Comparison of Four Covariate Adjustment Methods in Analysis of Mathematics Achievement in Randomised Controlled Trials among Senior Secondary School Students (2022)
"The study investigated the Mathematics achievement of senior secondary school students in Mathematics in Saki using Project Based Learning Method (PBLM) in enhancing the Mathematics achievement in senior Mathematics among senior secondary school students in Saki. To ascertain the degree of treatment efficacy, a randomised sample of 45 senior secondary school students with 30 and 15 participants each from two different senior secondary schools in Saki West Local Government areas in Saki were selected for the purpose. The randomised control trial design was employed, and a randomized randomised sample that undertook training in PBLM and a control group were used. The instrument used for data collection is Mathematics achievement tests sampled from the National Examination Council (NECO) for 2017 & 2018 objective Mathematics papers. Since the instrument was standardised test prepared by Public Examination body there is no need for revalidation. A pair of pre-test and post-test data was obtained from each participant who formed the basis of the findings using ANCOVA for data analyses. This study present the empirical application of four statistical methods (pre and post-treatment scores with analysis of covariance, post-test scores, difference in pre and post-treatment scores and percent difference in pre and post-treatment scores), using data from a randomised controlled trial of post-test among the senior secondary school students on Mathematics achievement using Project Based Learning Method (PBLM), with and without PBLM treatment, a Randomized Controlled Study trials. Analysis of covariance (ANCOVA) was used to determine the effectiveness of treatment, to adjust for baseline measures and to provide an unbiased estimate of the mean group difference of the post-treatment scores in Mathematics achievement among the participants used. Robustness tests were done by comparing ANCOVA with three comparative methods: the post-treatment scores, change in scores, and percentage change from baseline. All the four methods showed similar direction of effect; however, ANCOVA (93.503; 95% confidence interval [CI]: 89.332, 97.668; p = 0.019) and the post-treatment score (94.100; 95% CI: 89.733, 98.467; p = 0.001) method provided the highest precision of estimate compared with the change score (38.300; 95% CI: 34.215, 42.385; p = 0.001) and percent change (68.462; 95% CI: 60.358, 76.565; p = 0.001). The empirical studies provide the best statistical estimation for analyzing the best statistical estimation for analyzing continuous outcomes requiring covariate adjustment. Our empirical findings support the use of ANCOVA as an optimal method in both design and analysis of trials with a continuous primary outcome.