Improving University Students’ Data Analysis Outputs through Effective Data Collection, Cleaning, Screening and Normalisation.

Abstract

Practical data analysis reflects an improved approach to data collection, cleaning, and screening. However, very few studies reported the techniques used to clean and screen their collected data, leading to questionable final results and interpretations, especially among university students. To address this issue, the current study examines the rigorous data collection, cleaning, and screening processes for data normalization among university students in Nigeria. Using a multi-stage research methodology, 372 adapted survey instrument items were administered via snowball sampling. Finally, 365 were retrieved from the respondents. Missing data were all imputed using the Series Mean (SMEAN), and outliers were appropriately addressed using z-scores and chi-square criteria. Descriptive statistical measures were used to examine the dataset and presented in several tables, a histogram, a scatterplot, and a standard probability plot. The collected, cleaned, and screened data were found to have a normal distribution, facilitating analysis and understanding of the parametric distribution, variation, and normalization. The findings provide valuable guidance for university students, academics, policymakers, and practitioners in data collection, cleaning, and screening. It was recommended that university students, lecturers, researchers, and research institutions prioritize thorough data collection, embrace transparent data cleaning, screening, and reporting practices, and adopt standardized procedures to enhance data accuracy, reliability, and normalization, thereby enabling better data analysis and the interpretation of research findings.