This meta-analysis attempts to synthesize the Monte Carlo (MC) literature for the linear mixed model under a longitudinal framework. The meta-analysis aims to inform researchers about conditions that are important to consider when evaluating model assumptions and adequacy. In addition, the meta-analysis may be helpful to those wishing to design future MC simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and MC simulation conditions will be coded to serve as moderator variables. The type I error rate for the fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects respectively. Meta-regression and proportional odds models were used to explore variation in the empirical type I error rate effect sizes. Implications for applied researchers and researchers planning new MC studies will be explored.
Evaluation use is a complex and multifaceted construct comprising domains of findings and process use and sub-categories of instrumental use, conceptual use, and persuasive (legitimative/symbolic) use. It is an important aspect of evaluation practice which has received much attention from evaluation scholars and practitioners. However, evaluation and evaluation use in countries outside North America, Europe and Australasia is under-investigated. This study
As the volume of multimedia data exchanged over the Internet continues to grow, the problem of preventing the unauthorized duplication of copyright-protected images or the dissemination of undesirable material such as pornographic pictures has become increasingly important. The majority of the images circulated over the Internet are coded in the JPEG format. Thus, the present study proposes a novel digital feature retrieval scheme designed to detect unlawful dissemination of preselected JPEG images. In the proposed approach, the packet-level features of the target JPEG image are collected in advance and are stored in a feature database. Thereafter, the features of all the packets passing through a nominated router are compared on-the-fly with those in the feature database in order to detect any transmissions of the target images. The experimental results show that the proposed packet-level forensic scheme is far more efficient than existing applicationlevel schemes. As a result, the proposed scheme provides a viable solution for the background monitoring of JPEG streams over large-scale network environments such as the Internet in order to detect unlawful transmissions and to compile digital evidence of such transmissions for possible future legal proceedings.
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