MULTIPLE LINEAR REGRESSION ANALYSIS OF FACTORS INFLUENCING THE ABILITY TO USE DATA ANALYSIS SOFTWARE

Abstract

The ability to operate data analysis software is a fundamental competency for statistics students in the digital era; however, varying levels of proficiency are assumed to be influenced by internal factors (knowledge and attitudes) as well as external factors (usage frequency and barriers). This study aims to determine the influence of Data Literacy Knowledge (X1), Attitudes and Perceptions (X2), Usage Frequency and Intensity (X3), and Challenges and Barriers (X4) on the Ability of Statistics Students at Universitas Sumatera Utara to Use Data Analysis Software (Y). Using a quantitative method with an associative approach, the research involved 155 students selected through Stratified Random Sampling, with data collected via an online questionnaire and analyzed using multiple linear regression assisted by SPSS and validated with SmartPLS. The findings show that all four factors simultaneously have a significant effect on students’ ability, contributing 66.2% (R² = 0.662; F-statistic = 75.502), where Knowledge, Attitudes, and Usage Frequency have a positive and significant effect, while Challenges and Barriers have a negative and significant effect, and Usage Frequency and Intensity emerges as the most dominant factor. These results indicate that knowledge, attitudes, and frequency of use positively enhance students’ technical competence, whereas technical constraints diminish it, suggesting that improvement strategies should prioritize increasing practice intensity and minimizing technical obstacles.