Reason for using frequency and percentage to analyze categorical data

Data analysis is an important part of the research process because it enables researchers to organise, summarise and interpret information collected from respondents. One of the most commonly used methods for analysing categorical data is frequency and percentage analysis. Categorical data are information that can be grouped into distinct categories, such as gender, marital status, educational qualification, occupation, type of institution and responses to questionnaire items. Frequency refers to the number of respondents or observations that fall into a particular category, while percentage shows the proportion of respondents represented by each category. This article examines the reasons for using frequency and percentage to analyse categorical data. It discusses their relevance to both non-Likert and Likert-scale data and explains how they assist researchers in presenting findings clearly and making meaningful comparisons. The article also discusses the use of a 50% benchmark in interpreting questionnaire responses. Although some researchers use 50% as a decision benchmark for determining whether a majority of respondents have a particular opinion or characteristic, the benchmark should be regarded as a predetermined research criterion rather than a universal statistical rule.