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Journeys in Science 5 Canadian Edition
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Quality Research in Literacy and Science Education: International Perspectives and Gold Standards
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Condizione: Very Good. 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, and 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.
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Quality Research in Literacy and Science Education: International Perspectives and Gold Standards
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Hardcover. Condizione: Very good. Hardcover. 9 1/2" X 6 1/2". xxix, 666pp. Very mild shelf wear to covers, corners, and edges of paper over boards. Pages are clean and unmarked. Binding is sound. ABOUT THIS BOOK: 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.(Publisher).
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Journeys in Science
Lingua: Inglese
Editore: Collier Macmillan Publishers, 1988
ISBN 10: 0844553050 ISBN 13: 9780844553054
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Quality Research in Literacy and Science Education: International Perspectives and Gold Standards
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Quality Research in Literacy and Science Education
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QUALITY RESEARCH IN LITERACY AND SCIENCE
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Quality Research in Literacy and Science Education
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Quality Research in Literacy and Science Education | International Perspectives and Gold Standards | Mack C. Shelley (u. a.) | Taschenbuch | xxix | Englisch | 2010 | Springer | EAN 9789048178773 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Seiten: 666 | Sprache: Englisch | Produktart: Bücher | 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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Quality Research in Literacy and Science Education
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Quality Research in Literacy and Science Education : International Perspectives and Gold Standards
Lingua: Inglese
Editore: Springer Netherlands, Springer, 2010
ISBN 10: 9048178770 ISBN 13: 9789048178773
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Quality Research in Literacy and Science Education : International Perspectives and Gold Standards
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