Using Ordinal Data to Assess Distance Learning

TL;DR

This paper advocates for treating Likert scale data as ordinal, employing non-parametric tests and clustering to validate significant variables in the context of distance learning assessment.

Contribution

It introduces a methodology that uses ordinal data analysis techniques to evaluate distance learning effectiveness, emphasizing non-parametric testing and clustering.

Findings

Non-parametric tests effectively identify significant variables.

Clustering validates the significance of variables.

Ordinal treatment provides reliable insights into distance learning data.

Abstract

There is some disagreement on whether Likert scale data should be treated as ordinal or continuous. This paper treats Likert data as ordinal, uses non-parametric hypothesis testing, and clustering to validate those variables that have significant results from hypothesis testing.