diff --git a/NAMESPACE b/NAMESPACE
index 233ea5b6..3f138851 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -238,6 +238,7 @@ export(t_to_w)
 export(tschuprows_t)
 export(v_to_t)
 export(v_to_w)
+export(variance_ratio)
 export(vd_a)
 export(w_to_c)
 export(w_to_fei)
diff --git a/NEWS.md b/NEWS.md
index bfd8f3f0..0875f261 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -6,7 +6,7 @@
 
 - `repeated_measures_d()` to compute standardized mean differences (SMD) for repeated measures data.
   - Also supported in `effectsize(<t.test(paired = TRUE)>)`
-  
+- `variance_ratio()` to compute variance ratios for independent or paired samples.
 ## Bug fixes
 
 - `nnt()` now properly accepts the `y` argument.
diff --git a/R/sysdata.rda b/R/sysdata.rda
index 50806d57..9c9957fc 100644
Binary files a/R/sysdata.rda and b/R/sysdata.rda differ
diff --git a/R/vars_ratio.R b/R/vars_ratio.R
new file mode 100644
index 00000000..d3f7ffc9
--- /dev/null
+++ b/R/vars_ratio.R
@@ -0,0 +1,146 @@
+#' Ratio of Variances
+#'
+#' Computes the ratio of two variances of independent or paired samples. For
+#' paired data, this can also be thought of as the ratio of marginal (squared)
+#' scales of a bivariate normal distribution. For independent samples, this is a
+#' convenient wrapper around [stats::var.test()] (for a paired version of this
+#' test - the Pitman-Morgan test, see `PairedData::Var.test()`).
+#'
+#' @inheritParams cohens_d
+#' @inheritParams means_ratio
+#'
+#' @details
+#'
+#' # Confidence (Compatibility) Intervals (CIs)
+#' For independent samples, confidence intervals are estimated using
+#' [stats::var.test()]. For paired samples, confidence intervals are estimated
+#' using the standard parametric method (see Pitman, 1939; Morgan, 1929).
+#'
+#' @inheritSection effectsize_CIs CIs and Significance Tests
+#' @inheritSection print.effectsize_table Plotting with `see`
+#'
+#' @return A data frame with the effect size (`Vars_ratio`) and its CIs
+#'   (`CI_low` and `CI_high`).
+#'
+#' @seealso [means_ratio()], [stats::var.test()] and `PairedData::Var.test()`
+#'   for the _Pitman-Morgan Test_ for paired samples.
+#'
+#' @references
+#' - Morgan, W. A. (1939). A test for the significance of the difference between
+#' the two variances in a sample from a normal bivariate population. Biometrika,
+#' 31(1/2), 13-19.
+#' - Pitman, E. J. G. (1939). A note on normal correlation. Biometrika, 31(1/2), 9-12.
+#'
+#' @examples
+#' # Two Independent Samples ----------
+#'
+#' x <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
+#' y <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
+#'
+#' variance_ratio(x, y)
+#'
+#' # More options:
+#' variance_ratio(x, y, alternative = "less")
+#' variance_ratio(x, y, log = TRUE)
+#'
+#' # The ratio is scale invariant
+#' variance_ratio(x * 3, y * 3)
+#'
+#'
+#'
+#' # Paired Samples ----------
+#'
+#' sleep2 <- reshape(sleep,
+#'   direction = "wide",
+#'   idvar = "ID", timevar = "group"
+#' )
+#' variance_ratio(Pair(extra.1, extra.2) ~ 1, data = sleep2)
+#'
+#' @export
+variance_ratio <- function(x, y = NULL, data = NULL,
+                           paired = FALSE, log = FALSE,
+                           ci = 0.95, alternative = "two.sided",
+                           verbose = TRUE, ...) {
+  alternative <- .match.alt(alternative)
+  out <- .get_data_2_samples(x, y, data, paired = paired, verbose = verbose, ...)
+  x <- out[["x"]]
+  y <- out[["y"]]
+  paired <- out[["paired"]]
+
+  if (is.null(y)) {
+    insight::format_error("'y' is missing.")
+  }
+
+  if (paired) {
+    out <- .var_ratio_dep(x, y, ci, alternative)
+  } else {
+    out <- .var_ratio_ind(x, y, ci, alternative)
+  }
+
+  if (log) {
+    i <- intersect(colnames(out), c("Vars_ratio", "CI_low", "CI_high"))
+    out[i] <- sapply(out[i], log)
+    colnames(out)[1] <- "log_Vars_ratio"
+  }
+
+  out
+}
+
+#' @keywords internal
+.var_ratio_ind <- function(x, y, ci, alternative) {
+  conf.level <- if (is.null(ci)) 0.95 else ci
+  vt <- stats::var.test(x, y, alternative = alternative, conf.level = conf.level)
+  pars <- parameters::model_parameters(vt)
+  pars$CI <- ci
+  out <- pars[intersect(colnames(pars), c("Estimate", "CI", "CI_low", "CI_high"))]
+  if (is.null(ci)) {
+    out$CI_low <- out$CI_high <- NULL
+  }
+  colnames(out)[1] <- "Vars_ratio"
+  ci_method <- list(method = "F")
+
+  class(out) <- c("effectsize_table", "see_effectsize_table", "data.frame")
+  .someattributes(out) <- .nlist(
+    paired = FALSE, ci, ci_method, alternative,
+    approximate = FALSE
+  )
+  return(out)
+}
+
+#' @keywords internal
+.var_ratio_dep <- function(x, y, ci, alternative) {
+  v1 <- stats::var(x)
+  v2 <- stats::var(y)
+  w <- v1 / v2
+  out <- data.frame(Vars_ratio = w)
+
+  if (.test_ci(ci)) {
+    # Add cis
+    out$CI <- ci
+    ci.level <- .adjust_ci(ci, alternative)
+    alpha <- 1 - ci.level
+
+    # Pitman 1939, pp 11
+    n <- length(x)
+    df <- n - 2
+    r <- stats::cor(x, y)
+    qs <- qt(alpha / 2, df = n - 2)
+    K <- 1 + (2 * (1 - r^2) * qs^2) / (n - 2)
+    CONF <- w * (K + c(-1, 1) * sqrt(K^2 - 1))
+
+    out$CI_low <- CONF[1]
+    out$CI_high <- CONF[2]
+    ci_method <- list(method = "t")
+    out <- .limit_ci(out, alternative, 0, Inf)
+  } else {
+    ci_method <- alternative <- NULL
+  }
+
+
+  class(out) <- c("effectsize_table", "see_effectsize_table", "data.frame")
+  .someattributes(out) <- .nlist(
+    paired = TRUE, ci, ci_method, alternative,
+    approximate = FALSE
+  )
+  return(out)
+}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 4defa812..077e81fc 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -19,6 +19,7 @@ reference:
       - rank_biserial
       - p_superiority
       - means_ratio
+      - variance_ratio
 
   - subtitle: "For contingency tables"
     desc: >
diff --git a/data-raw/es_info.R b/data-raw/es_info.R
index 88044de6..4c5c413b 100644
--- a/data-raw/es_info.R
+++ b/data-raw/es_info.R
@@ -12,8 +12,10 @@ es_info <- tibble::tribble(
   "Mahalanobis_D", "Mahalanobis' D", NA, "onetail", 0, Inf, 0,
   "Means_ratio", "Means Ratio", NA, "twotail", 0, Inf, 1,
   "Means_ratio_adjusted", "Means Ratio (adj.)", NA, "twotail", 0, Inf, 1,
-  "log_Means_ratio", "log(Means Ratio)", NA, "twotail", 0, Inf, 1,
-  "log_Means_ratio_adjusted", "log(Means Ratio, adj.)", NA, "twotail", 0, Inf, 1,
+  "log_Means_ratio", "log(Means Ratio)", NA, "twotail", -Inf, Inf, 0,
+  "log_Means_ratio_adjusted", "log(Means Ratio, adj.)", NA, "twotail", -Inf, Inf, 0,
+  "Vars_ratio", "Variance Ratio", NA, "twotail", 0, Inf, 1,
+  "log_Vars_ratio", "log(Variance Ratio)", NA, "twotail", -Inf, Inf, 0,
 
   ## xtab cor
   "Cohens_w", "Cohen's w", NA, "onetail", 0, Inf, 0,
diff --git a/man/variance_ratio.Rd b/man/variance_ratio.Rd
new file mode 100644
index 00000000..66d1023f
--- /dev/null
+++ b/man/variance_ratio.Rd
@@ -0,0 +1,126 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/vars_ratio.R
+\name{variance_ratio}
+\alias{variance_ratio}
+\title{Ratio of Variances}
+\usage{
+variance_ratio(
+  x,
+  y = NULL,
+  data = NULL,
+  paired = FALSE,
+  log = FALSE,
+  ci = 0.95,
+  alternative = "two.sided",
+  verbose = TRUE,
+  ...
+)
+}
+\arguments{
+\item{x, y}{A numeric vector, or a character name of one in \code{data}.
+Any missing values (\code{NA}s) are dropped from the resulting vector.
+\code{x} can also be a formula (see \code{\link[stats:t.test]{stats::t.test()}}), in which case \code{y} is
+ignored.}
+
+\item{data}{An optional data frame containing the variables.}
+
+\item{paired}{If \code{TRUE}, the values of \code{x} and \code{y} are considered as paired.
+This produces an effect size that is equivalent to the one-sample effect
+size on \code{x - y}. See also \code{\link[=repeated_measures_d]{repeated_measures_d()}} for more options.}
+
+\item{log}{Should the log-ratio be returned? Defaults to \code{FALSE}.
+Normally distributed and useful for meta-analysis.}
+
+\item{ci}{Confidence Interval (CI) level}
+
+\item{alternative}{a character string specifying the alternative hypothesis;
+Controls the type of CI returned: \code{"two.sided"} (default, two-sided CI),
+\code{"greater"} or \code{"less"} (one-sided CI). Partial matching is allowed (e.g.,
+\code{"g"}, \code{"l"}, \code{"two"}...). See \emph{One-Sided CIs} in \link{effectsize_CIs}.}
+
+\item{verbose}{Toggle warnings and messages on or off.}
+
+\item{...}{Arguments passed to or from other methods. When \code{x} is a formula,
+these can be \code{subset} and \code{na.action}.}
+}
+\value{
+A data frame with the effect size (\code{Vars_ratio}) and its CIs
+(\code{CI_low} and \code{CI_high}).
+}
+\description{
+Computes the ratio of two variances of independent or paired samples. For
+paired data, this can also be thought of as the ratio of marginal (squared)
+scales of a bivariate normal distribution. For independent samples, this is a
+convenient wrapper around \code{\link[stats:var.test]{stats::var.test()}} (for a paired version of this
+test - the Pitman-Morgan test, see \code{PairedData::Var.test()}).
+}
+\section{Confidence (Compatibility) Intervals (CIs)}{
+For independent samples, confidence intervals are estimated using
+\code{\link[stats:var.test]{stats::var.test()}}. For paired samples, confidence intervals are estimated
+using the standard parametric method (see Pitman, 1939; Morgan, 1929).
+}
+
+\section{CIs and Significance Tests}{
+"Confidence intervals on measures of effect size convey all the information
+in a hypothesis test, and more." (Steiger, 2004). Confidence (compatibility)
+intervals and p values are complementary summaries of parameter uncertainty
+given the observed data. A dichotomous hypothesis test could be performed
+with either a CI or a p value. The 100 (1 - \eqn{\alpha})\% confidence
+interval contains all of the parameter values for which \emph{p} > \eqn{\alpha}
+for the current data and model. For example, a 95\% confidence interval
+contains all of the values for which p > .05.
+\cr\cr
+Note that a confidence interval including 0 \emph{does not} indicate that the null
+(no effect) is true. Rather, it suggests that the observed data together with
+the model and its assumptions combined do not provided clear evidence against
+a parameter value of 0 (same as with any other value in the interval), with
+the level of this evidence defined by the chosen \eqn{\alpha} level (Rafi &
+Greenland, 2020; Schweder & Hjort, 2016; Xie & Singh, 2013). To infer no
+effect, additional judgments about what parameter values are "close enough"
+to 0 to be negligible are needed ("equivalence testing"; Bauer & Kiesser,
+1996).
+}
+
+\section{Plotting with \code{see}}{
+
+The \code{see} package contains relevant plotting functions. See the \href{https://easystats.github.io/see/articles/effectsize.html}{plotting vignette in the \code{see} package}.
+}
+
+\examples{
+# Two Independent Samples ----------
+
+x <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
+y <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
+
+variance_ratio(x, y)
+
+# More options:
+variance_ratio(x, y, alternative = "less")
+variance_ratio(x, y, log = TRUE)
+
+# The ratio is scale invariant
+variance_ratio(x * 3, y * 3)
+
+
+
+# Paired Samples ----------
+
+sleep2 <- reshape(sleep,
+  direction = "wide",
+  idvar = "ID", timevar = "group"
+)
+variance_ratio(Pair(extra.1, extra.2) ~ 1, data = sleep2)
+
+}
+\references{
+\itemize{
+\item Morgan, W. A. (1939). A test for the significance of the difference between
+the two variances in a sample from a normal bivariate population. Biometrika,
+31(1/2), 13-19.
+\item Pitman, E. J. G. (1939). A note on normal correlation. Biometrika, 31(1/2), 9-12.
+}
+}
+\seealso{
+\code{\link[=means_ratio]{means_ratio()}}, \code{\link[stats:var.test]{stats::var.test()}} and \code{PairedData::Var.test()}
+for the \emph{Pitman-Morgan Test} for paired samples.
+}
diff --git a/tests/testthat/test-rov.R b/tests/testthat/test-rov.R
new file mode 100644
index 00000000..27b758e0
--- /dev/null
+++ b/tests/testthat/test-rov.R
@@ -0,0 +1,37 @@
+test_that("variance_ratio | independent", {
+  x <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
+  y <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
+
+  rov1 <- variance_ratio(x, y)
+  rov2 <- variance_ratio(y, x)
+
+  expect_equal(1 / rov2[[1]], rov1[[1]])
+  expect_equal(1 / rov2$CI_low, rov1$CI_high)
+  expect_equal(1 / rov2$CI_high, rov1$CI_low)
+
+  lrov1 <- variance_ratio(x, y, log = TRUE)
+  lrov2 <- variance_ratio(y, x, log = TRUE)
+
+  expect_equal(-lrov2[[1]], lrov1[[1]])
+  expect_equal(-lrov2$CI_low, lrov1$CI_high)
+  expect_equal(-lrov2$CI_high, lrov1$CI_low)
+
+  expect_error(variance_ratio(x), regexp = "missing")
+})
+
+
+test_that("variance_ratio | paired", {
+  sleep2 <- reshape(sleep,
+    direction = "wide",
+    idvar = "ID", timevar = "group"
+  )
+  pdat1 <- Pair(sleep2$extra.1, sleep2$extra.2)
+  pdat2 <- Pair(sleep2$extra.2, sleep2$extra.1)
+
+  rov1 <- variance_ratio(pdat1)
+  rov2 <- variance_ratio(pdat2)
+
+  expect_equal(1 / rov2[[1]], rov1[[1]])
+  expect_equal(1 / rov2$CI_low, rov1$CI_high)
+  expect_equal(1 / rov2$CI_high, rov1$CI_low)
+})