Statements can be made only with regard to the overall quality of life of a child. In the studies of early childhood development, it is difficult to differentiate between correlates of post facto resiliency outcomes and attributes of age-appropriate positive functioning. At present, the measurement is of crucial importance for studies of resilience considered as a dynamic characteristic of functioning. Empirical evidence does not support the linear increase of resilience with the child’s age. The overall effect sizes are small, the total number of participants is 19 300. Our findings suggest that a child’s individual characteristics are somewhat more related to resilience than his / her interpersonal relations or the setting of a community network.
connectedness with peers and other competent adults), and characteristics of Community. Attributes of resilience were treated as moderator variables and assigned to one of three categories according to the framework of the study, namely, individual characteristics (classified through the domains of child cognition, self-perception and temperament / personality traits), characteristics of Interpersonal relatedness (domain of close relationships within family, domain of relations outside family, i. We used the Comprehensive Meta-Analysis V2 software program and applied the guidelines for psychometric meta-analysis. The aim of the study was to investigate the attributes of a child’s positive functioning in face of maltreatment. Domain-specific resources accounted for the majority of attributes of resilience. In face of substantial and unbiased empirical evidence (published in scientific databases before 2010), research questions were raised about extant verifiable explanatory knowledge as well as implications for countries just starting such research. Moreover, OpenMeta features flexible plotting functionality through R.The growing field of empirical studies on child’s resilience encouraged us to conduct a meta-analysis in order to integrate the findings across studies targeted at child’s adaptive functioning after experiences of maltreatment. It provides many advanced meta-analytic routines and features an intuitive GUI. OpenMeta has been designed to handle complex data structures, including multiple treatment groups, multiple follow-ups (network meta-analysis) and multiple outcomes. Results & Conclusions: We introduce OpenMeta a new, cross-platform, entirely open-source version of our Meta-Analyst software. Both R and Python are themselves open-source and cross-platform. However, the underlying use of R is transparent to end-users OpenMeta is a stand-alone program, with a spreadsheet-based graphical user interface (GUI) written in the Python programming language. Methods: All analytic methods in OpenMeta are written and executed in the R programming language this allows us to leverage previously written meta-analysis code, including Bayesian applications such as network meta-analysis written in BUGS Moreover, researchers can implement their own methods in R and plug them into the software. To this end, we are developing a new R package that contains both basic and advanced meta-analytic methods, including an interface to OpenBugs to fit Bayesian models, with a consistent Application Programming Interface (API), and a Graphical User Interface (GUI) that allows novice analysts (who may not speak R) to easily use this package. Objectives: To combine the strengths of general statistical packages (flexibility) and dedicated meta-analysis programs (ease-of-use) for performing meta-analyses in a stand-alone, cross-platform, open-source meta-analysis program. The theory and statistical foundations of meta-analysis continually evolve, providing solutions to many new and challenging problems.
Background: Meta-analysis is increasingly used as a key source of evidence synthesis to inform clinical practice.
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