diff --git a/Project.toml b/Project.toml
index 848c78c3..88532b6c 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "MCMCDiagnosticTools"
 uuid = "be115224-59cd-429b-ad48-344e309966f0"
 authors = ["David Widmann"]
-version = "0.3.1"
+version = "0.3.2"
 
 [deps]
 AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"
diff --git a/src/ess_rhat.jl b/src/ess_rhat.jl
index a07f8633..5328dd4e 100644
--- a/src/ess_rhat.jl
+++ b/src/ess_rhat.jl
@@ -203,6 +203,7 @@ end
     ess(
         samples::AbstractArray{<:Union{Missing,Real},3};
         kind=:bulk,
+        relative::Bool=false,
         autocov_method=AutocovMethod(),
         split_chains::Int=2,
         maxlag::Int=250,
@@ -215,6 +216,8 @@ Estimate the effective sample size (ESS) of the `samples` of shape
 Optionally, the `kind` of ESS estimate to be computed can be specified (see below). Some
 `kind`s accept additional `kwargs`.
 
+If `relative` is `true`, the relative ESS is returned, i.e. `ess / (draws * chains)`.
+
 $_DOC_SPLIT_CHAINS There must be at least 3 draws in each chain after splitting.
 
 `maxlag` indicates the maximum lag for which autocovariance is computed and must be greater
@@ -447,6 +450,7 @@ end
 function _ess_rhat(
     ::Val{:basic},
     chains::AbstractArray{<:Union{Missing,Real},3};
+    relative::Bool=false,
     autocov_method::AbstractAutocovMethod=AutocovMethod(),
     split_chains::Int=2,
     maxlag::Int=250,
@@ -485,8 +489,8 @@ function _ess_rhat(
     # define cache for the computation of the autocorrelation
     esscache = build_cache(autocov_method, samples, chain_var)
 
-    # set maximum ess for antithetic chains, see below
-    ess_max = ntotal * log10(oftype(one(T), ntotal))
+    # set maximum relative ess for antithetic chains, see below
+    rel_ess_max = log10(oftype(one(T), ntotal))
 
     # for each parameter
     for (i, chains_slice) in zip(eachindex(ess), eachslice(chains; dims=3))
@@ -565,8 +569,13 @@ function _ess_rhat(
         ρ_even = maxlag > 1 ? 1 - inv_var₊ * (W - mean_autocov(k, esscache)) : zero(ρ_even)
         τ = max(0, 2 * sum_pₜ + max(0, ρ_even) - 1)
 
-        # estimate the effective sample size
-        ess[i] = min(ntotal / τ, ess_max)
+        # estimate the relative effective sample size
+        ess[i] = min(inv(τ), rel_ess_max)
+    end
+
+    if !relative
+        # absolute effective sample size
+        ess .*= ntotal
     end
 
     return (; ess, rhat)
diff --git a/test/ess_rhat.jl b/test/ess_rhat.jl
index dc251db5..ee8c7461 100644
--- a/test/ess_rhat.jl
+++ b/test/ess_rhat.jl
@@ -88,6 +88,21 @@ mymean(x) = mean(x)
             @test_throws ArgumentError ess(x2; kind=mymean)
         end
 
+        @testset "relative=true" begin
+            @testset for kind in (:rank, :bulk, :tail, :basic),
+                niter in (50, 100),
+                nchains in (2, 4)
+
+                ss = niter * nchains
+                x = rand(niter, nchains, 2)
+                kind === :rank || @test ess(x; kind, relative=true) ≈ ess(x; kind) / ss
+                S, R = ess_rhat(x; kind)
+                S2, R2 = ess_rhat(x; kind, relative=true)
+                @test S2 ≈ S / ss
+                @test R2 == R
+            end
+        end
+
         @testset "Union{Missing,Float64} eltype" begin
             @testset for kind in (:rank, :bulk, :tail, :basic)
                 x = Array{Union{Missing,Float64}}(undef, 1000, 4, 3)