Hendriks et al. (2015). Wie sich das von Laien in Experten gesetzte Vertrauen im digitalen Zeitalter messen lässt: „The Muenster Epistemic Trustworthiness Inventory (METI)“: Drei Forschungsdatensätze.

Bibliografische Informationen

Ersteller: Hendriks, Friederike; Kienhues, Dorothe; Bromme, Rainer

Mitwirkende: Hendriks, Friederike; Kienhues, Dorothe; Bromme, Rainer

Förderung: Deutsche Forschungsgemeinschaft (DFG): Graduiertenkolleg 1712 „Vertrauen und Kommunikation in einer digitalisierten Welt“

Titel: Wie sich das von Laien in Experten gesetzte Vertrauen im digitalen Zeitalter messen lässt: „The Muenster Epistemic Trustworthiness Inventory (METI)“: Drei Forschungsdatensätze.

Jahr der Publikation: 2015

Zitation: Hendriks, F., Kienhues, D., & Bromme, R. (2015). Wie sich das von Laien in Experten gesetzte Vertrauen im digitalen Zeitalter messen lässt: „The Muenster Epistemic Trustworthiness Inventory (METI)“: Drei Forschungsdatensätze. (Version 1.0.0) [Daten und Dokumentation]. Trier: Forschungsdatenzentrum des Leibniz Institut für Psychologie ZPID. https://doi.org/10.5160/psychdata.hsfe15mu08

Zusammenfassung

Aufgrund ihres fehlenden Hintergrundwissens benötigen Laien die Hilfe von Experten, wenn sie sich mit wissenschaftlichen Informationen auseinandersetzen. Um zu entscheiden, auf wessen Hilfe sie sich verlassen können, müssen Laien Experten in Bezug auf ihre epistemische Vertrauenswürdigkeit bestehend aus Kompetenz, Einhaltung wissenschaftlicher Standards und guten Absichten einschätzen. Online kann diese Einschätzung schwierig sein, da häufig nur begrenzte und manchmal wenig vertrauenswürdige Informationen zur Verfügung stehen. Um zu messen, wie Experten (in Online-Medien) von Laien beurteilt werden, wurde ein Inventar zur Erfassung epistemischer Vetrauenswürdigkeit mit den Dimensionen Expertise, Integrität und Wohlwollen konstruiert. Sowohl mit explorativer (n=237) wie auch konfirmatorischer Faktorenanalyse (n=345) konnte belegt werden, dass der Muenster Epistemic Trustworthiness Inventory (METI) sich aus diesen drei Faktoren zusammensetzt. In einer anschließenden experimentellen Untersuchung zeigte sich, dass alle drei Dimensionen des METI veränderungssensitiv sind. Die Autoren schlagen daher vor, dieses Inventar zur Messung von epistemischer Vertrauenswürdigkeit einzusetzen; sprich alle Einschätzungen, die Laien vornehmen, um zu entscheiden, ob sie epistemisches Vertrauen in einen Experten setzen und sich nach ihm richten, um ein wissenschaftliches Problem zu erfassen, das sie nicht verstehen.

Kodebuch

Kodebuch_hsfe15mu08_hendricks_kb1
PositionNameLabelValid_valuesMissing_values
1SUBJECT_IDsubject ID1-237 "identification number"-777 "missing value"
2DURATIONTime to complete survey128-50797 "seconds"-1 "missing value due to interrupted participation"
3AGEAge19-47 "years"9 "missing value"
4GENDERGender1 "male"
2 "female"
-77 "missing value"
5EDUCATIONHighest education degree1 "no degree"
2 "9 years in German education"
3 "10 years in German education"
4 "qualification for college entry"
5 "qualification for university entry"
6 "university or college degree"
7 "vocational training"
-77 "missing value"
6V_1Rating of the scientific expert: Competence1 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
7V_2Rating of the scientific expert: Intelligence1 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
8V_3Rating of the scientific expert: Education1 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
9V_4Rating of the scientific expert: Professionalism1 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unprofessional"
-77 "missing value"
10V_5Rating of the scientific expert: Experience1 "experienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexperienced"
-77 "missing value"
11V_6Rating of the scientific expert: Qualification1 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
12V_7Rating of the scientific expert: Reliability1 "reliable"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unreliable"
-77 "missing value"
13V_8Rating of the scientific expert: Sincereness1 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
14V_9Rating of the scientific expert: Honesty1 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
15V_10Rating of the scientific expert: Morality1 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
16V_11Rating of the scientific expert: Ethical1 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
17V_12Rating of the scientific expert: Impartiality1 "impartial"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "partial"
-77 "missing value"
18V_13Rating of the scientific expert: Responsibility1 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresponsible"
-77 "missing value"
19V_14Rating of the scientific expert: Justice1 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "injust"
-77 "missing value"
20V_15Rating of the scientific expert: Selfishness1 "unselfish"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "selfish"
-77 "missing value"
21V_16Rating of the scientific expert: Considerateness1 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
22V_17Rating of the scientific expert: Helpfulness1 "helpful"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unhelpful"
-77 "missing value"
23V_18Rating of the scientific expert: Fairness1 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
24EXPERTISE_MEANMean expertise (derived variable)1-7 "expertise-no expertise"9 "missing value"
25INTEGRITY_MEANMean integrity (derived variable)1-7 "integrity-no integrity"9 "missing value"
26BENEVOLENCE_MEANMean benevolence (derived variable)1-7 "benevolence-no benevolence"9 "missing value"
Kodebuch_hsfe15mu08_hendricks_kb2
PositionNameLabelValid_valuesMissing_values
2DURATIONtime to complete survey182-10089 "seconds"-1 "missing value due to interrupted participation"
3AGEAge18-50 "years"-77 "missing value"
4GENDERGender1 "male"
2 "female"
-77 "missing value"
5EDUCATIONHighest educational degree1 "no degree"
2 "9 years in German education"
3 "10 years in German education"
4 "qualification for college entry"
5 "qualification for university entry"
6 "university or college degree"
7 "vocational training"
-77 "missing value"
6E_1Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 11 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
7E_2Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 21 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
8E_3Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 31 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
9E_4Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 41 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "improfessional"
-77 "missing value"
10E_5Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 51 "expierienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexpierienced"
-77 "missing value"
11E_6Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 61 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
12I_1Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 11 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
13I_2Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 21 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
14W_1Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 11 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
15W_2Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 21 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
16W_3Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 31 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresponsible"
-77 "missing value"
17I_3Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 31 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "injust"
-77 "missing value"
18I_4Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 41 "unselfish"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "selfish"
-77 "missing value"
19W_4Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 41 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
20E_7hilfreich - hinderlich1 "helpful"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unhelpful"
-77 "missing value"
21I_5Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 51 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
22EXPERTISE_MEANMean expertise (derived variable)1-7 "expertise-no expertise"9 "missing value"
23INTEGRITY_MEANMean integrity (derived variable)1-7 "integrity-no integrity"9 "missing value"
24BENEVOLENCE_MEANMean benevolence (derived variable)1-7 "benevolence-no benevolence"9 "missing value"
1SUBJECT_IDsubject ID1-345 "identification number"-777 "missing value"
Kodebuch_hsfe15mu08_hendricks_kb3
PositionNameLabelValid_valuesMissing_values
63E_2_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Expertise, Item 21 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
2CONSENTConsent1 "yes"
2 "no"
-77 "missing value"
3AGEAge19-53 "years"-77 "missing value"
4GENDERGender1 "male"
2 "female"
-77 "missing value"
5EDUCATIONHighest education degree1 "no degree"
2 " 9 years in German education"
3 "10 years in German education"
4 "qualification for college entry"
5 "qualification for university entry"
6 " university or college degree"
7 " vocational training"
-77 "missing value"
6E_1_EXPERTISELOWExperimental condition: Low expertise. Muenster Epistemic Trustworthiness Inventory (METI): Expertise, Item 11 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
7E_2_EXPERTISELOWExperimental condition: Low expertise. METI: Expertise, Item 21 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
8E_3_EXPERTISELOWExperimental condition: Low expertise. METI: Expertise, Item 31 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
9E_4_EXPERTISELOWExperimental condition: Low expertise. METI: Expertise, Item 41 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "improfessional"
-77 "missing value"
10E_5_EXPERTISELOWExperimental condition: Low expertise. METI: Expertise, Item 51 "experienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexperienced"
-77 "missing value"
11E_6_EXPERTISELOWExperimental condition: Low expertise. METI: Expertise, Item 61 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
12I_1_EXPERTISELOWExperimental condition: Low expertise. METI: Integrity, Item 11 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
13I_2_EXPERTISELOWExperimental condition: Low expertise. METI: Integrity, Item 21 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
14W_1_EXPERTISELOWExperimental condition: Low expertise. METI: Benevolence, Item 11 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
15W_2_EXPERTISELOWExperimental condition: Low expertise. METI: Benevolence, Item 21 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
16W_3_EXPERTISELOWExperimental condition: Low expertise. METI: Benevolence, Item 31 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresposible"
-77 "missing value"
17I_3_EXPERTISELOWExperimental condition: Low expertise. METI: Integrity, Item 31 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unjust"
-77 "missing value"
18W_4_EXPERTISELOWExperimental condition: Low expertise. METI: Benevolence, Item 41 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
19I_5_EXPERTISELOWExperimental condition: Low expertise. METI: Integrity, Item 51 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
20E_1_EXPERTISEHIGHExperimental condition: High expertise. METI: Expertise, Item 11 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
21E_2_EXPERTISEHIGHExperimental condition: High expertise. METI: Expertise, Item 21 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
22E_3_EXPERTISEHIGHExperimental condition: High expertise. METI: Expertise, Item 31 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
23E_4_EXPERTISEHIGHExperimental condition: High expertise. METI: Expertise, Item 41 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "improfessional"
-77 "missing value"
24E_5_EXPERTISEHIGHExperimental condition: High expertise. METI: Expertise, Item 51 "expierienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexpierienced"
-77 "missing value"
25E_6_EXPERTISEHIGHExperimental condition: High expertise. METI: Expertise, Item 61 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
26I_1_EXPERTISEHIGHExperimental condition: High expertise. METI: Integrity, Item 11 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
27I_2_EXPERTISEHIGHExperimental condition: High expertise. METI: Integrity, Item 21 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
28W_1_EXPERTISEHIGHExperimental condition: High expertise. METI: Benevolence, Item 11 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
29W_2_EXPERTISEHIGHExperimental condition: High expertise. METI: Benevolence, Item 21 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
30W_3_EXPERTISEHIGHExperimental condition: High expertise. METI: Benevolence, Item 31 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresposible"
-77 "missing value"
31I_3_EXPERTISEHIGHExperimental condition: High expertise. METI: Integrity, Item 31 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unjust"
-77 "missing value"
32W_4_EXPERTISEHIGHExperimental condition: High expertise. METI: Benevolence, Item 41 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
33I_5_EXPERTISEHIGHExperimental condition: High expertise. METI: Integrity, Item 51 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
34E_1_INTEGRITYLOWExperimental condition: Low Integrity. METI: Expertise, Item 11 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
35E_2_INTEGRITYLOWExperimental condition: Low Integrity. METI: Expertise, Item 21 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
36E_3_INTEGRITYLOWExperimental condition: Low Integrity. METI: Expertise, Item 31 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
37E_4_INTEGRITYLOWExperimental condition: Low Integrity. METI: Expertise, Item 41 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "improfessional"
-77 "missing value"
38E_5_INTEGRITYLOWExperimental condition: Low Integrity. METI: Experience, Item 51 "experienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexperienced"
-77 "missing value"
39E_6_INTEGRITYLOWExperimental condition: Low Integrity. METI: Expertise, Item 61 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
40I_1_INTEGRITYLOWExperimental condition: Low Integrity. METI: Integrity, Item 11 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
41I_2_INTEGRITYLOWExperimental condition: Low Integrity. METI: Integrity, Item 21 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
42W_1_INTEGRITYLOWExperimental condition: Low Integrity. METI: Benevolence, Item 11 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
43W_2_INTEGRITYLOWExperimental condition: Low Integrity. METI: Benevolence, Item 21 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
44W_3_INTEGRITYLOWExperimental condition: Low Integrity. METI: Benevolence, Item 31 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresposible"
-77 "missing value"
45I_3_INTEGRITYLOWExperimental condition: Low Integrity. METI: Integrity, Item 31 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unjust"
-77 "missing value"
46W_4_INTEGRITYLOWExperimental condition: Low Integrity. METI: Benevolence, Item 41 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
47I_5_INTEGRITYLOWExperimental condition: Low Integrity. METI: Integrity, Item 51 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
48E_1_INTEGRITYHIGHExperimental condition: High Integrity. METI: Expertise, Item 11 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
49E_2_INTEGRITYHIGHExperimental condition: High Integrity. METI: Expertise, Item 21 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
50E_3_INTEGRITYHIGHExperimental condition: High Integrity. METI: Expertise, Item 31 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
51E_4_INTEGRITYHIGHExperimental condition: High Integrity. METI: Expertise, Item 41 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "improfessional"
-77 "missing value"
52E_5_INTEGRITYHIGHExperimental condition: High Integrity. METI: Expertise, Item 51 "expierienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexpierienced"
-77 "missing value"
53E_6_INTEGRITYHIGHExperimental condition: High Integrity. METI: Expertise, Item 61 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
54I_1_INTEGRITYHIGHExperimental condition: High Integrity. METI: Integrity, Item 11 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
55I_2_INTEGRITYHIGHExperimental condition: High Integrity. METI: Integrity, Item 21 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
56W_1_INTEGRITYHIGHExperimental condition: High Integrity. METI: Benevolence, Item 11 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
57W_2_INTEGRITYHIGHExperimental condition: High Integrity. METI: Benevolence, Item 21 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
58W_3_INTEGRITYHIGHExperimental condition: High Integrity. METI: Benevolence, Item 31 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresposible"
-77 "missing value"
59I_3_INTEGRITYHIGHExperimental condition: High Integrity. METI: Integrity, Item 31 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unjust"
-77 "missing value"
60W_4_INTEGRITYHIGHExperimental condition: High Integrity. METI: Benevolence, Item 41 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
61I_5_INTEGRITYHIGHExperimental condition: High Integrity. METI: Integrity, Item 51 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
62E_1_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Expertise, Item 11 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
64E_3_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Expertise, Item 31 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
65E_4_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Expertise, Item 41 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "improfessional"
-77 "missing value"
66E_5_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Expertise, Item 51 "expierienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexpierienced"
-77 "missing value"
67E_6_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Expertise, Item 61 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
68I_1_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Integrity, Item 11 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
69I_2_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Integrity, Item 21 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
70W_1_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Benevolence, Item 11 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
71W_2_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Benevolence, Item 21 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
72W_3_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Benevolence, Item 31 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresposible"
-77 "missing value"
73I_3_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Integrity, Item 31 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unjust"
-77 "missing value"
74W_4_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Benevolence, Item 41 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
75I_5_BENEVOLENCELOWExperimental condition: Low Benevolence. METI: Integrity, Item 51 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
76E_1_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Expertise, Item 11 "competent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "incompetent"
-77 "missing value"
77E_2_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Expertise, Item 21 "intelligent"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unintelligent"
-77 "missing value"
78E_3_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Expertise, Item 31 "well educated"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "poorly educated"
-77 "missing value"
79E_4_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Expertise, Item 41 "professional"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "improfessional"
-77 "missing value"
80E_5_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Expertise, Item 51 "experienced"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inexperienced"
-77 "missing value"
81E_6_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Expertise, Item 61 "qualified"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unqualified"
-77 "missing value"
82I_1_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Integrity, Item 11 "sincere"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "insincere"
-77 "missing value"
83I_2_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Integrity, Item 21 "honest"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "dishonest"
-77 "missing value"
84W_1_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Benevolence, Item 11 "moral"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "immoral"
-77 "missing value"
85W_2_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Benevolence, Item 21 "ethical"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unethical"
-77 "missing value"
86W_3_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Benevolence, Item 31 "responsible"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "irresposible"
-77 "missing value"
87I_3_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Integrity, Item 31 "just"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unjust"
-77 "missing value"
88W_4_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Benevolence, Item 41 "considerate"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "inconsiderate"
-77 "missing value"
89I_5_BENEVOLENCEHIGHExperimental condition: High Benevolence. METI: Integrity, Item 51 "fair"
2 "2"
3 "3"
4 "4"
5 "5"
6 "6"
7 "unfair"
-77 "missing value"
90E1U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "incompetent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "competent"
9 "missing value"
91E2U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "unintelligent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "intelligent"
9 "missing value"
92E3U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
93E4U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
94E5U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
95E6U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
96I1U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
97I2U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
98W1U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
99W2U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
100W3U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
101I3U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
102W4U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
103I5U_EXPERTISELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)3,0-7,0 ""9 "missing value"
104E1U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "incompetent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "competent"
9 "missing value"
105E2U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "unintelligent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "intelligent"
9 "missing value"
106E3U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
107E4U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
108E5U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
109E6U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
110I1U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
111I2U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)3,0-7,0 ""9 "missing value"
112W1U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
113W2U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
114W3U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
115I3U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
116W4U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
117I5U_EXPERTISEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
118E1U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "incompetent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "competent"
9 "missing value"
119E2U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "unintelligent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "intelligent"
9 "missing value"
120E3U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
121E4U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
122E5U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
123E6U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
124I1U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
125I2U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
126W1U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
127W2U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
128W3U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
129I3U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
130W4U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
131I5U_INTEGRITYLOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
132E1U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "incompetent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "competent"
9 "missing value"
133E2U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "unintelligent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "intelligent"
9 "missing value"
134E3U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
135E4U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
136E5U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
137E6U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
138I1U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
139I2U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
140W1U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
141W2U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
142W3U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
143I3U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
144W4U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
145I5U_INTEGRITYHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
146E1U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "incompetent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "competent"
9 "missing value"
147E2U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "unintelligent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "intelligent"
9 "missing value"
148E3U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
149E4U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
150E5U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
151E6U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
152I1U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
153I2U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
154W1U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
155W2U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
156W3U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
157I3U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
158W4U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
159I5U_BENEVOLENCELOWrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-6,0 ""9 "missing value"
160E1U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "incompetent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "competent"
9 "missing value"
161E2U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1 "unintelligent"
2 ""
3 ""
4 ""
5 ""
6 ""
7 "intelligent"
9 "missing value"
162E3U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)3,0-7,0 ""9 "missing value"
163E4U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
164E5U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
165E6U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
166I1U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
167I2U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
168W1U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
169W2U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)1,0-7,0 ""9 "missing value"
170W3U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
171I3U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
172W4U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)2,0-7,0 ""9 "missing value"
173I5U_BENEVOLENCEHIGHrecoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable)4,0-7,0 ""9 "missing value"
174EXPERTISE_EXPERTISELOWMean expertise in condition expertise low (derived variable)1-7 "no expertise-expertise"9 "missing value"
175INTEGRITY_EXPERTISELOWMean integrity in condition expertise low (derived variable)1-7 "no integrity-integrity"9 "missing value"
176BENEVOLENCE_EXPERTISELOWMean benevolence in condition expertise low (derived variable)1-7 "no benevolence-benevolence"9 "missing value"
177EXPERTISE_EXPERTISEHIGHMean expertise in condition expertise high (derived variable)1-7 "no expertise-expertise"9 "missing value"
178INTEGRITY_EXPERTISEHIGHMean integrity in condition expertise high (derived variable)1-7 "no integrity-integrity"9 "missing value"
179BENEVOLENCE_EXPERTISEHIGHMean benevolence in condition expertise high (derived variable)1-7 "no benevolence-benevolence"9 "missing value"
180EXPERTISE_INTEGRITYLOWMean expertise in condition integrity low (derived variable)1-7 "no expertise-expertise"9 "missing value"
181INTEGRITY_INTEGRITYLOWMean integrity in condition integrity low (derived variable)1-7 "no integrity-integrity"9 "missing value"
182BENEVOLENCE_INTEGRITYLOWMean benevolence in condition integrity low (derived variable)1-7 "no benevolence-benevolence"9 "missing value"
183EXPERTISE_INTEGRITYHIGHMean expertise in condition integrity high (derived variable)1-7 "no expertise-expertise"9 "missing value"
184INTEGRITY_INTEGRITYHIGHMean integrity in condition integrity high (derived variable)1-7 "no integrity-integrity"9 "missing value"
185BENEVOLENCE_INTEGRITYHIGHMean benevolence in condition integrity high (derived variable)1-7 "no benevolence-benevolence"9 "missing value"
186EXPERTISE_BENEVOLENCELOWMean expertise in condition benevolence low (derived variable)1-7 "no expertise-expertise"9 "missing value"
187INTEGRITY_BENEVOLENCELOWMean integrity in condition benevolence low (derived variable)1-7 "no integrity-integrity"9 "missing value"
188BENEVOLENCE_BENEVOLENCELOWMean benevolence in condition benevolence low (derived variable)1-7 "no benevolence-benevolence"9 "missing value"
189EXPERTISE_BENEVOLENCEHIGHMean expertise in condition benevolence high (derived variable)1-7 "no expertise-expertise"9 "missing value"
190INTEGRITY_BENEVOLENCEHIGHMean integrity in condition benevolence high (derived variable)1-7 "no integrity-integrity"9 "missing value"
191BENEVOLENCE_BENEVOLENCEHIGHMean benevolence in condition benevolence high (derived variable)1-7 "no benevolence-benevolence"9 "missing value"
1SUBJECT_IDsubject ID1-137 "identification number"-777 "missing value"

Studienbeschreibung

Forschungsfragen/Hypothesen:

1. Epistemisches Vertrauen lässt sich in die Dimensionen Expertise, Integrität und Wohlwollen unterteilen.
2. Diese Dimensionen sind trennbar, wenn auch möglicherweise korreliert.

Forschungsdesign: Forschungsinstrument; mehrmalige Erhebung

Messinstrumente/Apparate:

Studie 1: Die Probanden erhielten 18 Adjektiv-Gegensatzpaare, anhand derer sie einen wissenschaftlichen Experten auf einer Likert-Skala von 1 (z.B. fachmännisch) bis 7 (z.B. unfachmännisch) einschätzen sollten. Eine explorative Faktorenanalyse ergab 3 Faktoren – „Expertise“, „Integrität“ und „Wohlwollen“ – die insgesamt 61,66% der Varianz aufklärten.

Studie 2: Das zu konstruierende Muenster Epistemic Trustworthiness Inventory (METI) wurde auf 16 Items reduziert. Um die drei-dimensionale Struktur des Inventars zu überprüfen, wurde ein neuer Datensatz erhoben und eine konfirmatorische Faktorenanalyse gerechnet. Die Ergebnisse bestätigten die drei-dimensionale Struktur.
Nach Eliminierung von zwei Items besteht die endgültige Version des METI aus 14 Items. Alle drei Faktoren laden auf dem theoretischen Konstrukt „epistemische Vetrauenswürdigkeit“.

Studie 3: Die endgültige Version des METI mit 14 Items wurde in einer experimentellen Untersuchung eingesetzt. Die Teilnehmer sollten 6 fiktive Personen, die potentielle Autoren eines Blog-Eintrags darstellten, bezüglich ihrer epistemischen Vertrauenswürdigkeit einschätzen. Der Blog-Eintrag behandelte eine Untersuchung aus dem Bereich der Neurologie. Die Beschreibungen der Autoren wurden systematisch in den Dimensionen von epistemischer Vertrauenswürdigkeit (low vs. high expertise, low vs. high integrity, low vs. high benevolence) variiert. Die Ergebnisse zeigten, dass es der METI ermöglicht, epistemische Vertrauenswürdigkeit in differenzierter Form zu erfassen.
Für detaillierte Informationen zu den Studien siehe Hendriks, Kienhues & Bromme (2015).

Datenerhebungsmethode:

Erhebung in Abwesenheit eines Versuchsleiters
– Online-Erhebung

Population: Junge deutsche Akademiker

Erhebungszeitraum:

Studie 1: August & September 2013
Studie 2: Januar, Februar & März 2014
Studie 3: Juli & August 2015

Stichprobe: Anfallende Stichprobe

Geschlechtsverteilung:

Studie 1:
75,5% weibliche Probanden
24,5% männliche Probanden

Studie 2:
69,3% weibliche Probanden
30,7% männliche Probanden

Studie 3:
75,2& weibliche Probanden
24,8% männliche Probanden


Altersverteilung: Studie 1: 19 bis 47 Jahre; Studie 2: 18 bis 50 Jahre; Studie 3: 19 bis 53 Jahre

Räumlicher Erfassungsbereich (Land/Region/Stadt): Deutschland

Probandenrekrutierung:

Studie 1: Mitglieder einer Datenbank für Studienteilnahme an Studien der Arbeitseinheit Bromme wurden einmalig per Email kontaktiert, der Link zur Umfrage war darin enthalten. Die Teilnehmerinnen konnten selbstständig über Ort und Zeit der Teilnahme entscheiden. Es wurde eine Verlosung von Amazongutscheinen im Wert von insgesamt 200€ durchgeführt.
Studie 2: In Vorlesungen der Universität Münster wurde um die Emailadressen von Studierenden geworben. Diese wurden daraufhin einmalig per Email kontaktiert, der Link zur Umfrage war darin enthalten. Die Teilnehmerinnen konnten selbstständig über Ort und Zeit der Teilnahme entscheiden.
Es wurde eine Verlosung von Amazongutscheinen im Wert von insgesamt 200€ durchgeführt.
Studie 3: Personen aus einer Datenbank zur Studienteilnahme an der AE Bromme wurden einmalig per Email kontaktiert, der Link zur Umfrage war darin enthalten. Weiterhin wurde über den Studierendennewsletter der Universität Münster sowie die Website von Psychologie Heute geworben. Die Teilnehmerinnen konnten selbstständig über Ort und Zeit der Teilnahme entscheiden.
Es wurde eine Verlosung von Amazongutscheinen im Wert von insgesamt 100€ durchgeführt.

Stichprobengröße: Studie 1: 237 Individuen; Studie 2: 345 Individuen; Studie 3: 137 Individuen

Rücklauf/Ausfall:

Study 1: Der Fragebogen wurde von 300 Personen angeklickt (online). Nur die Daten derjenigen wurden verwendet, die den Fragebogen nicht abbrachen (79%).
Study 2: Der Fragebogen wurde von 406 Personen angeklickt (online). Nur die Daten derjenigen wurden verwendet, die den Fragebogen nicht abbrachen (85%).
Study 3: Der Fragebogen wurde von 243 Personen angeklickt (online). Nur die Daten derjenigen wurden verwendet, die den Fragebogen nicht abbrachen und die nicht weniger als 5.20 min oder mehr als 42.54 min für die Durchführung benötigten (56%).

Literatur

Unmittelbar auf den Datensatz bezogene Veröffentlichungen
Unmittelbar auf den Datensatz bezogene Veröffentlichungen
Hendriks, F., Kienhues, D., Bromme, R. (2015). Measuring Laypeople's Trust in Experts in a Digital Age: The Muenster Epistemic Trustworthiness Inventory (METI). PLoS ONE 10(10): e0139309. doi:10.1371/journal.pone.0139309Datensatz 0305481
Weiterführende Literatur
Weiterführende Literatur
Bromme, R., & Goldman, S.R. (2014). The public's Bounded Understanding of Science. Educational Psychologist, 49 (2), 59-69. DOI:10.1080/00461520.2014.921572Datensatz 0282214
Bromme, R., Kienhues, D. & Porsch, T. (2010). Who knows what and who can we believe? Epistemological beliefs are beliefs about knowledge (mostly) to be attained from others. In L. D. Bendixen & F. C. Feucht (Eds.), Personal epistemology in the classroom: Theory, research, and implications for practice (pp. 163-193). Cambridge: Cambridge University Press. Datensatz 0232884
Origgi G. (2004). Is trust an epistemological notion? Episteme, 1(1), 61–72.
Origgi G. (2014). Epistemic trust. In P. Capet & T. Delavallade (Eds.), Information Evaluation (pp. 35-54). New York: John Wiley & Sons. DOI: 10.1002/9781118899151.ch2
Sinatra, G. M., Kienhues, D., & Hofer, B. K. (2014). Addressing challenges to public understanding of science: Epistemic cognition, motivated reasoning, and conceptual change. Educational Psychologist, 49 (2), 123-138. DOI: 10.1080/00461520.2014.916216 Datensatz 0282216
Sperber, D., Clément, F., Heintz, C., Mascaro, O., Mercier, H., Origgi, G., & Wilson, D. (2010). Epistemic vigilance. Mind & Language, 25 (4), 359–393. DOI: 10.1111/j.1468-0017.2010.01394.x
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