence interval: ``` r fit_iv_mod_std_ci <- stdmod_lavaan(fit = fit, x = "iv", y = "med", w = "mod", x_w = "iv:mod", boot_ci = TRUE) fit_iv_mod_std_ci #> #> Call: #> stdmod_lavaan(fit = fit, x = "iv", y = "med", w = "mod", x_w = "iv:mod", #> boot_ci = TRUE) #> #> Variable #> Focal Variable iv #> Moderator mod #> Outcome Variable med #> Product Term iv:mod #> #> lhs op rhs est se z pvalue ci.lower ci.upper #> Original med ~ iv:mod 0.257 0.035 7.298 0 0.184 0.322 #> Standardized med ~ iv:mod 0.440 NA NA NA 0.322 0.539 #> #> Confidence interval of standardized moderation effect: #> - Level of confidence: 95% #> - Bootstrapping Method: Nonparametric #> - Type: Percentile #> - Number of bootstrap samples requests: #> - Number of bootstrap samples with valid results: 2000 #> #> NOTE: Bootstrapping conducted by the method in 0.2.7.5 or later. To use #> the method in the older versions for reproducing previous results, set #> 'use_old_version' to 'TRUE'. ``` The 95% confidence interval of the standardized moderation effect is 0.322 to 0.539. The second approach, not covered here, uses [`do_boot()`](https://sfcheung.github.io/manymome/articles/do_boot.html) from the [`manymome`](https://sfcheung.github.io/manymome/index.html) package. to generate bootstrap estimates. To use the stored bootstrap estimates, set `boot_out` to the output of `do_boot()`. The stored bootstrap estimates will then be used. This method can be used when non-bootstrapping confidence intervals are needed when fitting the model. # Remarks The function `stdmod_lavaan()` can be used for more complicated path models. The computation of the standardized moderation effect in a path model depends only on the standard deviations of the three variables involved (`x`, `w`, and `y`). # Reference(s) The computation of the standardized moderation effect is based on the simple formula presented in the following manuscript, using the standard deviations of the outcome variable, focal variable, and the moderator: Cheung, S. F., Cheung, S.-H., Lau, E. Y. Y., Hui, C. H., & Vong, W. N. (2022) Improving an old way to measure moderation effect in standardized units. *Health Psychology*, *41*(7), 502-505. https://doi.org/10.1037/hea0001188.