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BackgroundStandard methods for analysing data from large-scale assessments (LSA) cannot merely be adopted if hierarchical (or multilevel) regression modelling should be applied. Currently various approaches exist; they all follow generally a design-based model of estimation using the pseudo maximum likelihood method and adjusted weights for the corresponding hierarchies. Specifically, several different approaches to using and scaling sampling weights in hierarchical models are promoted, yet no study has compared them to provide evidence of which method performs best and therefore should be preferred. Furthermore, different software programs implement different estimation algorithms, leading to different results.Objective and methodIn this study, we determine based on a simulation, the estimation procedure showing the smallest distortion to the actual population features. We consider different estimation, optimization and acceleration methods, and different approaches on using sampling weights. Three scenarios have been simulated using the statistical program R. The analyses have been performed with two software packages for hierarchical modelling of LSA data, namely Mplus and SAS.Results and conclusionsThe simulation results revealed three weighting approaches performing best in retrieving the true population parameters. One of them implies using only level two weights (here: final school weights) and is because of its simple implementation the most favourable one. This finding should provide a clear recommendation to researchers for using weights in multilevel modelling (MLM) when analysing LSA data, or data with a similar structure. Further, we found only little differences in the performance and default settings of the software programs used, with the software package Mplus providing slightly more precise estimates. Different algorithm starting settings or different accelerating methods for optimization could cause these distinctions. However, it should be emphasized that with the recommended weighting approach, both software packages perform equally well. Finally, two scaling techniques for student weights have been investigated. They provide both nearly identical results. We use data from the Programme for International Student Assessment (PISA) 2015 to illustrate the practical importance and relevance of weighting in analysing large-scale assessment data with hierarchical models.

The present study, pertaining to a national information literacy training program for both farmers and rural communities in Taiwan, explores factors that affect learners' perception of learning information and communication technology (ICT). It further analyzes effects of individual characteristics and varied training designs on learners' perception of learning ICT. Data used for analyses derive from the Farmers' ICT Training Project of 2005, conducted by the Council of Agriculture in Taiwan (COAT), including evaluations of 4,405 trainees. Findings of hierarchical linear model (HLM) analysis supported both predictors of personal and organizational levels. Results of this study also revealed that learners' characteristics and varied training designs do have effects on learner's perception of training effectiveness. Specific impacts of learners' personal characteristics on their perception of learning effectiveness in different types of ICT training are also analyzed and discussed. In conclusion, implications founded on learner's perspective and suggestions for future research are illustrated in this paper.

Information and Communication Technology (ICT) has become an indispensable part of the 21st century. Having basic ICT skills is now seen as an important attribute that members of the young cohort should possess in order to be successful in life. Thus, countries all over the world, including Turkey, have attempted to adjust their educational policies to this new phenomenon of ICT. In this context, this study aims to investigate the impact of both household- and school-level factors on the ICT usage of students in Turkey. Given the multilevel structure of the data, the hierarchical linear modeling (HLM) method was used for statistical analyses. The results of the analyses show that there are significant differences between schools in terms of students' ICT usage, both for entertainment and school-related tasks. These differences, however, are mostly explained by the household-level factors. The only school-related factor that seems to be important for students' ICT usage is the student's involvement in ICT-related tasks at school. [PUBLICATION ABSTRACT]