This volume is of interest to science educators, graduate students, and classroom teachers. The book will also be an important addition to any scholarly library focusing on science education, science literacy, and writing. This book is unique in that it synthesizes the research of the three leading researchers in the field of writing to learn science: Carolyn S. Wallace, Brian Hand, and Vaughan Prain. It includes a comprehensive review of salient literature in the field, detailed reports of the authors' own research studies, and current and future issues on writing in science. The book is the first to definitely answer the question, "Does writing improve science learning?". Further, it provides evidence for some of the mechanisms through which learning occurs. It combines both theory and practice in a unique way. Although primarily a tool for research, classroom teachers will also find many practical suggestions for using writing in the science classroom.

This volume is of interest to science educators, graduate students, and classroom teachers. The book will also be an important addition to any scholarly library focusing on science education, science literacy, and writing. This book is unique in that it synthesizes the research of the three leading researchers in the field of writing to learn science: Carolyn S. Wallace, Brian Hand, and Vaughan Prain. It includes a comprehensive review of salient literature in the field, detailed reports of the authors'' own research studies, and current and future issues on writing in science. The book is the first to definitely answer the question, "Does writing improve science learning?". Further, it provides evidence for some of the mechanisms through which learning occurs. It combines both theory and practice in a unique way. Although primarily a tool for research, classroom teachers will also find many practical suggestions for using writing in the science classroom.

This volume is of interest to science educators, graduate students, and classroom teachers. The book will also be an important addition to any scholarly library focusing on science education, science literacy, and writing. This book is unique in that it synthesizes the research of the three leading researchers in the field of writing to learn science: Carolyn S. Wallace, Brian Hand, and Vaughan Prain. It includes a comprehensive review of salient literature in the field, detailed reports of the authors' own research studies, and current and future issues on writing in science. The book is the first to definitely answer the question, "Does writing improve science learning?". Further, it provides evidence for some of the mechanisms through which learning occurs. It combines both theory and practice in a unique way. Although primarily a tool for research, classroom teachers will also find many practical suggestions for using writing in the science classroom.

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.

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.

This volume is of interest to science educators, graduate students, and classroom teachers. The book will also be an important addition to any scholarly library focusing on science education, science literacy, and writing. This book is unique in that it synthesizes the research of the three leading researchers in the field of writing to learn science: Carolyn S. Wallace, Brian Hand, and Vaughan Prain. It includes a comprehensive review of salient literature in the field, detailed reports of the authors' own research studies, and current and future issues on writing in science. The book is the first to definitely answer the question, "Does writing improve science learning?". Further, it provides evidence for some of the mechanisms through which learning occurs. It combines both theory and practice in a unique way. Although primarily a tool for research, classroom teachers will also find many practical suggestions for using writing in the science classroom.

This volume is of interest to science educators, graduate students, and classroom teachers. The book will also be an important addition to any scholarly library focusing on science education, science literacy, and writing. This book is unique in that it synthesizes the research of the three leading researchers in the field of writing to learn science: Carolyn S. Wallace, Brian Hand, and Vaughan Prain. It includes a comprehensive review of salient literature in the field, detailed reports of the authors'' own research studies, and current and future issues on writing in science. The book is the first to definitely answer the question, "Does writing improve science learning?". Further, it provides evidence for some of the mechanisms through which learning occurs. It combines both theory and practice in a unique way. Although primarily a tool for research, classroom teachers will also find many practical suggestions for using writing in the science classroom.

This volume is of interest to science educators, graduate students, and classroom teachers. The book will also be an important addition to any scholarly library focusing on science education, science literacy, and writing. This book is unique in that it synthesizes the research of the three leading researchers in the field of writing to learn science: Carolyn S. Wallace, Brian Hand, and Vaughan Prain. It includes a comprehensive review of salient literature in the field, detailed reports of the authors' own research studies, and current and future issues on writing in science. The book is the first to definitely answer the question, "Does writing improve science learning?". Further, it provides evidence for some of the mechanisms through which learning occurs. It combines both theory and practice in a unique way. Although primarily a tool for research, classroom teachers will also find many practical suggestions for using writing in the science classroom.

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.

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.