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The AVE of accountability was just below the 0.5 threshold (0.483), due to the values of 3 loading coefficients, which were just <0.7.
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The model was reliable, consistent and had discriminant validity suggesting that the results confirmed that the hypothesised structural paths were real, and not a mere result of statistical discrepancies. The CTA showed the reflective structure of the model. Six hypotheses were formulated and tested. The analytical approach adopted was the one suggested by Sarstedt and Ringle in 2017. The researchers developed a conceptual model for visualising the connections among the inner variables (latent variables) which displayed the hypotheses and the variables relationships estimated by the PLS-SEM analysis. The highest percentage of students (53%) was in biomedical science and the lowest in pharmacy (18%). The student population was a mix of 3 different disciplines: pharmacy, foundation year and biomedical sciences. This study aimed to identify paths and predictive power of students’ satisfaction during TBL activities in the faculty of life sciences. Therefore, student satisfaction has a moderate/substantial predictive power, while accountability has weak predictive power ( Table 7). The effect size (f 2) shows how strong one exogenous construct contributes to explaining a certain endogenous construct in terms of R 2. The R 2 for accountability was 0.303 showing a weak predictive power, while the R 2 (0.678) of student satisfaction was closer to the substantial predictive power ( Table 7). The coefficients of determination (R 2) were calculated for obtaining an in-sample prediction. R 2 ranges between 0 and 1 with a larger value indicating higher levels of explanatory power. R 2 is a measure of the model explanatory power and represents the amount of variance in the endogenous construct (e.g., student satisfaction) explained by all the exogenous constructs linked to it (e.g., TBL, lectures). To the best of our knowledge PLS-SEM has not been used to evaluate students’ accountability, preference for TBL or lectures and satisfaction as measured using the TBL-SAI in the United Kingdom. A few studies conducted in the United Kingdom analysed the use of TBL with the team-based learning students assessment instruments (TBL-SAI). PLS-SEM has been used to explore pharmacists’ job satisfaction and the effects of different indicators on job satisfaction, and more recently to explore the influence of pharmacists’ expertise on the prescribing decisions of physicians. Partial least squares structural equation modelling (PLS-SEM) is a prediction-oriented variance-based approach that focuses on endogenous target constructs in the model and aims at maximising their explained variance (e.g., looking at the coefficient of determination value).
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Structural equation modelling (SEM) represents a group of statistical techniques that have become very popular in business and social sciences search. the primary learning objective of TBL is to go beyond simply covering content and focus on ensuring that students have the opportunity to practise using course concepts to solve problems. Team-based learning (TBL) is an evidence-based collaborative learning and teaching strategy designed around units of instruction, known as “modules,” that are taught in a three-step cycle: preparation, in-class readiness assurance testing, and application-focused exercise.