Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior.

Sustainable construction and demolition waste management relies heavily on the attitudes and actions of its constituents; nevertheless, deep analysis for introducing the best estimator is rarely attained. The main objective of this study is to perform a comparison analysis among different approaches of Structural Equation Modeling (SEM) in Construction and Demolition Waste Management (C&DWM) modeling based on an Extended Theory of Planned Behaviour (Extended TPB). The introduced research model includes twelve latent variables, six independent variables, one mediator, three control variables, and one dependent variable. Maximum likelihood (ML), partial least square (PLS), and Bayesian estimators were considered in this study. The output of SEM with the Bayesian estimator was 85.8%, and among effectiveness of six main variables on C&DWM Behavioral (Depenmalaydent variables), five of them have significant relations. Meanwhile, the variation based on SEM with ML estimator was equal to 78.2%, and four correlations with dependent variable have significant relationship. At the conclusion, the R-square of SEM with the PLS estimator was equivalent to 73.4% and three correlations with the dependent variable had significant relationships. At the same time, the values of the three statistical indices include root mean square error (RMSE), mean absolute percentage error (MPE), and mean absolute error (MSE) with involving Bayesian estimator are lower than both ML and PLS estimators. Therefore, compared to both PLS and ML, the predicted values of the Bayesian estimator are closer to the observed values. The lower values of MPE, RMSE, and MSE and the higher values of R-square will generate better goodness of fit for SEM with a Bayesian estimator. Moreover, the SEM with a Bayesian estimator revealed better data fit than both the PLS and ML estimators. The pattern shows that the relationship between research variables can change with different estimators. Hence, researchers using the SEM technique must carefully consider the primary estimator for their data analysis. The precaution is necessary because higher error means different regression coefficients in the research model.

Evaluation of depression and obesity indices based on applications of ANOVA, regression, structural equation modeling and Taguchi algorithm process.

Introduction: Depression and obesity are the main threat among women which have been considered by many research scholars in psychology studies. In their analysis for measuring and estimating obesity and depression they were involving statistical functions. Methods: Regression, Analysis of Variance (ANOVA), and in the last two decades Structural Equation Modeling are the most familiar statistical methods among research scholars. Taguchi algorism process is one the statistical methods which mostly have been applying in engineering studies. In this study we are looking at two main objectives. The first one is to introduce Taguchi algorism process and apply it in a case study in psychology area. The second objective is challenging among four statistical techniques include ANOVA, regression, SEM, and Taguchi technique in a same data. To achieve those aims we involved depression and obesity indices with other familiar indicators contain socioeconomic, screen time, sleep time, and usage fitness and nutrition mobile applications. Results and discussion: Outputs proved that Taguchi technique is able to analyze some correlations which are not achieved by applying ANOVA, regression, and SEM. Moreover, SEM has a special capability to estimate some hidden correlations which are not possible to evaluate them by using ANOVA, regression, and even Taguchi method. In the last, we found that some correlations are significant by SEM, however, in the same data with regression those correlation were not significant. This paper could be a warning for psychology research scholars to be more careful with involving statistical methods for measuring and estimating of their research variables.

Estimation in regret-regression using quadratic inference functions with ridge estimator.

In this paper, we propose a new estimation method in estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) can be used to estimate the parameters of the mean response at each visit, conditional on previous states and actions. Singularity issues may arise during computation when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. A simulation study and an application to anticoagulation dataset were conducted to investigate the model's performance in parameter estimation. The results show that estimations using rQIF-MRr are more efficient than the QIF-MRr.

Effects of simple knee sleeves on pain and knee adduction moment in early unilateral knee osteoarthritis.

Knee sleeves are often prescribed to alleviate pain in people with early knee osteoarthritis. However, the biomechanics underlying their pain-relieving effect are still not well understood. This pre-post study aims at evaluating and comparing the effects of two different types of knee sleeves on knee adduction moment. Patients with clinically diagnosed knee osteoarthritis were recruited from the University of Malaya Medical Centre and were randomly assigned to two test groups using (1) a simple knee sleeve and (2) a simple sleeve with patella cutout. Knee adduction moment was collected using the Vicon motion capture system with two Kistler force plates. Pain, stiffness and physical functions were recorded using the Western Ontario and McMaster Universities Osteoarthritis Index. All measurements were taken before, immediately after and at the completion of 6 weeks of application (primary time point). In total, 17 participants with early unilateral knee osteoarthritis (47.7 (9.7) years) completed the study. Overall results show significant reduction in pain, early stance and late stance knee adduction moment and increased walking speed after 6 weeks of both knee sleeves application. This study results suggest that knee sleeves can reduce knee adduction moments in early unilateral knee osteoarthritis by 14.0% and 12.1% using the simple sleeve and the sleeve with patella cutout, respectively, and can potentially delay disease progression. In addition, knee sleeve with patella cutout does not provide additional benefits when compared to the simple knee sleeve.