Statistics Without Assumptions: Assessing the Application of Nonparametric Methods in Data Analysis Practices

This study assessed the application of nonparametric statistical methods in data analysis practices at Davao de Oro State College (DDOSC) using a mixed-methods research design. Faculty members and student researchers with experience in conducting quantitative research were selected through purposive sampling. Data were gathered using a researcher-made questionnaire and analyzed through frequency, percentage, mean, standard deviation, and thematic analysis. The findings revealed that nonparametric statistical methods are generally applied in research practices at DDOSC. The Chi-Square Test emerged as the most commonly applied method, indicating frequent analysis of categorical data in institutional research. This was followed by Spearman Rank Correlation, suggesting regular use in examining relationships involving ordinal or Likert-scale variables. The Mann-Whitney U Test and Kruskal-Wallis Test were also applied, demonstrating respondents’ awareness of nonparametric procedures for group comparisons, although these methods were utilized less frequently. Meanwhile, the Wilcoxon Signed-Rank Test obtained the lowest level of application, indicating limited exposure to paired-sample analysis. Overall, the results revealed that researchers tend to rely on basic and commonly used nonparametric techniques, while more specialized statistical procedures remain less utilized. Statistical software applications, particularly IBM SPSS Statistics and Microsoft Excel, were also highly utilized, highlighting their importance in facilitating data organization, computation, and analysis. The qualitative findings revealed several challenges encountered by respondents in applying nonparametric statistical methods, including limited knowledge of statistical concepts, difficulty in selecting appropriate tests based on research design and data characteristics, limited skills in using statistical software, inadequate training and mentoring opportunities, and challenges in interpreting statistical outputs. These findings suggest that while researchers possess familiarity with commonly used statistical tools, gaps remain in their deeper understanding and application of advanced nonparametric techniques. To address these concerns, respondents recommended the implementation of regular statistical training workshops, strengthened research mentoring programs, and hands-on learning activities focused on statistical application and interpretation. They also emphasized the development of user-friendly guides for SPSS and Excel and the provision of accessible reference materials and statistical manuals to support independent learning and enhance researchers’ analytical competence.

Nonparametric Statistical Methods, Data Analysis Practices, Mixed-Methods Research, Statistical Competence, SPSS, Philippines

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