Data report overview

The dataset examined has the following dimensions:

Feature Result
Number of observations 695
Number of variables 15

Codebook summary table

Label Variable Class # unique values Missing Description
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 the trait referenced was described as a high or low level Level factor 2 0.00 %
Factorial variable from the condition manipulating whether the trait referenced was morality or intelligence Trait factor 2 0.00 %
Perceived intelligence - ‘How intelligent do you think X is?’ (1 = not at all; 7= very much) Intelligent numeric 7 0.00 %
Perceived intelligence in comparison to average person (-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 %
Perceived morality - ‘How moral do you think X is?’ (1 = not at all; 7= very much) Moral numeric 7 0.00 %
Perceived morality in comparison to average person (-3 = much less than average person; 0 = equal to the average person; 3 = much more than an average person) Moral_Comparison numeric 7 0.00 %
Perceived trustworthiness - ‘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 %
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 4 0.00 %
Participant age, in numeric form Age numeric 60 0.14 %
Participant gender recoded to as a factor to be male, female, non-binary/other, and not say Gender character 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) (*Only asked for participants in the AI conditions, in this study) Familiarity numeric 8 50.22 %
Self-reported familiarity with AI, means-centered Familiarity_c numeric 8 50.22 %

Variable list

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 the trait referenced was described as a high or low level

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

Trait

Factorial variable from the condition manipulating whether the trait referenced was morality or intelligence

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

Intelligent

Perceived intelligence - ‘How 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 5
1st and 3rd quartiles 3; 6
Min. and max. 1; 7

Intelligent_Comparison

Perceived intelligence in comparison to 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 -1; 2
Min. and max. -3; 3

Moral

Perceived morality - ‘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 4
1st and 3rd quartiles 2; 6
Min. and max. 1; 7

Moral_Comparison

Perceived morality in comparison to 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; 1
Min. and max. -3; 3

Trust

Perceived trustworthiness - ‘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 2; 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 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 4
Mode “2”

Age

Participant age, in numeric form

Feature Result
Variable type numeric
Number of missing obs. 1 (0.14 %)
Number of unique values 59
Median 38
1st and 3rd quartiles 30; 50
Min. and max. 18; 83

Gender

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

Feature Result
Variable type character
Number of missing obs. 0 (0 %)
Number of unique values 4
Mode “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) (Only asked for participants in the AI conditions, in this study)*

Feature Result
Variable type numeric
Number of missing obs. 349 (50.22 %)
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. 349 (50.22 %)
Number of unique values 7
Median -0.59
1st and 3rd quartiles -0.59; 1.41
Min. and max. -2.59; 3.41

Report generation information:

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

  • Report creation time: Sun Aug 17 2025 11:55:42

  • 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_3_Data_Summary, render = TRUE, mode = c("summarize", "visualize"), smartNum = FALSE, file = "Study_1b_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 3 Codebook", add.codebook = TRUE, smart.order = FALSE)