Política de educação e discurso político: The American Federation for Children redes

This article presents findings from an analysis of the AFC policy network using tools from network ethnography and qualitative content analysis. Specifically, we examined tax forms and carried out extensive web searches to spatialize and map the AFC network, mined text from policy-actors in the AFC network, and analyzed the policy discourse promoted by these network actors to achieve their political goals. The task for this study was to use AFC as a heuristic device to explore the complexity of the education policy field and to understand how network policy-actors work to achieve their policy goals through advocacy and marketing. Findings from the study indicate that the AFC network demonstrates a hierarchical ordering, this hierarchical ordering is reflective of the elite planning and social engineering associated with neoliberal reforms, and that the policy-actors in the AFC network employ discursive strategies to frame an elite political project to advance school choice policies as an anti-elite movement oriented toward political empowerment and educational justice.Este artículo presenta los resultados de un análisis de la red política American Federation for Children Network (AFC) utilizando herramientas de red de la etnografía y análisis de contenido cualitativo. En concreto, examinamos formularios de impuestos y realizó extensas investigaciones en la web para espacializar y asignar la red AFC, texto extraído de políticos-actores en la red AFC, y analizados por el discurso político Promovido Estos actores de la red para alcanzar sus objetivos políticos. La tarea para este estudio fue utilizar el AFC como un dispositivo heurístico para explorar la complejidad de la política educativa y entender cómo los actores de política de red trabajan para alcanzar sus objetivos de política a través de la defensa y el marketing. Las conclusiones del estudio indican que la red AFC demuestra una ordenación jerárquica, esta ordenación jerárquica es el reflejo de la planificación de la elite y la ingeniería social asociadas a las reformas neoliberales, y que la política-actores al servicio de la red estrategias discursivas AFC para encuadrar un proyecto político élite para hacer avanzar las políticas de elección de la escuela como un movimiento político anti-elite orientado hacia la capacitación y la justicia educativa.Este artigo apresenta os resultados de uma análise da rede política American Federation for Children Network (AFC) utilizando ferramentas de rede da etnografia e análise de conteúdo qualitativa. Especificamente, nós examinamos formulários de impostos e realizou extensas pesquisas na web para espacializar e mapear a rede AFC, texto extraído de políticos-atores na rede AFC, e analisados pelo discurso político Promovido Estes atores da rede para atingir seus objetivos políticos. A tarefa para este estudo foi usar o AFC como um dispositivo heurístico para explorar a complexidade da política educacional e entender como os atores de política de rede trabalham para alcançar seus objetivos de política por meio de advocacy e marketing. Conclusões do estudo indicam que a rede AFC Demonstra uma ordenação hierárquica, esta ordenação hierárquica é o reflexo do planejamento elite e engenharia social associados às reformas neoliberais, e que a política-atores a serviço da rede estratégias discursivas AFC para enquadrar um projeto político elite para fazer avançar as políticas de escolha da escola como um movimento político anti-elite voltada para a capacitação e justiça educacional

Quasi-experimental study designs series –Paper 9: Collecting Data from Quasi-Experimental Studies

Objective: To identify variables that must be coded when synthesizing primary studies that use quasi-experimental designs.  Study Design and Setting: All quasi-experimental (QE) designs.  Results: When designing a systematic review of QE studies potential sources of heterogeneity – both theory-based and methodological – must be identified. We outline key components of inclusion criteria for syntheses of quasi-experimental studies. We provide recommendations for coding content-relevant and methodological variables, and outlined the distinction between bivariate effect sizes and partial (i.e., adjusted) effect sizes. Designs used and controls employed are viewed as of greatest importance. Potential sources of bias and confounding are also addressed.  Conclusion: Careful consideration must be given to inclusion criteria and the coding of theoretical and methodological variables during the design phase of a synthesis of quasi-experimental studies. The success of the meta-regression analysis relies on the data available to the meta-analyst. Omission of critical moderator variables (i.e., effect modifiers) will undermine the conclusions of a meta-analysis

Quasi-experimental study designs series – Paper 10: Synthesizing evidence for effects collected from quasi-experimental studies presents surmountable challenges

Objective: To outline issues of importance to analytic approaches to the synthesis of quasi-experiments (QEs), and to provide a statistical model for use in analysis. Study Design and Setting: We drew on the literatures of statistics, epidemiology, and social-science methodology to outline methods for synthesis of QE studies. The design and conduct of quasi-experiments, effect sizes from QEs, and moderator variables for the analysis of those effect sizes were discussed. Results: Biases, confounding, design complexities and comparisons across designs offer serious challenges to syntheses of QEs. Key components of meta-analyses of QEs were identified, including the aspects of QE study design to be coded and analyzed. Of utmost importance are the design and statistical controls implemented in the QEs. Such controls and any potential sources of bias and confounding must be modeled in analyses, along with aspects of the interventions and populations studied. Because of such controls, effect sizes from QEs are more complex than those from randomized experiments. A statistical meta-regression model that incorporates important features of the QEs under review was presented. Conclusion: Meta-analyses of quasi-experiments provide particular challenges, but thorough coding of intervention characteristics and study methods, along with careful analysis, should allow for sound inferences

Quasi-experimental study designs series-paper 6: risk of bias assessment.

OBJECTIVES: Rigorous and transparent bias assessment is a core component of high-quality systematic reviews. We assess modifications to existing risk of bias approaches to incorporate rigorous quasi-experimental approaches with selection on unobservables. These are nonrandomized studies using design-based approaches to control for unobservable sources of confounding such as difference studies, instrumental variables, interrupted time series, natural experiments, and regression-discontinuity designs. STUDY DESIGN AND SETTING: We review existing risk of bias tools. Drawing on these tools, we present domains of bias and suggest directions for evaluation questions. RESULTS: The review suggests that existing risk of bias tools provide, to different degrees, incomplete transparent criteria to assess the validity of these designs. The paper then presents an approach to evaluating the internal validity of quasi-experiments with selection on unobservables. CONCLUSION: We conclude that tools for nonrandomized studies of interventions need to be further developed to incorporate evaluation questions for quasi-experiments with selection on unobservables

Estimating outcome-specific effects in meta-analyses of multiple outcomes: A simulation study

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size. However, researchers are sometimes interested in obtaining separate effect size estimates for different types of outcomes. A recent simulation study (Park & Beretvas, 2019) has compared the performance of the three-level approach and the RVE method in estimating outcome-specific effects when several effect sizes are reported for different types of outcomes within studies. The goal of this paper is to extend that study by incorporating additional simulation conditions and by exploring the performance of additional models, such as the multivariate model, a three-level model that specifies different study-effects for each type of outcome, a three-level model that specifies a common study-effect for all outcomes, and separate three-level models for each type of outcome. Additionally, we also tested whether the a posteriori application of the RV correction improves the standard error estimates and the 95% confidence intervals. Results show that the application of separate three-level models for each type of outcome is the only approach that consistently gives adequate standard error estimates. Also, the a posteriori application of the RV correction results in correct 95% confidence intervals in all models, even if they are misspecified, meaning that Type I error rate is adequate when the RV correction is implemented.status: publishe