Addressing this concern, this paper explains how to evaluate the results of higher-order constructs in PLS-SEM using the repeated indicators and the two-stage approaches, which feature prominently in applied social sciences research. Unfortunately, researchers frequently confuse the specification, estimation, and validation of higher-order constructs, for example, when it comes to assessing their reliability and validity. Higher-order constructs, which facilitate modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become an increasingly visible trend in applications of partial least squares structural equation modeling (PLS-SEM). With α = 5%, the research results conclude that mathematical reasoning positively influences algorithm programming ability with an R score of 0.999, and that the most influential variable among mathematical reasoning abilities was algebra with an R score of 0.732. The data analysis used was variant-based Structural Equation Modelling, also known as Partial Least Squares - Structural Equation Modelling based on Smart-PLS 3. Mathematical reasoning tests incorporated linear algebraic, basic calculus, and mathematical logic. The research instruments were mathematical reasoning and basic algorithm programming test. The research subjects were second-semester information technology students in several private universities in Surabaya, Indonesia. The purpose of this research is to find the most influential factor in learning programming algorithm using a quantitative approach. This paper examines the limited proficiency to engage in programming algorithms among university students in information technology and information system in several universities across Surabaya, Indonesia.
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