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stress_at_work_analysis
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Add ANOVA for adherence and gender + country.

communication
junos 2021-06-08 17:16:08 +02:00
parent c96307b430
commit 1294ee40a0
1 changed files with 27 additions and 0 deletions
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27
statistical_analysis/concordance.py
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@ -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)