Computer Application for Statistical Mirroring-Based Ordinalysis
Description
The Statistical Mirroring-Based Ordinalysis [3] Application (SM-Based Ordinalysis Application) is a computer tool designed for descriptive analysis of ordinal data (numerically encoded and homogeneous ordinal scores) using statistical mirroring-based ordinalysis methodology. A user-friendly interface facilitates in-depth analysis of ordinal assessment data, including estimates of how closely or distant an individual’s composite set of ordinal assessment scores is to the highest positive ordinal scale point. Utilizing statistical mirroring [2], and Kabirian-based isomorphic optinalysis [1], the application allows users to select preprocessing parameters and compute estimates based on the task needs. Users can enter ordinal score manually and directly or upload CSV or Excel files, choose analysis parameters, and generate detailed results. The SM-Based Ordinalysis Application's intuitive design and powerful features make it an essential resource for researchers, students, and professionals seeking to explore and describe ordinal assessment data effectively. Its broad applicability spans areas such as survey data analysis, clinical assessments, psychometrics, market research, and public health studies where ordinal data are common. References: [1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. doi: 10.1016/j.mex.2023.102400 [2] K.B. Abdullahi, Statistical mirroring: A robust method for statistical dispersion estimation, MethodsX 12 (2024) 102682. https://doi.org/10.1016/j.mex.2024.102682 [3] K.B. Abdullahi, Statistical mirroring-based ordinalysis: A sensitive, robust, efficient, and ordinality-preserving descriptive method for analyzing ordinal assessment data, (2024). [You can follow the published version of the paper for the other reference details).