Multiple linear regression modelling on the factors affecting student’s awareness on cleanliness: a case study in tertiary institutions / Faten Ainon Najwa Arba … [et al.]

• Main Authors: Arba, Faten Ainon Najwa, Hamzah, Nor Hazreeni, Ibrahim, Nur Safwati, Ibrahim, Nurul Bariyah, Mohamed Zukri, Shamsunarnie, Mohd Fauzi, Noor Zafarina
• Format: Article
• Language: English
• Published: Unit Penerbitan, UiTM Kelantan 2022
• Subjects: Surveys. Sampling. Statistical survey methodology; Regression. Correlation; Path analysis. Structural equation modeling
• Online Access: https://ir.uitm.edu.my/id/eprint/72442/1/72442.pdf; https://ir.uitm.edu.my/id/eprint/72442/; https://jmcs.com.my/

Cleanliness is a state of being clean. Today’s pandemic of Coronavirus 2019, also known as Covid-19, is greatly associated with cleanliness where those who practice good hygiene are less likely to be infected. This research was done to identify the awareness of cleanliness among students of UiTM Kota Bharu based on gender, academic achievement, peer, and environmental influence. One of the aims of this research was to identify the mean different between gender and academic achievement on the awareness of cleanliness. Besides, this research also aimed to identify the influential factor towards the awareness of cleanliness. A cross-sectional correlational design was used in this research with a sample of 301 respondents. Independent t-test, one-way ANOVA and multiple linear regression was used in order to answer the objectives. It was found that both gender and academic achievement had no significant difference on the awareness of cleanliness. However, it was revealed that peer and environmental influence significantly trigger the awareness of cleanliness among students. Environmental influence contributed more on the awareness of cleanliness as compared to peer influence. For future research, it is recommended for other researchers to add more influential factors to the awareness of cleanliness and enlarge the sample size to improve the quality of data analysis.