The Application of the Nominal Scale of Measurement in Research Data Analysis

ppropriate measurement scales are fundamental in data analysis, allowing researchers to 
categorise, select appropriate statistical methods, and analyse and interpret their data 
accurately. The nominal scale is one such measurement scale in behavioural sciences, which 
is crucial in organising data into distinct categories. This paper provides an overview of the 
nominal measurementscale in research data analysis. It explains the characteristics and role 
of the nominal scale in organising data into distinct categories. The paper discusses methods 
of collecting nominal scale data, including surveys and observations. It explores the use of the 
nominal scale in descriptive (such as frequency counts, measures of dispersion and central 
tendencies), and inferential statistics (such as point biserial correlation, independent ttest, 
analysis of variance, logistic regression, discriminant analysis, differential item functioning, 
chi-square test of independence, Kruskal-Walli’s test, and Mann-Whitney U Test). Each 
technique is explained with assumptions and application areas. In conclusion, the paper 
emphasises the significance of the nominal scale in data analysis and its contribution to various 
statistical techniques. It serves as a comprehensive guide for researchers and practitioners 
looking to understand and utilise the nominal measurement scale in their data analysis.
Keywords: ANOVA, categorical, data, logistic, regression