diff --git a/DESCRIPTION b/DESCRIPTION
index d77d1950d..cdca7f002 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.21.2
+Version: 0.21.2.1
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
diff --git a/NEWS.md b/NEWS.md
index c52c21fde..d8bcfe9f6 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,10 @@
+# parameters 0.21.3
+
+## Changes
+
+* `principal_components()` and `factor_analysis()` now also work when argument
+  `n = 1`.
+
 # parameters 0.21.2
 
 ## Changes
diff --git a/R/principal_components.R b/R/principal_components.R
index 371befa30..2166507eb 100644
--- a/R/principal_components.R
+++ b/R/principal_components.R
@@ -7,13 +7,14 @@
 #' Details section).
 #'
 #' @param x A data frame or a statistical model.
-#' @param n Number of components to extract. If `n="all"`, then `n` is
-#'   set as the number of variables minus 1 (`ncol(x)-1`). If
-#'   `n="auto"` (default) or `n=NULL`, the number of components is
-#'   selected through [`n_factors()`] resp. [`n_components()`].
-#'   In [`reduce_parameters()`], can also be `"max"`, in which case
-#'   it will select all the components that are maximally pseudo-loaded (i.e.,
-#'   correlated) by at least one variable.
+#' @param n Number of components to extract. If `n="all"`, then `n` is set as
+#'   the number of variables minus 1 (`ncol(x)-1`). If `n="auto"` (default) or
+#'   `n=NULL`, the number of components is selected through [`n_factors()`] resp.
+#'   [`n_components()`]. Else, if `n` is a number, `n` components are extracted.
+#'   If `n` exceeds number of variables in the data, it is automatically set to
+#'   the maximum number (i.e. `ncol(x)`). In [`reduce_parameters()`], can also
+#'   be `"max"`, in which case it will select all the components that are
+#'   maximally pseudo-loaded (i.e., correlated) by at least one variable.
 #' @param rotation If not `"none"`, the PCA / FA will be computed using the
 #'   **psych** package. Possible options include `"varimax"`,
 #'   `"quartimax"`, `"promax"`, `"oblimin"`, `"simplimax"`,
@@ -418,15 +419,9 @@ principal_components.data.frame <- function(x,
   } else if (n == "all") {
     n <- ncol(x) - 1
   } else if (n >= ncol(x)) {
-    n <- ncol(x) - 1
-  }
-
-  ## TODO: the next if-statement was removed by Dom, but this breaks
-  ## performance code. Need to check, so we for now add this back
-
-  # sanity check - we need at least two factors
-  if (n < 2 && ncol(x) >= 2) {
-    n <- 2
+    n <- ncol(x)
+  } else if (n < 1) {
+    n <- 1
   }
   n
 }
diff --git a/man/principal_components.Rd b/man/principal_components.Rd
index ebfbbdcd2..c0a644356 100644
--- a/man/principal_components.Rd
+++ b/man/principal_components.Rd
@@ -53,13 +53,14 @@ closest_component(pca_results)
 \arguments{
 \item{x}{A data frame or a statistical model.}
 
-\item{n}{Number of components to extract. If \code{n="all"}, then \code{n} is
-set as the number of variables minus 1 (\code{ncol(x)-1}). If
-\code{n="auto"} (default) or \code{n=NULL}, the number of components is
-selected through \code{\link[=n_factors]{n_factors()}} resp. \code{\link[=n_components]{n_components()}}.
-In \code{\link[=reduce_parameters]{reduce_parameters()}}, can also be \code{"max"}, in which case
-it will select all the components that are maximally pseudo-loaded (i.e.,
-correlated) by at least one variable.}
+\item{n}{Number of components to extract. If \code{n="all"}, then \code{n} is set as
+the number of variables minus 1 (\code{ncol(x)-1}). If \code{n="auto"} (default) or
+\code{n=NULL}, the number of components is selected through \code{\link[=n_factors]{n_factors()}} resp.
+\code{\link[=n_components]{n_components()}}. Else, if \code{n} is a number, \code{n} components are extracted.
+If \code{n} exceeds number of variables in the data, it is automatically set to
+the maximum number (i.e. \code{ncol(x)}). In \code{\link[=reduce_parameters]{reduce_parameters()}}, can also
+be \code{"max"}, in which case it will select all the components that are
+maximally pseudo-loaded (i.e., correlated) by at least one variable.}
 
 \item{rotation}{If not \code{"none"}, the PCA / FA will be computed using the
 \strong{psych} package. Possible options include \code{"varimax"},
diff --git a/man/reduce_parameters.Rd b/man/reduce_parameters.Rd
index 40c6ff827..a039544e7 100644
--- a/man/reduce_parameters.Rd
+++ b/man/reduce_parameters.Rd
@@ -15,13 +15,14 @@ reduce_data(x, method = "PCA", n = "max", distance = "euclidean", ...)
 \item{method}{The feature reduction method. Can be one of \code{"PCA"}, \code{"cMDS"},
 \code{"DRR"}, \code{"ICA"} (see the 'Details' section).}
 
-\item{n}{Number of components to extract. If \code{n="all"}, then \code{n} is
-set as the number of variables minus 1 (\code{ncol(x)-1}). If
-\code{n="auto"} (default) or \code{n=NULL}, the number of components is
-selected through \code{\link[=n_factors]{n_factors()}} resp. \code{\link[=n_components]{n_components()}}.
-In \code{\link[=reduce_parameters]{reduce_parameters()}}, can also be \code{"max"}, in which case
-it will select all the components that are maximally pseudo-loaded (i.e.,
-correlated) by at least one variable.}
+\item{n}{Number of components to extract. If \code{n="all"}, then \code{n} is set as
+the number of variables minus 1 (\code{ncol(x)-1}). If \code{n="auto"} (default) or
+\code{n=NULL}, the number of components is selected through \code{\link[=n_factors]{n_factors()}} resp.
+\code{\link[=n_components]{n_components()}}. Else, if \code{n} is a number, \code{n} components are extracted.
+If \code{n} exceeds number of variables in the data, it is automatically set to
+the maximum number (i.e. \code{ncol(x)}). In \code{\link[=reduce_parameters]{reduce_parameters()}}, can also
+be \code{"max"}, in which case it will select all the components that are
+maximally pseudo-loaded (i.e., correlated) by at least one variable.}
 
 \item{distance}{The distance measure to be used. Only applies when
 \code{method = "cMDS"}. This must be one of \code{"euclidean"}, \code{"maximum"},
diff --git a/tests/testthat/test-pca.R b/tests/testthat/test-pca.R
index dd7791410..b6dba5bd1 100644
--- a/tests/testthat/test-pca.R
+++ b/tests/testthat/test-pca.R
@@ -19,10 +19,17 @@ test_that("principal_components", {
     tolerance = 0.01
   )
 
-  expect_equal(
-    colnames(x),
-    c("Variable", "RC1", "RC2", "Complexity", "Uniqueness", "MSA")
-  )
+  expect_named(x, c("Variable", "RC1", "RC2", "Complexity", "Uniqueness", "MSA"))
+})
+
+
+test_that("principal_components, n", {
+  data(iris)
+  x <- parameters::principal_components(iris[1:4], n = 2)
+  expect_named(x, c("Variable", "PC1", "PC2", "Complexity"))
+
+  x <- parameters::principal_components(iris[1:4], n = 1)
+  expect_named(x, c("Variable", "PC1", "Complexity"))
 })
 
 
@@ -43,17 +50,13 @@ test_that("principal_components", {
     tolerance = 0.01
   )
 
-  expect_equal(
-    colnames(x),
-    c("Variable", "PC1", "PC2", "Complexity")
-  )
+  expect_named(x, c("Variable", "PC1", "PC2", "Complexity"))
 })
 
 
 # predict ----------------------
 # N.B tests will fail if `GPArotation` package is not installed
 
-require("GPArotation", quietly = TRUE)
 d <- na.omit(psych::bfi[, 1:25])
 model <- psych::fa(d, nfactors = 5)
 mp <- model_parameters(model, sort = TRUE, threshold = "max")