Non-Parametric Data Analysis for Likert Scale Variables in Quantitative Research Methodologies

The analysis of Likert scale data presents unique challenges in research methodology, requiring careful consideration of statistical approaches that respect the nature of ordinal measurements. This comprehensive discussion explores the theoretical foundations, methodological considerations, and practical applications of non-parametric data analysis techniques when working with Likert scale variables. Drawing upon the methodological framework established in Van Wert’s (2025) research study of renewable energy facility siting processes, this examination provides insights into how researchers can effectively analyze ordinal data and the process of ranking while maintaining statistical integrity and producing meaningful results (Satianugraha, 2025). Under the framework established by Kuzon et al. (1996) in their "seven deadly sins of statistical analysis," the first transgression (Sin 1) involves applying parametric statistical methods to ordinal Likert scale data. This error occurs when researchers fail to acknowledge the fundamental properties and constraints of the population parameters under investigation. Essentially, this sin represents the methodological mistake of using analytical techniques that assume interval or ratio-level measurements when working with data that only possesses rank-order properties. Silan (2025) addresses the Likert data debate by proposing a shift in analytical approach from individual items to composite scales. Citing Carifio and Perla (2008), Silan contends that while individual Likert items possess ordinal properties, the aggregation of multiple related items through summation or averaging creates a quasi-continuous distribution.