Appropriate 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 measurement scale 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 t-test, 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. (shrink)

Research assessment and grant funding are vital to higher education. However, the reliance on quantitative metrics in these processes has raised concerns about their validity and potential negative consequences. This study aims to investigate the game of numbers in research assessment and grant funding, focusing on the perspectives of experienced researchers from around the globe. Accidental sampling elicited responses from more than 15 experienced researchers across different academic disciplines, institutions, and countries. The data were collected from the popular “Reviewer 2 (...) Must be Stopped!” Facebook platform, which includes more than 135,000 members across the globe. Two posts were made, allowing participants to share their experiences, perspectives, and concerns related to metrics and numbers in research assessment and grant funding. The results from the thematic analysis revealed diverse perspectives among experienced researchers. Some participants expressed concerns about the dominance of quantitative metrics, highlighting the limitations and potential biases associated with their use. Others acknowledged the value of certain indicators in showcasing research impact. Moreover, the impact of metrics on grant funding awards was also documented. The study highlights the necessity for a more balanced and context-aware approach to research assessment and grant funding, incorporating qualitative measures and acknowledging the diverse nature of research impact. (shrink)

Previous research has assessed school facilities, administrative expenditures and curriculum and their relative contributions to students’ cognitive learning outcomes. This suggested the need to investigate further how these predictors may impact students’ affective and psychomotor outcomes. The current research studied the combined and relative prediction of school facilities, administrative expenses and curriculum on students’ overall cognitive, affective and psychomotor learning outcomes in public secondary schools. A cross-sectional research design was employed in this study, involving 87 school administrators and a randomly (...) selected group of 915 senior secondary class II (SS2) students. For data collection, we utilised the School Inputs Questionnaire (SIQ) and Educational Outcomes Questionnaire (EOQ), both developed by the researchers and validated through expert assessments, including content validity, Exploratory Factor Analysis (EFA) for dimensionality, Confirmatory Factor Analysis (CFA) for goodness of fit, and reliability using Cronbach's alpha. The results of these assessments demonstrated acceptable outcomes aligned with international standards. Hierarchical regression analysis was conducted to analyse the collected data. The findings indicated that enhancing the provision of quality school facilities, administrative expenses, and school curricula improved students' overall cognitive, affective, and psychomotor learning outcomes. Specifically, administrative expenses and school curriculum had significant predictive power for students’ overall cognitive, affective, and psychomotor learning outcomes. However, while school facilities significantly predicted students' overall, affective, and psychomotor dimensions, they did not significantly predict the cognitive dimension. These findings offer valuable insights for policymakers and educators aiming to enhance the educational quality in public secondary schools. (shrink)