<![CDATA[In the current digital era, quantitative data analysis remains a critical yet demanding stage of the research process, particularly for researchers without a strong statistical background. Complex analysis procedures, inconsistent method selection, and manual data handling increase the risk of human error and can threaten the validity of research findings. This study addresses this problem by designing and developing the CQR (Calculation of Quantitative Research) Application, a web-based predictive quantitative-analytics system intended to simplify statistical data processing for researchers regardless of their statistical expertise. The system was developed using the Research and Development (RD) method combined with the Waterfall model, comprising problem identification, data collection, system design, implementation, and testing, and was built using the Laravel 11 framework with a MySQL database. System design was guided by an analysis of functional and non-functional requirements and modelled using Use Case and Activity Diagrams. The developed system was evaluated through black-box testing across four core modules-user authentication and three data-upload calculator modules for the System Usability Scale (SUS). All eleven scenarios (100%) produced the expected output. A preliminary usability evaluation of the CQR Application itself, using the System Usability Scale with 32 student respondents, yielded a mean score of 76.09 (SD = 9.67), corresponding to an “Acceptable” rating and a curved grade of B. These findings indicate that the CQR Application is functionally reliable and perceived as usable, while quantitative benchmarking of analytical accuracy and processing time against established statistical software, together with a complementary User Experience Questionnaire evaluation, are identified as the immediate next steps for strengthening the system's scientific and practical contribution. The CQR Application is expected to help researchers, particularly those without extensive statistical training, conduct quantitative analysis that is more consistent, efficient, and credible.]]>
