diff --git a/DESCRIPTION b/DESCRIPTION
index 0fe42daee..be27f7c8a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -183,7 +183,7 @@ Suggests:
     panelr,
     pbkrtest,
     PCDimension,
-    performance (>= 0.12.0),
+    performance (>= 0.14.0),
     plm,
     PMCMRplus,
     poorman,
diff --git a/R/1_model_parameters.R b/R/1_model_parameters.R
index d19f6c6ce..ec3760895 100644
--- a/R/1_model_parameters.R
+++ b/R/1_model_parameters.R
@@ -129,7 +129,7 @@
 #'
 #' - **pseudo** (*for 2-level (G)LMMs only*): In this (post-hoc) method, the
 #' response and the predictor are standardized based on the level of prediction
-#' (levels are detected with [performance::check_heterogeneity_bias()]): Predictors
+#' (levels are detected with [performance::check_group_variation()]): Predictors
 #' are standardized based on their SD at level of prediction (see also
 #' [datawizard::demean()]); The outcome (in linear LMMs) is standardized based
 #' on a fitted random-intercept-model, where `sqrt(random-intercept-variance)`
diff --git a/R/standardize_info.R b/R/standardize_info.R
index 1c067e2cd..a7cc7c612 100644
--- a/R/standardize_info.R
+++ b/R/standardize_info.R
@@ -415,7 +415,8 @@ standardize_info.default <- function(model,
 
   f <- if (two_sd) 2 else 1
 
-  within_vars <- unclass(performance::check_heterogeneity_bias(model))
+  gv <- performance::check_group_variation(model)
+  within_vars <- gv[gv$Variation %in% c("both", "within"), "Variable"]
   id <- insight::get_random(model)[[1]]
   w <- insight::get_weights(model, remove_na = TRUE)
 
diff --git a/R/standardize_parameters.R b/R/standardize_parameters.R
index ef4c91a8c..8e8bcf2fd 100644
--- a/R/standardize_parameters.R
+++ b/R/standardize_parameters.R
@@ -64,7 +64,7 @@
 #' delta.
 #' - **pseudo** (*for 2-level (G)LMMs only*): In this (post-hoc) method, the
 #' response and the predictor are standardized based on the level of prediction
-#' (levels are detected with [`performance::check_heterogeneity_bias()`]): Predictors
+#' (levels are detected with [`performance::check_group_variation()`]): Predictors
 #' are standardized based on their SD at level of prediction (see also
 #' [`datawizard::demean()`]); The outcome (in linear LMMs) is standardized based
 #' on a fitted random-intercept-model, where `sqrt(random-intercept-variance)`
diff --git a/man/model_parameters.Rd b/man/model_parameters.Rd
index 628bfee85..553796137 100644
--- a/man/model_parameters.Rd
+++ b/man/model_parameters.Rd
@@ -142,7 +142,7 @@ As a results, the coefficients for effects of factors are similar to a Glass'
 delta.
 \item \strong{pseudo} (\emph{for 2-level (G)LMMs only}): In this (post-hoc) method, the
 response and the predictor are standardized based on the level of prediction
-(levels are detected with \code{\link[performance:check_heterogeneity_bias]{performance::check_heterogeneity_bias()}}): Predictors
+(levels are detected with \code{\link[performance:check_group_variation]{performance::check_group_variation()}}): Predictors
 are standardized based on their SD at level of prediction (see also
 \code{\link[datawizard:demean]{datawizard::demean()}}); The outcome (in linear LMMs) is standardized based
 on a fitted random-intercept-model, where \code{sqrt(random-intercept-variance)}
diff --git a/man/standardize_parameters.Rd b/man/standardize_parameters.Rd
index db5c3945a..82cd6c2c5 100644
--- a/man/standardize_parameters.Rd
+++ b/man/standardize_parameters.Rd
@@ -119,7 +119,7 @@ As a results, the coefficients for effects of factors are similar to a Glass'
 delta.
 \item \strong{pseudo} (\emph{for 2-level (G)LMMs only}): In this (post-hoc) method, the
 response and the predictor are standardized based on the level of prediction
-(levels are detected with \code{\link[performance:check_heterogeneity_bias]{performance::check_heterogeneity_bias()}}): Predictors
+(levels are detected with \code{\link[performance:check_group_variation]{performance::check_group_variation()}}): Predictors
 are standardized based on their SD at level of prediction (see also
 \code{\link[datawizard:demean]{datawizard::demean()}}); The outcome (in linear LMMs) is standardized based
 on a fitted random-intercept-model, where \code{sqrt(random-intercept-variance)}
diff --git a/tests/testthat/test-standardize_parameters.R b/tests/testthat/test-standardize_parameters.R
index c3dbeeb73..639a15d05 100644
--- a/tests/testthat/test-standardize_parameters.R
+++ b/tests/testthat/test-standardize_parameters.R
@@ -329,6 +329,7 @@ test_that("standardize_parameters (Bayes)", {
 # Pseudo - GLMM --------------------------------
 test_that("standardize_parameters (Pseudo - GLMM)", {
   skip_if_not_installed("datawizard", minimum_version = "0.12.0")
+  skip_if_not_installed("performance", minimum_version = "0.14.0")
   skip_on_cran()
   skip_if_not_installed("lme4")
   set.seed(1)
diff --git a/vignettes/demean.Rmd b/vignettes/demean.Rmd
index 27f31725e..14e884d1e 100644
--- a/vignettes/demean.Rmd
+++ b/vignettes/demean.Rmd
@@ -85,11 +85,11 @@ turn, results in biased estimates, because both the within- *and* between-effect
 are captured in *one* estimate.
 
 You can check if your model may suffer from heterogeneity bias using the
-`check_heterogeneity_bias()` function:
+`check_group_variation()` function:
 
 ```{r}
 library(performance)
-check_heterogeneity_bias(qol_cancer, select = c("phq4", "education"), by = "ID")
+check_group_variation(qol_cancer, select = c("phq4", "education"), by = "ID")
 ```
 
 # Adressing heterogeneity bias: the Fixed Effects Regression (FE) approach