diff --git a/R/lencode_mixed.R b/R/lencode_mixed.R
index 7490fb2c..8020ad26 100644
--- a/R/lencode_mixed.R
+++ b/R/lencode_mixed.R
@@ -45,7 +45,7 @@
 #' For novel levels, a slightly timmed average of the coefficients is returned.
 #'
 #' A hierarchical generalized linear model is fit using [lme4::lmer()] or
-#' [lme4::glmer()], depending on the nature of the outcome, and no intercept via
+#' [lme4::glmer()], depending on the nature of the outcome, and an intercept via
 #'
 #' ```
 #'   lmer(outcome ~ 1 + (1 | predictor), data = data, ...)
diff --git a/man/step_lencode_mixed.Rd b/man/step_lencode_mixed.Rd
index ae9075f4..c8554f9a 100644
--- a/man/step_lencode_mixed.Rd
+++ b/man/step_lencode_mixed.Rd
@@ -71,7 +71,7 @@ the \emph{first} level of the factor.
 For novel levels, a slightly timmed average of the coefficients is returned.
 
 A hierarchical generalized linear model is fit using \code{\link[lme4:lmer]{lme4::lmer()}} or
-\code{\link[lme4:glmer]{lme4::glmer()}}, depending on the nature of the outcome, and no intercept via
+\code{\link[lme4:glmer]{lme4::glmer()}}, depending on the nature of the outcome, and an intercept via
 
 \if{html}{\out{<div class="sourceCode">}}\preformatted{  lmer(outcome ~ 1 + (1 | predictor), data = data, ...)
 }\if{html}{\out{</div>}}