There is a clear call to use evidence-based practice (EBP) in schools, and a growing knowledge base of practices that have proven to be effective in helping students achieve in educational settings. In addition, the current trends of Positive Behavior Supports (PBS) and Response to Intervention (RtI) advocate for preventative and proactive strategies. Token economies (TE) are one intervention that is proactive and can be flexible to use with students across a wide range of behaviors and settings. According to Higgins, Williams, and McLaughlin, token economy (TE) is an effective way to improve classroom behavior. Unfortunately, limited recent research is available that evaluated the effects and moderators of token economies in classroom settings. The purpose of this investigation was to Meta-analyze the single case research on TE implemented in school and is the first to offer effect size analysis and identify moderators. The use of TE's has been widely established as an evidence-based intervention for use in prisons, psychiatric hospitals, and school settings. However, very few articles discuss size of effects to expect, the essential elements required, or the practical implementation issues within a classroom. Many myths surround the use of a TE, i.e., many assume a token system is effective only for individuals and this is not so, as TE is effective for groups as well as individuals. In an age of accountability and emphasis on preventative evidence based practice evidence for using a TE and how to implement a TE is needed in our literature. Empirical evidence for the use of a token economy in a classroom is presented along with suggested implementation ideas. Twenty four studies were included in this Meta-analysis with an overall combined Tau-U ES of .78 of data showing improvement between phase A and B with CI90 [.72, .83]. Tau-U effect sizes ranged from .35 to 1.0. TE is effective with all ages evaluated (ages 3 - 15); however, statistically significant results indicated it was more effective with ages 6 - 15. Active ingredients (i.e. procedural steps) were evaluated, combined, and reported. Results indicate that TE is an evidence-based intervention to increase academic readiness behaviors and to decrease inappropriate behaviors.

Yes / In the last few years, several studies have examined the predictors of mobile banking (m-banking) adoption using the unified theory of acceptance and use of technology (UTAUT). However, contradictory results in some of the UTAUT relationships were found in the existing literature. Therefore, we aim to clarify and synthesize the empirical findings from the m-banking studies published since 2004 by conducting weight and meta-analysis with a focus on the UTAUT theory. We also seek to identify the roles of moderating variables on each UTAUT path. A total of 364 path coefficients from 127 studies were relevant for data analysis. CMA software V3 was employed to combine the effect sizes. All UTAUT relationships were found to be significant. Performance expectancy emerged as the strongest antecedent of usage intention. We also find that usage intention is the most critical predictor of use behavior. It was also revealed that sample size and culture significantly moderated the linkages between facilitating conditions and usage intention, effort expectancy and usage intention, and usage intention and use behavior. Theoretical contributions and managerial implications are also discussed toward the end.

Although replication is considered an indispensable part of the scientific method in software engineering, few replication studies are published each year. The rate of replication, however, is not surprising given that replication theory in software engineering is immature. Not only are replication taxonomies varied and difficult to reconcile, but opinions on the role of replication contradict. In general, we have no clear sense of how to build knowledge via replication, particularly given the practical realities of our research field. Consequently, most replications in software engineering yield little useful information. In particular, the vast majority of external replications (i.e., replications performed by researchers unaffiliated with the original study) not only fail to reproduce the original results, but defy explanation. The net effect is that, as a research field, we consistently fail to produce usable (i.e., transferable) knowledge, and thus, our research results have little if any impact on industry. In this dissertation, we dissect the problem of replication into four primary concerns: 1) rate and explicitness of replication; 2) theoretical foundations of replication; 3) tractability of methods for context analysis; and 4) effectiveness of inter-study communication. We address each of the four concerns via a two-part research strategy involving both a theoretical and a practical component. The theoretical component consists of a grounded theory study in which we integrate and then apply external replication theory to problems of replication in empirical software engineering. The theoretical component makes three key contributions to the literature: first, it clarifies the role of replication with respect to the overall process of science; second, it presents a flexible framework for reconciling disparate replication terminology; and third, it informs a broad range of practical replication concerns. The practical component involves a series of replication studies, through which we explore a variety of replication concepts and empirical methods, ultimately culminating in the development of a tractable method for context analysis (TCA). TCA enables the quantitative evaluation of context variables in greater detail, with greater statistical power, and via considerably smaller datasets than previously possible. As we show (via a complex, real-world example), the method ultimately enables the empirically and statistically-grounded reconciliation and generalization of otherwise contradictory results across dissimilar replications—which problem has previously remained unsolved in software engineering.