Reason for Using Frequency and Percentage to Analyse Categorical Data

Data analysis is an essential component of quantitative research because it enables researchers to organise, summarise, interpret, and communicate information obtained from respondents or observations. One of the most common forms of data encountered in research is categorical data. Categorical data consist of observations classified into distinct groups or categories, such as gender, marital status, occupation, educational qualification, employment status, disease status, and levels of agreement. Because categorical data do not necessarily possess numerical properties that justify the calculation of means and standard deviations, frequency and percentage are widely used to describe them. This article examines the reasons for using frequency and percentage to analyse categorical data. It discusses the nature of categorical data, the meaning and functions of frequency and percentage, and their relevance to nominal and ordinal variables. The article further explains their application to questionnaire responses and Likert-type scales. Frequency provides the actual number of observations in each category, while percentage expresses each category relative to the total number of observations. Together, they provide a simple, transparent, and meaningful description of categorical distributions. The article also explains that frequency and percentage are primarily descriptive statistics and should not automatically be regarded as substitutes for inferential statistical procedures where the objective is to test relationships, associations, or statistical significance. The article concludes that frequency and percentage remain fundamental methods for analysing categorical data because they preserve the categorical nature of observations while making research findings easy to understand, compare, and communicate.