Methodological Appraisal and Credibility Assessment checklist (MACA)

This dataset accompanies the Methodological Appraisal and Credibility Assessment checklist (MACA), developed to evaluate the methodological quality of studies applying computational and statistical methods for composite indicator construction. The repository includes three reproducible components: MACA_ICC-KAPPA: Scripts and results for inter-rater reliability analysis (Cohen’s Kappa, Gwet’s AC1, PABAK, and ICC) of the MACA checklist. MACA_FinalScore: Script and output for calculating the averaged and total scores of the final 17-item fused version of MACA, based on two independent evaluators. MACA_heatmap: Python script and visualization for the heatmap summarizing methodological quality across studies. All scripts are written in Python and include example input and output files for transparency and reproducibility. The dataset supports the umbrella review on methodological quality assessment in computational and statistical methods applied to public health and composite indicators.本数据集配套于方法论评价与可信度评估清单（Methodological Appraisal and Credibility Assessment, MACA），该清单专为评估采用计算与统计方法构建综合指标的相关研究的方法学质量而开发。 本仓库包含三个可复现的组件： 1. MACA_ICC-KAPPA：用于对MACA清单开展评定者间信度分析的脚本与结果，分析方法涵盖Cohen's Kappa系数、Gwet's AC1系数、PABAK系数及组内相关系数（ICC）； 2. MACA_FinalScore：基于两名独立评估者的评分，用于计算MACA最终17项融合版本的平均分与总分的脚本及输出文件； 3. MACA_heatmap：用于生成汇总各项研究方法学质量的热图的Python脚本与可视化结果。 所有脚本均采用Python编写，并附带示例输入与输出文件，以保障研究的透明度与可复现性。 本数据集可支撑针对公共卫生及综合指标领域所应用的计算与统计方法的方法学质量评估的伞状综述研究。