A synthetic dataset modeled after large-scale social surveys with demographics, attitudes, and survey design features. Created for testing and demonstration purposes without licensing concerns.
Format
A data frame with 2,500 rows and 16 variables:
- id
Unique identifier (1-2500)
- age
Age in years (18-95)
- gender
Gender (Male, Female)
- region
Region (East, West)
- education
Education level (Basic Secondary, Intermediate Secondary, Academic Secondary, University)
- income
Monthly household income in EUR (800-15000)
- employment
Employment status (Student, Employed, Unemployed, Retired, Other)
- political_orientation
Political orientation (1=very left to 5=very right)
- environmental_concern
Environmental concern (1=not concerned to 5=very concerned)
- life_satisfaction
Life satisfaction (1=very dissatisfied to 5=very satisfied)
- trust_government
Trust in government (1=no trust to 5=complete trust)
- trust_media
Trust in media (1=no trust to 5=complete trust)
- trust_science
Trust in science (1=no trust to 5=complete trust)
- sampling_weight
Post-stratification sampling weight (0.7-1.4)
- stratum
Stratification variable combining region and age group
- interview_mode
Interview mode (Face-to-face, Telephone, Online)
Source
Generated synthetically using realistic demographic and attitudinal patterns. No real survey data was used.
Details
This dataset contains realistic patterns of correlation between variables:
Education correlates with income and political attitudes
Regional differences reflect East/West patterns
Age effects on employment and attitudes
Realistic missing data patterns (3-12% depending on sensitivity)
Survey weights for post-stratification adjustment
The data includes proper variable labels and follows social survey conventions for coding and structure. Generated with set.seed(2024) for reproducibility.
Examples
# Load required packages and data
library(dplyr)
data(survey_data)
# Basic descriptive statistics
survey_data %>% describe(age, income, weights = sampling_weight)
#>
#> Weighted Descriptive Statistics
#> -------------------------------
#> Variable Mean Median SD Range IQR Skewness Effective_N
#> age 50.514 50 17.084 77 25 0.159 2468.8
#> income 3743.099 3500 1423.966 7200 1900 0.725 2158.9
#> ----------------------------------------
# Frequency analysis
survey_data %>% frequency(education, region, weights = sampling_weight)
#> Frequency: education [Weighted]
#> 4 categories, N valid = 2516, missing = 0
#> Frequency: region [Weighted]
#> 2 categories, N valid = 2516, missing = 0
#> Use summary() for detailed output.
# Group comparisons
survey_data %>%
group_by(region) %>%
t_test(life_satisfaction, group = gender, weights = sampling_weight)
#> [region = 1]
#> t-Test: life_satisfaction by gender [Weighted]
#> t(484.7) = 0.641, p = 0.522 , g = 0.058 (negligible), N = 488
#> [region = 2]
#> t-Test: life_satisfaction by gender [Weighted]
#> t(1901.1) = -1.548, p = 0.122 , g = -0.070 (negligible), N = 1949