Data report overview

The dataset examined has the following dimensions:

Feature Result
Number of observations 365
Number of variables 31

Codebook summary table

Label Variable Class # unique values Missing Description
Participant number, auto-assigned based on rows in data preparation Participant integer 365 0.00 %
Factorial variable from the condition manipulating whether the agent is human or AI Agent factor 2 0.00 %
Perceived intelligence - ’As a result of this new breakthrough, how do you think this affected X’s intelligence? Remember, intelligence should be understood here as the ability to competently and effectively achieve one’s goals, whatever they may be’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Intelligent numeric 40 0.00 %
Combined score of moral knowledge, moral prediction, and moral explanation (-50 = drastically reduced, 0 = stay the same, 50 = drastically increased) Moral_Competence numeric 132 0.00 %
Combined scores of moral harm, help, and fairness (-50 = drastically reduced, 0 = stay the same, 50 = drastically increased) Moral_Motivation numeric 124 0.00 %
Perceived trust - ‘To what extent do you think that X would be trustworthy?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Trust numeric 68 0.00 %
Perceived danger - ‘To what extent do you think that X would be dangerous?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Danger numeric 77 0.00 %
Self-reported familiarity with AI - ‘How much do you think you know about AI, how it works, and how it is used?’ (1 = not at all; 7= very much) Familiarity numeric 7 0.00 %
Number of correct PEW knowledge items across the 6 items Pew_Correct integer 8 0.27 %
Percentage of correct PEW knowledge items across the 6 items Pew_Percent numeric 8 0.27 %
Question from Pew on knowledge on AI PEW1 numeric 5 0.00 %
Question from Pew on knowledge on AI PEW2 numeric 6 0.27 %
Question from Pew on knowledge on AI PEW3 numeric 6 0.27 %
Question from Pew on knowledge on AI PEW4 numeric 4 0.00 %
Question from Pew on knowledge on AI PEW5 numeric 5 0.00 %
Question from Pew on knowledge on AI PEW6 numeric 5 0.00 %
Self-reported familiarity with AI, mean-centered Familiarity_c numeric 7 0.00 %
Number of correct PEW knowledge items across the 5 items, mean-centered Pew_Correct_c numeric 8 0.27 %
Perceived intelligence, means-centered Intelligence_c numeric 40 0.00 %
Perceived moral competence across the three items, means-centered Moral_Competence_c numeric 132 0.00 %
Perceived moral motivation across the three items, means-centered Moral_Motivation_c numeric 124 0.00 %
Attention Check 1 (Tiktok) AttentionCheck character 1 0.00 %
Attention Check 2 (Post-Manipulation) - ‘Earlier in this study you were presented with some information about someone or something that was then changed in some way. What was described?’ (1 = An AI became rapidly more intelligent; 2 = The AI became rapidly more moral; 3 = A person became rapidly more intelligent; 4 = A person became rapidly more moral; 5 = An algorithm became better at image identification) AttentionTwo character 2 0.00 %
Participant age, in numeric form Age numeric 56 0.00 %
Participant gender recoded to be male, female, non-binary/other, and not say Gender factor 4 0.00 %
Perceived moral knowledge - ’As a result of this new breakthrough, how do you think this affected X’s moral knowledge? That is, the extent X knows about the moral norms we have, and understands when and why we say certain things are morally wrong. (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Moral_Knowledge numeric 61 0.00 %
Perceived moral prediction ability - ‘As a result of this new breakthrough, how much do you think this affected X’s ability to predict when their actions might have morally good and bad outcomes?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Moral_Predict numeric 63 0.00 %
Perceived moral explanation ability - ‘As a result of this new breakthrough, how do you think this affected X’s ability to explain or justify why their action was right or wrong?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Moral_Explain numeric 58 0.00 %
Perceived moral motivation to avoid harm - ’As a result of this new breakthrough, how do you think this affected how much X would be concerned with avoiding harm?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Moral_Harm numeric 67 0.00 %
Perceived motivation to help - ’As a result of this new breakthrough, how do you think this affected X’s level of motivation to help others?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Moral_Help numeric 56 0.00 %
Perceived motivation for fairness - ’As a result of this new breakthrough, how do you think this affected X’s fairness? That is, their sensitivity to concerns about equality, discrimination, and motivation to ensure they are being unbiased and impartial’(-50 = drastically reduced, 0 = remain the same, 50 = drastically increased) Moral_Fair numeric 62 0.00 %

Variable list

Participant

Participant number, auto-assigned based on rows in data preparation

Feature Result
Variable type integer
Number of missing obs. 0 (0 %)
Number of unique values 365
Median 200
1st and 3rd quartiles 101; 299
Min. and max. 1; 399

Agent

Factorial variable from the condition manipulating whether the agent is human or AI

Feature Result
Variable type factor
Number of missing obs. 0 (0 %)
Number of unique values 2
Mode “AI”
Reference category Human

Intelligent

Perceived intelligence - ’As a result of this new breakthrough, how do you think this affected X’s intelligence? Remember, intelligence should be understood here as the ability to competently and effectively achieve one’s goals, whatever they may be’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 40
Median 48
1st and 3rd quartiles 40; 50
Min. and max. -11; 50

Moral_Competence

Combined score of moral knowledge, moral prediction, and moral explanation (-50 = drastically reduced, 0 = stay the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 132
Median 18
1st and 3rd quartiles 7.33; 31.33
Min. and max. -15; 50

Moral_Motivation

Combined scores of moral harm, help, and fairness (-50 = drastically reduced, 0 = stay the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 124
Median 11
1st and 3rd quartiles 0.33; 22.67
Min. and max. -31.33; 50

Trust

Perceived trust - ‘To what extent do you think that X would be trustworthy?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 68
Median 2
1st and 3rd quartiles 0; 25
Min. and max. -50; 50

Danger

Perceived danger - ‘To what extent do you think that X would be dangerous?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 77
Median 2
1st and 3rd quartiles 0; 29
Min. and max. -50; 50

Familiarity

Self-reported familiarity with AI - ‘How much do you think you know about AI, how it works, and how it is used?’ (1 = not at all; 7= very much)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 7
Median 4
1st and 3rd quartiles 3; 5
Min. and max. 1; 7

Pew_Correct

Number of correct PEW knowledge items across the 6 items

Feature Result
Variable type integer
Number of missing obs. 1 (0.27 %)
Number of unique values 7
Median 5
1st and 3rd quartiles 4; 6
Min. and max. 0; 6

Pew_Percent

Percentage of correct PEW knowledge items across the 6 items

Feature Result
Variable type numeric
Number of missing obs. 1 (0.27 %)
Number of unique values 7
Median 83.33
1st and 3rd quartiles 66.67; 100
Min. and max. 0; 100

PEW1

Question from Pew on knowledge on AI

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 5
Median 4
1st and 3rd quartiles 4; 4
Min. and max. 1; 5

PEW2

Question from Pew on knowledge on AI

Feature Result
Variable type numeric
Number of missing obs. 1 (0.27 %)
Number of unique values 5
Median 2
1st and 3rd quartiles 2; 2
Min. and max. 1; 5

PEW3

Question from Pew on knowledge on AI

Feature Result
Variable type numeric
Number of missing obs. 1 (0.27 %)
Number of unique values 5
Median 3
1st and 3rd quartiles 3; 3
Min. and max. 1; 5

PEW4

Question from Pew on knowledge on AI

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 4
Median 1
1st and 3rd quartiles 1; 1
Min. and max. 1; 5

PEW5

Question from Pew on knowledge on AI

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 5
Median 3
1st and 3rd quartiles 3; 3
Min. and max. 1; 5

PEW6

Question from Pew on knowledge on AI

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 5
Median 2
1st and 3rd quartiles 2; 2
Min. and max. 1; 5

Familiarity_c

Self-reported familiarity with AI, mean-centered

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 7
Median 0.24
1st and 3rd quartiles -0.76; 1.24
Min. and max. -2.76; 3.24

Pew_Correct_c

Number of correct PEW knowledge items across the 5 items, mean-centered

Feature Result
Variable type numeric
Number of missing obs. 1 (0.27 %)
Number of unique values 7
Median 0.12
1st and 3rd quartiles -0.88; 1.12
Min. and max. -4.88; 1.12

Intelligence_c

Perceived intelligence, means-centered

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 40
Median 5.69
1st and 3rd quartiles -2.31; 7.69
Min. and max. -53.31; 7.69

Moral_Competence_c

Perceived moral competence across the three items, means-centered

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 132
Median -1.32
1st and 3rd quartiles -11.99; 12.01
Min. and max. -34.32; 30.68

Moral_Motivation_c

Perceived moral motivation across the three items, means-centered

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 124
Median -2.03
1st and 3rd quartiles -12.69; 9.64
Min. and max. -44.36; 36.97

AttentionCheck

Attention Check 1 (Tiktok)

  • The variable only takes one (non-missing) value: "9". The variable contains 0 % missing observations.

AttentionTwo

Attention Check 2 (Post-Manipulation) - ‘Earlier in this study you were presented with some information about someone or something that was then changed in some way. What was described?’ (1 = An AI became rapidly more intelligent; 2 = The AI became rapidly more moral; 3 = A person became rapidly more intelligent; 4 = A person became rapidly more moral; 5 = An algorithm became better at image identification)

Feature Result
Variable type character
Number of missing obs. 0 (0 %)
Number of unique values 2
Mode “1”

Age

Participant age, in numeric form

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 56
Median 39
1st and 3rd quartiles 30; 52
Min. and max. 18; 83

Gender

Participant gender recoded to be male, female, non-binary/other, and not say

Feature Result
Variable type factor
Number of missing obs. 0 (0 %)
Number of unique values 4
Mode “Female”
Reference category Female

Moral_Knowledge

Perceived moral knowledge - ’As a result of this new breakthrough, how do you think this affected X’s moral knowledge? That is, the extent X knows about the moral norms we have, and understands when and why we say certain things are morally wrong. (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 61
Median 10
1st and 3rd quartiles 0; 29
Min. and max. -24; 50

Moral_Predict

Perceived moral prediction ability - ‘As a result of this new breakthrough, how much do you think this affected X’s ability to predict when their actions might have morally good and bad outcomes?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 63
Median 20
1st and 3rd quartiles 0; 33
Min. and max. -45; 50

Moral_Explain

Perceived moral explanation ability - ‘As a result of this new breakthrough, how do you think this affected X’s ability to explain or justify why their action was right or wrong?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 58
Median 25
1st and 3rd quartiles 10; 40
Min. and max. -27; 50

Moral_Harm

Perceived moral motivation to avoid harm - ’As a result of this new breakthrough, how do you think this affected how much X would be concerned with avoiding harm?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 67
Median 10
1st and 3rd quartiles 0; 30
Min. and max. -50; 50

Moral_Help

Perceived motivation to help - ’As a result of this new breakthrough, how do you think this affected X’s level of motivation to help others?’ (-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 56
Median 8
1st and 3rd quartiles 0; 30
Min. and max. -39; 50

Moral_Fair

Perceived motivation for fairness - ’As a result of this new breakthrough, how do you think this affected X’s fairness? That is, their sensitivity to concerns about equality, discrimination, and motivation to ensure they are being unbiased and impartial’(-50 = drastically reduced, 0 = remain the same, 50 = drastically increased)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 62
Median 4
1st and 3rd quartiles 0; 25
Min. and max. -48; 50

Report generation information:

  • Created by: Jim Everett (username: jimeverett).

  • Report creation time: Sun Aug 17 2025 11:57:12

  • Report was run from directory: /Users/jimeverett/Documents/Academic/Research/Current Projects/AI Orthogonality/Orthogonality Data Analysis/Data Preparation

  • dataReporter v1.0.5 [Pkg: 2025-04-13 from CRAN (R 4.5.0)]

  • R version 4.5.1 (2025-06-13).

  • Platform: aarch64-apple-darwin20(Europe/London).

  • Function call: dataReporter::makeDataReport(data = Orthogonality_Study_7_Data_Summary, render = TRUE, mode = c("summarize", "visualize"), smartNum = FALSE, file = "Study_7_Codebook.Rmd", replace = TRUE, checks = list( character = "showAllFactorLevels", factor = "showAllFactorLevels", labelled = "showAllFactorLevels", haven_labelled = "showAllFactorLevels", numeric = NULL, integer = NULL, logical = NULL, Date = NULL), listChecks = FALSE, maxProbVals = Inf, codebook = TRUE, reportTitle = "Orthogonality Study 7 Codebook", add.codebook = TRUE, smart.order = FALSE)