OPSTA: An Online Analysis Platform for Chinese Sports Science Thesis Data Based on the Shiny Framework

With the continuous expansion of postgraduate enrollment in sports science in China, problems related to research design and statistical analysis in masters theses have become increasingly prominent. Some postgraduate students have limited statistical training, which may lead to inappropriate research designs, improper selection of statistical methods, failure to check underlying assumptions, and insufficient reporting of effect sizes and confidence intervals, ultimately undermining the scientific rigor and credibility of research conclusions. To address these issues systematically, it is necessary to develop an easy-to-use data analysis tool tailored to the sports science domain and aligned with statistical reporting standards. Accordingly, this study developed the Online Platform for Sports Thesis Analysis (OPSTA). Built on the Shiny framework, OPSTA integrates statistical procedures commonly used in sports science research, including descriptive statistics, independent-samples and paired-samples t tests, one-way and multifactor analysis of variance (ANOVA), correlation analysis, regression analysis, and reliability and validity testing. Through an interactive and visualized interface, the platform guides users through data import, method selection, and result export. While lowering the barrier to statistical analysis, OPSTA emphasizes assumption checking and standardized result presentation, thereby reducing the misuse of statistical methods and improving reporting quality. The platform is available at: https://menglab.org.cn/opsta/.