diff --git a/R/data_plot.R b/R/data_plot.R
index 13f4730ef..11827629a 100644
--- a/R/data_plot.R
+++ b/R/data_plot.R
@@ -84,7 +84,7 @@
 #' x <- equivalence_test(model, verbose = FALSE)
 #' plot(x)
 #' @export
-data_plot <- function(x, data = NULL, ...) {
+data_plot <- function(x, ...) {
   UseMethod("data_plot")
 }
 
diff --git a/R/plot.check_model.R b/R/plot.check_model.R
index 70a1ccde6..8f3b4e87c 100644
--- a/R/plot.check_model.R
+++ b/R/plot.check_model.R
@@ -3,6 +3,7 @@
 plot.see_check_model <- function(x,
                                  style = theme_lucid,
                                  colors = NULL,
+                                 type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
                                  ...) {
   p <- list()
 
@@ -21,6 +22,7 @@ plot.see_check_model <- function(x,
   model_info <- attr(x, "model_info")
   overdisp_type <- attr(x, "overdisp_type")
 
+  type <- match.arg(type)
 
   # set default values for arguments ------
 
@@ -59,6 +61,7 @@ plot.see_check_model <- function(x,
     p$PP_CHECK <- plot.see_performance_pp_check(
       x$PP_CHECK,
       style = style,
+      type = type,
       check_model = TRUE,
       adjust_legend = TRUE,
       colors = colors[1:2]
diff --git a/R/plot.performance_pp_check.R b/R/plot.check_predictions.R
similarity index 51%
rename from R/plot.performance_pp_check.R
rename to R/plot.check_predictions.R
index e4796650c..98922e473 100644
--- a/R/plot.performance_pp_check.R
+++ b/R/plot.check_predictions.R
@@ -1,5 +1,5 @@
 #' @export
-data_plot.performance_pp_check <- function(x, ...) {
+data_plot.performance_pp_check <- function(x, type = "density", ...) {
   columns <- colnames(x)
   dataplot <- stats::reshape(
     x,
@@ -21,10 +21,11 @@ data_plot.performance_pp_check <- function(x, ...) {
 
   attr(dataplot, "info") <- list(
     "xlab" = attr(x, "response_name"),
-    "ylab" = "Density",
+    "ylab" = ifelse(identical(type, "density"), "Density", "Counts"),
     "title" = "Posterior Predictive Check",
     "check_range" = attr(x, "check_range"),
-    "bandwidth" = attr(x, "bandwidth")
+    "bandwidth" = attr(x, "bandwidth"),
+    "model_info" = attr(x, "model_info")
   )
 
   class(dataplot) <- unique(c("data_plot", "see_performance_pp_check", class(dataplot)))
@@ -40,6 +41,9 @@ data_plot.performance_pp_check <- function(x, ...) {
 #'
 #' @param line_alpha Numeric value specifying alpha of lines indicating `yrep`.
 #' @param style A ggplot2-theme.
+#' @param type Plot type for the posterior predictive checks plot. Can be `"density"`
+#' (default), `"discrete_dots"`, `"discrete_interval"` or `"discrete_both"` (the
+#' `discrete_*` options are only for models with binary, integer or ordinal outcomes).
 #' @inheritParams data_plot
 #' @inheritParams plot.see_check_normality
 #' @inheritParams plot.see_parameters_distribution
@@ -48,7 +52,18 @@ data_plot.performance_pp_check <- function(x, ...) {
 #'
 #' @examplesIf require("performance")
 #' model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
-#' check_posterior_predictions(model)
+#' check_predictions(model)
+#'
+#' # dot-plot style for count-models
+#' d <- iris
+#' d$poisson_var <- rpois(150, 1)
+#' model <- glm(
+#'   poisson_var ~ Species + Petal.Length + Petal.Width,
+#'   data = d,
+#'   family = poisson()
+#' )
+#' out <- check_predictions(model)
+#' plot(out, type = "discrete_dots")
 #' @export
 print.see_performance_pp_check <- function(x,
                                            size_line = 0.5,
@@ -56,15 +71,17 @@ print.see_performance_pp_check <- function(x,
                                            size_bar = 0.7,
                                            style = theme_lucid,
                                            colors = unname(social_colors(c("green", "blue"))),
+                                           type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
                                            ...) {
   orig_x <- x
   check_range <- isTRUE(attributes(x)$check_range)
+  type <- match.arg(type)
 
   if (!inherits(x, "data_plot")) {
-    x <- data_plot(x)
+    x <- data_plot(x, type)
   }
 
-  p1 <- .plot_pp_check(x, size_line, line_alpha, theme_style = style, colors = colors, ...)
+  p1 <- .plot_pp_check(x, size_line, line_alpha, theme_style = style, colors = colors, type = type, ...)
 
   if (isTRUE(check_range)) {
     p2 <- .plot_pp_check_range(orig_x, size_bar, colors = colors)
@@ -85,15 +102,17 @@ plot.see_performance_pp_check <- function(x,
                                           size_bar = 0.7,
                                           style = theme_lucid,
                                           colors = unname(social_colors(c("green", "blue"))),
+                                          type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
                                           ...) {
   orig_x <- x
   check_range <- isTRUE(attributes(x)$check_range)
+  type <- match.arg(type)
 
   if (!inherits(x, "data_plot")) {
-    x <- data_plot(x)
+    x <- data_plot(x, type)
   }
 
-  p1 <- .plot_pp_check(x, size_line, line_alpha, theme_style = style, colors = colors, ...)
+  p1 <- .plot_pp_check(x, size_line, line_alpha, theme_style = style, colors = colors, type = type, ...)
 
   if (isTRUE(check_range)) {
     p2 <- .plot_pp_check_range(orig_x, size_bar, colors = colors)
@@ -105,7 +124,7 @@ plot.see_performance_pp_check <- function(x,
 
 
 
-.plot_pp_check <- function(x, size_line, line_alpha, theme_style, colors, ...) {
+.plot_pp_check <- function(x, size_line, line_alpha, theme_style, colors, type = "density", ...) {
   info <- attr(x, "info")
 
   # default bandwidth, for smooting
@@ -114,7 +133,46 @@ plot.see_performance_pp_check <- function(x,
     bandwidth <- "nrd"
   }
 
-  out <- ggplot2::ggplot(x) +
+  minfo <- info$model_info
+  suggest_dots <- (minfo$is_bernoulli || minfo$is_count || minfo$is_ordinal || minfo$is_categorical)
+
+  if (!is.null(type) && type %in% c("discrete_dots", "discrete_interval", "discrete_both") && suggest_dots) {
+    out <- .plot_check_predictions_dots(x, colors, info, size_line, line_alpha, type, ...)
+  } else {
+    if (suggest_dots) {
+      insight::format_alert(
+        "The model has an integer or a categorical response variable.",
+        "It is recommended to switch to a dot-plot style, e.g. `plot(check_model(model), type = \"discrete_dots\"`."
+      )
+    }
+    # denity plot - for models that have no binary or count/ordinal outcome
+    out <- .plot_check_predictions_density(x, colors, info, size_line, line_alpha, bandwidth, ...)
+  }
+
+
+  dots <- list(...)
+  if (isTRUE(dots[["check_model"]])) {
+    out <- out + theme_style(
+      base_size = 10,
+      plot.title.space = 3,
+      axis.title.space = 5
+    )
+  }
+
+  if (isTRUE(dots[["adjust_legend"]]) || isTRUE(info$check_range)) {
+    out <- out + ggplot2::theme(
+      legend.position = "bottom",
+      legend.margin = ggplot2::margin(0, 0, 0, 0),
+      legend.box.margin = ggplot2::margin(-5, -5, -5, -5)
+    )
+  }
+
+  out
+}
+
+
+.plot_check_predictions_density <- function(x, colors, info, size_line, line_alpha, bandwidth, ...) {
+  ggplot2::ggplot(x) +
     ggplot2::stat_density(
       mapping = ggplot2::aes(
         x = .data$values,
@@ -159,26 +217,150 @@ plot.see_performance_pp_check <- function(x,
       color = ggplot2::guide_legend(reverse = TRUE),
       size = ggplot2::guide_legend(reverse = TRUE)
     )
+}
 
 
-  dots <- list(...)
-  if (isTRUE(dots[["check_model"]])) {
-    out <- out + theme_style(
-      base_size = 10,
-      plot.title.space = 3,
-      axis.title.space = 5
-    )
+.plot_check_predictions_dots <- function(x, colors, info, size_line, line_alpha, type = "discrete_dots", ...) {
+  # make sure we have a factor, so "table()" generates frequencies for all levels
+  # for each group - we need tables of same size to bind data frames
+  x$values <- as.factor(x$values)
+  x <- stats::aggregate(x["values"], list(grp = x$grp), table)
+  x <- cbind(data.frame(key = "Model-predicted data", stringsAsFactors = FALSE), x)
+  x <- cbind(x[1:2], as.data.frame(x[[3]]))
+  x$key[nrow(x)] <- "Observed data"
+  x <- datawizard::data_to_long(x, select = -1:-2, names_to = "x", values_to = "count")
+  if (insight::n_unique(x$x) > 8) {
+    x$x <- datawizard::to_numeric(x$x)
   }
 
-  if (isTRUE(dots[["adjust_legend"]]) || isTRUE(info$check_range)) {
-    out <- out + ggplot2::theme(
-      legend.position = "bottom",
-      legend.margin = ggplot2::margin(0, 0, 0, 0),
-      legend.box.margin = ggplot2::margin(-5, -5, -5, -5)
+  p1 <- p2 <- NULL
+
+  if (!is.null(type) && type %in% c("discrete_interval", "discrete_both")) {
+    centrality_dispersion <- function(i) {
+      c(
+        count = stats::median(i, na.rm = TRUE),
+        unlist(bayestestR::ci(i)[c("CI_low", "CI_high")])
+      )
+    }
+    x_errorbars <- stats::aggregate(x["count"], list(x$x), centrality_dispersion)
+    x_errorbars <- cbind(x_errorbars[1], as.data.frame(x_errorbars[[2]]))
+    colnames(x_errorbars) <- c("x", "count", "CI_low", "CI_high")
+    x_errorbars <- cbind(
+      data.frame(key = "Model-predicted data", stringsAsFactors = FALSE),
+      x_errorbars
     )
+
+    x_tmp <- x[x$key == "Observed data", ]
+    x_tmp$CI_low <- NA
+    x_tmp$CI_high <- NA
+    x_tmp$grp <- NULL
+
+    x_errorbars <- rbind(x_errorbars, x_tmp)
+    p1 <- ggplot2::ggplot() + ggplot2::geom_pointrange(
+      data = x_errorbars[x_errorbars$key == "Model-predicted data", ],
+      mapping = ggplot2::aes(
+        x = .data$x,
+        y = .data$count,
+        ymin = .data$CI_low,
+        ymax = .data$CI_high,
+        color = .data$key
+      ),
+      position = ggplot2::position_nudge(x = 0.2),
+      size = 1.5 * size_line,
+      linewidth = 1.5 * size_line,
+      stroke = 0,
+      shape = 16
+    ) +
+      ggplot2::geom_point(
+        data = x_errorbars[x_errorbars$key == "Observed data", ],
+        mapping = ggplot2::aes(
+          x = .data$x,
+          y = .data$count,
+          color = .data$key
+        ),
+        size = 6 * size_line,
+        stroke = 0,
+        shape = 16
+      )
   }
 
-  out
+  if (!is.null(type) && type %in% c("discrete_dots", "discrete_both")) {
+    if (is.null(p1)) {
+      p2 <- ggplot2::ggplot()
+    } else {
+      p2 <- p1
+    }
+    p2 <- p2 + ggplot2::geom_point(
+      data = x[x$key == "Model-predicted data", ],
+      mapping = ggplot2::aes(
+        x = .data$x,
+        y = .data$count,
+        group = .data$grp,
+        color = .data$key
+      ),
+      alpha = line_alpha,
+      position = ggplot2::position_jitter(width = 0.1, height = 0.02),
+      size = 4 * size_line,
+      stroke = 0,
+      shape = 16
+    ) +
+      # for legend
+      ggplot2::geom_point(
+        data = x[x$key == "Observed data", ],
+        mapping = ggplot2::aes(
+          x = .data$x,
+          y = .data$count,
+          group = .data$grp,
+          color = .data$key
+        ),
+        size = 4 * size_line
+      ) +
+      ggplot2::geom_point(
+        data = x[x$key == "Observed data", ],
+        mapping = ggplot2::aes(
+          x = .data$x,
+          y = .data$count
+        ),
+        size = 6 * size_line,
+        shape = 21,
+        colour = "white",
+        fill = colors[1]
+      )
+  }
+
+  if (is.null(p2)) {
+    p <- p1
+  } else {
+    p <- p2
+  }
+
+  if (type == "discrete_interval") {
+    subtitle <- "Model-predicted intervals should include observed data points"
+  } else {
+    subtitle <- "Model-predicted points should be close to observed data points"
+  }
+
+  p <- p +
+    ggplot2::scale_y_continuous() +
+    ggplot2::scale_color_manual(values = c(
+      "Observed data" = colors[1],
+      "Model-predicted data" = colors[2]
+    )) +
+    ggplot2::labs(
+      x = info$xlab,
+      y = info$ylab,
+      color = "",
+      size = "",
+      alpha = "",
+      title = "Posterior Predictive Check",
+      subtitle = subtitle
+    ) +
+    ggplot2::guides(
+      color = ggplot2::guide_legend(reverse = TRUE),
+      size = ggplot2::guide_legend(reverse = TRUE)
+    )
+
+  return(p)
 }
 
 
diff --git a/R/plot.compare_performance.R b/R/plot.compare_performance.R
index 61094dab5..85848f011 100644
--- a/R/plot.compare_performance.R
+++ b/R/plot.compare_performance.R
@@ -1,3 +1,4 @@
+#' @rdname data_plot
 #' @export
 data_plot.compare_performance <- function(x, data = NULL, ...) {
   x$Model <- sprintf("%s (%s)", x$Name, x$Model)
diff --git a/man/data_plot.Rd b/man/data_plot.Rd
index 725361f3f..4daef23d7 100644
--- a/man/data_plot.Rd
+++ b/man/data_plot.Rd
@@ -1,18 +1,21 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_plot.R
+% Please edit documentation in R/data_plot.R, R/plot.compare_performance.R
 \name{data_plot}
 \alias{data_plot}
+\alias{data_plot.compare_performance}
 \title{Prepare objects for plotting or plot objects}
 \usage{
-data_plot(x, data = NULL, ...)
+data_plot(x, ...)
+
+\method{data_plot}{compare_performance}(x, data = NULL, ...)
 }
 \arguments{
 \item{x}{An object.}
 
+\item{...}{Arguments passed to or from other methods.}
+
 \item{data}{The original data used to create this object. Can be a
 statistical model.}
-
-\item{...}{Arguments passed to or from other methods.}
 }
 \description{
 \code{data_plot()} extracts and transforms an object for plotting,
diff --git a/man/print.see_performance_pp_check.Rd b/man/print.see_performance_pp_check.Rd
index 2d4bc0a5d..9ab399a9d 100644
--- a/man/print.see_performance_pp_check.Rd
+++ b/man/print.see_performance_pp_check.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plot.performance_pp_check.R
+% Please edit documentation in R/plot.check_predictions.R
 \name{print.see_performance_pp_check}
 \alias{print.see_performance_pp_check}
 \alias{plot.see_performance_pp_check}
@@ -12,6 +12,7 @@
   size_bar = 0.7,
   style = theme_lucid,
   colors = unname(social_colors(c("green", "blue"))),
+  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
   ...
 )
 
@@ -22,6 +23,7 @@
   size_bar = 0.7,
   style = theme_lucid,
   colors = unname(social_colors(c("green", "blue"))),
+  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
   ...
 )
 }
@@ -39,6 +41,10 @@
 \item{colors}{Character vector of length two, indicating the colors (in
 hex-format) for points and line.}
 
+\item{type}{Plot type for the posterior predictive checks plot. Can be \code{"density"}
+(default), \code{"discrete_dots"}, \code{"discrete_interval"} or \code{"discrete_both"} (the
+\verb{discrete_*} options are only for models with binary, integer or ordinal outcomes).}
+
 \item{...}{Arguments passed to or from other methods.}
 }
 \value{
@@ -50,6 +56,17 @@ The \code{plot()} method for the \code{performance::check_predictions()} functio
 \examples{
 \dontshow{if (require("performance")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
-check_posterior_predictions(model)
+check_predictions(model)
+
+# dot-plot style for count-models
+d <- iris
+d$poisson_var <- rpois(150, 1)
+model <- glm(
+  poisson_var ~ Species + Petal.Length + Petal.Width,
+  data = d,
+  family = poisson()
+)
+out <- check_predictions(model)
+plot(out, type = "discrete_dots")
 \dontshow{\}) # examplesIf}
 }