Data report overview

The dataset examined has the following dimensions:

Feature Result
Number of observations 331
Number of variables 13

Codebook summary table

Label Variable Class # unique values Missing Description
Factorial variable manipulating whether the text described AI as likely to be more intelligent than humans, or never as intelligent as humans Condition factor 2 0.00 %
Perceived intelligence - ‘In general, how intelligent do you think AI will be in the near future? That is, how much do you think that the AI can reason, problem solve, and acquire new knowledge?’ (1 = not at all; 7= very much) Intelligent numeric 7 0.00 %
Perceived intelligence in comparison to average person - ‘Compared to a human, how intelligent do you think AI will be in the near future?’ (-3 = much less than human; 0 = equal to a human; 3 = much more than a human) Intelligent_Comparison numeric 7 0.00 %
Perceived morality - ’In general, how moral do you think AI will be in the near future? That is, how much do you think that AI will support the same kinds of ethical values and behaviors that humans agree are important?’ (1 = not at all; 7= very much) Moral numeric 7 0.00 %
Perceived morality in comparison to average person - ‘Compared to a human, how moral do you think AI will be in the near future?’ (-3 = much less than human; 0 = equal to a human; 3 = much more than a human) Moral_Comparison numeric 6 0.00 %
Perceived trust - ‘To what extent do you think that near-term AI would be trustworthy?’ (1 = not at all; 7= very much) Trust numeric 7 0.00 %
Perceived danger - ‘To what extent do you think that near-term AI is likely to present a danger to us?’ (1 = not at all; 7= very much) Danger numeric 7 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 what experts predict about AI. Which answer best represents what you were told?’ (1 = AI will become more intelligent than humans; 2) AI will never become intelligent in the way humans are; 3) AI will take people’s jobs; 4) AI requires very fast hardware; 5) AI can be run easily on mobiles) 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 3 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 %

Variable list

Condition

Factorial variable manipulating whether the text described AI as likely to be more intelligent than humans, or never as intelligent as humans

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 - ‘In general, how intelligent do you think AI will be in the near future? That is, how much do you think that the AI can reason, problem solve, and acquire new knowledge?’ (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; 7
Min. and max. 1; 7

Intelligent_Comparison

Perceived intelligence in comparison to average person - ‘Compared to a human, how intelligent do you think AI will be in the near future?’ (-3 = much less than human; 0 = equal to a human; 3 = much more than a human)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 7
Median 1
1st and 3rd quartiles -1; 2
Min. and max. -3; 3

Moral

Perceived morality - ’In general, how moral do you think AI will be in the near future? That is, how much do you think that AI will support the same kinds of ethical values and behaviors that humans agree are important?’ (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

Moral_Comparison

Perceived morality in comparison to average person - ‘Compared to a human, how moral do you think AI will be in the near future?’ (-3 = much less than human; 0 = equal to a human; 3 = much more than a human)

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 6
Median -1
1st and 3rd quartiles -2; -1
Min. and max. -3; 2

Trust

Perceived trust - ‘To what extent do you think that near-term AI 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 3; 4.5
Min. and max. 1; 7

Danger

Perceived danger - ‘To what extent do you think that near-term AI is likely to present a danger to us?’ (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

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 what experts predict about AI. Which answer best represents what you were told?’ (1 = AI will become more intelligent than humans; 2) AI will never become intelligent in the way humans are; 3) AI will take people’s jobs; 4) AI requires very fast hardware; 5) AI can be run easily on mobiles)

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

Age

Participant age, in numeric form

Feature Result
Variable type numeric
Number of missing obs. 0 (0 %)
Number of unique values 56
Median 38
1st and 3rd quartiles 30; 49.5
Min. and max. 20; 88

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 3
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 3
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 -0.57
1st and 3rd quartiles -0.57; 1.43
Min. and max. -2.57; 3.43

Report generation information:

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

  • Report creation time: Sun Aug 17 2025 11:53:53

  • 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_1a_Data_Summary, render = TRUE, mode = c("summarize", "visualize"), smartNum = FALSE, file = "Study_1a_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 1a Codebook", add.codebook = TRUE, smart.order = FALSE)