Measuring Laypeople's Trust in Experts in a Digital Age: The Muenster Epistemic Trustworthiness Inventory (METI): Three Datasets.

Educational Psychology

Authors(s) / Creator(s)

Hendriks, Friederike
Kienhues, Dorothee
Bromme, Rainer

Abstract

Given their lack of background knowledge, laypeople require expert help when dealing with scientific information. To decide whose help is dependable, laypeople must judge an expert's epistemic trustworthiness in terms of competence, adherence to scientific standards, and good intentions. Online, this may be difficult due to the often limited and sometimes unreliable source information available. To measure laypeople's evaluations of experts (encountered online), we constructed an inventory to assess epistemic trustworthiness on the dimensions expertise, integrity, and benevolence. Exploratory (n = 237) and confirmatory factor analyses (n = 345) showed that the Muenster Epistemic Trustworthiness Inventory (METI) is composed of these three factors. A subsequent experimental study (n = 137) showed that all three dimensions of the METI are sensitive to variation in source characteristics. We propose using this inventory to measure assignments of epistemic trustworthiness, that is, all judgments laypeople make when deciding whether to place epistemic trust in - and defer to - an expert in order to solve a scientific informational problem that is beyond their understanding.

Persistent Identifier

https://doi.org/10.5160/psychdata.hsfe15mu08

Year of Publication

2015

Funding

German Research Foundation (DFG) within the Research Training Group 1712 “Trust and Communication in a Digitized World”

Citation

Hendriks, F. ., Kienhues, D. & Bromme, R. (2015). Measuring Laypeople's Trust in Experts in a Digital Age: The Muenster Epistemic Trustworthiness Inventory (METI): Three Datasets. (Version 1.0.0) [Data and Documentation]. Trier: Research Data Center at ZPID. https://doi.org/10.5160/psychdata.hsfe15mu08

Study Description

Research Questions/Hypotheses:

  1. Epistemic trustworthiness can be differentiated in the three dimensions expertise, integrity and benevolence.
  2. These dimensions are separable, though to some extent correlated.

Research Design:

Research Instrument; repeated measurements

Measurement Instruments/Apparatus:

Study 1: Participants received 18 semantic opposites to rate a scientific expert on Likert scales ranging from 1 (e.g. professional) to 7 (e.g., unprofessional).
Exploratory factor analysis revealed three factors – “expertise”, “integrity” and “benevolence” – explaining 61.66% of the total variance.

Study 2: The constructed Muenster Epistemic Trustworthiness Inventory (METI) was reduced to 16 items. To test the three-factor structure of the inventory, a new data set was assessed with confirmatory factor analysis. The results confirmed the three-factor structure.
After elimination of two items, the final version of the METI consists of 14 items. All three factors are related to the comprehensive theoretical construct “epistemic trustworthiness”.

Study 3: The final version of the METI consisting of 14 items was administered in an experimental study. Participants had to rate the epistemic thrustworthiness of six ficticious persons, who were indicated as potential authors of a blog entry. The blog entry was about a study from the field of neurology. Descriptions of authors varied along each of the dimensions of epistemic trustworthiness (low vs. high expertise, low vs. high integrity, low vs. high benevolence). The results indicated that the METI is able to measure epistemic trustworthiness in a differentiated way.
For detailed information see Hendriks, Kienhues & Bromme (2015).

Data Collection Method:

Data collection in the absence of an experimenter

  • Online-Survey

Population:

Young German graduates

Survey Time Period:

Study 1: August & Septembre 2013
Study 2: January, February & March 2014
Study 3: July & August 2015

Sample:

Convenience Sample

Gender Distribution:

Study 1:
75,5% female subjects
24,5% male subjects

Study 2:
69,3% female subjects
30,7% male subjects

Study 3:
75,2% female subjects
24,8% male subjects


Age Distribution: Study 1: 19-47 years; Study 2: 18-50 years; Study 3: 19-53 years

Spatial Coverage (Country/Region/City): Germany

Subject Recruitment:

Study 1: Participants registered in the Bromme research unit’s internal volunteer database were contacted via e-mail containing the link to the online survey. Participants could choose the time and location of their participation. Subjects took part in an Amazon voucher lottery worth a total of 200 euro.


Study 2: Participants were recruited in lectures at the University of Muenster. Students interested in participation could leave their email adress for contact. They were contacted via e-mail containing the link to the online survey. Participants could choose the time and location of their participation. Subjects took part in an Amazon voucher lottery worth a total of 200 euro.


Study 3: Participants registered in the Bromme research unit’s internal volunteer database were contacted via email containing the link to the online survey. Furthermore, advertisements were posted in a newsletter for students at the University of Muenster and on the German magazine “Psychologie Heute”’s website. Participants could choose the time and location of their participation. Subjects took part in an Amazon voucher lottery worth a total of 100 euro.

Sample Size:

Study 1: 237 individuals; Study 2: 345 individuals; Study 3: 137 individuals

Return/Drop Out:

Study 1: 300 persons followed the link to open the online survey. Only those who completed the survey were included in the analysis (79%).
Study 2: 406 persons followed the link to open the online survey. Only those who completed the survey were included in the analysis (85%).
Study 3: 243 persons followed the link to open the online survey. Only those who completed the survey were included in the analysis (56%).

hsfe15mu08_readme.txt
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hsfe15mu08_fd1.txt
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Description: Research data file of study 1

hsfe15mu08_kb1.txt
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hsfe15mu08_fd2.txt
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hsfe15mu08_kb2.txt
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Description: English codebook of the research data file hsfe15mu08_fd2.txt

hsfe15mu08_fd3.txt
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Description: Research data file of study 3

hsfe15mu08_kb3.txt
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Description: English codebook of the research data file hsfe15mu08_fd3.txt

Position Name Label Valid Values Missing Values
1 SUBJECT_ID subject ID 1-237 "identification number" -777 "missing value"
2 DURATION Time to complete survey 128-50797 "seconds" -1 "missing value due to interrupted participation"
3 AGE Age 19-47 "years" 9 "missing value"
4 GENDER Gender 1 "male" 2 "female" -77 "missing value"
5 EDUCATION Highest education degree 1 "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"
6 V_1 Rating of the scientific expert: Competence 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
7 V_2 Rating of the scientific expert: Intelligence 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
8 V_3 Rating of the scientific expert: Education 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
9 V_4 Rating of the scientific expert: Professionalism 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unprofessional" -77 "missing value"
10 V_5 Rating of the scientific expert: Experience 1 "experienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexperienced" -77 "missing value"
11 V_6 Rating of the scientific expert: Qualification 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
12 V_7 Rating of the scientific expert: Reliability 1 "reliable" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unreliable" -77 "missing value"
13 V_8 Rating of the scientific expert: Sincereness 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
14 V_9 Rating of the scientific expert: Honesty 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
15 V_10 Rating of the scientific expert: Morality 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
16 V_11 Rating of the scientific expert: Ethical 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
17 V_12 Rating of the scientific expert: Impartiality 1 "impartial" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "partial" -77 "missing value"
18 V_13 Rating of the scientific expert: Responsibility 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresponsible" -77 "missing value"
19 V_14 Rating of the scientific expert: Justice 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "injust" -77 "missing value"
20 V_15 Rating of the scientific expert: Selfishness 1 "unselfish" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "selfish" -77 "missing value"
21 V_16 Rating of the scientific expert: Considerateness 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
22 V_17 Rating of the scientific expert: Helpfulness 1 "helpful" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unhelpful" -77 "missing value"
23 V_18 Rating of the scientific expert: Fairness 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
24 EXPERTISE_MEAN Mean expertise (derived variable) 1-7 "expertise-no expertise" 9 "missing value"
25 INTEGRITY_MEAN Mean integrity (derived variable) 1-7 "integrity-no integrity" 9 "missing value"
26 BENEVOLENCE_MEAN Mean benevolence (derived variable) 1-7 "benevolence-no benevolence" 9 "missing value"
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Items per page: 10 25 50
Position Name Label Valid Values Missing Values
1 SUBJECT_ID subject ID 1-345 "identification number" -777 "missing value"
2 DURATION time to complete survey 182-10089 "seconds" -1 "missing value due to interrupted participation"
3 AGE Age 18-50 "years" -77 "missing value"
4 GENDER Gender 1 "male" 2 "female" -77 "missing value"
5 EDUCATION Highest educational degree 1 "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"
6 E_1 Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 1 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
7 E_2 Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 2 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
8 E_3 Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 3 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
9 E_4 Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 4 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "improfessional" -77 "missing value"
10 E_5 Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 5 1 "expierienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexpierienced" -77 "missing value"
11 E_6 Muenster Epistemic Trustworthiness Inventory (METI). Expertise, Item 6 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
12 I_1 Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 1 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
13 I_2 Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 2 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
14 W_1 Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 1 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
15 W_2 Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 2 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
16 W_3 Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 3 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresponsible" -77 "missing value"
17 I_3 Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 3 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "injust" -77 "missing value"
18 I_4 Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 4 1 "unselfish" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "selfish" -77 "missing value"
19 W_4 Muenster Epistemic Trustworthiness Inventory (METI). Benevolence, Item 4 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
20 E_7 hilfreich - hinderlich 1 "helpful" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unhelpful" -77 "missing value"
21 I_5 Muenster Epistemic Trustworthiness Inventory (METI). Integrity, Item 5 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
22 EXPERTISE_MEAN Mean expertise (derived variable) 1-7 "expertise-no expertise" 9 "missing value"
23 INTEGRITY_MEAN Mean integrity (derived variable) 1-7 "integrity-no integrity" 9 "missing value"
24 BENEVOLENCE_MEAN Mean benevolence (derived variable) 1-7 "benevolence-no benevolence" 9 "missing value"
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Position Name Label Valid Values Missing Values
1 SUBJECT_ID subject ID 1-137 "identification number" -777 "missing value"
2 CONSENT Consent 1 "yes" 2 "no" -77 "missing value"
3 AGE Age 19-53 "years" -77 "missing value"
4 GENDER Gender 1 "male" 2 "female" -77 "missing value"
5 EDUCATION Highest education degree 1 "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"
6 E_1_EXPERTISELOW Experimental condition: Low expertise. Muenster Epistemic Trustworthiness Inventory (METI): Expertise, Item 1 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
7 E_2_EXPERTISELOW Experimental condition: Low expertise. METI: Expertise, Item 2 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
8 E_3_EXPERTISELOW Experimental condition: Low expertise. METI: Expertise, Item 3 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
9 E_4_EXPERTISELOW Experimental condition: Low expertise. METI: Expertise, Item 4 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "improfessional" -77 "missing value"
10 E_5_EXPERTISELOW Experimental condition: Low expertise. METI: Expertise, Item 5 1 "experienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexperienced" -77 "missing value"
11 E_6_EXPERTISELOW Experimental condition: Low expertise. METI: Expertise, Item 6 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
12 I_1_EXPERTISELOW Experimental condition: Low expertise. METI: Integrity, Item 1 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
13 I_2_EXPERTISELOW Experimental condition: Low expertise. METI: Integrity, Item 2 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
14 W_1_EXPERTISELOW Experimental condition: Low expertise. METI: Benevolence, Item 1 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
15 W_2_EXPERTISELOW Experimental condition: Low expertise. METI: Benevolence, Item 2 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
16 W_3_EXPERTISELOW Experimental condition: Low expertise. METI: Benevolence, Item 3 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresposible" -77 "missing value"
17 I_3_EXPERTISELOW Experimental condition: Low expertise. METI: Integrity, Item 3 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unjust" -77 "missing value"
18 W_4_EXPERTISELOW Experimental condition: Low expertise. METI: Benevolence, Item 4 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
19 I_5_EXPERTISELOW Experimental condition: Low expertise. METI: Integrity, Item 5 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
20 E_1_EXPERTISEHIGH Experimental condition: High expertise. METI: Expertise, Item 1 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
21 E_2_EXPERTISEHIGH Experimental condition: High expertise. METI: Expertise, Item 2 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
22 E_3_EXPERTISEHIGH Experimental condition: High expertise. METI: Expertise, Item 3 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
23 E_4_EXPERTISEHIGH Experimental condition: High expertise. METI: Expertise, Item 4 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "improfessional" -77 "missing value"
24 E_5_EXPERTISEHIGH Experimental condition: High expertise. METI: Expertise, Item 5 1 "expierienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexpierienced" -77 "missing value"
25 E_6_EXPERTISEHIGH Experimental condition: High expertise. METI: Expertise, Item 6 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
26 I_1_EXPERTISEHIGH Experimental condition: High expertise. METI: Integrity, Item 1 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
27 I_2_EXPERTISEHIGH Experimental condition: High expertise. METI: Integrity, Item 2 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
28 W_1_EXPERTISEHIGH Experimental condition: High expertise. METI: Benevolence, Item 1 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
29 W_2_EXPERTISEHIGH Experimental condition: High expertise. METI: Benevolence, Item 2 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
30 W_3_EXPERTISEHIGH Experimental condition: High expertise. METI: Benevolence, Item 3 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresposible" -77 "missing value"
31 I_3_EXPERTISEHIGH Experimental condition: High expertise. METI: Integrity, Item 3 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unjust" -77 "missing value"
32 W_4_EXPERTISEHIGH Experimental condition: High expertise. METI: Benevolence, Item 4 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
33 I_5_EXPERTISEHIGH Experimental condition: High expertise. METI: Integrity, Item 5 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
34 E_1_INTEGRITYLOW Experimental condition: Low Integrity. METI: Expertise, Item 1 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
35 E_2_INTEGRITYLOW Experimental condition: Low Integrity. METI: Expertise, Item 2 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
36 E_3_INTEGRITYLOW Experimental condition: Low Integrity. METI: Expertise, Item 3 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
37 E_4_INTEGRITYLOW Experimental condition: Low Integrity. METI: Expertise, Item 4 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "improfessional" -77 "missing value"
38 E_5_INTEGRITYLOW Experimental condition: Low Integrity. METI: Experience, Item 5 1 "experienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexperienced" -77 "missing value"
39 E_6_INTEGRITYLOW Experimental condition: Low Integrity. METI: Expertise, Item 6 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
40 I_1_INTEGRITYLOW Experimental condition: Low Integrity. METI: Integrity, Item 1 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
41 I_2_INTEGRITYLOW Experimental condition: Low Integrity. METI: Integrity, Item 2 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
42 W_1_INTEGRITYLOW Experimental condition: Low Integrity. METI: Benevolence, Item 1 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
43 W_2_INTEGRITYLOW Experimental condition: Low Integrity. METI: Benevolence, Item 2 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
44 W_3_INTEGRITYLOW Experimental condition: Low Integrity. METI: Benevolence, Item 3 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresposible" -77 "missing value"
45 I_3_INTEGRITYLOW Experimental condition: Low Integrity. METI: Integrity, Item 3 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unjust" -77 "missing value"
46 W_4_INTEGRITYLOW Experimental condition: Low Integrity. METI: Benevolence, Item 4 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
47 I_5_INTEGRITYLOW Experimental condition: Low Integrity. METI: Integrity, Item 5 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
48 E_1_INTEGRITYHIGH Experimental condition: High Integrity. METI: Expertise, Item 1 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
49 E_2_INTEGRITYHIGH Experimental condition: High Integrity. METI: Expertise, Item 2 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
50 E_3_INTEGRITYHIGH Experimental condition: High Integrity. METI: Expertise, Item 3 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
51 E_4_INTEGRITYHIGH Experimental condition: High Integrity. METI: Expertise, Item 4 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "improfessional" -77 "missing value"
52 E_5_INTEGRITYHIGH Experimental condition: High Integrity. METI: Expertise, Item 5 1 "expierienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexpierienced" -77 "missing value"
53 E_6_INTEGRITYHIGH Experimental condition: High Integrity. METI: Expertise, Item 6 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
54 I_1_INTEGRITYHIGH Experimental condition: High Integrity. METI: Integrity, Item 1 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
55 I_2_INTEGRITYHIGH Experimental condition: High Integrity. METI: Integrity, Item 2 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
56 W_1_INTEGRITYHIGH Experimental condition: High Integrity. METI: Benevolence, Item 1 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
57 W_2_INTEGRITYHIGH Experimental condition: High Integrity. METI: Benevolence, Item 2 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
58 W_3_INTEGRITYHIGH Experimental condition: High Integrity. METI: Benevolence, Item 3 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresposible" -77 "missing value"
59 I_3_INTEGRITYHIGH Experimental condition: High Integrity. METI: Integrity, Item 3 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unjust" -77 "missing value"
60 W_4_INTEGRITYHIGH Experimental condition: High Integrity. METI: Benevolence, Item 4 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
61 I_5_INTEGRITYHIGH Experimental condition: High Integrity. METI: Integrity, Item 5 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
62 E_1_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Expertise, Item 1 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
63 E_2_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Expertise, Item 2 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
64 E_3_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Expertise, Item 3 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
65 E_4_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Expertise, Item 4 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "improfessional" -77 "missing value"
66 E_5_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Expertise, Item 5 1 "expierienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexpierienced" -77 "missing value"
67 E_6_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Expertise, Item 6 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
68 I_1_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Integrity, Item 1 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
69 I_2_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Integrity, Item 2 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
70 W_1_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Benevolence, Item 1 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
71 W_2_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Benevolence, Item 2 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
72 W_3_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Benevolence, Item 3 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresposible" -77 "missing value"
73 I_3_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Integrity, Item 3 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unjust" -77 "missing value"
74 W_4_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Benevolence, Item 4 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
75 I_5_BENEVOLENCELOW Experimental condition: Low Benevolence. METI: Integrity, Item 5 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
76 E_1_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Expertise, Item 1 1 "competent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "incompetent" -77 "missing value"
77 E_2_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Expertise, Item 2 1 "intelligent" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unintelligent" -77 "missing value"
78 E_3_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Expertise, Item 3 1 "well educated" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "poorly educated" -77 "missing value"
79 E_4_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Expertise, Item 4 1 "professional" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "improfessional" -77 "missing value"
80 E_5_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Expertise, Item 5 1 "experienced" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inexperienced" -77 "missing value"
81 E_6_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Expertise, Item 6 1 "qualified" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unqualified" -77 "missing value"
82 I_1_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Integrity, Item 1 1 "sincere" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "insincere" -77 "missing value"
83 I_2_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Integrity, Item 2 1 "honest" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "dishonest" -77 "missing value"
84 W_1_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Benevolence, Item 1 1 "moral" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "immoral" -77 "missing value"
85 W_2_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Benevolence, Item 2 1 "ethical" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unethical" -77 "missing value"
86 W_3_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Benevolence, Item 3 1 "responsible" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "irresposible" -77 "missing value"
87 I_3_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Integrity, Item 3 1 "just" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unjust" -77 "missing value"
88 W_4_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Benevolence, Item 4 1 "considerate" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "inconsiderate" -77 "missing value"
89 I_5_BENEVOLENCEHIGH Experimental condition: High Benevolence. METI: Integrity, Item 5 1 "fair" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6" 7 "unfair" -77 "missing value"
90 E1U_EXPERTISELOW recoded 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"
91 E2U_EXPERTISELOW recoded 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"
92 E3U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
93 E4U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
94 E5U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
95 E6U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
96 I1U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
97 I2U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
98 W1U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
99 W2U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
100 W3U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
101 I3U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
102 W4U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
103 I5U_EXPERTISELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 3,0-7,0 "<!LABEL!>" 9 "missing value"
104 E1U_EXPERTISEHIGH recoded 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"
105 E2U_EXPERTISEHIGH recoded 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"
106 E3U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
107 E4U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
108 E5U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
109 E6U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
110 I1U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
111 I2U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 3,0-7,0 "<!LABEL!>" 9 "missing value"
112 W1U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
113 W2U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
114 W3U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
115 I3U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
116 W4U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
117 I5U_EXPERTISEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
118 E1U_INTEGRITYLOW recoded 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"
119 E2U_INTEGRITYLOW recoded 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"
120 E3U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
121 E4U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
122 E5U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
123 E6U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
124 I1U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
125 I2U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
126 W1U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
127 W2U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
128 W3U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
129 I3U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
130 W4U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
131 I5U_INTEGRITYLOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
132 E1U_INTEGRITYHIGH recoded 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"
133 E2U_INTEGRITYHIGH recoded 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"
134 E3U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
135 E4U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
136 E5U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
137 E6U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
138 I1U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
139 I2U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
140 W1U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
141 W2U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
142 W3U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
143 I3U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
144 W4U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
145 I5U_INTEGRITYHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
146 E1U_BENEVOLENCELOW recoded 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"
147 E2U_BENEVOLENCELOW recoded 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"
148 E3U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
149 E4U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
150 E5U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
151 E6U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
152 I1U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
153 I2U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
154 W1U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
155 W2U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
156 W3U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
157 I3U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
158 W4U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
159 I5U_BENEVOLENCELOW recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-6,0 "<!LABEL!>" 9 "missing value"
160 E1U_BENEVOLENCEHIGH recoded 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"
161 E2U_BENEVOLENCEHIGH recoded 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"
162 E3U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 3,0-7,0 "<!LABEL!>" 9 "missing value"
163 E4U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
164 E5U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
165 E6U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
166 I1U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
167 I2U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
168 W1U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
169 W2U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 1,0-7,0 "<!LABEL!>" 9 "missing value"
170 W3U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
171 I3U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
172 W4U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 2,0-7,0 "<!LABEL!>" 9 "missing value"
173 I5U_BENEVOLENCEHIGH recoded 1=7, 2=6, 3=5, 4=4, 5=3, 6=2, 7=1 (derived variable) 4,0-7,0 "<!LABEL!>" 9 "missing value"
174 EXPERTISE_EXPERTISELOW Mean expertise in condition expertise low (derived variable) 1-7 "no expertise-expertise" 9 "missing value"
175 INTEGRITY_EXPERTISELOW Mean integrity in condition expertise low (derived variable) 1-7 "no integrity-integrity" 9 "missing value"
176 BENEVOLENCE_EXPERTISELOW Mean benevolence in condition expertise low (derived variable) 1-7 "no benevolence-benevolence" 9 "missing value"
177 EXPERTISE_EXPERTISEHIGH Mean expertise in condition expertise high (derived variable) 1-7 "no expertise-expertise" 9 "missing value"
178 INTEGRITY_EXPERTISEHIGH Mean integrity in condition expertise high (derived variable) 1-7 "no integrity-integrity" 9 "missing value"
179 BENEVOLENCE_EXPERTISEHIGH Mean benevolence in condition expertise high (derived variable) 1-7 "no benevolence-benevolence" 9 "missing value"
180 EXPERTISE_INTEGRITYLOW Mean expertise in condition integrity low (derived variable) 1-7 "no expertise-expertise" 9 "missing value"
181 INTEGRITY_INTEGRITYLOW Mean integrity in condition integrity low (derived variable) 1-7 "no integrity-integrity" 9 "missing value"
182 BENEVOLENCE_INTEGRITYLOW Mean benevolence in condition integrity low (derived variable) 1-7 "no benevolence-benevolence" 9 "missing value"
183 EXPERTISE_INTEGRITYHIGH Mean expertise in condition integrity high (derived variable) 1-7 "no expertise-expertise" 9 "missing value"
184 INTEGRITY_INTEGRITYHIGH Mean integrity in condition integrity high (derived variable) 1-7 "no integrity-integrity" 9 "missing value"
185 BENEVOLENCE_INTEGRITYHIGH Mean benevolence in condition integrity high (derived variable) 1-7 "no benevolence-benevolence" 9 "missing value"
186 EXPERTISE_BENEVOLENCELOW Mean expertise in condition benevolence low (derived variable) 1-7 "no expertise-expertise" 9 "missing value"
187 INTEGRITY_BENEVOLENCELOW Mean integrity in condition benevolence low (derived variable) 1-7 "no integrity-integrity" 9 "missing value"
188 BENEVOLENCE_BENEVOLENCELOW Mean benevolence in condition benevolence low (derived variable) 1-7 "no benevolence-benevolence" 9 "missing value"
189 EXPERTISE_BENEVOLENCEHIGH Mean expertise in condition benevolence high (derived variable) 1-7 "no expertise-expertise" 9 "missing value"
190 INTEGRITY_BENEVOLENCEHIGH Mean integrity in condition benevolence high (derived variable) 1-7 "no integrity-integrity" 9 "missing value"
191 BENEVOLENCE_BENEVOLENCEHIGH Mean benevolence in condition benevolence high (derived variable) 1-7 "no benevolence-benevolence" 9 "missing value"
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Further Reading
Bromme, R., & Goldman, S.R. (2014). The public's Bounded Understanding of Science. Educational Psychologist, 49 (2), 59-69. DOI:10.1080/00461520.2014.921572 PSYNDEX
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. PSYNDEX
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 PSYNDEX
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