Despite wide applications of both mediation models and missing data techniques, formal discussion of mediation analysis with missing data is still rare. We introduce and compare four approaches to dealing with missing data in mediation analysis including listwise deletion, pairwise deletion, multiple imputation (MI), and a two-stage maximum likelihood (TS-ML) method. An R package bmem is developed to implement the four methods for mediation analysis with missing data in the structural equation modeling framework, and two real examples are used to illustrate the application of the four methods. The four methods are evaluated and compared under MCAR, MAR, and MNAR missing data mechanisms through simulation studies. Both MI and TS-ML perform well for MCAR and MAR data regardless of the inclusion of auxiliary variables and for AV-MNAR data with auxiliary variables. Although listwise deletion and pairwise deletion have low power and large parameter estimation bias in many studied conditions, they may provide useful information for exploring missing mechanisms.

Web service composition has become a promising technology in a variety of e-science or e-business areas. There are a variety of models and methods to deal with this issue from dierent aspects. Bioinspired algorithms are becoming main approaches and solutions. This paper reviews the current researches on web service composition based on bio-inspired algorithms, such as Ant Colony Optimization(ACO), Genetic Algorithm(GA), Evolutionary Al-gorithm(EA) and Particle Swarm Optimization(PSO). By analyzing and investigating dierent approaches, this paper gives an overview about the researches on bio-inspired algorithm in web service composition and point out future directions.

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Background and Objective. Several reviews have summarised studies on secondary school students’ moderate-to-vigorous physical activity (MVPA) in physical education (PE), but no systematic review with semiquantitative assessment has been conducted to specifically identify the correlates of their MVPA. This review aims to systematically summarise the existing literature, which investigated correlates of MVPA of secondary school students during their PE lessons. Methods. A systematic search using ERIC, SPORTDiscus, PubMed, PsycINFO, Academic Search Premier, and Web of Science was conducted to identify the correlates of the MVPA of secondary school students in PE. Studies were eligible if they were English published articles and examined the association with MVPA during secondary school PE lessons and cross-sectional and prospective longitudinal quantitative studies. Two reviewers independently examined the articles, assessed their methodological quality, and performed data extraction. The correlates of MVPA were synthesised and further assessed semiquantitatively. Results. Fifty-five studies were identified to correlate with secondary school students’ MVPA in PE lessons. Further analysis only included 43 studies (78.2%) that were of medium and high quality by methodological quality assessment. Out of 54 variables identified from these medium and high-quality studies, 11 were consistently associated with the MVPA. Sex (boys), ethnicity (White), class gender (boys-only), PE activities (team games), lesson location (outdoors), expectancy beliefs, subjective task values, and enjoyment were consistently and positively associated with MVPA. Other variables, namely, class gender (girls-only), PE activities (movement activities), and lesson context (knowledge), were consistently and negatively related to MVPA. Conclusions. Interventions focusing on the consistent variables are needed to build active lesson time in PE. This review also provides insights for future research.

This study introduces an item response theory–zero-inflated Poisson (IRT–ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are included to investigate item performance from both propensity and level perspectives. The application of the model was illustrated by analyzing the substance use data from the National Longitudinal Study of Youth. A simulation study based on the empirical data analysis scenario showed that the item parameters can be recovered accurately and precisely with adequate sample sizes. Limitations and future directions are discussed.

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