This book addresses implications for "Gold Standards" of education research-especially in science education and literacy. These standards are meant to provide evidence-based educational outcomes found effective in randomized controlled trials, following patterns of evidence used in medical research. Similar expectations have emerged in other countries-from education ministries, for researchers working with U.S. colleagues, and for researchers with multinational and non-profit support.

The current "Gold Standard" policy, developed in the United States through the 2001 "No Child Left Behind" [NCLB] Act and the 2002 Education Sciences Reform Act, attempts to improve the effects of schooling and enhance educational research. The contributions to this book explore perspectives on how best to implement multiple standards of education research.

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This book addresses implications for "Gold Standards" of education researchespecially in science education and literacy. These standards are meant to provide evidence-based educational outcomes found effective in randomized controlled trials, following patterns of evidence used in medical research. Similar expectations have emerged in other countriesfrom education ministries, for researchers working with U.S. colleagues, and for researchers with multinational and non-profit support.

Résumé
Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a response variable (sometimes called dependent variable) and at least one predictor variable (sometimes called independent or explanatory variable). When investigating a possible cause-and-effect type of relationship, the response variable is the putative effect and the predictors are the hypothesized causes. Typically, there is a main predictor variable of interest; other predictors in the model are called covariates. Unknown covariates or other independent variables not controlled in an experiment or analysis can affect the dependent or outcome variable and mislead the conclusions made from the inquiry (Bock, Velleman, & De Veaux, 2009). A p value (p) measures the statistical significance of the observed relationship; given the model, p is the probability that a relationship is seen by mere chance. The smaller the p value, the more confident we can be that the pattern seen in the data 2 is not random. In the type of models examined here, the R measures the prop- tion of the variation in the response variable that is explained by the predictors 2 specified in the model; if R is close to 1, then almost all the variation in the response variable has been explained. This measure is also known as the multiple correlation coefficient. Statistical studies can be grouped into two types: experimental and observational.

Contenu
General Introduction.- Education Research Meets the Gold Standard: Evaluation, Research Methods, and Statistics after No Child Left Behind.- Setting the Agenda: Science Education and Science-based Research.- Why Gold Standard Needs Another s: Results from the Gold Standard(s) in Science and Literacy Education Research Conference.- Research and Practice: A Complex Relationship?.- Moving Beyond the Gold Standard: Epistemological and Ontological Considerations of Research in Science Literacy.- Longitudinal Studies into Science Learning: Methodological Issues.- An International Perspective of Monitoring Educational Research Quality: Commonalities and Differences.- Considering Research Quality and Applicability Through the Eyes of Stakeholders.- Curriculum and Pedagogy.- Researching Effective Pedagogies for Developing the Literacies of Science: Some Theoretical and Practical Considerations.- Pedagogy, Implementation, and Professional Development for Teaching Science Literacy: How Students and Teachers Know and Learn.- Approaching Classroom Realities: The Use of Mixed Methods and Structural Equation Modeling in Science Education Research.- Mixed-methodology Research in Science Education: Opportunities and Challenges in Exploring and Enhancing Thinking Dispositions.- New Directions in Science Literacy Education.- Statistics, Research Methods, and Science Literacy.- Multilevel Modeling with HLM: Taking a Second Look at PISA.- Methods from Item Response Theory: Going Beyond Traditional Validity and Reliability in Standardizing Assessments.- Confounding in Observational Studies using Standardized Test Data: Careful Disentanglement of Statistical Interpretations and Explanations.- Predicting Group Membership using National Assessment of Educational Progress(NAEP) Mathematics Data.- Incorporating Exploratory Methods using Dynamic Graphics into Multivariate Statistics Classes: Curriculum Development.- Approaches to Broadening the Statistics Curricula.- Dr. Fox Rocks: Using Data-mining Techniques to Examine Student Ratings of Instruction.- Process Execution of Writing and Reading: Considering Text Quality, Learner and Task Characteristics.- Can We Make a Silk Purse from a Sow's Ear?.- Public Policy and Gold Standard(s) Research.- Speaking Truth to Power with Powerful Results: Impacting Public Awareness and Public Policy.- Funding Patterns and Priorities: An International Perspective.- Research Ethics Boards and the Gold Standard(s) in Literacy and Science Education Research.- Data Sharing: Disclosure, Confidentiality, and Security.- Stitching the Pieces Together to Reveal the Generalized Patterns: Systematic Research Reviews, Secondary Reanalyses, Case-to-case Comparisons, and Metasyntheses of Qualitative Research Studies.- The Gold Standard and Knowing What to Do.- Epilogue: New Standards, New Directions, and New Realities.- Reflections on Beyond the Gold Standards Era and Ways of Promoting Compelling Arguments about Science Literacy for All.