Reason for Using Frequency and Percentage to Analyze Categorical Data

Data analysis is an important stage of research because it enables the researcher to transform raw responses into meaningful information that can be interpreted in relation to the research objectives and questions. In many studies within Information Science, Social Sciences, Management and related disciplines, researchers collect categorical data such as sex, age group, educational qualification, location, occupation, organizational category, and responses to structured questionnaire items. Frequency and percentage are among the most appropriate descriptive statistical techniques for presenting such data because they show both the actual number of respondents in each category and the proportion represented by each category. This article examines the reasons for using frequency and percentage to analyze categorical data regardless of the academic degree or level of the researcher. It also discusses the rationale for using a 50% benchmark when interpreting responses obtained through both non-Likert and Likert-type questions. The article argues that frequency and percentage provide a simple, transparent and accessible means of summarizing categorical responses, while the 50% benchmark can serve as a practical descriptive reference point when it is clearly defined in the research methodology. However, the benchmark should not be treated as a universal test of statistical significance or as a substitute for inferential analysis. The article concludes that the choice of frequency and percentage should primarily be determined by the nature and level of measurement of the data rather than by the academic degree of the researcher.