Orthogonality Study 5 Codebook
Autogenerated data summary from dataReporter
2025-08-17 11:56:36.398072
Data report overview
The dataset examined has the following dimensions:
| Feature | Result |
|---|---|
| Number of observations | 353 |
| Number of variables | 42 |
Codebook summary table
| Label | Variable | Class | # unique values | Missing | Description |
|---|---|---|---|---|---|
| Participant number, auto-assigned based on rows in data preparation | Participant | integer | 353 | 0.00 % | |
| Factorial variable from the condition manipulating whether the agent is human or AI | Agent | factor | 2 | 0.00 % | |
| Factorial variable from the condition manipulating whether agent was described as being low or high (in intelligence) | Level | factor | 2 | 0.00 % | |
| Perceived intelligence - ‘How intelligent do you think X is, where intelligence should be understood here as the ability to competently and effectively achieve one’s goals, whatever they may be?’ (1 = not at all; 7= very much) | Intelligent | numeric | 7 | 0.00 % | |
| Perceived intelligence in comparison to average person - ‘Compared to an average person, how intelligent do you think X is?’(-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Intelligent_Comparison | numeric | 7 | 0.00 % | |
| Combined score of moral knowledge, moral prediction, and moral explanation | Moral_Competence | numeric | 19 | 0.00 % | |
| Combined scores of moral knowledge, moral prediction, and moral explanation, in comparison to average person | Moral_Competence_Comparison | numeric | 19 | 0.00 % | |
| Combined scores of moral harm, help, and fairness | Moral_Motivation | numeric | 19 | 0.00 % | |
| Combined scores of moral harm, help, and fairness, in comparison to the average person | Moral_Motivation_Comparison | numeric | 19 | 0.00 % | |
| Perceived trust - ‘To what extent do you think that X would be trustworthy?’ (1 = not at all; 7= very much) | Trust | numeric | 7 | 0.00 % | |
| Perceived danger - ‘To what extent do you think that X would be dangerous?’ (1 = not at all; 7= very much) | Danger | numeric | 7 | 0.00 % | |
| Perceived moral competence across the three items, means-centered | Moral_Competence_c | numeric | 19 | 0.00 % | |
| Perceived moral motivation across the three items, means-centered | Moral_Motivation_c | numeric | 19 | 0.00 % | |
| Perceived moral knowledge - ‘How much moral knowledge do you think X has? That is, to what extent does X know about the moral norms we have, and understand when and why we say certain things are morally wrong’ (1 = not at all; 7= very much) | Moral_Knowledge | numeric | 7 | 0.00 % | |
| Perceived moral knowledge in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Moral_Knowledge_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral prediction ability - ‘To what extent do you think X can predict when its actions might have morally good and bad outcomes?’(1 = not at all; 7= very much) | Moral_Predict | numeric | 7 | 0.00 % | |
| Perceived moral prediction ability in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Moral_Predict_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral explanation ability - ‘To what extent do you think X can explain or justify why its action was right or wrong?’ (1 = not at all; 7= very much) | Moral_Explain | numeric | 7 | 0.00 % | |
| Perceived moral explanation ability in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Moral_Explain_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral motivation to avoid harm - ‘How much do you think that X is concerned with avoiding harm?’ (1 = not at all; 7= very much) | Moral_Harm | numeric | 7 | 0.00 % | |
| Perceived moral motivation to avoid harm in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Moral_Harm_Comparison | numeric | 7 | 0.00 % | |
| Perceived motivation to help - ‘How motivated to help others do you think X is?’ (1 = not at all; 7= very much) | Moral_Help | numeric | 7 | 0.00 % | |
| Perceived moral motivation to help others in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Moral_Help_Comparison | numeric | 7 | 0.00 % | |
| Perceived motivation for fairness - ‘How fair do you think X is? That is, how much is X motivated by concerns about equality, discrimination, ensuring it is being unbiased and impartial?’ (1 = not at all; 7= very much) | Moral_Fair | numeric | 7 | 0.00 % | |
| Perceived moral motivation for fairness in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Moral_Fair_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral competence in comparison to average person, across the three items, means-centered | Moral_Competence_Comparison_c | numeric | 19 | 0.00 % | |
| Perceived moral motivation in comparison to average person, across the three items, means-centered | Moral_Motivation_Comparison_c | numeric | 19 | 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 an expert’s assessment. Which answer best represents what you were told?’ (1 = Experts assessed an AI on its level of intelligence; 2 = Experts assessed an AI on its level of morality; 3 = Experts assessed a person on his level of intelligence; 4 = Experts assessed a person on his level of morality; 5 = Experts assessed an AI on how human-like it was) | AttentionTwo | character | 2 | 0.00 % | |
| Participant age, in numeric form | Age | numeric | 55 | 0.00 % | |
| Participant gender recoded to be male, female, non-binary/other, and not say | Gender | factor | 4 | 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 % | |
| Self-reported familiarity with AI, mean-centered | Familiarity_c | numeric | 7 | 0.00 % | |
| Number of correct PEW knowledge items across the 6 items | Pew_Correct | integer | 8 | 0.28 % | |
| Number of correct PEW knowledge items across the 5 items, mean-centered | Pew_Correct_c | numeric | 8 | 0.28 % | |
| Percentage of correct PEW knowledge items across the 6 items | Pew_Percent | numeric | 8 | 0.28 % | |
| Question from Pew on knowledge on AI | PEW1 | numeric | 4 | 0.00 % | |
| Question from Pew on knowledge on AI | PEW2 | numeric | 5 | 0.00 % | |
| Question from Pew on knowledge on AI | PEW3 | numeric | 5 | 0.00 % | |
| Question from Pew on knowledge on AI | PEW4 | numeric | 4 | 0.00 % | |
| Question from Pew on knowledge on AI | PEW5 | numeric | 6 | 0.28 % | |
| Question from Pew on knowledge on AI | PEW6 | numeric | 5 | 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 | 353 |
| Median | 205 |
| 1st and 3rd quartiles | 100; 304 |
| Min. and max. | 1; 403 |
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 | “Human” |
| Reference category | Human |
Level
Factorial variable from the condition manipulating whether agent was described as being low or high (in intelligence)
| Feature | Result |
|---|---|
| Variable type | factor |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 2 |
| Mode | “Low Intelligence” |
| Reference category | Low Intelligence |
Intelligent
Perceived intelligence - ‘How intelligent do you think X is, where intelligence should be understood here as the ability to competently and effectively achieve one’s goals, whatever they may be?’ (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 | 2; 7 |
| Min. and max. | 1; 7 |
Intelligent_Comparison
Perceived intelligence in comparison to average person - ‘Compared to an average person, how intelligent do you think X is?’(-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | -1 |
| 1st and 3rd quartiles | -2; 3 |
| Min. and max. | -3; 3 |
Moral_Competence
Combined score of moral knowledge, moral prediction, and moral explanation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | 3.33 |
| 1st and 3rd quartiles | 2.33; 5 |
| Min. and max. | 1; 7 |
Moral_Competence_Comparison
Combined scores of moral knowledge, moral prediction, and moral explanation, in comparison to average person
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | -0.67 |
| 1st and 3rd quartiles | -2; 1 |
| Min. and max. | -3; 3 |
Moral_Motivation
Combined scores of moral harm, help, and fairness
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | 4 |
| 1st and 3rd quartiles | 3; 4.67 |
| Min. and max. | 1; 7 |
Moral_Motivation_Comparison
Combined scores of moral harm, help, and fairness, in comparison to the average person
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | -0.33 |
| 1st and 3rd quartiles | -1; 0.33 |
| Min. and max. | -3; 3 |
Trust
Perceived trust - ‘To what extent do you think that X would be trustworthy?’ (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 |
Danger
Perceived danger - ‘To what extent do you think that X would be dangerous?’ (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 |
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 | 19 |
| Median | -0.34 |
| 1st and 3rd quartiles | -1.34; 1.33 |
| Min. and max. | -2.67; 3.33 |
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 | 19 |
| Median | 0.19 |
| 1st and 3rd quartiles | -0.81; 0.85 |
| Min. and max. | -2.81; 3.19 |
Moral_Knowledge
Perceived moral knowledge - ‘How much moral knowledge do you think X has? That is, to what extent does X know about the moral norms we have, and understand when and why we say certain things are morally wrong’ (1 = not at all; 7= very much)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 3 |
| 1st and 3rd quartiles | 2; 5 |
| Min. and max. | 1; 7 |
Moral_Knowledge_Comparison
Perceived moral knowledge in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | -1 |
| 1st and 3rd quartiles | -2; 1 |
| Min. and max. | -3; 3 |
Moral_Predict
Perceived moral prediction ability - ‘To what extent do you think X can predict when its actions might have morally good and bad outcomes?’(1 = not at all; 7= very much)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 3 |
| 1st and 3rd quartiles | 2; 5 |
| Min. and max. | 1; 7 |
Moral_Predict_Comparison
Perceived moral prediction ability in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | -1 |
| 1st and 3rd quartiles | -2; 1 |
| Min. and max. | -3; 3 |
Moral_Explain
Perceived moral explanation ability - ‘To what extent do you think X can explain or justify why its action was right or wrong?’ (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 | 2; 6 |
| Min. and max. | 1; 7 |
Moral_Explain_Comparison
Perceived moral explanation ability in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | -1 |
| 1st and 3rd quartiles | -2; 1 |
| Min. and max. | -3; 3 |
Moral_Harm
Perceived moral motivation to avoid harm - ‘How much do you think that X is concerned with avoiding harm?’ (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 |
Moral_Harm_Comparison
Perceived moral motivation to avoid harm in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | -2; 0 |
| Min. and max. | -3; 3 |
Moral_Help
Perceived motivation to help - ‘How motivated to help others do you think X is?’ (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 |
Moral_Help_Comparison
Perceived moral motivation to help others in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | -1; 1 |
| Min. and max. | -3; 3 |
Moral_Fair
Perceived motivation for fairness - ‘How fair do you think X is? That is, how much is X motivated by concerns about equality, discrimination, ensuring it is being unbiased and impartial?’ (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 |
Moral_Fair_Comparison
Perceived moral motivation for fairness in comparison to the average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | -1; 1 |
| Min. and max. | -3; 3 |
Moral_Competence_Comparison_c
Perceived moral competence in comparison to average person, across the three items, means-centered
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | -0.16 |
| 1st and 3rd quartiles | -1.49; 1.51 |
| Min. and max. | -2.49; 3.51 |
Moral_Motivation_Comparison_c
Perceived moral motivation in comparison to average person, across the three items, means-centered
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | 0 |
| 1st and 3rd quartiles | -0.67; 0.66 |
| Min. and max. | -2.67; 3.33 |
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 an expert’s assessment. Which answer best represents what you were told?’ (1 = Experts assessed an AI on its level of intelligence; 2 = Experts assessed an AI on its level of morality; 3 = Experts assessed a person on his level of intelligence; 4 = Experts assessed a person on his level of morality; 5 = Experts assessed an AI on how human-like it was)
| Feature | Result |
|---|---|
| Variable type | character |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 2 |
| Mode | “3” |
Age
Participant age, in numeric form
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 55 |
| Median | 40 |
| 1st and 3rd quartiles | 30; 51 |
| Min. and max. | 18; 77 |
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 |
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 |
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 | 0.22 |
| 1st and 3rd quartiles | -0.78; 1.22 |
| Min. and max. | -2.78; 3.22 |
Pew_Correct
Number of correct PEW knowledge items across the 6 items
| Feature | Result |
|---|---|
| Variable type | integer |
| Number of missing obs. | 1 (0.28 %) |
| Number of unique values | 7 |
| Median | 5 |
| 1st and 3rd quartiles | 4; 6 |
| Min. and max. | 0; 6 |
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 (0.28 %) |
| Number of unique values | 7 |
| Median | 0.22 |
| 1st and 3rd quartiles | -0.78; 1.22 |
| Min. and max. | -4.78; 1.22 |
Pew_Percent
Percentage of correct PEW knowledge items across the 6 items
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 1 (0.28 %) |
| 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 | 4 |
| 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. | 0 (0 %) |
| 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. | 0 (0 %) |
| 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. | 1 (0.28 %) |
| 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 |
Report generation information:
Created by: Jim Everett (username:
jimeverett).Report creation time: Sun Aug 17 2025 11:56:36
Report was run from directory:
/Users/jimeverett/Documents/Academic/Research/Current Projects/AI Orthogonality/Orthogonality Data Analysis/Data PreparationdataReporter 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_5_Data_Summary, render = TRUE, mode = c("summarize", "visualize"), smartNum = FALSE, file = "Study_5_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 5 Codebook", add.codebook = TRUE, smart.order = FALSE)