Orthogonality Study 4 Codebook
Autogenerated data summary from dataReporter
2025-08-17 11:56:19.066217
Data report overview
The dataset examined has the following dimensions:
| Feature | Result |
|---|---|
| Number of observations | 739 |
| Number of variables | 29 |
Codebook summary table
| Label | Variable | Class | # unique values | Missing | Description |
|---|---|---|---|---|---|
| Participant number, auto-assigned based on rows in data preparation | Participant | integer | 739 | 0.00 % | |
| Factorial variable from the condition manipulating whether the change was morality or intelligence | Trait | factor | 2 | 0.00 % | |
| Factorial variable from the condition manipulating whether the agent is human or AI | Agent | factor | 2 | 0.00 % | |
| Perceived intelligence, prior to the augmentation - ‘How generally intelligent do you think X is?’ (1 = not at all; 7= very much) | Pre_Intelligent | numeric | 7 | 0.00 % | |
| Perceived intelligence in comparison to average person, prior to the augmentation - ’Compared to an average person, how generally 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) | Pre_Intelligent_Comparison | numeric | 7 | 0.00 % | |
| Perceived morality, prior to the augmentation - ‘In general, how moral do you think X is?’ (1 = not at all; 7= very much) | Pre_Moral | numeric | 7 | 0.00 % | |
| Perceived morality, prior to the augmentation - ’Compared to an average person, how moral do you think X is?’ (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Pre_Moral_Comparison | numeric | 7 | 0.00 % | |
| Perceived danger, prior to the augmentation - ‘To what extent do you think that X would be dangerous?’ (1 = not at all; 7= very much) | Pre_Danger | numeric | 7 | 0.00 % | |
| Perceived trustworthiness, prior to the augmentation - ‘To what extent do you think that X would be trustworthy?’ (1 = not at all; 7= very much) | Pre_Trust | numeric | 7 | 0.00 % | |
| Perceived intelligence, after the augmentation - ‘As a result of this new breakthrough, how generally intelligent do you think X is now?’ (1 = not at all; 7= very much) | Post_Intelligent | numeric | 7 | 0.00 % | |
| Perceived intelligence in comparison to average person, after the augmentation - ‘As a result of this new breakthrough, compared to an average person, how generally intelligent do you think X is now?’ (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) | Post_Intelligent_Comparison | numeric | 7 | 0.00 % | |
| Perceived morality, after the augmentation - ‘As a result of this new breakthrough, how moral do you think X is now?’ (1 = not at all; 7= very much) | Post_Moral | numeric | 7 | 0.00 % | |
| Perceived morality in comparison to average person, after the augmentation - ‘As a result of this new breakthrough, compared to an average person, how moral do you think X is now?’ (1 = not at all; 7= very much) | Post_Moral_Comparison | numeric | 7 | 0.00 % | |
| Perceived danger, after the augmentation - ‘As a result of this new breakthrough, to what extent do you think that X would now be dangerous?’ (1 = not at all; 7= very much) | Post_Danger | numeric | 7 | 0.00 % | |
| Perceived trustworthiness, after the augmentation - ‘As a result of this new breakthrough, to what extent do you think that X would now be trustworthy?’ (1 = not at all; 7= very much) | Post_Trust | numeric | 7 | 0.00 % | |
| Attention Check 1 (Tiktok) | AttentionCheck | numeric | 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 | numeric | 4 | 0.00 % | |
| Participant age, in numeric form | Age | numeric | 61 | 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, means-centered | Familiarity_c | numeric | 7 | 0.00 % | |
| Number of correct PEW knowledge items across the 6 items | Pew_Correct | integer | 8 | 0.95 % | |
| Percentage of correct PEW knowledge items across the 6 items | Pew_Percent | numeric | 8 | 0.95 % | |
| Question from Pew on knowledge on AI | PEW1 | numeric | 5 | 0.14 % | |
| Question from Pew on knowledge on AI | PEW2 | numeric | 6 | 0.41 % | |
| Question from Pew on knowledge on AI | PEW3 | numeric | 6 | 0.27 % | |
| Question from Pew on knowledge on AI | PEW4 | numeric | 5 | 0.14 % | |
| Question from Pew on knowledge on AI | PEW5 | numeric | 6 | 0.14 % | |
| Question from Pew on knowledge on AI | PEW6 | numeric | 6 | 0.27 % |
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 | 739 |
| Median | 423 |
| 1st and 3rd quartiles | 212.5; 630.5 |
| Min. and max. | 1; 848 |
Trait
Factorial variable from the condition manipulating whether the change was morality or intelligence
| Feature | Result |
|---|---|
| Variable type | factor |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 2 |
| Mode | “Increased Intelligence” |
| Reference category | Increased Morality |
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 |
Pre_Intelligent
Perceived intelligence, prior to the augmentation - ‘How generally intelligent 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 | 3 |
| 1st and 3rd quartiles | 2; 4 |
| Min. and max. | 1; 7 |
Pre_Intelligent_Comparison
Perceived intelligence in comparison to average person, prior to the augmentation - ’Compared to an average person, how generally 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; 0 |
| Min. and max. | -3; 3 |
Pre_Moral
Perceived morality, prior to the augmentation - ‘In general, how moral 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 | 2 |
| 1st and 3rd quartiles | 2; 4 |
| Min. and max. | 1; 7 |
Pre_Moral_Comparison
Perceived morality, prior to the augmentation - ’Compared to an average person, how moral 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 | -2 |
| 1st and 3rd quartiles | -2; 0 |
| Min. and max. | -3; 3 |
Pre_Danger
Perceived danger, prior to the augmentation - ‘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 | 5 |
| 1st and 3rd quartiles | 3; 6 |
| Min. and max. | 1; 7 |
Pre_Trust
Perceived trustworthiness, prior to the augmentation - ‘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 | 3 |
| 1st and 3rd quartiles | 2; 4 |
| Min. and max. | 1; 7 |
Post_Intelligent
Perceived intelligence, after the augmentation - ‘As a result of this new breakthrough, how generally intelligent do you think X is now?’ (1 = not at all; 7= very much)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 6 |
| 1st and 3rd quartiles | 4; 7 |
| Min. and max. | 1; 7 |
Post_Intelligent_Comparison
Perceived intelligence in comparison to average person, after the augmentation - ‘As a result of this new breakthrough, compared to an average person, how generally intelligent do you think X is now?’ (-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 | 0; 2 |
| Min. and max. | -3; 3 |
Post_Moral
Perceived morality, after the augmentation - ‘As a result of this new breakthrough, how moral do you think X is now?’ (1 = not at all; 7= very much)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 5 |
| 1st and 3rd quartiles | 4; 6 |
| Min. and max. | 1; 7 |
Post_Moral_Comparison
Perceived morality in comparison to average person, after the augmentation - ‘As a result of this new breakthrough, compared to an average person, how moral do you think X is now?’ (1 = not at all; 7= very much)
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | 0; 2 |
| Min. and max. | -3; 3 |
Post_Danger
Perceived danger, after the augmentation - ‘As a result of this new breakthrough, to what extent do you think that X would now 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 |
Post_Trust
Perceived trustworthiness, after the augmentation - ‘As a result of this new breakthrough, to what extent do you think that X would now 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 | 5 |
| 1st and 3rd quartiles | 4; 5.5 |
| Min. and max. | 1; 7 |
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 | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 4 |
| Median | 2 |
| 1st and 3rd quartiles | 1.5; 3 |
| Min. and max. | 1; 4 |
Age
Participant age, in numeric form
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 61 |
| Median | 43 |
| 1st and 3rd quartiles | 33; 54 |
| Min. and max. | 18; 86 |
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, means-centered
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0.2 |
| 1st and 3rd quartiles | -0.8; 1.2 |
| Min. and max. | -2.8; 3.2 |
Pew_Correct
Number of correct PEW knowledge items across the 6 items
| Feature | Result |
|---|---|
| Variable type | integer |
| Number of missing obs. | 7 (0.95 %) |
| 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. | 7 (0.95 %) |
| 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. | 1 (0.14 %) |
| 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. | 3 (0.41 %) |
| 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. | 2 (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. | 1 (0.14 %) |
| 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 (0.14 %) |
| 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. | 2 (0.27 %) |
| 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 11:56:19
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_4_Data_Summary, render = TRUE, mode = c("summarize", "visualize"), smartNum = FALSE, file = "Study_4_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 4 Codebook", add.codebook = TRUE, smart.order = FALSE)