Residential Environmental Quality and Perceived Value in the Greek Real Estate Market: Survey Data and Statistical Analysis Code
Description
This dataset provides supporting materials for a postgraduate dissertation investigating real estate professionals' perceptions of residential environmental quality and perceived property value in Greece. The study is based on an exploratory cross-sectional survey of 28 professionals and uses descriptive statistics and 16 non-parametric statistical tests, including Spearman correlation, Wilcoxon signed-rank, Mann–Whitney U and Friedman tests, with Holm correction for multiple comparisons. The repository provides reproducibility materials, including statistical analysis code and accompanying documentation. Individual-level survey data are included only where their disclosure is permitted by participant consent and applicable data protection requirements.
Files
Steps to reproduce
## Steps to Reproduce 1. Download the supplementary Excel workbook, `Mendeley_Complete_Survey_and_Analysis_EN_PRIVATE.xlsx`, and the Python script, `G3_Statistical_Analysis_Reproduce.py`. 2. Open the Excel workbook. It contains eight worksheets: START HERE, Survey Responses, Question Codebook, 16 Statistical Tests, 21 Table Audit, Sensitivity Analysis, README English, and Python Source Code. 3. Review the Survey Responses and Question Codebook worksheets to understand the survey variables and the 28 observations. 4. Install Python 3 and the required packages: NumPy, SciPy, python-docx, and artifact_tool. 5. Prepare the original survey response workbook (`ΑΠΑΝΤΗΣΕΙΣ(4).xlsx`) and the dissertation document (`Karountzos_Diplomatiki_Anadiorganomeni(1).docx`). Place these files and the Python script in the same working directory. The script uses the original Greek-language source data rather than the translated Excel workbook. 6. Execute the script using the following command: `python G3_Statistical_Analysis_Reproduce.py` 7. The script generates three CSV reports: `G3_table_audit.csv`, `G3_16_tests.csv`, and `G3_sensitivity.csv`. 8. Compare the generated results with the corresponding worksheets in the supplementary Excel workbook. The analysis covers 16 non-parametric statistical tests, including Spearman rank correlations, Wilcoxon signed-rank tests, Mann–Whitney U tests, and Friedman tests. Holm correction is applied to account for multiple comparisons. 9. Review the sensitivity analysis and verify that the calculated statistics, sample sizes, and adjusted p-values agree with the dissertation results. 10. Note that the script was developed to audit dissertation version (1). The latest dissertation version (2) requires a separate consistency check. **Data availability and reproducibility limitations:** The combined Excel workbook contains translated survey responses and previously generated statistical results. It does not independently execute the Python analysis. Full computational reproduction requires the original source data, the specified dissertation document, and the executable Python script. Access to individual-level responses is subject to participant consent, anonymization requirements, and applicable data protection regulations.