Developing an Environmental Culture Index for Household Waste Sorting
Description
This dataset supports the development and validation of the KAUC Cultural Index (KCI) presented in the study “Developing an Environmental Culture Index for Household Waste Sorting”. It contains 445 complete, de-identified responses collected through a household waste-sorting survey conducted in Hai Phong, Vietnam, from July to August 2025. The dataset includes 74 five-point Likert-scale items measuring domain-specific constructs related to Knowledge, Action, Utility, Contribution, and theoretically related validation variables. The shared files enable researcher(s)/user(s) to reproduce the construction, calibration, robustness testing, sequence analysis, and nomological validation of the KCI. All direct personal identifiers and timestamps have been removed to protect participant confidentiality.
Files
Steps to reproduce
The data were collected during the first survey wave in Hai Phong City, Vietnam, from July 10 to August 18, 2025, under Vietnam National University project QG.24.92. A mixed-mode approach was used, combining face-to-face interviews with online administration through Google Forms. The original Vietnamese questionnaire contained 74 five-point Likert-scale items measuring constructs related to Knowledge, Action, Utility, Contribution, and theoretically relevant validation variables. Of 487 submitted responses, 11 respondents who had not completed the preceding project surveys, 29 respondents who reported no household waste-sorting activity and therefore did not complete the subsequent measurement sections, and two incomplete pilot records were excluded. The final analytical dataset contains 445 complete, de-identified responses. Names, contact details, residential addresses, timestamps, and other direct identifiers were removed before publication. To reproduce the analyses, download the dataset, codebook, questionnaires, four Python scripts, and the Google Colab notebook included in this repository. Open KCI_Reproducibility_Run.ipynb in Google Colab and run the cells in order. When prompted, upload data_445_deidentified.csv, kci_analysis.py, vif_pruning.py, measurement_models.py, and validate_package.py. The notebook installs the required packages, including NumPy, pandas, SciPy, statsmodels, semopy, and openpyxl; checks the dataset structure and value ranges; runs the primary KCI analysis; reproduces the deterministic VIF-pruning procedure; regenerates standardized loadings, reliability, AVE, and HTMT diagnostics; and verifies the manuscript-linked results. The workflow creates respondent-level KCI scores, descriptive statistics, bottleneck classifications, Guttman sequence diagnostics, calibration and sensitivity analyses, construct-substitution tests, nomological validation results, measurement-model outputs, log files, and a consolidated Excel summary. Successful reproduction is confirmed when the notebook reports Manuscript linkage checks: PASS and PASS: all manuscript linkage checks passed. The English questionnaire and codebook provide the exact correspondence between survey items, variable names, constructs, and KAUC layers.
Institutions
- Vietnam National University, HanoiAn Giang Province, Hanoi