The study recognized the worth of understanding the how’s of handling censoring and censored data in survival analysis and the potential biases it might cause if researchers fail to identify and handle the concepts with utmost care. We systematically reviewed the concepts of censoring and how researchers have handled censored data and brought all the ideas under one umbrella. The review was done on articles written in the English language spanning from the late fifties to the present time. We googled through NCBI, PubMed, Google scholar and other websites and identified theories and publications on the research topic. Revelation was that censoring has the potential of biasing results and reducing the statistical power of analyses if not handled with the appropriate techniques it requires. We also found that, besides the four main approaches (complete-data analysis method; imputation approach; dichotomizing the data; the likelihood-based approach) to handling censored data, there were several other innovative approaches to handling censored data. These methods include censored network estimation; conditional mean imputation method; inverse probability of censoring weighting; maximum likelihood estimation; Buckley-Janes least squares algorithm; simple multiple imputation strategy; filter algorithm; Bayesian framework; β -substitution method; search-and-score-hill-climbing algorithm and constraint-based conditional independence algorithm; frequentist; Markov chain Monte Carlo for imputed data; quantile regression; random effects hierarchical Cox proportional hazards; Lin’s Concordance Correlation Coefficient; classical maximum likelihood estimate. We infer that the presence of incomplete information about subjects does not necessarily mean that such information must be discarded, rather they must be incorporated into the study for they might carry certain relevant information that holds the key to the understanding of the research. We anticipate that through this review, researchers will develop a deeper understanding of this concept in survival analysis and select the appropriate statistical procedures for such studies devoid of biases.
Cooperative learning strategies have the tendency to enhance the academic strength of learners. In this paper, the independent variable, type of cooperative strategy, included three levels: Jig-Saw, Think-Pair-Share, and Brainstorming. The dependent variable was the students' individual mathematics achievement scores and the covariate was the students' group score when the cooperative strategy was used. A preliminary analysis that sought to assess the homogeneity-of-regression assumption indicated that the relationship between the covariate and the dependent variable did not differ significantly as a function of the independent variable, F (2, 81) =.045, p =.956. Principal component analysis (PCA) was used to reduce the three covariates to one score factor for ANCOVA procedure. A significant relationship was found between academic achievement score with respect to a cooperative strategy used and the individual academic achievement scores, F (2, 83) = 249.030, p <. 05. About 86% of the total variance in individual mathematics achievement score was accounted for by the three levels of cooperative strategy controlling for the students' academic group scores. Jigsaw cooperative strategy (Mean: 3.4, SE: 0.068, p < 0.01) had the most impact on individual achievement in mathematics with students obtaining an average grade of B+. The findings also showed from the PCA that the mathematics achievement scores of the group treated with Jig-Saw cooperative strategy explained most (about 39%) of the total variance, followed by Think-Pair-share with the least being Brainstorming. Explained in another way, when students use Jig-saw learning strategy in Mathematics, their individual academic potentials are enhanced well than when Think-Pair-Share or Brainstorming is used. It is therefore recommended for Jig-saw strategy to be the preferred strategy for learning when mathematics teachers seek to improve deep learning and problem solving among students.
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