A cross-cultural dataset from the Many-Analysts Religion Project (MARP), which investigated the relationship between religiosity and well-being across 24 countries and diverse religious traditions.
Usage
data(marp)Format
A data frame with 10,535 rows (participants) and 48 variables:
- subject
Unique subject identifier (integer).
- country
Country of residence (character string).
- rel_1
Importance of religion in daily life (0–10 scale).
- rel_2
Frequency of religious service attendance (ordinal).
- rel_3
Self-rated religiosity (0–10 scale).
- rel_4
Belief in God (binary: yes/no).
- rel_5
Prayer frequency (ordinal).
- rel_6
Bible/study frequency (ordinal).
- rel_7
Religious upbringing (binary: yes/no).
- rel_8
Current religious denomination (categorical).
- rel_9
Change in religiosity over lifetime (ordinal).
- cnorm_1
Perceived cultural norm: importance of religious lifestyle for average person in country (0–10).
- cnorm_2
Perceived cultural norm: importance of belief in God for average person in country (0–10).
- wb_gen_1
Overall life satisfaction (1–5 Likert).
- wb_gen_2
Overall happiness (1–5 Likert).
- wb_phys_1
Energy level (1–5).
- wb_phys_2
Sleep quality (1–5).
- wb_phys_3
Appetite (1–5).
- wb_phys_4
Physical pain/discomfort (1–5).
- wb_phys_5
General health (1–5).
- wb_phys_6
Exercise frequency (1–5).
- wb_phys_7
Illness burden (1–5).
- wb_psych_1
Positive affect (1–5).
- wb_psych_2
Negative affect (reverse coded; 1–5).
- wb_psych_3
Meaning in life (1–5).
- wb_psych_4
Purpose in life (1–5).
- wb_psych_5
Hopefulness (1–5).
- wb_psych_6
Anxiety (reverse coded; 1–5).
- wb_soc_1
Social support (1–5).
- wb_soc_2
Loneliness (reverse coded; 1–5).
- wb_soc_3
Community belonging (1–5).
- wb_overall_mean
Mean of all well-being items (numeric).
- wb_phys_mean
Mean of physical well-being items (numeric).
- wb_psych_mean
Mean of psychological well-being items (numeric).
- wb_soc_mean
Mean of social well-being items (numeric).
- age
Age in years (integer).
- gender
Self-reported gender (character: e.g., "Male", "Female", "Other").
- ses
Socioeconomic status composite (numeric).
- education
Highest education level completed (ordinal integer).
- ethnicity
Self-reported ethnicity (character).
- denomination
Religious denomination (character).
- gdp
GDP per capita (PPP, USD) for country (numeric).
- gdp_scaled
Scaled GDP (mean = 0, sd = 1) used in analyses (numeric).
- sample_type
Recruitment method: e.g., "online panel", "student sample" (character).
- compensation
Type of compensation: e.g., "monetary", "entry into lottery" (character).
- attention_check
Score on embedded attention check task (integer).
Source
Hoogeveen, S., Sarafoglou, A., Aczel, B., et al. (2022). A many-analysts approach to the relation between religiosity and well-being. Religion, Brain & Behavior. doi:10.1080/2153599X.2023.2254980
Examples
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
data(marp)
# Dimensions
dim(marp)
#> [1] 10535 46
# Quick overview
if (requireNamespace("dplyr", quietly = TRUE)) {
library(dplyr)
marp |>
group_by(country) |>
summarise(
mean_wb = mean(wb_overall_mean, na.rm = TRUE),
.groups = "drop"
)
}
#> # A tibble: 24 × 2
#> country mean_wb
#> <chr> <dbl>
#> 1 Australia 3.68
#> 2 Belgium 3.77
#> 3 Brazil 3.54
#> 4 Canada 3.59
#> 5 Chile 3.71
#> 6 China 3.61
#> 7 Croatia 3.82
#> 8 Denmark 3.77
#> 9 France 3.59
#> 10 Germany 3.83
#> # ℹ 14 more rows