This dataset, included in the tidyML package, is a simulated dataset (Martínez et al., 2025) designed to capture relationships among psychological and demographic variables influencing psychological wellbeing, the primary outcome variable. It comprises data for 1,000 individuals.
data(sim_data)A data frame with 1,000 rows and 10 columns:
Psychological Wellbeing Indicator. Continuous with (0,100)
Psychological Wellbeing Binary Indicator. Factor with ("Low", "High")
Psychological Wellbeing Polytomic Indicator. Factor with ("Low", "Somewhat", "Quite a bit", "Very Much")
Patient Gender. Factor ("Female", "Male")
Patient Age. Continuous (18, 85)
Socioeconomial Status Indicator. Factor ("Low", "Medium", "High")
Emotional Intelligence Indicator. Continuous (24, 120)
Resilience Indicator. Continuous (4, 20)
Depression Indicator. Continuous (0, 63)
Life Satisfaction Indicator. Continuous (5, 35)
The predictor variables include gender (50.7% female), age (range: 18-85 years, mean = 51.63, median = 52, SD = 17.11), and socioeconomic status, categorized as Low (n = 343), Medium (n = 347), and High (n = 310). Additional predictors are emotional intelligence (range: 24-120, mean = 71.97, median = 71, SD = 23.79), resilience (range: 4-20, mean = 11.93, median = 12, SD = 4.46), life satisfaction (range: 5-35, mean = 20.09, median = 20, SD = 7.42), and depression (range: 0-63, mean = 31.45, median = 32, SD = 14.85). The primary outcome variable is emotional wellbeing, measured on a scale from 0 to 100 (mean = 50.22, median = 49, SD = 24.45).
The dataset incorporates correlations as conditions for the simulation. Psychological wellbeing is positively correlated with emotional intelligence (r = 0.50), resilience (r = 0.40), and life satisfaction (r = 0.60), indicating that higher levels of these factors are associated with better emotional health outcomes. Conversely, a strong negative correlation exists between depression and psychological wellbeing (r = -0.80), suggesting that higher depression scores are linked to lower emotional wellbeing. Age shows a slight positive correlation with emotional wellbeing (r = 0.15), reflecting the expectation that older individuals might experience greater emotional stability. Gender and socioeconomic status are included as potential predictors, but the simulation assumes no statistically significant differences in psychological wellbeing across these categories.
Additionally, the dataset includes categorical transformations of psychological wellbeing into binary and polytomous formats: a binary version ("Low" = 477, "High" = 523) and a polytomous version with four levels: "Low" (n = 161), "Somewhat" (n = 351), "Quite a bit" (n = 330), and "Very much" (n = 158). The polytomous transformation uses the 25th, 50th, and 75th percentiles as thresholds for categorizing psychological wellbeing scores. These transformations enable analyses using machine learning models for regression (continuous outcome) and classification (binary or polytomous outcomes) tasks.
Martínez-García, J., Montaño, J.J., Jiménez, R., Gervilla, E., Cajal, B., Núñez-Prats, A., Leguizamo-Barroso, F., & Sesé, A. (2025). Decoding Artificial Intelligence: A tutorial on Neural Networks in Behavioral Research. Clinical and Health, 36(2). https://doi.org/10.5093/clh2025a13