Reason for Using Frequency and Percentage to Analyse Categorical Data

Abstract

Categorical data, unlike continuous numerical data, cannot be meaningfully summarised using measures such as the mean or standard deviation because its values represent group membership rather than quantity. This article examines why frequency and percentage remain the most appropriate and widely used statistical tools for analysing categorical data. Frequency counts the number of cases falling into each category, while percentage expresses that count relative to the total sample, allowing comparison across groups of different sizes. Drawing on established research methodology literature, including Kaur, Stoltzfus, and Yellapu's (2018) account of descriptive statistics and Aliyu's (2023) application of frequency and percentage in a descriptive study, the article discusses the nature of categorical data, the descriptive and interpretive value of frequency and percentage, their advantages over other statistical measures, and their role in supporting further inferential analysis such as the chi-square test. The article concludes that frequency and percentage offer a simple, accurate, and universally understood means of summarising categorical variables, and recommends their consistent use alongside appropriate visual displays in quantitative research reporting.