A comparison of Information Technology Education students’ achievement in statistics using Kruskal-Wallis H-test statistic: a nonparametric analysis
Purpose The study sought to investigate whether variations in information technology students’ achievement across four related classes were statistically significant, thereby addressing concerns about performance consistency and instructional effectiveness in statistical results. Methodology/design/approach This study adopted a quantitative comparative research design to examine the differences in Statistics achievement among Information Technology Education (ITE) students, involving 40 purposively sampled second-year students enrolled in a statistics course. Data for the study were obtained from students’ statistics achievement results with high internal consistency (Cronbach's α = 0.887). Kruskal–Wallis H-Test statistic, a nonparametric test, was applied as an alternative to a one-way ANOVA for independent groups. Findings The computed Kruskal–Wallis H-Test statistic ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="IM1"> <mml:mi>H</mml:mi> <mml:mo>=</mml:mo> <mml:mspace width="0.25em"/> <mml:mn>20.675862</mml:mn> </mml:math> ) was substantially higher than the critical value (7.815), leading to the acceptance of the alternative hypothesis. This indicates that there was a statistically significant variation in students’ statistical success among the four linked groups. Originality/novelty The study contributes methodologically to statistical education research by applying the Kruskal–Wallis H-Test statistic, a nonparametric test, addressing a notable gap in the literature where parametric methods dominate, and offering a statistically robust alternative for analyzing non-normally distributed educational performance data.