Government scrutiny and intensified oversight have dramatically changed the landscape of education in recent years. Observers want to know how schools compare, which district is best, which states are spending the most per student on education, whether reforms are making a difference, and why so many students are failing. Some of these questions require technical answers that educators historically redirected to outside experts, but the questions leveled at all educators have become so acute and persistent that they can no longer be outsourced. This text helps educators develop the tools and the conceptual understanding needed to provide definitive answers to difficult statistical questions facing education today.

The chief executive officer of a corporation is not much different from a public school administrator. While CEOs base many of their decisions on data, for school administrators, this type of research may conjure up miserable memories of searching for information to meet a graduate school requirement. However, the value of data-based decision making will continue to escalate and the school community—students, teachers, parents and the general public—expect this information to come from their administrators. Administrators are called on to be accountable, but few are capable of presenting the mountain of data that they collect in a cohesive and strategic manner. Most statistical books are focused on statistical theory versus application, but Statistics Made Simple for School Leaders presents statistics in a simple, practical, conceptual, and immediately applicable manner. It enables administrators to take their data and manage it into strategic information so the results can be used for action plans that benefit the school system. The approach is 'user friendly' and leaves the reader with a confident can-do attitude to communicate results and plans to staff and the community.

"Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text's integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: assessment; statistics; instructional and differentiated psychology; classroom management."--Publisher's description.

In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of evidence, such as scores on students’ assessments, classroom observations etc. This book supports policy-makers, people working with schools, researchers and school leaders and teachers in the use of data, by bringing together the current research conducted on data use across multiple countries into a single volume. Some of these studies are ‘best practice’ studies, where effective data use has led to improvements in student learning. Others provide insight into challenges in both policy and practice environments. Each of them draws on research and literature in the field.

Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.

Improve instructional leadership practice with proven, easy-to-understand strategies for data-based decision making! This reader-friendly second edition of Schools and Data provides real-world examples and step-by-step procedures for collecting and organizing data, providing every school leader with the means to facilitate more appropriate and effective decision making. With a highly practical method for statistical analysis, this highly accessible resource places special emphasis on: Connecting statistics and educators’ daily work Integrating Excel and SPSS technology Strengthening educators’ data interpretation skills Increasing the focus on correlation and regression Building strong skills in problem analysis, program evaluation, data-driven decision making, and report preparation

With the new federal law, No Child Left Behind, there is ever increasing pressure on schools to be accountable for improving student achievement. That pressure is taking the form of focused efforts around data-driven decision making. However, very little is known about what data-driven decision making can really tell one about improving achievement nor is there a full explanation available about what it really takes to do this work. The few examples that do exist, while proposing to get at some of these issues, make huge assumptions about educators' knowledge base and available resources necessary for success. In this book, Philip Streifer fills the gaps by laying out how this work can be done and then explains what is knowable when one actually conducts these analyses and what follow-up steps are needed to make true improvements. He provides readers with a comprehensive understanding of what data-driven decision making can and cannot tell educators about student achievement and addresses the related issues for leadership, policy development, and accountability. Senior level district administration for policy development, school level administrators who have to put policy into practice, and graduate college professors teaching data-driven decision making will find this book most useful.

Statistics education as proposed in this framework can promote the must-have competencies for graduates to thrive in the modern world.

Streifer (educational leadership, U. of Connecticut) urges the application of data-driven decision making to educational questions. He explains the processes and methodologies involved and outlines their uses in the educational sector. Chapters concentrate on longitudinal analysis for program evaluation, equity issues, the local context, budgetary control and reallocation, school improvement and systems thinking, data extraction and analysis, and information technology. Annotation copyrighted by Book News, Inc., Portland, OR

This book presents the breadth and diversity of empirical and practical work done on statistics education around the world. A wide range of methods are used to respond to the research questions that form it's base. Case studies of single students or teachers aimed at understanding reasoning processes, large-scale experimental studies attempting to generalize trends in the teaching and learning of statistics are both employed. Various epistemological stances are described and utilized. The teaching and learning of statistics is presented in multiple contexts in the book. These include designed settings for young children, students in formal schooling, tertiary level students, vocational schools, and teacher professional development. A diversity is evident also in the choices of what to teach (curriculum), when to teach (learning trajectory), how to teach (pedagogy), how to demonstrate evidence of learning (assessment) and what challenges teachers and students face when they solve statistical problems (reasoning and thinking).