diff --git a/R/check_outliers.R b/R/check_outliers.R
index 9234796fb..161bc65e7 100644
--- a/R/check_outliers.R
+++ b/R/check_outliers.R
@@ -392,6 +392,9 @@ check_outliers.default <- function(x,
     several.ok = TRUE
   )
 
+  # Get model information
+  m_info <- insight::model_info(x)
+
   # Get data
   my_data <- insight::get_data(x, verbose = FALSE)
 
@@ -405,8 +408,12 @@ check_outliers.default <- function(x,
     )
   }
 
-  # Remove non-numerics, but only check predictors
-  model_predictors <- unique(insight::find_predictors(x, flatten = TRUE))
+  # Remove non-numerics, but in case of binomial, only check predictors
+  if (m_info$is_binomial) {
+    model_predictors <- unique(insight::find_predictors(x, flatten = TRUE))
+  } else {
+    model_predictors <- colnames(my_data)
+  }
   my_data <- datawizard::data_select(
     my_data[model_predictors],
     select = is.numeric,
@@ -451,7 +458,7 @@ check_outliers.default <- function(x,
   }
 
   # Cook
-  if ("cook" %in% method && !insight::model_info(x)$is_bayesian && !inherits(x, "bife")) {
+  if ("cook" %in% method && !m_info$is_bayesian && !inherits(x, "bife")) {
     data_cook <- .check_outliers_cook(
       x,
       threshold = thresholds$cook
@@ -491,7 +498,7 @@ check_outliers.default <- function(x,
   }
 
   # Pareto
-  if ("pareto" %in% method && insight::model_info(x)$is_bayesian) {
+  if ("pareto" %in% method && m_info(x)$is_bayesian) {
     data_pareto <- .check_outliers_pareto(
       x,
       threshold = thresholds$pareto