Reason for Using Frequency and Percentage to Analyse Categorical Data

Descriptive statistics form the foundation of quantitative data analysis, and among the various tools available, frequency and percentage remain the most widely applied techniques for summarising categorical data. This article examines the rationale for using frequency and percentage as the appropriate descriptive statistical tools for analysing nominal and categorical data generated from survey-based research, particularly demographic variables and non-Likert research questions. Drawing on established literature in biostatistics and survey research methodology, the article discusses the nature of categorical data, the properties that make frequency and percentage suitable for such data, and the role of a response-rate benchmark in interpreting survey results. The discussion further considers how percentages are compared against a benchmark, usually 50%, to determine the direction of respondents' views, especially where data do not lend themselves to Likert-type scoring. The article concludes that frequency and percentage are not merely convenient but statistically appropriate for categorical, nominal-level data, and recommends that researchers handling descriptive, non-experimental studies with nominal variables adopt these measures rather than statistics meant for interval or ratio data.