Orthogonality Study 6 Codebook
Autogenerated data summary from dataReporter
2025-08-17 11:56:54.292964
Data report overview
The dataset examined has the following dimensions:
| Feature | Result |
|---|---|
| Number of observations | 362 |
| Number of variables | 57 |
Codebook summary table
| Label | Variable | Class | # unique values | Missing | Description |
|---|---|---|---|---|---|
| Participant number, auto-assigned based on rows in data preparation | Participant | integer | 362 | 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 | Pre_Intelligent | numeric | 7 | 0.00 % | |
| Perceived intelligence in comparison to average person, prior to the augmentation | Pre_Intelligent_Comparison | numeric | 7 | 0.00 % | |
| Combined score of moral knowledge, moral prediction, and moral explanation, prior to the augmentation | Pre_Moral_Competence | numeric | 16 | 0.00 % | |
| Combined score of moral knowledge, moral prediction, and moral explanation, in comparison to average person, prior to the augmentation | Pre_Moral_Competence_Comparison | numeric | 16 | 0.00 % | |
| Combined scores of moral harm, help, and fairness, prior to the augmentation | Pre_Moral_Motivation | numeric | 18 | 0.00 % | |
| Combined scores of moral harm, help, and fairness in comparison to average person, prior to the augmentation | Pre_Moral_Motivation_Comparison | numeric | 16 | 0.00 % | |
| Perceived trust, prior to the augmentation - ‘To what extent do you think that X would be trustworthy?’ | Pre_Trust | numeric | 7 | 0.00 % | |
| Perceived danger, prior to the augmentation - ‘To what extent do you think that X would be dangerous?’ | Pre_Danger | numeric | 7 | 0.00 % | |
| Perceived intelligence, after the augmentation | Post_Intelligent | numeric | 6 | 0.00 % | |
| Perceived intelligence in comparison to average person, after the augmentation | Post_Intelligent_Comparison | numeric | 6 | 0.00 % | |
| Combined score of moral knowledge, moral prediction, and moral explanation, after the augmentation | Post_Moral_Competence | numeric | 19 | 0.00 % | |
| Combined score of moral knowledge, moral prediction, and moral explanation, in comparison to average person, after the augmentation | Post_Moral_Competence_Comparison | numeric | 19 | 0.00 % | |
| Combined scores of moral harm, help, and fairness, after the augmentation | Post_Moral_Motivation | numeric | 19 | 0.00 % | |
| Combined scores of moral harm, help, and fairness, in comparison to average person, after the augmentation | Post_Moral_Motivation_Comparison | numeric | 18 | 0.00 % | |
| Perceived trust, after the augmentation - ‘To what extent do you think that X would be trustworthy?’ | Post_Trust | numeric | 7 | 0.00 % | |
| Perceived danger, after to the augmentation - ‘To what extent do you think that X would be dangerous?’ | Post_Danger | numeric | 7 | 0.00 % | |
| Perceived moral knowledge, prior to the augmentation - ‘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?’ | Pre_Moral_Knowledge | numeric | 7 | 0.00 % | |
| Perceived moral knowledge in comparison to the average person, prior to the augmentation | Pre_Moral_Knowledge_Comparison | numeric | 6 | 0.00 % | |
| Perceived moral prediction ability, prior to the augmentation - ‘To what extent do you think X can predict when its actions might have morally good and bad outcomes?’ | Pre_Moral_Predict | numeric | 7 | 0.00 % | |
| Perceived moral prediction ability in comparison to the average person, prior to the augmentation | Pre_Moral_Predict_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral explanation ability, prior to the augmentation - ‘To what extent do you think X can explain or justify why its action was right or wrong?’ | Pre_Moral_Explain | numeric | 7 | 0.00 % | |
| Perceived moral explanation ability in comparison to the average person, prior to the augmentation | Pre_Moral_Explain_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral motivation to avoid harm, prior to the augmentation - ‘How much do you think that X is concerned with avoiding harm?’ | Pre_Moral_Harm | numeric | 7 | 0.00 % | |
| Perceived moral motivation to avoid harm in comparison to the average person, prior to the augmentation | Pre_Moral_Harm_Comparison | numeric | 7 | 0.00 % | |
| Perceived motivation to help, prior to the augmentation - ‘How motivated to help others do you think X is?’ | Pre_Moral_Help | numeric | 7 | 0.00 % | |
| Perceived moral motivation to help others in comparison to the average person, prior to the augmentation | Pre_Moral_Help_Comparison | numeric | 7 | 0.00 % | |
| Perceived motivation for fairness, prior to the augmentation - ‘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?’ | Pre_Moral_Fair | numeric | 7 | 0.00 % | |
| Perceived moral motivation for fairness in comparison to the average person, prior to the augmentation | Pre_Moral_Fair_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral knowledge, after the augmentation - ‘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?’ | Post_Moral_Knowledge | numeric | 7 | 0.00 % | |
| Perceived moral knowledge in comparison to the average person, after the augmentation | Post_Moral_Knowledge_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral prediction ability, after the augmentation - ‘To what extent do you think X can predict when its actions might have morally good and bad outcomes?’ | Post_Moral_Predict | numeric | 7 | 0.00 % | |
| Perceived moral prediction ability in comparison to the average person, after the augmentation | Post_Moral_Predict_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral explanation ability, after the augmentation - ‘To what extent do you think X can explain or justify why its action was right or wrong?’ | Post_Moral_Explain | numeric | 7 | 0.00 % | |
| Perceived moral explanation ability in comparison to the average person, after the augmentation | Post_Moral_Explain_Comparison | numeric | 7 | 0.00 % | |
| Perceived moral motivation to avoid harm, after the augmentation - ‘How much do you think that X is concerned with avoiding harm?’ | Post_Moral_Harm | numeric | 7 | 0.00 % | |
| Perceived moral motivation to avoid harm in comparison to the average person, after the augmentation | Post_Moral_Harm_Comparison | numeric | 7 | 0.00 % | |
| Perceived motivation to help, after the augmentation - ‘How motivated to help others do you think X is?’ | Post_Moral_Help | numeric | 8 | 0.28 % | |
| Perceived moral motivation to help others in comparison to the average person, after the augmentation | Post_Moral_Help_Comparison | numeric | 7 | 0.00 % | |
| Perceived motivation for fairness, after the augmentation - ‘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?’ | Post_Moral_Fair | numeric | 7 | 0.00 % | |
| Perceived moral motivation for fairness in comparison to the average person, after the augmentation | Post_Moral_Fair_Comparison | 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 | 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 % | |
| 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 | 7 | 0.00 % | |
| Number of correct PEW knowledge items across the 6 items, means-centered | Pew_Correct_c | numeric | 7 | 0.00 % | |
| Percentage of correct PEW knowledge items across the 6 items | Pew_Percent | numeric | 7 | 0.00 % | |
| Question from Pew on knowledge on AI | PEW1 | numeric | 3 | 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 | 5 | 0.00 % | |
| Question from Pew on knowledge on AI | PEW5 | numeric | 4 | 0.00 % | |
| 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 | 362 |
| Median | 200.5 |
| 1st and 3rd quartiles | 105.25; 297.75 |
| Min. and max. | 1; 395 |
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 |
Pre_Intelligent
Perceived intelligence, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 2 |
| 1st and 3rd quartiles | 2; 2 |
| Min. and max. | 1; 7 |
Pre_Intelligent_Comparison
Perceived intelligence in comparison to average person, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | -2 |
| 1st and 3rd quartiles | -3; -1 |
| Min. and max. | -3; 3 |
Pre_Moral_Competence
Combined score of moral knowledge, moral prediction, and moral explanation, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 16 |
| Median | 2.67 |
| 1st and 3rd quartiles | 2; 3.33 |
| Min. and max. | 1; 6 |
Pre_Moral_Competence_Comparison
Combined score of moral knowledge, moral prediction, and moral explanation, in comparison to average person, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 16 |
| Median | -1.33 |
| 1st and 3rd quartiles | -2.33; -0.67 |
| Min. and max. | -3; 2 |
Pre_Moral_Motivation
Combined scores of moral harm, help, and fairness, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 18 |
| Median | 3.67 |
| 1st and 3rd quartiles | 2.67; 4.33 |
| Min. and max. | 1; 7 |
Pre_Moral_Motivation_Comparison
Combined scores of moral harm, help, and fairness in comparison to average person, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 16 |
| Median | -0.67 |
| 1st and 3rd quartiles | -1.67; 0 |
| Min. and max. | -3; 2 |
Pre_Trust
Perceived trust, prior to the augmentation - ‘To what extent do you think that X would be trustworthy?’
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 4 |
| 1st and 3rd quartiles | 2; 5 |
| Min. and max. | 1; 7 |
Pre_Danger
Perceived danger, prior to the augmentation - ‘To what extent do you think that X would be dangerous?’
| 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_Intelligent
Perceived intelligence, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 6 |
| Median | 7 |
| 1st and 3rd quartiles | 6; 7 |
| Min. and max. | 1; 7 |
Post_Intelligent_Comparison
Perceived intelligence in comparison to average person, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 6 |
| Median | 3 |
| 1st and 3rd quartiles | 2; 3 |
| Min. and max. | -3; 3 |
Post_Moral_Competence
Combined score of moral knowledge, moral prediction, and moral explanation, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | 5.33 |
| 1st and 3rd quartiles | 4.42; 6.33 |
| Min. and max. | 1; 7 |
Post_Moral_Competence_Comparison
Combined score of moral knowledge, moral prediction, and moral explanation, in comparison to average person, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | 1 |
| 1st and 3rd quartiles | 0; 2 |
| Min. and max. | -3; 3 |
Post_Moral_Motivation
Combined scores of moral harm, help, and fairness, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 19 |
| Median | 4.67 |
| 1st and 3rd quartiles | 4; 5.33 |
| Min. and max. | 1; 7 |
Post_Moral_Motivation_Comparison
Combined scores of moral harm, help, and fairness, in comparison to average person, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 18 |
| Median | 0.33 |
| 1st and 3rd quartiles | 0; 1 |
| Min. and max. | -3; 3 |
Post_Trust
Perceived trust, after the augmentation - ‘To what extent do you think that X would be trustworthy?’
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 4 |
| 1st and 3rd quartiles | 4; 5 |
| Min. and max. | 1; 7 |
Post_Danger
Perceived danger, after to the augmentation - ‘To what extent do you think that X would be dangerous?’
| 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 |
Pre_Moral_Knowledge
Perceived moral knowledge, prior to the augmentation - ‘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?’
| 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_Moral_Knowledge_Comparison
Perceived moral knowledge in comparison to the average person, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 6 |
| Median | -1 |
| 1st and 3rd quartiles | -3; 0 |
| Min. and max. | -3; 3 |
Pre_Moral_Predict
Perceived moral prediction ability, prior to the augmentation - ‘To what extent do you think X can predict when its actions might have morally good and bad outcomes?’
| 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_Moral_Predict_Comparison
Perceived moral prediction ability in comparison to the average person, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | -1 |
| 1st and 3rd quartiles | -3; -1 |
| Min. and max. | -3; 3 |
Pre_Moral_Explain
Perceived moral explanation ability, prior to the augmentation - ‘To what extent do you think X can explain or justify why its action was right or wrong?’
| 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_Moral_Explain_Comparison
Perceived moral explanation ability in comparison to the average person, prior to the augmentation
| 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 |
Pre_Moral_Harm
Perceived moral motivation to avoid harm, prior to the augmentation - ‘How much do you think that X is concerned with avoiding harm?’
| 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_Moral_Harm_Comparison
Perceived moral motivation to avoid harm in comparison to the average person, prior to the augmentation
| 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_Help
Perceived motivation to help, prior to the augmentation - ‘How motivated to help others do you think X is?’
| 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 |
Pre_Moral_Help_Comparison
Perceived moral motivation to help others in comparison to the average person, prior to the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | -1; 0 |
| Min. and max. | -3; 3 |
Pre_Moral_Fair
Perceived motivation for fairness, prior to the augmentation - ‘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?’
| 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_Moral_Fair_Comparison
Perceived moral motivation for fairness in comparison to the average person, prior to the augmentation
| 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 |
Post_Moral_Knowledge
Perceived moral knowledge, after the augmentation - ‘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?’
| 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_Knowledge_Comparison
Perceived moral knowledge in comparison to the average person, after the augmentation
| 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_Predict
Perceived moral prediction ability, after the augmentation - ‘To what extent do you think X can predict when its actions might have morally good and bad outcomes?’
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 5 |
| 1st and 3rd quartiles | 5; 6 |
| Min. and max. | 1; 7 |
Post_Moral_Predict_Comparison
Perceived moral prediction ability in comparison to the average person, after the augmentation
| 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_Explain
Perceived moral explanation ability, after the augmentation - ‘To what extent do you think X can explain or justify why its action was right or wrong?’
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 6 |
| 1st and 3rd quartiles | 5; 7 |
| Min. and max. | 1; 7 |
Post_Moral_Explain_Comparison
Perceived moral explanation ability in comparison to the average person, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 1 |
| 1st and 3rd quartiles | 0; 3 |
| Min. and max. | -3; 3 |
Post_Moral_Harm
Perceived moral motivation to avoid harm, after the augmentation - ‘How much do you think that X is concerned with avoiding harm?’
| 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_Harm_Comparison
Perceived moral motivation to avoid harm in comparison to the average person, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | 0; 1 |
| Min. and max. | -3; 3 |
Post_Moral_Help
Perceived motivation to help, after the augmentation - ‘How motivated to help others do you think X is?’
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 1 (0.28 %) |
| Number of unique values | 7 |
| Median | 5 |
| 1st and 3rd quartiles | 4; 6 |
| Min. and max. | 1; 7 |
Post_Moral_Help_Comparison
Perceived moral motivation to help others in comparison to the average person, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | 0; 1 |
| Min. and max. | -3; 3 |
Post_Moral_Fair
Perceived motivation for fairness, after the augmentation - ‘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?’
| 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_Fair_Comparison
Perceived moral motivation for fairness in comparison to the average person, after the augmentation
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0 |
| 1st and 3rd quartiles | 0; 1 |
| Min. and max. | -3; 3 |
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 | 2 |
| Median | 3 |
| 1st and 3rd quartiles | 1; 3 |
| Min. and max. | 1; 3 |
Age
Participant age, in numeric form
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 56 |
| Median | 40 |
| 1st and 3rd quartiles | 31; 52 |
| Min. and max. | 19; 82 |
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 | 0.26 |
| 1st and 3rd quartiles | -0.74; 1.26 |
| Min. and max. | -2.74; 3.26 |
Pew_Correct
Number of correct PEW knowledge items across the 6 items
| Feature | Result |
|---|---|
| Variable type | integer |
| Number of missing obs. | 0 (0 %) |
| 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 6 items, means-centered
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| Number of unique values | 7 |
| Median | 0.17 |
| 1st and 3rd quartiles | -0.83; 1.17 |
| Min. and max. | -4.83; 1.17 |
Pew_Percent
Percentage of correct PEW knowledge items across the 6 items
| Feature | Result |
|---|---|
| Variable type | numeric |
| Number of missing obs. | 0 (0 %) |
| 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 | 3 |
| 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 | 5 |
| 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 | 4 |
| 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 11:56:54
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_6_Data_Summary, render = TRUE, mode = c("summarize", "visualize"), smartNum = FALSE, file = "Study_6_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 6 Codebook", add.codebook = TRUE, smart.order = FALSE)