Data analysis and validation in investigating cognitive biases in the Singaporean workplace

This paper details the data analysis and validation procedures employed in a quantitative study focusing on cognitive biases in employee decision-making in the Singaporean workplace. Building on prior methodological frameworks, the study ensured data quality through systematic data management, outlier detection and normality testing. Outliers were identified using univariate and multivariate approaches, while normality was evaluated using statistical (Shapiro–Wilk and Kolmogorov–Smirnov) and visual (skewness, kurtosis, histograms and QQ plots) approaches. Demographic and descriptive analyses provided baseline insights into sample characteristics and variable distributions. The reliability of measurement instruments was confirmed using Cronbach’s alpha and item–total correlations. These procedures demonstrate data integrity and offer guidance for similar studies in behavioural economics and management, particularly in under-researched geographical contexts such as Singapore. Results also establish the basis for future studies on how overconfidence, herding and decision avoidance biases influence workplace decision-making, including evaluating information, searching information and procrastination.