diff --git a/.quarto/preview/lock b/.quarto/preview/lock
index 42d1d9a..6b602dc 100644
--- a/.quarto/preview/lock
+++ b/.quarto/preview/lock
@@ -1 +1 @@
-32703
\ No newline at end of file
+33158
\ No newline at end of file
diff --git a/docs/pages/anova_demo_between.html b/docs/pages/anova_demo_between.html
index 26ec5c0..99f800f 100644
--- a/docs/pages/anova_demo_between.html
+++ b/docs/pages/anova_demo_between.html
@@ -369,7 +369,7 @@
 };
 
 // Store cell data
-globalThis.qwebrCellDetails = [{"code":"# download all libraries\nlibrary(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)\n# load data in workspace\ndownload.file(\"https://kogpsy.github.io/anova-demo/data/akademisch.csv\", \n              \"akademisch.csv\")","id":1,"options":{"autorun":"true","classes":"","comment":"","context":"setup","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-1","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"library(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)","id":2,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-2","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch)\n\nsummary(anovaBETWEEN)","id":3,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-3","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"leveneTest(akad_swk ~ bildung_mutter * bildung_vater, data = akademisch, center = median)","id":4,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-4","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"summary(lm(akad_swk ~ bildung_mutter * bildung_vater, data = akademisch))","id":5,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-5","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_obs <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch, \n                          observed = c(\"bildung_mutter\", \"bildung_vater\"))\n\nsummary(anovaBETWEEN_obs)","id":6,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-6","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_obs$Anova\n\nanovaBETWEEN_obs$Anova$`Sum Sq` |> round(3)","id":7,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-7","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_obs2 <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch, \n                           observed = c(\"bildung_mutter\", \"bildung_vater\"), \n                           anova_table = list(es = c(\"ges\", \"pes\")))\n\nsummary(anovaBETWEEN_obs2)","id":8,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-8","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_eff <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch, \n                          anova_table = list(es = c(\"ges\", \"pes\")))\n\nsummary(anovaBETWEEN_eff)","id":9,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-9","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"afex_plot(object = anovaBETWEEN,\n          x = \"bildung_mutter\",\n          trace = \"bildung_vater\")","id":10,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-10","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p <- afex_plot(object = anovaBETWEEN,\n               x = \"bildung_mutter\",\n               trace = \"bildung_vater\",\n               mapping = c(\"color\", \"shape\"), \n               # Faktorstufen mit unterschiedlichen Farben und Formen\n               dodge = 0.2, \n               # Verschiebung der Faktorstufen anpassen (default: 0.5)\n               error_ci = FALSE, \n               # Standardfehler statt Konfidenzintervalle\n               legend_title = \"Bildung Vater\",\n               # Legendentitel ist nur hier anpassbar!\n               data_plot = FALSE) \n               # Datenpunkte weglassen","id":11,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-11","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p +\n    scale_color_manual(values = c(\"#000000\", \"#E69F00\")) +\n    scale_y_continuous(limits = c(4, 6)) +\n    theme_classic() +                                     \n    ggtitle(\"Bildung der Eltern und Selbstwirksamkeit\") +    \n    xlab(\"Bildung Mutter\") +                        \n    ylab(\"Akademische SWK\")","id":12,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-12","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"joint_tests(anovaBETWEEN, by = \"bildung_vater\")","id":13,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-13","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"joint_tests(anovaBETWEEN, by = \"bildung_mutter\")","id":14,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-14","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"compar <- emmeans(anovaBETWEEN, ~ bildung_mutter * bildung_vater)","id":15,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-15","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, simple = \"bildung_mutter\")","id":16,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-16","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, simple = \"bildung_vater\")","id":17,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-17","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, adjust = \"none\")\npairs(compar, adjust = \"tukey\")","id":18,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-18","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pwpm(compar)","id":19,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-19","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"plot(compar, comparisons = TRUE)","id":20,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-20","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pwpp(compar)","id":21,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-21","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}}];
+globalThis.qwebrCellDetails = [{"code":"# download all libraries\nlibrary(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)\n# load data in workspace\ndownload.file(\"https://kogpsy.github.io/anova-demo/data/akademisch.csv\", \n              \"akademisch.csv\")","id":1,"options":{"autorun":"true","classes":"","comment":"","context":"setup","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-1","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"library(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)","id":2,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-2","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"akademisch <- read_csv(\"akademisch.csv\") |> \n    mutate(bildung_vater = factor(bildung_vater, \n                                  levels = (c(\"niedrig\", \"hoch\"))),\n           bildung_mutter = factor(bildung_mutter, \n                                   levels = (c(\"niedrig\", \"hoch\"))))\n\nakademisch # Datensatz anschauen","id":3,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-3","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"akademisch |> \n  group_by(bildung_mutter, bildung_vater) |> \n  summarize(mean_swk = mean(akad_swk),\n            .groups = \"drop\")","id":4,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-4","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch)\n\nsummary(anovaBETWEEN)","id":5,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-5","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"leveneTest(akad_swk ~ bildung_mutter * bildung_vater, data = akademisch, center = median)","id":6,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-6","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"summary(lm(akad_swk ~ bildung_mutter * bildung_vater, data = akademisch))","id":7,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-7","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_obs <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch, \n                          observed = c(\"bildung_mutter\", \"bildung_vater\"))\n\nsummary(anovaBETWEEN_obs)","id":8,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-8","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_obs$Anova\n\nanovaBETWEEN_obs$Anova$`Sum Sq` |> round(3)","id":9,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-9","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_obs2 <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch, \n                           observed = c(\"bildung_mutter\", \"bildung_vater\"), \n                           anova_table = list(es = c(\"ges\", \"pes\")))\n\nsummary(anovaBETWEEN_obs2)","id":10,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-10","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaBETWEEN_eff <- aov_4(akad_swk ~ bildung_mutter * bildung_vater + (1 | id), data = akademisch, \n                          anova_table = list(es = c(\"ges\", \"pes\")))\n\nsummary(anovaBETWEEN_eff)","id":11,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-11","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"afex_plot(object = anovaBETWEEN,\n          x = \"bildung_mutter\",\n          trace = \"bildung_vater\")","id":12,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-12","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p <- afex_plot(object = anovaBETWEEN,\n               x = \"bildung_mutter\",\n               trace = \"bildung_vater\",\n               mapping = c(\"color\", \"shape\"), \n               # Faktorstufen mit unterschiedlichen Farben und Formen\n               dodge = 0.2, \n               # Verschiebung der Faktorstufen anpassen (default: 0.5)\n               error_ci = FALSE, \n               # Standardfehler statt Konfidenzintervalle\n               legend_title = \"Bildung Vater\",\n               # Legendentitel ist nur hier anpassbar!\n               data_plot = FALSE) \n               # Datenpunkte weglassen","id":13,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-13","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p +\n    scale_color_manual(values = c(\"#000000\", \"#E69F00\")) +\n    scale_y_continuous(limits = c(4, 6)) +\n    theme_classic() +                                     \n    ggtitle(\"Bildung der Eltern und Selbstwirksamkeit\") +    \n    xlab(\"Bildung Mutter\") +                        \n    ylab(\"Akademische SWK\")","id":14,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-14","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"joint_tests(anovaBETWEEN, by = \"bildung_vater\")","id":15,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-15","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"joint_tests(anovaBETWEEN, by = \"bildung_mutter\")","id":16,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-16","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"compar <- emmeans(anovaBETWEEN, ~ bildung_mutter * bildung_vater)","id":17,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-17","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, simple = \"bildung_mutter\")","id":18,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-18","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, simple = \"bildung_vater\")","id":19,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-19","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, adjust = \"none\")\npairs(compar, adjust = \"tukey\")","id":20,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-20","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pwpm(compar)","id":21,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-21","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"plot(compar, comparisons = TRUE)","id":22,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-22","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pwpp(compar)","id":23,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-23","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}}];
 
 </script>
 <script type="module">
@@ -2041,20 +2041,14 @@ <h2 class="anchored" data-anchor-id="a-daten">A) Daten</h2>
 <section id="daten-einlesen-und-konvertieren" class="level3">
 <h3 class="anchored" data-anchor-id="daten-einlesen-und-konvertieren">1) Daten einlesen und konvertieren</h3>
 <p>Lesen Sie den Datensatz <code>akademisch</code> ein und konvertieren Sie die Variablen <code>bildung_vater</code> und <code>bildung_mutter</code> zu Faktoren. Definieren Sie für beide <code>niedrig</code> als erste Faktorstufe und <code>hoch</code> als zweite Faktorstufe.</p>
-<p>```{webr-r message=FALSE, warning=FALSE} akademisch &lt;- read_csv(“akademisch.csv”) |&gt; mutate(bildung_vater = factor(bildung_vater, levels = (c(“niedrig”, “hoch”))), bildung_mutter = factor(bildung_mutter, levels = (c(“niedrig”, “hoch”))))</p>
-<p>akademisch # Datensatz anschauen</p>
-<pre><code>
-
-
-### 2) Mittelwerte berechnen
-
-Berechnen Sie mittels `group_by()` und `summarize()` die Mittelwerte von `akad_swk` getrennt nach `bildung_vater` und `bildung_mutter`.
-
-```{webr-r}
-akademisch |&gt; 
-  group_by(bildung_mutter, bildung_vater) |&gt; 
-  summarize(mean_swk = mean(akad_swk),
-            .groups = "drop")</code></pre>
+<div id="qwebr-insertion-location-3"></div>
+<noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
+</section>
+<section id="mittelwerte-berechnen" class="level3">
+<h3 class="anchored" data-anchor-id="mittelwerte-berechnen">2) Mittelwerte berechnen</h3>
+<p>Berechnen Sie mittels <code>group_by()</code> und <code>summarize()</code> die Mittelwerte von <code>akad_swk</code> getrennt nach <code>bildung_vater</code> und <code>bildung_mutter</code>.</p>
+<div id="qwebr-insertion-location-4"></div>
+<noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 </section>
 <section id="b-2-x-2-between-anova-mit-afex-und-emmeans" class="level2">
@@ -2062,31 +2056,31 @@ <h2 class="anchored" data-anchor-id="b-2-x-2-between-anova-mit-afex-und-emmeans"
 <section id="anova-modell-berechnen" class="level3">
 <h3 class="anchored" data-anchor-id="anova-modell-berechnen">1) ANOVA-Modell berechnen</h3>
 <p>Führen Sie die 2x2 Between ANOVA mit <code>aov_4()</code> durch:</p>
-<div id="qwebr-insertion-location-3"></div>
+<div id="qwebr-insertion-location-5"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Für den Levene-Test benutzen wir <code>leveneTest()</code> aus car:</p>
-<div id="qwebr-insertion-location-4"></div>
+<div id="qwebr-insertion-location-6"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Leider erhalten wir keinen Gesamteffekt. Diesen bekommen wir als letzte Zeile eines mit <code>lm()</code> gefitteten Modells:</p>
-<div id="qwebr-insertion-location-5"></div>
+<div id="qwebr-insertion-location-7"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="fokus-effektgrössen" class="level3">
 <h3 class="anchored" data-anchor-id="fokus-effektgrössen">2) Fokus: Effektgrössen</h3>
 <p>Standardmässig werden (wie z.B. auch in jamovi oder SPSS) die partiellen Effektgrössen berechnet. Diese werden bei experimentell manipulierten Faktoren empfohlen. Bei beobachteten Faktoren wie in diesem Beispiel sollte man auf nicht-partielle Effektgrössen zurückgreifen mit Hilfe des Arguments <code>observed = c("bildung_mutter", "bildung_vater")</code>.</p>
-<div id="qwebr-insertion-location-6"></div>
+<div id="qwebr-insertion-location-8"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Um das besser zu verstehen, müssen wir dem Modell noch etwas “unter die Haube schauen”:</p>
-<div id="qwebr-insertion-location-7"></div>
+<div id="qwebr-insertion-location-9"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Wir berechnen beispielhaft das partielle <span class="math inline">\(\hat\eta^2_p\)</span> und nicht-partielle <span class="math inline">\(\hat\eta^2\)</span> für den Haupteffekt von “Bildung Mutter (BM)”:</p>
 <p><span class="math inline">\(\hat\eta^2_{p(BM)} = \frac{QS_{BM}}{QS_{BM} + QS_{Res}} = \frac{9.122}{9.122 + 143.216} = 0.05988\)</span></p>
 <p><span class="math inline">\(\hat\eta^2_{(BM)} = \frac{QS_{BM}}{QS_{TOT}} = \frac{9.122}{9.122 + 0.027 + 1.487 + 143.216} = 0.05929\)</span></p>
 <p>Wenn die Faktoren als <code>observed</code> definiert sind, kann man beide (partielle und nicht-partielle) Effektgrössen auch direkt anfordern mit <code>anova_table = list(es = c("ges", "pes"))</code>:</p>
-<div id="qwebr-insertion-location-8"></div>
+<div id="qwebr-insertion-location-10"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Wenn das nicht der Fall ist, wird bei Anforderung beider Effektgrössen <code>pes</code> und <code>ges</code> für beide die partielle Effektgrösse ausgegeben, weil diese bei experimentell manipulierten Variablen auch dem <code>ges</code> (generalized eta square) entspricht:</p>
-<div id="qwebr-insertion-location-9"></div>
+<div id="qwebr-insertion-location-11"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Man kann auch nur <strong>einen</strong> von beiden Faktoren als <code>observed</code> definieren, dann wird nur die Quadratsumme dieses Faktors im Nenner der Effektgrösse des anderen (manipulierten) Faktors sowie der Interaktion berücksichtigt. Es handelt sich dann um eine semi-partielle Effektgrösse. Diese Möglichkeit schauen wir hier aber nicht an.</p>
 </section>
@@ -2095,13 +2089,13 @@ <h3 class="anchored" data-anchor-id="afex-plot-der-mittelwerte">3) afex-Plot der
 <p>Für den Plot verwenden wir die Funktion <code>afex_plot(object, x)</code>.</p>
 <p>Als erstes Argument der Funktion wird das Ergebnisobjekt der ANOVA benötigt. Für das Argument <code>x =</code> muss die Gruppierungsvariable/Faktor angegeben werden. Für das Argument <code>trace =</code> muss derjenige Faktor angegeben werden, für den separate Linien (mit Legende) ausgegeben werden sollen.</p>
 <p>Im Plot werden per default Mittelwerte mit den 95 %-Konfidenzintervallen pro Stufe der unabhängigen Variable angezeigt. Mit <code>error_ci = FALSE</code> erhält man stattdessen die Standardfehler der Mittelwerte. In Grau werden ausserdem die beobachteten Datenpunkte angezeigt.</p>
-<div id="qwebr-insertion-location-10"></div>
+<div id="qwebr-insertion-location-12"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Mit <code>afex_plot()</code> kann auch ein ggplot-Objekt erstellt werden, welches wir weiter verändern können. Jetzt mit weiteren Optionen (alle Optionen siehe <code>help(afex_plot)</code>):</p>
-<div id="qwebr-insertion-location-11"></div>
+<div id="qwebr-insertion-location-13"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Verschönern Sie den Plot weiter, indem Sie z.B. folgende Elemente hinzufügen:</p>
-<div id="qwebr-insertion-location-12"></div>
+<div id="qwebr-insertion-location-14"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="bedingte-haupteffekte" class="level3">
@@ -2109,35 +2103,35 @@ <h3 class="anchored" data-anchor-id="bedingte-haupteffekte">4) Bedingte Haupteff
 <p>Bedingte Haupteffekte erhalten wir mit der Funktion <code>joint_tests(object, by)</code> aus <code>emmeans</code>. Als erstes Argument wird das <code>afex</code>-Ergebnisobjekt der ANOVA benötigt. Für das Argument <code>by</code> muss derjenige Faktor angegeben werden, getrennt für dessen Stufen wir die bedingten Haupteffekte des jeweils anderen Faktors erhalten wollen.</p>
 <p>Trotz der knappen Nicht-Signifikanz des Interaktionseffekts im Beispiel wollen wir uns hier die bedingten Haupteffekte aus beiden Richtungen anschauen: sowohl den Vergleich der Bildungsstufen der Mutter separat für (= <code>by</code>) die Stufen der Bildung des Vaters als auch andersherum.</p>
 <p>Bedingte Haupteffekte <code>bildung_mutter</code>:</p>
-<div id="qwebr-insertion-location-13"></div>
+<div id="qwebr-insertion-location-15"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Bedingte Haupteffekte <code>bildung_vater</code>:</p>
-<div id="qwebr-insertion-location-14"></div>
+<div id="qwebr-insertion-location-16"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>In diesem Fall entsprechen die Tests für bedingte Haupteffekte den Einzelvergleichen Bildung hoch vs.&nbsp;niedrig eines Elternteils auf einer bestimmten Bildungsstufe des anderen Elternteils. Daher können wir alternativ auch Einzelvergleiche rechnen. Dazu muss zuerst ein <code>emmeans</code>-Objekt für den Interaktionseffekt erstellt werden:</p>
-<div id="qwebr-insertion-location-15"></div>
+<div id="qwebr-insertion-location-17"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Jetzt können wir <code>pairs()</code> mit <code>simple</code> für denjenigen Faktor verwenden, für den die bedingten Haupteffekte berechnet werden sollen.</p>
 <p>Bedingte Haupteffekte <code>bildung_mutter</code>:</p>
-<div id="qwebr-insertion-location-16"></div>
+<div id="qwebr-insertion-location-18"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Bedingte Haupteffekte <code>bildung_vater</code>:</p>
-<div id="qwebr-insertion-location-17"></div>
+<div id="qwebr-insertion-location-19"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="post-hoc-einzelvergleiche-aller-zellen-untereinander" class="level3">
 <h3 class="anchored" data-anchor-id="post-hoc-einzelvergleiche-aller-zellen-untereinander">5) Post-hoc-Einzelvergleiche aller Zellen untereinander</h3>
 <p>Vergleiche aller vier Bedingungen untereinander (inkl. “diagonale Bedingungen”, d.h. Bedingungen, die sich auf beiden Faktoren unterscheiden), einmal ohne und einmal mit Alpha-Korrektur:</p>
-<div id="qwebr-insertion-location-18"></div>
+<div id="qwebr-insertion-location-20"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Als Matrix:</p>
-<div id="qwebr-insertion-location-19"></div>
+<div id="qwebr-insertion-location-21"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Hier als Plot mit Konfidenzintervallen + Pfeilen, deren Nicht-Überschneidung von zwei Bedingungen Signifikanz anzeigt:</p>
-<div id="qwebr-insertion-location-20"></div>
+<div id="qwebr-insertion-location-22"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Und zuletzt hier als Pairwise-P-value-Plot:</p>
-<div id="qwebr-insertion-location-21"></div>
+<div id="qwebr-insertion-location-23"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 
 
diff --git a/docs/pages/anova_demo_mixed.html b/docs/pages/anova_demo_mixed.html
index 64fc47a..cf71bb8 100644
--- a/docs/pages/anova_demo_mixed.html
+++ b/docs/pages/anova_demo_mixed.html
@@ -369,7 +369,7 @@
 };
 
 // Store cell data
-globalThis.qwebrCellDetails = [{"code":"# download all libraries\nlibrary(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)\n# load data in workspace\ndownload.file(\"https://kogpsy.github.io/anova-demo/data/therapie.csv\", \n              \"therapie.csv\")","id":1,"options":{"autorun":"true","classes":"","comment":"","context":"setup","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-1","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"library(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)","id":2,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-2","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"therapie_long <-  therapie_long |>  \n    mutate(messzeitpunkt = factor(messzeitpunkt, \n                                  levels = c(\"beginn\", \"mitte\", \"ende\")))\n\nlevels(therapie_long$messzeitpunkt) # Reihenfolge bestätigen","id":3,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-3","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"summary(therapie_long)","id":4,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-4","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"therapie_long |> \n  group_by(bedingung, messzeitpunkt) |> \n  summarize(wohlbefinden = mean(wohlbefinden),\n            .groups = \"drop\")","id":5,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-5","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaMIXED <- aov_4(wohlbefinden ~ bedingung + (messzeitpunkt | id), \n                    data = therapie_long)\n\nsummary(anovaMIXED)","id":6,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-6","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaMIXED$anova_table","id":7,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-7","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"leveneTest(wohlbefinden ~ bedingung, data = therapie_long, center = median)","id":8,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-8","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p <- afex_plot(object = anovaMIXED,\n               x = \"messzeitpunkt\",\n               trace = \"bedingung\",\n               mapping = c(\"color\", \"shape\"), \n               dodge = 0.2,\n               error_ci = FALSE, \n               legend_title = \"Bedingung\")","id":9,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-9","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p +    \n  scale_color_manual(values = c(\"#000000\", \"#E69F00\")) +\n  scale_y_continuous(limits = c(1, 6), breaks = 1:6) +\n  scale_x_discrete(labels = c(\"Beginn\", \"Mitte\", \"Ende\")) +\n  theme_classic() +                              \n  ggtitle(\"Wohlbefinden im Therapieverlauf\") +    \n  xlab(\"Messzeitpunkt\") +                         \n  ylab(\"Wohlbefinden\")                          ","id":10,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-10","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"joint_tests(object = anovaMIXED, by = \"bedingung\")","id":11,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-11","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"compar <- emmeans(anovaMIXED, ~ messzeitpunkt * bedingung)","id":12,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-12","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, simple = \"messzeitpunkt\", adjust = \"tukey\")","id":13,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-13","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"plot(compar, by = \"bedingung\", comparisons = TRUE)","id":14,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-14","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pwpp(compar, by = \"bedingung\")","id":15,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-15","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"contr <- emmeans(anovaMIXED, ~ messzeitpunkt | bedingung)","id":16,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-16","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"kontrast <- list(\"Beginn vs. Mitte & Ende\" = c(-1, 1/2, 1/2))\ncontrast(object = contr, method = kontrast)","id":17,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-17","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"contrast(object = contr, method = \"poly\", adjust = \"none\")","id":18,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-18","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}}];
+globalThis.qwebrCellDetails = [{"code":"# download all libraries\nlibrary(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)\n# load data in workspace\ndownload.file(\"https://kogpsy.github.io/anova-demo/data/therapie.csv\", \n              \"therapie.csv\")","id":1,"options":{"autorun":"true","classes":"","comment":"","context":"setup","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-1","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"library(tidyverse)\nlibrary(afex)\nlibrary(emmeans)\nlibrary(car)","id":2,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-2","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"therapie <- read_csv(\"therapie.csv\") # einlesen\n\ntherapie <- therapie |> \n    mutate(id = as.factor(id),\n           bedingung = as.factor(bedingung))\n\ntherapie # Datensatz anschauen","id":3,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-3","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"therapie_long <- therapie |>\n    pivot_longer(cols = beginn:ende, \n                 names_to = \"messzeitpunkt\", \n                 values_to = \"wohlbefinden\")\n\ntherapie_long # anschauen","id":4,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-4","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"therapie_long <-  therapie_long |>  \n    mutate(messzeitpunkt = factor(messzeitpunkt, \n                                  levels = c(\"beginn\", \"mitte\", \"ende\")))\n\nlevels(therapie_long$messzeitpunkt) # Reihenfolge bestätigen","id":5,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-5","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"summary(therapie_long)","id":6,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-6","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"therapie_long |> \n  group_by(bedingung, messzeitpunkt) |> \n  summarize(wohlbefinden = mean(wohlbefinden),\n            .groups = \"drop\")","id":7,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-7","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaMIXED <- aov_4(wohlbefinden ~ bedingung + (messzeitpunkt | id), \n                    data = therapie_long)\n\nsummary(anovaMIXED)","id":8,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-8","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"anovaMIXED$anova_table","id":9,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-9","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"leveneTest(wohlbefinden ~ bedingung, data = therapie_long, center = median)","id":10,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-10","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p <- afex_plot(object = anovaMIXED,\n               x = \"messzeitpunkt\",\n               trace = \"bedingung\",\n               mapping = c(\"color\", \"shape\"), \n               dodge = 0.2,\n               error_ci = FALSE, \n               legend_title = \"Bedingung\")","id":11,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-11","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"p +    \n  scale_color_manual(values = c(\"#000000\", \"#E69F00\")) +\n  scale_y_continuous(limits = c(1, 6), breaks = 1:6) +\n  scale_x_discrete(labels = c(\"Beginn\", \"Mitte\", \"Ende\")) +\n  theme_classic() +                              \n  ggtitle(\"Wohlbefinden im Therapieverlauf\") +    \n  xlab(\"Messzeitpunkt\") +                         \n  ylab(\"Wohlbefinden\")                          ","id":12,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-12","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"joint_tests(object = anovaMIXED, by = \"bedingung\")","id":13,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-13","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"compar <- emmeans(anovaMIXED, ~ messzeitpunkt * bedingung)","id":14,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-14","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pairs(compar, simple = \"messzeitpunkt\", adjust = \"tukey\")","id":15,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-15","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"plot(compar, by = \"bedingung\", comparisons = TRUE)","id":16,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-16","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"pwpp(compar, by = \"bedingung\")","id":17,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-17","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"contr <- emmeans(anovaMIXED, ~ messzeitpunkt | bedingung)","id":18,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-18","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"kontrast <- list(\"Beginn vs. Mitte & Ende\" = c(-1, 1/2, 1/2))\ncontrast(object = contr, method = kontrast)","id":19,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-19","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}},{"code":"contrast(object = contr, method = \"poly\", adjust = \"none\")","id":20,"options":{"autorun":"false","classes":"","comment":"","context":"interactive","dpi":72,"editor-font-scale":"1","editor-max-height":"","editor-quick-suggestions":"false","editor-word-wrap":"true","fig-cap":"","fig-height":5,"fig-width":7,"label":"unnamed-chunk-20","message":"true","out-height":"","out-width":"700px","output":"true","read-only":"false","results":"markup","warning":"true"}}];
 
 </script>
 <script type="module">
@@ -2041,34 +2041,25 @@ <h2 class="anchored" data-anchor-id="a-daten">A) Daten</h2>
 <section id="daten-einlesen-und-konvertieren" class="level3">
 <h3 class="anchored" data-anchor-id="daten-einlesen-und-konvertieren">1) Daten einlesen und konvertieren</h3>
 <p>Lesen Sie den Datensatz <code>therapie</code> ein und konvertieren Sie die Variablen <code>id</code> und <code>bedingung</code> zu Faktoren:</p>
-<p>```{webr-r message=FALSE, warning=FALSE} therapie &lt;- read_csv(“therapie.csv”) # einlesen</p>
-<p>therapie &lt;- therapie |&gt; mutate(id = as.factor(id), bedingung = as.factor(bedingung))</p>
-<p>therapie # Datensatz anschauen</p>
-<pre><code>
-
-
-### 2) Datensatz transformieren
-
-Führen Sie eine wide-to-long Transformation durch und bilden Sie damit einen Dataframe `therapie_long` mit einer Variable `messzeitpunkt` und der (Outcome-)Variablen `wohlbefinden`. 
-
-```{webr-r}
-therapie_long &lt;- therapie |&gt;
-    pivot_longer(cols = beginn:ende, 
-                 names_to = "messzeitpunkt", 
-                 values_to = "wohlbefinden")
-
-therapie_long # anschauen</code></pre>
-<p>Definieren Sie die Variable <code>messzeitpunkt</code> als Faktor mit den Faktorstufen “beginn”, “mitte” und “ende” (in dieser Reihenfolge).</p>
 <div id="qwebr-insertion-location-3"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
-<p>Zusammenfassung mit <code>summary()</code>:</p>
+</section>
+<section id="datensatz-transformieren" class="level3">
+<h3 class="anchored" data-anchor-id="datensatz-transformieren">2) Datensatz transformieren</h3>
+<p>Führen Sie eine wide-to-long Transformation durch und bilden Sie damit einen Dataframe <code>therapie_long</code> mit einer Variable <code>messzeitpunkt</code> und der (Outcome-)Variablen <code>wohlbefinden</code>.</p>
 <div id="qwebr-insertion-location-4"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
+<p>Definieren Sie die Variable <code>messzeitpunkt</code> als Faktor mit den Faktorstufen “beginn”, “mitte” und “ende” (in dieser Reihenfolge).</p>
+<div id="qwebr-insertion-location-5"></div>
+<noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
+<p>Zusammenfassung mit <code>summary()</code>:</p>
+<div id="qwebr-insertion-location-6"></div>
+<noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="mittelwerte-berechnen" class="level3">
 <h3 class="anchored" data-anchor-id="mittelwerte-berechnen">3) Mittelwerte berechnen</h3>
 <p>Berechnen Sie mittels <code>group_by()</code> und <code>summarize()</code> die Mittelwerte von <code>wohlbefinden</code> getrennt nach <code>bedingung</code> und <code>messzeitpunkt</code>.</p>
-<div id="qwebr-insertion-location-5"></div>
+<div id="qwebr-insertion-location-7"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 </section>
@@ -2077,14 +2068,14 @@ <h2 class="anchored" data-anchor-id="b-2-x-3-mixed-design-anova-mit-afex-und-emm
 <section id="mixed-design-anova" class="level3">
 <h3 class="anchored" data-anchor-id="mixed-design-anova">1) Mixed Design ANOVA</h3>
 <p>Führen Sie die Mixed Design ANOVA mit <code>aov_4()</code> durch.</p>
-<div id="qwebr-insertion-location-6"></div>
+<div id="qwebr-insertion-location-8"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Für die Effektstärke Generalized Eta-Square (ges) brauchen wir noch den ANOVA-Table:</p>
-<div id="qwebr-insertion-location-7"></div>
+<div id="qwebr-insertion-location-9"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Hier werden standardmässig (unabhängig vom Ergebnis des Mauchly-Tests) die Greenhouse-Geisser(GG)-korrigierten F-Tests berichtet.</p>
 <p>Für den Levene-Test benutzen wir <code>leveneTest()</code> aus car:</p>
-<div id="qwebr-insertion-location-8"></div>
+<div id="qwebr-insertion-location-10"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="afex-plot-der-mittelwerte" class="level3">
@@ -2093,44 +2084,44 @@ <h3 class="anchored" data-anchor-id="afex-plot-der-mittelwerte">2) afex-Plot der
 <p>Als erstes Argument der Funktion wird das Ergebnisobjekt der ANOVA benötigt. Für das Argument <code>x =</code> muss die Gruppierungsvariable/Faktor angegeben werden. Für das Argument <code>trace =</code> muss derjenige Faktor angegeben werden, für den separate Linien (mit Legende) ausgegeben werden sollen.</p>
 <p>In einem Mixed-Design wie unserem ist es am sinnvollsten, den Faktor <code>messzeitpunkt</code> auf der X-Achse darzustellen und getrennte Linien für den Between-Faktor <code>bedingung</code> zu verwenden. Der Legendentitel kann mit dem Argument <code>legend.title</code> angepasst werden.</p>
 <p>Wir erstellen mit <code>afex_plot()</code> ein ggplot-Objekt, welches wir weiter verändern können:</p>
-<div id="qwebr-insertion-location-9"></div>
+<div id="qwebr-insertion-location-11"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Verschönern Sie den Plot, indem Sie folgende Elemente hinzufügen:</p>
-<div id="qwebr-insertion-location-10"></div>
+<div id="qwebr-insertion-location-12"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="bedingte-haupteffekte" class="level3">
 <h3 class="anchored" data-anchor-id="bedingte-haupteffekte">3) Bedingte Haupteffekte</h3>
 <p>Bedingte Haupteffekte erhalten wir mit der Funktion <code>joint_tests(object, by)</code> aus <code>emmeans</code>. Als erstes Argument wird das <code>afex</code>-Ergebnisobjekt der ANOVA benötigt. Für das Argument <code>by</code> muss derjenige Faktor angegeben werden, getrennt für dessen Stufen wir die bedingten Haupteffekte des jeweils anderen Faktors erhalten wollen.</p>
 <p>Hier interessieren wir uns für die bedingten Haupteffekte des within-subject Faktors <code>messzeitpunkt</code> (getrennt für - <code>by</code> - die Stufen des between-subjects Faktors <code>bedingung</code>).</p>
-<div id="qwebr-insertion-location-11"></div>
+<div id="qwebr-insertion-location-13"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="post-hoc-einzelvergleiche-für-die-bedingten-haupteffekte" class="level3">
 <h3 class="anchored" data-anchor-id="post-hoc-einzelvergleiche-für-die-bedingten-haupteffekte">4) Post-hoc-Einzelvergleiche für die bedingten Haupteffekte</h3>
 <p>Die bedingten Haupteffekte zeigen nur, dass es insgesamt signifikante Unterschiede zwischen den Messzeitpunkten gibt. Jetzt wollen wir noch wissen, welche Messzeitpunkten sich jeweils signifikant unterscheiden (und welche nicht).</p>
 <p>Um diese Einzelvergleiche/Kontraste durchführen zu können, muss zuerst ein <code>emmeans</code>-Objekt (hier: <code>compar</code>) erstellt werden, und zwar für die Interaktion <code>messzeitpunkt * bedingung</code>:</p>
-<div id="qwebr-insertion-location-12"></div>
+<div id="qwebr-insertion-location-14"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Post-hoc-Einzelvergleiche zwischen den Zeitpunkten (<code>simple = "messzeitpunkt"</code>) getrennt für die beiden Bedingungen:</p>
-<div id="qwebr-insertion-location-13"></div>
+<div id="qwebr-insertion-location-15"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Plots für diese Vergleiche:</p>
-<div id="qwebr-insertion-location-14"></div>
+<div id="qwebr-insertion-location-16"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
-<div id="qwebr-insertion-location-15"></div>
+<div id="qwebr-insertion-location-17"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 </section>
 <section id="kontrastanalysen-für-die-bedingten-haupteffekte" class="level3">
 <h3 class="anchored" data-anchor-id="kontrastanalysen-für-die-bedingten-haupteffekte">5) Kontrastanalysen für die bedingten Haupteffekte</h3>
 <p>Wir wollen nun zusätzlich zu den Einzelvergleichen auch noch Kontrastanalysen für den Faktor <code>messzeitpunkt</code> innerhalb beider Bedingungen durchführen. Dazu muss zuerst nochmal ein <code>emmeans</code>-Objekt (hier: <code>contr</code>) erstellt werden (jetzt explizit für genau diese bedingten Haupteffekte):</p>
-<div id="qwebr-insertion-location-16"></div>
+<div id="qwebr-insertion-location-18"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Wir wollen das Wohlbefinden zu <code>beginn</code> mit dem Durchschnitt des Wohlbefindens von <code>mitte</code> und <code>ende</code> vergleichen. Dazu muss ein Kontrast (hier: <code>kontrast</code>) spezifiziert werden und dieser dann mit <code>contrast()</code> evaluiert werden:</p>
-<div id="qwebr-insertion-location-17"></div>
+<div id="qwebr-insertion-location-19"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 <p>Zuletzt wollen wir noch Polynomiale Trendkontraste in Bezug auf den Zeitverlauf getrennt für die beiden Bedingungen testen. Dazu können wir dasselbe <code>emmeans</code>-Objekt benutzen und als Kontrast-Methode <code>poly</code> spezifizieren:</p>
-<div id="qwebr-insertion-location-18"></div>
+<div id="qwebr-insertion-location-20"></div>
 <noscript>Please enable JavaScript to experience the dynamic code cell content on this page.</noscript>
 
 
diff --git a/docs/search.json b/docs/search.json
index a15647b..76d0e34 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -24,7 +24,7 @@
     "href": "pages/anova_demo_mixed.html#a-daten",
     "title": "2 x 3 Mixed Design ANOVA",
     "section": "A) Daten",
-    "text": "A) Daten\n\n1) Daten einlesen und konvertieren\nLesen Sie den Datensatz therapie ein und konvertieren Sie die Variablen id und bedingung zu Faktoren:\n```{webr-r message=FALSE, warning=FALSE} therapie &lt;- read_csv(“therapie.csv”) # einlesen\ntherapie &lt;- therapie |&gt; mutate(id = as.factor(id), bedingung = as.factor(bedingung))\ntherapie # Datensatz anschauen\n\n\n\n### 2) Datensatz transformieren\n\nFühren Sie eine wide-to-long Transformation durch und bilden Sie damit einen Dataframe `therapie_long` mit einer Variable `messzeitpunkt` und der (Outcome-)Variablen `wohlbefinden`. \n\n```{webr-r}\ntherapie_long &lt;- therapie |&gt;\n    pivot_longer(cols = beginn:ende, \n                 names_to = \"messzeitpunkt\", \n                 values_to = \"wohlbefinden\")\n\ntherapie_long # anschauen\nDefinieren Sie die Variable messzeitpunkt als Faktor mit den Faktorstufen “beginn”, “mitte” und “ende” (in dieser Reihenfolge).\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.\nZusammenfassung mit summary():\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.\n\n\n3) Mittelwerte berechnen\nBerechnen Sie mittels group_by() und summarize() die Mittelwerte von wohlbefinden getrennt nach bedingung und messzeitpunkt.\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.",
+    "text": "A) Daten\n\n1) Daten einlesen und konvertieren\nLesen Sie den Datensatz therapie ein und konvertieren Sie die Variablen id und bedingung zu Faktoren:\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.\n\n\n2) Datensatz transformieren\nFühren Sie eine wide-to-long Transformation durch und bilden Sie damit einen Dataframe therapie_long mit einer Variable messzeitpunkt und der (Outcome-)Variablen wohlbefinden.\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.\nDefinieren Sie die Variable messzeitpunkt als Faktor mit den Faktorstufen “beginn”, “mitte” und “ende” (in dieser Reihenfolge).\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.\nZusammenfassung mit summary():\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.\n\n\n3) Mittelwerte berechnen\nBerechnen Sie mittels group_by() und summarize() die Mittelwerte von wohlbefinden getrennt nach bedingung und messzeitpunkt.\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.",
     "crumbs": [
       "2 x 3 Mixed Design ANOVA"
     ]
@@ -71,7 +71,7 @@
     "href": "pages/anova_demo_between.html#a-daten",
     "title": "2 x 2 Between ANOVA",
     "section": "A) Daten",
-    "text": "A) Daten\n\n1) Daten einlesen und konvertieren\nLesen Sie den Datensatz akademisch ein und konvertieren Sie die Variablen bildung_vater und bildung_mutter zu Faktoren. Definieren Sie für beide niedrig als erste Faktorstufe und hoch als zweite Faktorstufe.\n```{webr-r message=FALSE, warning=FALSE} akademisch &lt;- read_csv(“akademisch.csv”) |&gt; mutate(bildung_vater = factor(bildung_vater, levels = (c(“niedrig”, “hoch”))), bildung_mutter = factor(bildung_mutter, levels = (c(“niedrig”, “hoch”))))\nakademisch # Datensatz anschauen\n\n\n\n### 2) Mittelwerte berechnen\n\nBerechnen Sie mittels `group_by()` und `summarize()` die Mittelwerte von `akad_swk` getrennt nach `bildung_vater` und `bildung_mutter`.\n\n```{webr-r}\nakademisch |&gt; \n  group_by(bildung_mutter, bildung_vater) |&gt; \n  summarize(mean_swk = mean(akad_swk),\n            .groups = \"drop\")",
+    "text": "A) Daten\n\n1) Daten einlesen und konvertieren\nLesen Sie den Datensatz akademisch ein und konvertieren Sie die Variablen bildung_vater und bildung_mutter zu Faktoren. Definieren Sie für beide niedrig als erste Faktorstufe und hoch als zweite Faktorstufe.\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.\n\n\n2) Mittelwerte berechnen\nBerechnen Sie mittels group_by() und summarize() die Mittelwerte von akad_swk getrennt nach bildung_vater und bildung_mutter.\n\nPlease enable JavaScript to experience the dynamic code cell content on this page.",
     "crumbs": [
       "2 x 2 Between ANOVA"
     ]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 6ddb71c..5c648e5 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -2,7 +2,7 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
   <url>
     <loc>https://psylu.github.io/anova_demo/pages/anova_demo_mixed.html</loc>
-    <lastmod>2024-12-18T09:02:02.974Z</lastmod>
+    <lastmod>2024-12-18T09:20:45.739Z</lastmod>
   </url>
   <url>
     <loc>https://psylu.github.io/anova_demo/index.html</loc>
@@ -10,6 +10,6 @@
   </url>
   <url>
     <loc>https://psylu.github.io/anova_demo/pages/anova_demo_between.html</loc>
-    <lastmod>2024-12-18T09:02:04.641Z</lastmod>
+    <lastmod>2024-12-18T09:20:32.157Z</lastmod>
   </url>
 </urlset>
diff --git a/pages/anova_demo_between.qmd b/pages/anova_demo_between.qmd
index fc9c617..b8be46c 100644
--- a/pages/anova_demo_between.qmd
+++ b/pages/anova_demo_between.qmd
@@ -49,7 +49,7 @@ library(car)
 
 Lesen Sie den Datensatz `akademisch` ein und konvertieren Sie die Variablen `bildung_vater` und `bildung_mutter` zu Faktoren. Definieren Sie für beide `niedrig` als erste Faktorstufe und `hoch` als zweite Faktorstufe. 
 
-```{webr-r message=FALSE, warning=FALSE}
+```{webr-r}
 akademisch <- read_csv("akademisch.csv") |> 
     mutate(bildung_vater = factor(bildung_vater, 
                                   levels = (c("niedrig", "hoch"))),
diff --git a/pages/anova_demo_mixed.qmd b/pages/anova_demo_mixed.qmd
index 9141497..47453d4 100644
--- a/pages/anova_demo_mixed.qmd
+++ b/pages/anova_demo_mixed.qmd
@@ -50,7 +50,7 @@ library(car)
 
 Lesen Sie den Datensatz `therapie` ein und konvertieren Sie die Variablen `id` und `bedingung` zu Faktoren:
 
-```{webr-r message=FALSE, warning=FALSE}
+```{webr-r}
 therapie <- read_csv("therapie.csv") # einlesen
 
 therapie <- therapie |>