diff --git a/statistical_analysis/concordance.py b/statistical_analysis/concordance.py
index 5d19faa..45d2b9c 100644
--- a/statistical_analysis/concordance.py
+++ b/statistical_analysis/concordance.py
@@ -58,3 +58,30 @@ print("-------------------------------------")
 print(tbl_session_outcomes/len(df_session_counts))
 
 # %%
+VARIABLES_TO_TRANSLATE = {
+    "Gebruikersnaam": "username",
+    "Geslacht": "gender",
+    "Geboortedatum": "date_of_birth"    
+}
+baseline_inactive.rename(columns=VARIABLES_TO_TRANSLATE, copy=False, inplace=True)
+now = pd.Timestamp('now')
+baseline_inactive = baseline_inactive.assign(date_of_birth = lambda x: pd.to_datetime(x.date_of_birth),
+                                             age = lambda x: now - x.date_of_birth)
+
+# %%
+df_session_counts
+
+# %%
+df_adherence = baseline_inactive[["username", "gender", "age", "startlanguage"]].merge(df_esm_preprocessed[["username", "participant_id"]].drop_duplicates(), how="left", on="username")
+
+# %%
+df_esm_preprocessed_adherence = df_esm_preprocessed.merge(df_session_counts.reset_index(), how="left", on=["participant_id", "device_id", "esm_session"])
+#df_esm_finished = df_esm_preprocessed_adherence[df_esm_preprocessed_adherence["session_response"]=="esm_finished"]
+
+# %%
+df_adherence = df_adherence.merge(df_esm_preprocessed_adherence[df_esm_preprocessed_adherence["session_response"] == "esm_finished"].groupby("participant_id").count()["session_response"], how="left", on="participant_id")
+
+# %%
+lm_adherence = ols('session_response ~ C(gender, Sum) + C(startlanguage, Sum)', data=df_adherence).fit()
+table = sm.stats.anova_lm(lm_adherence, typ=2) # Type 2 ANOVA DataFrame
+print(table)