Correlation and Regression Modeling of Perceived Local Social Support and Household Waste-Management Compliance: A Sample Survey Analysis in Indonesia

Survey-based studies of environmental behavior require measurement and modeling procedures that yield reliable and robust estimates of association. This study applies a correlation–regression modeling framework to quantify the association between perceived local social support and residents’ compliance with household waste-management practices in Indonesia. Data were obtained from a cross-sectional sample survey of 385 adult residents, with the minimum sample size determined using Cochran’s formula. Both constructs were measured using five-point Likert-scale instruments, and instrument reliability was evaluated using corrected item–total correlations and Cronbach’s alpha. After distributional assumptions were examined, the association was estimated using Pearson’s product–moment correlation with a 95% confidence interval, verified using Spearman’s rank correlation as a nonparametric robustness check, and modeled using simple linear regression. Both scales showed high internal consistency (α = 0.923 and α = 0.928). The Pearson correlation was positive and moderate (r = 0.523, 95% CI [0.446, 0.592], p < 0.001), and the Spearman estimate was consistent (ρ = 0.531, p < 0.001). The regression model produced a positive slope (B = 0.502, p < 0.001) with a coefficient of determination of R² = 0.274. The agreement between parametric and nonparametric estimates indicates that the association is robust to distributional assumptions. Because the design is cross-sectional and the sample was drawn purposively, the estimates describe association rather than causation. The study illustrates a transparent statistical workflow for analyzing Likert-scale survey data in applied environmental research.