williams: Stereotyping of high-wealth individuals across ecologies

Description

Data from a study by Williams et al. testing whether high-wealth individuals are perceived as having faster life history strategies (e.g., more impulsive, less invested) when associated with "desperate" ecological conditions compared to "hopeful" ones.

Usage

data(williams)

Arguments

Format

A data frame with 224 rows (one per participant) and 25 variables:

subject: Unique subject identifier (integer).
ecology: Experimental condition: "Desperate" or "Hopeful" (character).
age: Participant's age in years (numeric).
gender: Self-reported gender: 1 = Male, 2 = Female (numeric); may be recoded as factor.
duration_in_seconds: Time taken to complete the survey (numeric).
attention_1: First attention check response: 1 = correct, 0 = incorrect (numeric).
attention_2: Second attention check response: 1 = correct, 0 = incorrect (numeric).
SexUnres_1: Perceived sexual unrestrictedness: "likely to have short-term relationships" (1–7 Likert).
SexUnres_2: "likely to engage in casual sex" (1–7).
SexUnres_3: "not interested in long-term commitment" (1–7).
SexUnres_4_r: "faithful to romantic partners" — reverse-coded (1–7).
SexUnres_5_r: "committed in relationships" — reverse-coded (1–7).
Impuls_1: "acts without thinking" (1–7).
Impuls_2_r: "thinks carefully before acting" — reverse-coded (1–7).
Impul_3_r: "plans ahead" — reverse-coded (1–7).
Opport_1: "opportunities for long-term planning exist" (1–7).
Opport_2: "can save money for the future" (1–7).
Opport_3: "can make career plans" (1–7).
Opport_4: "can plan for retirement" (1–7).
Opport_5: "has control over future outcomes" (1–7).
Opport_6_r: "life is unpredictable" — reverse-coded (1–7).
InvEdu_1_r: "invests in education" — reverse-coded (1–7).
InvEdu_2_r: "values academic achievement" — reverse-coded (1–7).
InvChild_1: "invests time and resources in children" (1–7).
InvChild_2_r: "neglects parental responsibilities" — reverse-coded (1–7).

Examples

Run this code

data(williams)
str(williams)
table(williams$ecology)

# Compute composite scores (example)
if (requireNamespace("dplyr", quietly = TRUE)) {
  library(dplyr)

  williams_composites <- williams |>
    rowwise() |>
    mutate(
      sexual_unrestrictedness = mean(c(SexUnres_1, SexUnres_2, SexUnres_3,
                                     8 - SexUnres_4_r, 8 - SexUnres_5_r), na.rm = TRUE),
      impulsivity = mean(c(Impuls_1, 8 - Impuls_2_r, 8 - Impul_3_r), na.rm = TRUE),
      opportunity = mean(c(Opport_1, Opport_2, Opport_3, Opport_4, Opport_5,
                           8 - Opport_6_r), na.rm = TRUE),
      investment = mean(c(8 - InvEdu_1_r, 8 - InvEdu_2_r, InvChild_1,
                          8 - InvChild_2_r), na.rm = TRUE)
    ) |>
    ungroup()

  summary(williams_composites[ , c("sexual_unrestrictedness", "impulsivity")])
}

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