Commit 0c8e64b5 authored by Katie Winkle's avatar Katie Winkle
Browse files

final chunk of hri 2021 rebuttal stuff

parent 6ee2c345
,katie,winkle-machine,03.01.2021 19:58,file:///home/katie/.config/libreoffice/4;
\ No newline at end of file
,katie,winkle-machine,04.01.2021 09:23,file:///home/katie/.config/libreoffice/4;
\ No newline at end of file
%% Cell type:code id: tags:
``` python
import pandas as pd
import numpy as np
# for stats
from scipy import stats
from scipy.stats import f_oneway
from scipy.stats import ttest_rel, ttest_ind
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.stats.multicomp import MultiComparison
from statsmodels.stats.anova import AnovaRM
```
%% Cell type:markdown id: tags:
## Social agency: primed participants
%% Cell type:code id: tags:
``` python
# Social agency data = APX = agency primed; exercise ordering 1-6
AP1df=pd.read_csv("AP1.csv")
AP1df=AP1df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AP1df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AP1df["condition_order"]="A-P-N" #consider replacing this with concat label
AP1df = AP1df[2:] #discard excess headings
AP2df=pd.read_csv("AP2.csv")
AP2df=AP2df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AP2df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AP2df["condition_order"]="A-N-P"
AP2df = AP2df[2:] #discard excess headings
AP3df=pd.read_csv("AP3.csv")
AP3df=AP3df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AP3df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AP3df["condition_order"]="P-A-N"
AP3df = AP3df[2:] #discard excess headings
AP4df=pd.read_csv("AP4.csv")
AP4df=AP4df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AP4df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AP4df["condition_order"]="P-N-A"
AP4df = AP4df[2:] #discard excess headings
AP5df=pd.read_csv("AP5.csv")
AP5df=AP5df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AP5df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AP5df["condition_order"]="N-A-P"
AP5df = AP5df[2:] #discard excess headings
AP6df=pd.read_csv("AP6.csv")
AP6df=AP6df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AP6df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AP6df["condition_order"]="N-P-A"
AP6df = AP6df[2:] #discard excess headings
```
%% Cell type:code id: tags:
``` python
# cast integers as floats + calculate condition subscales for ease of concatenation
# expertise
AP1df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AP1df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AP1df["subscale_expertise_AS"] = AP1df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1, numeric_only=True)
AP2df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AP2df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AP2df["subscale_expertise_AS"] = AP2df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AP3df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AP3df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AP3df["subscale_expertise_AS"] = AP3df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AP4df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AP4df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AP4df["subscale_expertise_AS"] = AP4df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AP5df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AP5df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AP5df["subscale_expertise_AS"] = AP5df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AP6df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AP6df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AP6df["subscale_expertise_AS"] = AP6df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AP1df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AP1df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AP1df["subscale_expertise_PS"] = AP1df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AP2df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AP2df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AP2df["subscale_expertise_PS"] = AP2df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AP3df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AP3df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AP3df["subscale_expertise_PS"] = AP3df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AP4df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AP4df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AP4df["subscale_expertise_PS"] = AP4df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AP5df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AP5df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AP5df["subscale_expertise_PS"] = AP5df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AP6df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AP6df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AP6df["subscale_expertise_PS"] = AP6df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AP1df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AP1df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AP1df["subscale_expertise_NS"] = AP1df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AP2df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AP2df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AP2df["subscale_expertise_NS"] = AP2df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AP3df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AP3df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AP3df["subscale_expertise_NS"] = AP3df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AP4df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AP4df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AP4df["subscale_expertise_NS"] = AP4df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AP5df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AP5df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AP5df["subscale_expertise_NS"] = AP5df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AP6df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AP6df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AP6df["subscale_expertise_NS"] = AP6df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
# trustworthiness
AP1df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AP1df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AP1df["subscale_trustworthiness_AS"] = AP1df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AP2df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AP2df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AP2df["subscale_trustworthiness_AS"] = AP2df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AP3df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AP3df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AP3df["subscale_trustworthiness_AS"] = AP3df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AP4df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AP4df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AP4df["subscale_trustworthiness_AS"] = AP4df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AP5df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AP5df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AP5df["subscale_trustworthiness_AS"] = AP5df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AP6df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AP6df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AP6df["subscale_trustworthiness_AS"] = AP6df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AP1df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AP1df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AP1df["subscale_trustworthiness_PS"] = AP1df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AP2df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AP2df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AP2df["subscale_trustworthiness_PS"] = AP2df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AP3df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AP3df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AP3df["subscale_trustworthiness_PS"] = AP3df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AP4df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AP4df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AP4df["subscale_trustworthiness_PS"] = AP4df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AP5df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AP5df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AP5df["subscale_trustworthiness_PS"] = AP5df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AP6df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AP6df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AP6df["subscale_trustworthiness_PS"] = AP6df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AP1df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AP1df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AP1df["subscale_trustworthiness_NS"] = AP1df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AP2df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AP2df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AP2df["subscale_trustworthiness_NS"] = AP2df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AP3df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AP3df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AP3df["subscale_trustworthiness_NS"] = AP3df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AP4df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AP4df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AP4df["subscale_trustworthiness_NS"] = AP4df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AP5df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AP5df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AP5df["subscale_trustworthiness_NS"] = AP5df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AP6df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AP6df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AP6df["subscale_trustworthiness_NS"] = AP6df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
# goodwill
AP1df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AP1df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AP1df["subscale_goodwill_AS"] = AP1df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AP2df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AP2df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AP2df["subscale_goodwill_AS"] = AP2df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AP3df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AP3df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AP3df["subscale_goodwill_AS"] = AP3df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AP4df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AP4df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AP4df["subscale_goodwill_AS"] = AP4df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AP5df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AP5df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AP5df["subscale_goodwill_AS"] = AP5df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AP6df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AP6df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AP6df["subscale_goodwill_AS"] = AP6df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AP1df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AP1df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AP1df["subscale_goodwill_PS"] = AP1df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AP2df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AP2df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AP2df["subscale_goodwill_PS"] = AP2df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AP3df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AP3df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AP3df["subscale_goodwill_PS"] = AP3df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AP4df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AP4df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AP4df["subscale_goodwill_PS"] = AP4df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AP5df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AP5df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AP5df["subscale_goodwill_PS"] = AP5df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AP6df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AP6df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AP6df["subscale_goodwill_PS"] = AP6df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AP1df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AP1df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AP1df["subscale_goodwill_NS"] = AP1df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AP2df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AP2df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AP2df["subscale_goodwill_NS"] = AP2df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AP3df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AP3df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AP3df["subscale_goodwill_NS"] = AP3df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AP4df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AP4df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AP4df["subscale_goodwill_NS"] = AP4df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AP5df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AP5df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AP5df["subscale_goodwill_NS"] = AP5df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AP6df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AP6df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AP6df["subscale_goodwill_NS"] = AP6df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
# # additional credibility + godspeed subscales should go here if interested in results
# # AP1df["subscale_anthro_AS"] = AP1df[["AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly"]].mean(axis=1)
# # AP2df["subscale_anthro_AS"] = AP2df[["AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly"]].mean(axis=1)
# # AP3df["subscale_anthro_AS"] = AP3df[["AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly"]].mean(axis=1)
# # AP4df["subscale_anthro_AS"] = AP4df[["AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly"]].mean(axis=1)
# # AP5df["subscale_anthro_AS"] = AP5df[["AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly"]].mean(axis=1)
# # AP6df["subscale_anthro_AS"] = AP6df[["AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly"]].mean(axis=1)
# # AP1df["subscale_anthro_PS"] = AP1df[["PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly"]].mean(axis=1)
# # AP2df["subscale_anthro_PS"] = AP2df[["PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly"]].mean(axis=1)
# # AP3df["subscale_anthro_PS"] = AP3df[["PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly"]].mean(axis=1)
# # AP4df["subscale_anthro_PS"] = AP4df[["PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly"]].mean(axis=1)
# # AP5df["subscale_anthro_PS"] = AP5df[["PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly"]].mean(axis=1)
# # AP6df["subscale_anthro_PS"] = AP6df[["PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly"]].mean(axis=1)
# # AP1df["subscale_anthro_NS"] = AP1df[["NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly"]].mean(axis=1)
# # AP2df["subscale_anthro_NS"] = AP2df[["NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly"]].mean(axis=1)
# # AP3df["subscale_anthro_NS"] = AP3df[["NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly"]].mean(axis=1)
# # AP4df["subscale_anthro_NS"] = AP4df[["NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly"]].mean(axis=1)
# # AP5df["subscale_anthro_NS"] = AP5df[["NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly"]].mean(axis=1)
# # AP6df["subscale_anthro_NS"] = AP6df[["NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly"]].mean(axis=1)
# # likeability
AP1df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AP1df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AP1df["subscale_likeability_AS"] = AP1df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AP2df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AP2df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AP2df["subscale_likeability_AS"] = AP2df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AP3df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AP3df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AP3df["subscale_likeability_AS"] = AP3df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AP4df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AP4df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AP4df["subscale_likeability_AS"] = AP4df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AP5df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AP5df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AP5df["subscale_likeability_AS"] = AP5df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AP6df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AP6df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AP6df["subscale_likeability_AS"] = AP6df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AP1df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]] = AP1df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].astype(float)
AP1df["subscale_likeability_PS"] = AP1df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].mean(axis=1)
AP2df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]] = AP2df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].astype(float)
AP2df["subscale_likeability_PS"] = AP2df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].mean(axis=1)
AP3df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]] = AP3df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].astype(float)
AP3df["subscale_likeability_PS"] = AP3df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].mean(axis=1)
AP4df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]] = AP4df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].astype(float)
AP4df["subscale_likeability_PS"] = AP4df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].mean(axis=1)
AP5df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]] = AP5df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].astype(float)
AP5df["subscale_likeability_PS"] = AP5df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].mean(axis=1)
AP6df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]] = AP6df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].astype(float)
AP6df["subscale_likeability_PS"] = AP6df[["PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice"]].mean(axis=1)
AP1df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]] = AP1df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].astype(float)
AP1df["subscale_likeability_NS"] = AP1df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].mean(axis=1)
AP2df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]] = AP2df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].astype(float)
AP2df["subscale_likeability_NS"] = AP2df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].mean(axis=1)
AP3df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]] = AP3df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].astype(float)
AP3df["subscale_likeability_NS"] = AP3df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].mean(axis=1)
AP4df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]] = AP4df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].astype(float)
AP4df["subscale_likeability_NS"] = AP4df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].mean(axis=1)
AP5df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]] = AP5df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].astype(float)
AP5df["subscale_likeability_NS"] = AP5df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].mean(axis=1)
AP6df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]] = AP6df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].astype(float)
AP6df["subscale_likeability_NS"] = AP6df[["NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice"]].mean(axis=1)
# get rid of NaNs (incomplete surveys etc.)
AP2df = AP2df[:-4]
AP5df = AP5df[:-1]
AP6df = AP6df[:-1]
```
%% Cell type:code id: tags:
``` python
# recode most motivating, prefer to wotk with deceptive answers based on group ordering
AP1df["most_motivating"]=AP1df["most_motivating"].astype(int)
AP1df["most_motivating"]=AP1df["most_motivating"].apply(lambda x: "first person" if x == 1 else ("third person" if x == 2 else "control"))
AP1df["prefer_work_with"]=AP1df["prefer_work_with"].astype(int)
AP1df["prefer_work_with"]=AP1df["prefer_work_with"].apply(lambda x: "first person" if x == 1 else ("third person" if x == 2 else "control"))
AP1df["deceptive"]=AP1df["deceptive"].astype(int)
AP1df["deceptive"]=AP1df["deceptive"].apply(lambda x: "first person" if x == 1 else ("third person" if x == 2 else ("control" if x == 3 else "none")))
AP1df["would_debrief_change_answers"]=AP1df["would_debrief_change_answers"].astype(int)
AP1df["would_debrief_change_answers"]=AP1df["would_debrief_change_answers"].apply(lambda x: "yes" if x == 1 else ("no" if x == 2 else "don't know"))
AP2df["most_motivating"]=AP2df["most_motivating"].astype(int)
AP2df["most_motivating"]=AP2df["most_motivating"].apply(lambda x: "first person" if x == 1 else ("third person" if x == 3 else "control"))
AP2df["prefer_work_with"]=AP2df["prefer_work_with"].astype(int)
AP2df["prefer_work_with"]=AP2df["prefer_work_with"].apply(lambda x: "first person" if x == 1 else ("third person" if x == 3 else "control"))
AP2df["deceptive"]=AP2df["deceptive"].astype(int)
AP2df["deceptive"]=AP2df["deceptive"].apply(lambda x: "first person" if x == 1 else ("third person" if x == 3 else ("control" if x == 2 else "none")))
AP2df["would_debrief_change_answers"]=AP2df["would_debrief_change_answers"].astype(int)
AP2df["would_debrief_change_answers"]=AP2df["would_debrief_change_answers"].apply(lambda x: "yes" if x == 1 else ("no" if x == 2 else "don't know"))
AP3df["most_motivating"]=AP3df["most_motivating"].astype(int)
AP3df["most_motivating"]=AP3df["most_motivating"].apply(lambda x: "first person" if x == 2 else ("third person" if x == 1 else "control"))
AP3df["prefer_work_with"]=AP3df["prefer_work_with"].astype(int)
AP3df["prefer_work_with"]=AP3df["prefer_work_with"].apply(lambda x: "first person" if x == 2 else ("third person" if x == 1 else "control"))
AP3df["deceptive"]=AP3df["deceptive"].astype(int)
AP3df["deceptive"]=AP3df["deceptive"].apply(lambda x: "first person" if x == 2 else ("third person" if x == 1 else ("control" if x == 3 else "none")))
AP3df["would_debrief_change_answers"]=AP3df["would_debrief_change_answers"].astype(int)
AP3df["would_debrief_change_answers"]=AP3df["would_debrief_change_answers"].apply(lambda x: "yes" if x == 1 else ("no" if x == 2 else "don't know"))
AP4df["most_motivating"]=AP4df["most_motivating"].astype(int)
AP4df["most_motivating"]=AP4df["most_motivating"].apply(lambda x: "first person" if x == 3 else ("third person" if x == 1 else "control"))
AP4df["prefer_work_with"]=AP4df["prefer_work_with"].astype(int)
AP4df["prefer_work_with"]=AP4df["prefer_work_with"].apply(lambda x: "first person" if x == 3 else ("third person" if x == 1 else "control"))
AP4df["deceptive"]=AP4df["deceptive"].astype(int)
AP4df["deceptive"]=AP4df["deceptive"].apply(lambda x: "first person" if x == 3 else ("third person" if x == 1 else ("control" if x == 2 else "none")))
AP4df["would_debrief_change_answers"]=AP4df["would_debrief_change_answers"].astype(int)
AP4df["would_debrief_change_answers"]=AP4df["would_debrief_change_answers"].apply(lambda x: "yes" if x == 1 else ("no" if x == 2 else "don't know"))
AP5df["most_motivating"]=AP5df["most_motivating"].astype(int)
AP5df["most_motivating"]=AP5df["most_motivating"].apply(lambda x: "first person" if x == 2 else ("third person" if x == 3 else "control"))
AP5df["prefer_work_with"]=AP5df["prefer_work_with"].astype(int)
AP5df["prefer_work_with"]=AP5df["prefer_work_with"].apply(lambda x: "first person" if x == 2 else ("third person" if x == 2 else "control"))
AP5df["deceptive"]=AP5df["deceptive"].astype(int)
AP5df["deceptive"]=AP5df["deceptive"].apply(lambda x: "first person" if x == 2 else ("third person" if x == 3 else ("control" if x == 1 else "none")))
AP5df["would_debrief_change_answers"]=AP5df["would_debrief_change_answers"].astype(int)
AP5df["would_debrief_change_answers"]=AP5df["would_debrief_change_answers"].apply(lambda x: "yes" if x == 1 else ("no" if x == 2 else "don't know"))
AP6df["most_motivating"]=AP6df["most_motivating"].astype(int)
AP6df["most_motivating"]=AP6df["most_motivating"].apply(lambda x: "first person" if x == 3 else ("third person" if x == 2 else "control"))
AP6df["prefer_work_with"]=AP6df["prefer_work_with"].astype(int)
AP6df["prefer_work_with"]=AP6df["prefer_work_with"].apply(lambda x: "first person" if x == 3 else ("third person" if x == 2 else "control"))
AP6df["deceptive"]=AP6df["deceptive"].astype(int)
AP6df["deceptive"]=AP6df["deceptive"].apply(lambda x: "first person" if x == 3 else ("third person" if x == 2 else ("control" if x == 1 else "none")))
AP6df["would_debrief_change_answers"]=AP6df["would_debrief_change_answers"].astype(int)
AP6df["would_debrief_change_answers"]=AP6df["would_debrief_change_answers"].apply(lambda x: "yes" if x == 1 else ("no" if x == 2 else "don't know"))
```
%% Cell type:code id: tags:
``` python
# define new, ordered data sets
AP1df2 = AP1df[["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"subscale_expertise_AS", "subscale_trustworthiness_AS", "subscale_goodwill_AS", "subscale_likeability_AS",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"subscale_expertise_PS", "subscale_trustworthiness_PS", "subscale_goodwill_PS", "subscale_likeability_PS",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"subscale_expertise_NS", "subscale_trustworthiness_NS", "subscale_goodwill_NS", "subscale_likeability_NS",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers",
"condition_order"
]]
AP2df2 = AP2df[["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"subscale_expertise_AS", "subscale_trustworthiness_AS", "subscale_goodwill_AS", "subscale_likeability_AS",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"subscale_expertise_PS", "subscale_trustworthiness_PS", "subscale_goodwill_PS", "subscale_likeability_PS",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"subscale_expertise_NS", "subscale_trustworthiness_NS", "subscale_goodwill_NS", "subscale_likeability_NS",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers",
"condition_order"
]]
AP3df2 = AP3df[["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"subscale_expertise_AS", "subscale_trustworthiness_AS", "subscale_goodwill_AS", "subscale_likeability_AS",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"subscale_expertise_PS", "subscale_trustworthiness_PS", "subscale_goodwill_PS", "subscale_likeability_PS",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"subscale_expertise_NS", "subscale_trustworthiness_NS", "subscale_goodwill_NS", "subscale_likeability_NS",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers",
"condition_order"
]]
AP4df2 = AP4df[["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"subscale_expertise_AS", "subscale_trustworthiness_AS", "subscale_goodwill_AS", "subscale_likeability_AS",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"subscale_expertise_PS", "subscale_trustworthiness_PS", "subscale_goodwill_PS", "subscale_likeability_PS",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"subscale_expertise_NS", "subscale_trustworthiness_NS", "subscale_goodwill_NS", "subscale_likeability_NS",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers",
"condition_order"
]]
AP5df2 = AP5df[["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"subscale_expertise_AS", "subscale_trustworthiness_AS", "subscale_goodwill_AS", "subscale_likeability_AS",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"subscale_expertise_PS", "subscale_trustworthiness_PS", "subscale_goodwill_PS", "subscale_likeability_PS",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"subscale_expertise_NS", "subscale_trustworthiness_NS", "subscale_goodwill_NS", "subscale_likeability_NS",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers",
"condition_order"
]]
AP6df2 = AP6df[["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"subscale_expertise_AS", "subscale_trustworthiness_AS", "subscale_goodwill_AS", "subscale_likeability_AS",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"subscale_expertise_PS", "subscale_trustworthiness_PS", "subscale_goodwill_PS", "subscale_likeability_PS",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"subscale_expertise_NS", "subscale_trustworthiness_NS", "subscale_goodwill_NS", "subscale_likeability_NS",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers",
"condition_order"
]]
# concatenate before doing population wide scale manipulations
combined_primed_df = pd.concat([AP1df2, AP2df2, AP3df2, AP4df2, AP5df2, AP6df2], ignore_index=True, keys=['1','2','3','4','5','6'])
combined_primed_df["priming"] = "primed"
#combined_primed_df
```
%% Cell type:markdown id: tags:
## Social agency: un-primed participants
%% Cell type:code id: tags:
``` python
# Social agency data = AUX = agency unprimed; exercise ordering 1-6
AU1df=pd.read_csv("AU1.csv")
AU1df=AU1df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AU1df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AU1df["condition_order"]="A-P-N" #consider replacing this with concat label
AU1df = AU1df[2:] #discard excess headings
AU2df=pd.read_csv("AU2.csv")
AU2df=AU2df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AU2df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AU2df["condition_order"]="A-N-P"
AU2df = AU2df[2:] #discard excess headings
AU3df=pd.read_csv("AU3.csv")
AU3df=AU3df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AU3df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AU3df["condition_order"]="P-A-N"
AU3df = AU3df[2:] #discard excess headings
AU4df=pd.read_csv("AU4.csv")
AU4df=AU4df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AU4df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AU4df["condition_order"]="P-N-A"
AU4df = AU4df[2:] #discard excess headings
AU5df=pd.read_csv("AU5.csv")
AU5df=AU5df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AU5df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AU5df["condition_order"]="N-A-P"
AU5df = AU5df[2:] #discard excess headings
AU6df=pd.read_csv("AU6.csv")
AU6df=AU6df[["Q16","Q17","Q18_1_TEXT","Q19","Q20_1_TEXT","Q21","Q22_1","Q23_1","Q23_2","Q23_3","Q23_4","Q23_5","Q23_6","Q23_7","Q23_8","Q23_9","Q23_10","Q24_1","Q24_2","Q24_3","Q24_4","Q24_5","Q24_6","Q24_7","Q24_8","Q24_9","Q24_10","Q24_11","Q28_1","Q28_2","Q28_3","Q28_4","Q28_5","Q28_6","Q28_7","Q28_8","Q29_1","Q29_2","Q29_3","Q29_4","Q29_5","Q29_6","Q29_7","Q29_8","Q29_9","Q30_1","Q30_2","Q30_3","Q30_4","Q30_5","Q30_6","Q31_1","Q31_2","Q31_3","Q31_4","Q32_1","Q32_2","Q32_3","Q32_4","Q33_1","Q33_2","Q33_3","Q36_1","Q36_2","Q36_3","Q36_4","Q36_5","Q37_1","Q37_2","Q37_3","Q37_4","Q38_1","Q38_2","Q38_3","Q38_4","Q38_5","Q41_1","Q41_2","Q107_1","Q108_1","Q110_1","Q111_1","Q114_1","Q114_2","Q114_3","Q114_4","Q114_5","Q114_6","Q114_7","Q114_8","Q115_1","Q115_2","Q115_3","Q115_4","Q115_5","Q115_6","Q115_7","Q115_8","Q115_9","Q116_1","Q116_2","Q116_3","Q116_4","Q116_5","Q116_6","Q117_1","Q117_2","Q117_3","Q117_4","Q118_1","Q118_2","Q118_3","Q118_4","Q119_1","Q119_2","Q119_3","Q120_1","Q120_2","Q120_3","Q120_4","Q120_5","Q121_1","Q121_2","Q121_3","Q121_4","Q122_1","Q122_2","Q122_3","Q122_4","Q122_5","Q123_1","Q123_2","Q124_1","Q125_1","Q126_1","Q127_1","Q130_1","Q130_2","Q130_3","Q130_4","Q130_5","Q130_6","Q130_7","Q130_8","Q131_1","Q131_2","Q131_3","Q131_4","Q131_5","Q131_6","Q131_7","Q131_8","Q131_9","Q132_1","Q132_2","Q132_3","Q132_4","Q132_5","Q132_6","Q133_1","Q133_2","Q133_3","Q133_4","Q134_1","Q134_2","Q134_3","Q134_4","Q135_1","Q135_2","Q135_3","Q136_1","Q136_2","Q136_3","Q136_4","Q136_5","Q137_1","Q137_2","Q137_3","Q137_4","Q138_1","Q138_2","Q138_3","Q138_4","Q138_5","Q139_1","Q139_2","Q140_1","Q141_1","Q142_1","Q143_1","Q79","Q80","Q99","Q86"]]
AU6df.columns = ["age","gender","native_language","ethnicity", "country",
"experience_therapy",
"familiarity",
"extrovert1", "agreeable1_rev", "conscientious1", "stable1_rev", "open1", "extrovert2_rev", "agreeable2", "conscientious2_rev", "stable2", "open2_rev",
"nars1", "nars2", "nars3", "nars4", "nars5", "nars6", "nars7", "nars8", "nars9", "nars10", "nars11",
"NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright",
"NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine",
"NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding",
"NS_bold","NS_verbal","NS_aggressive","NS_talkative",
"NS_nervous","NS_tense","NS_anxious","NS_excitable",
"NS_irritable","NS_gloomy","NS_unfriendly",
"NS_natural","NS_humanlike","NS_conscious","NS_lifelike","NS_moving_elegantly",
"NS_alive","NS_lively","NS_organic","NS_interactive",
"NS_like","NS_friendly","NS_kind","NS_pleasant","NS_nice",
"NS_patient_developed_relationship","NS_robot_developed_relationship",
"NS_therapist_responsibility_monitoring","NS_robot_responsibility_monitoring","NS_therapist_responsibility_advice","NS_robot_responsibility_advice",
"PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright",
"PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine",
"PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding",
"PS_bold","PS_verbal","PS_aggressive","PS_talkative",
"PS_nervous","PS_tense","PS_anxious","PS_excitable",
"PS_irritable","PS_gloomy","PS_unfriendly",
"PS_natural","PS_humanlike","PS_conscious","PS_lifelike","PS_moving_elegantly",
"PS_alive","PS_lively","PS_organic","PS_interactive",
"PS_like","PS_friendly","PS_kind","PS_pleasant","PS_nice",
"PS_patient_developed_relationship","PS_robot_developed_relationship",
"PS_therapist_responsibility_monitoring","PS_robot_responsibility_monitoring","PS_therapist_responsibility_advice","PS_robot_responsibility_advice",
"AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright",
"AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine",
"AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding",
"AS_bold","AS_verbal","AS_aggressive","AS_talkative",
"AS_nervous","AS_tense","AS_anxious","AS_excitable",
"AS_irritable","AS_gloomy","AS_unfriendly",
"AS_natural","AS_humanlike","AS_conscious","AS_lifelike","AS_moving_elegantly",
"AS_alive","AS_lively","AS_organic","AS_interactive",
"AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice",
"AS_patient_developed_relationship","AS_robot_developed_relationship",
"AS_therapist_responsibility_monitoring","AS_robot_responsibility_monitoring","AS_therapist_responsibility_advice","AS_robot_responsibility_advice",
"most_motivating","prefer_work_with","deceptive","would_debrief_change_answers"
]
AU6df["condition_order"]="N-P-A"
AU6df = AU6df[2:] #discard excess headings
```
%% Cell type:code id: tags:
``` python
# cast integers as floats + calculate condition subscales for ease of concatenation
# expertise
AU1df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AU1df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AU1df["subscale_expertise_AS"] = AU1df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1, numeric_only=True)
AU2df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AU2df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AU2df["subscale_expertise_AS"] = AU2df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AU3df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AU3df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AU3df["subscale_expertise_AS"] = AU3df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AU4df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AU4df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AU4df["subscale_expertise_AS"] = AU4df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AU5df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AU5df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AU5df["subscale_expertise_AS"] = AU5df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AU6df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]] = AU6df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].astype(float)
AU6df["subscale_expertise_AS"] = AU6df[["AS_experienced","AS_informed","AS_trained","AS_qualified","AS_skilled","AS_intelligent","AS_competent","AS_bright"]].mean(axis=1)
AU1df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AU1df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AU1df["subscale_expertise_PS"] = AU1df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AU2df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AU2df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AU2df["subscale_expertise_PS"] = AU2df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AU3df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AU3df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AU3df["subscale_expertise_PS"] = AU3df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AU4df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AU4df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AU4df["subscale_expertise_PS"] = AU4df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AU5df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AU5df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AU5df["subscale_expertise_PS"] = AU5df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AU6df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]] = AU6df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].astype(float)
AU6df["subscale_expertise_PS"] = AU6df[["PS_experienced","PS_informed","PS_trained","PS_qualified","PS_skilled","PS_intelligent","PS_competent","PS_bright"]].mean(axis=1)
AU1df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AU1df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AU1df["subscale_expertise_NS"] = AU1df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AU2df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AU2df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AU2df["subscale_expertise_NS"] = AU2df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AU3df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AU3df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AU3df["subscale_expertise_NS"] = AU3df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AU4df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AU4df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AU4df["subscale_expertise_NS"] = AU4df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AU5df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AU5df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AU5df["subscale_expertise_NS"] = AU5df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
AU6df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]] = AU6df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].astype(float)
AU6df["subscale_expertise_NS"] = AU6df[["NS_experienced","NS_informed","NS_trained","NS_qualified","NS_skilled","NS_intelligent","NS_competent","NS_bright"]].mean(axis=1)
# trustworthiness
AU1df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AU1df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AU1df["subscale_trustworthiness_AS"] = AU1df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AU2df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AU2df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AU2df["subscale_trustworthiness_AS"] = AU2df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AU3df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AU3df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AU3df["subscale_trustworthiness_AS"] = AU3df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AU4df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AU4df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AU4df["subscale_trustworthiness_AS"] = AU4df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AU5df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AU5df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AU5df["subscale_trustworthiness_AS"] = AU5df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AU6df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]] = AU6df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].astype(float)
AU6df["subscale_trustworthiness_AS"] = AU6df[["AS_honest","AS_trustworthy","AS_open_minded","AS_just","AS_fair","AS_unselfish","AS_moral","AS_ethical","AS_genuine"]].mean(axis=1)
AU1df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AU1df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AU1df["subscale_trustworthiness_PS"] = AU1df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AU2df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AU2df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AU2df["subscale_trustworthiness_PS"] = AU2df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AU3df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AU3df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AU3df["subscale_trustworthiness_PS"] = AU3df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AU4df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AU4df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AU4df["subscale_trustworthiness_PS"] = AU4df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AU5df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AU5df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AU5df["subscale_trustworthiness_PS"] = AU5df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AU6df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]] = AU6df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].astype(float)
AU6df["subscale_trustworthiness_PS"] = AU6df[["PS_honest","PS_trustworthy","PS_open_minded","PS_just","PS_fair","PS_unselfish","PS_moral","PS_ethical","PS_genuine"]].mean(axis=1)
AU1df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AU1df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AU1df["subscale_trustworthiness_NS"] = AU1df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AU2df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AU2df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AU2df["subscale_trustworthiness_NS"] = AU2df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AU3df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AU3df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AU3df["subscale_trustworthiness_NS"] = AU3df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AU4df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AU4df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AU4df["subscale_trustworthiness_NS"] = AU4df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AU5df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AU5df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AU5df["subscale_trustworthiness_NS"] = AU5df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
AU6df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]]=AU6df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].astype(float)
AU6df["subscale_trustworthiness_NS"] = AU6df[["NS_honest","NS_trustworthy","NS_open_minded","NS_just","NS_fair","NS_unselfish","NS_moral","NS_ethical","NS_genuine"]].mean(axis=1)
# goodwill
AU1df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AU1df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AU1df["subscale_goodwill_AS"] = AU1df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AU2df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AU2df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AU2df["subscale_goodwill_AS"] = AU2df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AU3df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AU3df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AU3df["subscale_goodwill_AS"] = AU3df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AU4df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AU4df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AU4df["subscale_goodwill_AS"] = AU4df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AU5df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AU5df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AU5df["subscale_goodwill_AS"] = AU5df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AU6df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]] = AU6df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].astype(float)
AU6df["subscale_goodwill_AS"] = AU6df[["AS_cares_about_patient","AS_patient_interest_at_heart","AS_not_self_centred","AS_concerned_patient_wellbeing","AS_sensitive","AS_understanding"]].mean(axis=1)
AU1df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AU1df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AU1df["subscale_goodwill_PS"] = AU1df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AU2df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AU2df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AU2df["subscale_goodwill_PS"] = AU2df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AU3df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AU3df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AU3df["subscale_goodwill_PS"] = AU3df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AU4df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AU4df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AU4df["subscale_goodwill_PS"] = AU4df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AU5df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AU5df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AU5df["subscale_goodwill_PS"] = AU5df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AU6df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]] = AU6df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].astype(float)
AU6df["subscale_goodwill_PS"] = AU6df[["PS_cares_about_patient","PS_patient_interest_at_heart","PS_not_self_centred","PS_concerned_patient_wellbeing","PS_sensitive","PS_understanding"]].mean(axis=1)
AU1df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AU1df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AU1df["subscale_goodwill_NS"] = AU1df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AU2df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AU2df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AU2df["subscale_goodwill_NS"] = AU2df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AU3df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AU3df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AU3df["subscale_goodwill_NS"] = AU3df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AU4df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AU4df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AU4df["subscale_goodwill_NS"] = AU4df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AU5df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AU5df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AU5df["subscale_goodwill_NS"] = AU5df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
AU6df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]] = AU6df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].astype(float)
AU6df["subscale_goodwill_NS"] = AU6df[["NS_cares_about_patient","NS_patient_interest_at_heart","NS_not_self_centred","NS_concerned_patient_wellbeing","NS_sensitive","NS_understanding"]].mean(axis=1)
# additional credibility + godspeed subscales should go here if interested in results
# likeability
AU1df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AU1df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AU1df["subscale_likeability_AS"] = AU1df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AU2df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AU2df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AU2df["subscale_likeability_AS"] = AU2df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AU3df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AU3df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AU3df["subscale_likeability_AS"] = AU3df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AU4df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AU4df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].astype(float)
AU4df["subscale_likeability_AS"] = AU4df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]].mean(axis=1)
AU5df[["AS_like","AS_friendly","AS_kind","AS_pleasant","AS_nice"]] = AU5df[["AS_like","AS_f