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Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition
by Charles M. Judd Gary H. McClelland Carey S. RyanData Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: a new chapter on logistic regression; expanded treatment of mixed models for data with multiple random factors; updated examples; an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.
Data Analysis for Continuous School Improvement
by Victoria L. BernhardtThis book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of data. The updated edition provides a wealth of tools, protocols, timelines, examples, and strategies that will help schools and districts become genuine learning organizations. Data Analysis for Continuous School Improvement provides a new definition of school improvement, away from a singular focus on compliance, toward a true commitment to excellence.
Data Analysis for Continuous School Improvement
by Victoria L. BernhardtThis book is a call to action. It is about inspiring schools and school districts to commit to continuous school improvement by providing a framework that will result in improving teaching for every teacher and learning for every student through the comprehensive use of data. A culmination of over 30 years of doing the hard work in schools and districts both nationally and internationally, Data Analysis for Continuous School Improvement shares new, evidence-based learnings about how to analyze, report, communicate, and use multiple measures of data. The updated edition provides a wealth of tools, protocols, timelines, examples, and strategies that will help schools and districts become genuine learning organizations. Data Analysis for Continuous School Improvement provides a new definition of school improvement, away from a singular focus on compliance, toward a true commitment to excellence.
Data Analysis for the Social Sciences: Integrating Theory and Practice
by Professor Douglas Bors'This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.' —Ruth Horry, Psychology, Swansea University 'This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.' —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
Data Analytics and Adaptive Learning: Research Perspectives
by Patsy D. Moskal, Charles D. Dziuban, and Anthony G. PiccianoData Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K–12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
Data Analytics and Adaptive Learning: Research Perspectives
Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K–12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
Data Analytics Approaches in Educational Games and Gamification Systems (Smart Computing and Intelligence)
by Ahmed Tlili Maiga ChangGame-based learning environments and learning analytics are attracting increasing attention from researchers and educators, since they both can enhance learning outcomes. This book focuses on the application of data analytics approaches and research on human behaviour analysis in game-based learning environments, namely educational games and gamification systems, to provide smart learning. Specifically, it discusses the purposes, advantages and limitations of applying such approaches in these environments. Additionally, the various smart game-based learning environments presented help readers integrate learning analytics in their educational games and gamification systems to, for instance, assess and model students (e.g. their computational thinking) or enhance the learning process for better outcomes. Moreover, the book presents general guidelines on various aspects, such as collecting data for analysis, game-based learning environment design, system architecture and applied algorithms, which facilitate incorporating learning analytics into educational games and gamification systems.After a general introduction to help readers become familiar with the subject area, the individual chapters each discuss a different aim of applying data analytics approaches in educational games and gamification systems. Lastly, the conclusion provides a summary and presents general guidelines and frameworks to consider when designing smart game-based learning environments with learning analytics.
Data and Policy Change: The Fragility of Data in the Policy Context
by David DeryThis is a work on "hostile" data and the conditions under which they are accepted and rejected. What is the place of data in politics and organization? Why are politicians and administrators so often hostile to research data, or why do they tend to perceive data as hostile to them? How can data become relevant to policy? These questions are the focus of this book. In answer I try to show how political and administrative institutions cope with "hostile" data; how they seek to maintain closedness to disconfirming data, and how they are led, in a free society, to change their policies despite the epistemological bias in favor of the already known and the initial inclination to resist change. At the same time, I demonstrate that data producers must learn that while their research findings may be subjected to science's own standards of verifiability, such data must also meet standards of contestability by the various interests involved in political and administrative decisions. The production and "appropriate" publication of a research report may at best buy one an admission ticket to participate in political and administrative contests, but not the power nor the justification to determine the outcomes of the contest. I begin with two hypotheses: Hypothesis 1: Politicians or administrators reject data that do not coincide with behavior they are unwilling to change. Hypothesis II: Politicians or administrators change behavior that does not coincide with data they are unwilling to reject.
Data, Architecture and the Experience of Place
by Anastasia KarandinouThe notion of data is increasingly encountered in spatial, creative and cultural studies. Big data and artificial intelligence are significantly influencing a number of disciplines. Processes, methods and vocabularies from sciences, architecture, arts are borrowed, discussed and tweaked, and new cross-disciplinary fields emerge. More and more, artists and designers are drawing on hard data to interpret the world and to create meaningful, sensuous environments. Architects are using neurophysiological data to improve their understanding of people’s experiences in built spaces. Different disciplines collaborate with scientists to visualise data in different and creative ways, revealing new connections, interpretations and readings. This often demonstrates a genuine desire to comprehend human behaviour and experience and to – possibly – inform design processes accordingly. At the same time, this opens up questions as to why this desire and curiosity is emerging now, how it relates to recent technological advances and how it converses with the cultural, philosophical and methodological context of the disciplines with which it engages. Questions are also raised as to how the use of data and data-informed methods may serve, support, promote and/or challenge political agendas. Data, Architecture and the Experience of Place provides an overview of new approaches on this significant subject and is ideal for students and researchers in digital architecture, architectural theory, design, digital media, sensory studies and related fields.
Data, Architecture and the Experience of Place
by Anastasia KarandinouThe notion of data is increasingly encountered in spatial, creative and cultural studies. Big data and artificial intelligence are significantly influencing a number of disciplines. Processes, methods and vocabularies from sciences, architecture, arts are borrowed, discussed and tweaked, and new cross-disciplinary fields emerge. More and more, artists and designers are drawing on hard data to interpret the world and to create meaningful, sensuous environments. Architects are using neurophysiological data to improve their understanding of people’s experiences in built spaces. Different disciplines collaborate with scientists to visualise data in different and creative ways, revealing new connections, interpretations and readings. This often demonstrates a genuine desire to comprehend human behaviour and experience and to – possibly – inform design processes accordingly. At the same time, this opens up questions as to why this desire and curiosity is emerging now, how it relates to recent technological advances and how it converses with the cultural, philosophical and methodological context of the disciplines with which it engages. Questions are also raised as to how the use of data and data-informed methods may serve, support, promote and/or challenge political agendas. Data, Architecture and the Experience of Place provides an overview of new approaches on this significant subject and is ideal for students and researchers in digital architecture, architectural theory, design, digital media, sensory studies and related fields.
Data-based Decision Making in Education: Challenges and Opportunities (Studies in Educational Leadership #17)
by Kim Schildkamp, Mei Kuin Lai and Lorna EarlIn 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 Center Virtualization Certification: Everything you need to achieve 2V0-622 certification – with exam tips and exercises
by Andrea Mauro Paolo ValsecchiDeploy and configure vSphere infrastructure and learn to effectively create and administer vSphere virtual machines Key Features Implement advanced network virtualization techniques Configure and administer vSphere high availability Enhance your data center virtualization skills with practice questions and mock tests Book Description This exam guide enables you to install, configure, and manage the vSphere 6.5 infrastructure in all its components: vCenter Server, ESXi hosts, and virtual machines, while helping you to prepare for the industry standard certification. This data center book will assist you in automating administration tasks and enhancing your environment's capabilities. You will begin with an introduction to all aspects related to security, networking, and storage in vSphere 6.5. Next, you will learn about resource management and understand how to back up and restore the vSphere 6.5 infrastructure. As you advance, you will also cover troubleshooting, deployment, availability, and virtual machine management. This is followed by two mock tests that will test your knowledge and challenge your understanding of all the topics included in the exam. By the end of this book, you will not only have learned about virtualization and its techniques, but you'll also be prepared to pass the VCP6.5-DCV (2V0-622) exam. What you will learn Deploy and configure vSphere infrastructure Create and administer vSphere virtual machines Optimize, secure, and troubleshoot all vSphere components Implement vSphere HA on a vSAN cluster Understand how to back up and restore your vSphere 6.5 infrastructure Test your understanding of key concepts required through sample questions Who this book is for If you are interested in achieving Data Center Virtualization certification, this is the book is for you. You will also benefit from this book if you are a system administrator or network engineer. Some prior knowledge of virtualization can assist you in understanding key concepts covered in the book.
Data Center Virtualization Certification: Everything you need to achieve 2V0-622 certification – with exam tips and exercises
by Andrea Mauro Paolo ValsecchiDeploy and configure vSphere infrastructure and learn to effectively create and administer vSphere virtual machines Key Features Implement advanced network virtualization techniques Configure and administer vSphere high availability Enhance your data center virtualization skills with practice questions and mock tests Book Description This exam guide enables you to install, configure, and manage the vSphere 6.5 infrastructure in all its components: vCenter Server, ESXi hosts, and virtual machines, while helping you to prepare for the industry standard certification. This data center book will assist you in automating administration tasks and enhancing your environment's capabilities. You will begin with an introduction to all aspects related to security, networking, and storage in vSphere 6.5. Next, you will learn about resource management and understand how to back up and restore the vSphere 6.5 infrastructure. As you advance, you will also cover troubleshooting, deployment, availability, and virtual machine management. This is followed by two mock tests that will test your knowledge and challenge your understanding of all the topics included in the exam. By the end of this book, you will not only have learned about virtualization and its techniques, but you'll also be prepared to pass the VCP6.5-DCV (2V0-622) exam. What you will learn Deploy and configure vSphere infrastructure Create and administer vSphere virtual machines Optimize, secure, and troubleshoot all vSphere components Implement vSphere HA on a vSAN cluster Understand how to back up and restore your vSphere 6.5 infrastructure Test your understanding of key concepts required through sample questions Who this book is for If you are interested in achieving Data Center Virtualization certification, this is the book is for you. You will also benefit from this book if you are a system administrator or network engineer. Some prior knowledge of virtualization can assist you in understanding key concepts covered in the book.
Data Collection: Planning for and Collecting All Types of Data (Measurement and Evaluation Series #175)
by Patricia Pulliam Phillips Cathy A. StawarskiData Collection Data Collection is the second of six books in the Measurement and Evaluation Series from Pfeiffer. The proven ROI Methodology--developed by the ROI Institute--provides a practical system for evaluation planning, data collection, data analysis, and reporting. All six books in the series offer the latest tools, most current research, and practical advice for measuring ROI in a variety of settings. Data Collection offers an effective process for collecting data that is essential to the implementation of the ROI Methodology. The authors outline the techniques, processes, and critical issues involved in successful data collection. The book examines the various methods of data collection, including questionnaires, interviews, focus groups, observation, action plans, performance contracts, and monitoring records. Written for evaluators, facilitators, analysts, designers, coordinators, and managers, Data Collection is a valuable guide for collecting data that are adequate in quantity and quality to produce a complete and credible analysis.
Data Collection: Planning for and Collecting All Types of Data (Measurement and Evaluation Series #175)
by Patricia Pulliam Phillips Cathy A. StawarskiData Collection Data Collection is the second of six books in the Measurement and Evaluation Series from Pfeiffer. The proven ROI Methodology--developed by the ROI Institute--provides a practical system for evaluation planning, data collection, data analysis, and reporting. All six books in the series offer the latest tools, most current research, and practical advice for measuring ROI in a variety of settings. Data Collection offers an effective process for collecting data that is essential to the implementation of the ROI Methodology. The authors outline the techniques, processes, and critical issues involved in successful data collection. The book examines the various methods of data collection, including questionnaires, interviews, focus groups, observation, action plans, performance contracts, and monitoring records. Written for evaluators, facilitators, analysts, designers, coordinators, and managers, Data Collection is a valuable guide for collecting data that are adequate in quantity and quality to produce a complete and credible analysis.
Data Conversion: Calculating the Monetary Benefits (Measurement and Evaluation Series #176)
by Patricia Pulliam Phillips Holly BurkettThis book tackles the third major challenge and the second most difficult step in the ROI methodology: converting data to monetary values. When a particular project or program is connected to a business measure, the next logical question is: what is the monetary value of that impact? For ROI analysis, it is at this critical point where the monetary benefits are developed to compare to the costs of the program to calculate the ROI. Includes: the importance of converting data to monetary value; preliminary issues; standard values: the standard values: where to find them; using internal experts, using external databases; linking with other measures; using estimates; when to abandon conversion efforts and leave data as intangible, analyzing the intangibles; and reporting the intangibles.
Data Cultures in Higher Education: Emergent Practices and the Challenge Ahead (Higher Education Dynamics #59)
by Juliana E. Raffaghelli Albert SangràThis collection focuses on the role of higher education institutions concerning datafication as a complex phenomenon. It explores how the universities can develop data literac(ies) shaping tomorrow skills and “formae mentis” to face the most deleterious effects of datafication, but also to engage in creative and constructive ways with data. Notably, the book spots data practices within the two most relevant sides of academics’ professional practice, namely, research and teaching. Hence, the collection seeks to reflect on faculty’s professional learning about data infrastructures and practices.The book draws on a range of studies covering the higher education response to the several facets of data in society, from data surveillance and the algorithmic control of human behaviour to empowerment through the use of open data. The research reported ranges from literature overviews to multi-case and in-depth case studies illustrating institutional and educational responses to different problems connected to data. The ultimate intention is to provide conceptual bases and practical examples relating to universities’ faculty development policies to overcome data practices and discourses' fragmentation and contradictions: in a nutshell, to build “fair data cultures” in higher education.
Data, Data Everywhere: Bringing All the Data Together for Continuous School Improvement
by Victoria L. BernhardtThe latest book from best-selling author Victoria L. Bernhardt is an easy-to-read primer that describes what it takes to achieve student learning growth at every grade level, in every subject area, and with every student group. In this new edition, readers will learn how to use data to inform their continuous school improvement as they develop an appreciation of the various types of data, uses for data, and how data are involved in the process. This accessible, updated edition provides a wealth of straightforward and accessible strategies that will allow educators to become comfortable with the many uses of data in increasing student improvement. Data, Data Everywhere, 2nd edition, provides a framework and summary of the continuous school improvement framework. It is a perfect resource for teachers, administrators, support staff, and students of leadership to guide comprehensive school improvement that will make a difference for all students.
Data, Data Everywhere: Bringing All the Data Together for Continuous School Improvement
by Victoria L. BernhardtThe latest book from best-selling author Victoria L. Bernhardt is an easy-to-read primer that describes what it takes to achieve student learning growth at every grade level, in every subject area, and with every student group. In this new edition, readers will learn how to use data to inform their continuous school improvement as they develop an appreciation of the various types of data, uses for data, and how data are involved in the process. This accessible, updated edition provides a wealth of straightforward and accessible strategies that will allow educators to become comfortable with the many uses of data in increasing student improvement. Data, Data Everywhere, 2nd edition, provides a framework and summary of the continuous school improvement framework. It is a perfect resource for teachers, administrators, support staff, and students of leadership to guide comprehensive school improvement that will make a difference for all students.
The Data Deluge: Can Libraries Cope with E-Science?
by Deanna B. Marcum Gerald GeorgeAn essential collection of essays for librarians looking to support E-science programs and capabilities to their institutions.From the frontiers of contemporary information science research comes this helpful and timely volume for libraries preparing for the deluge of data that E-science can deliver to their patrons and institutions. The Data Deluge: Can Libraries Cope with E-Science? brings together nine of the world's foremost authorities on the capabilities and requirements of E-science, offering their perspectives to librarians hoping to develop similar programs for their own institutions.The essays contained in The Data Deluge were adapted from papers first delivered at the prestigious annual Library Round Table at the Kanazawa Institute of Technology, where E-science has been the theme from the past two annual conferences. Now this groundbreaking work is available in convenient printed format for the first time. The essays are divided into three parts: an overview of E-science challenges for libraries; perspectives on E-science; and perspectives from individual research libraries.
Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings (Lecture Notes in Computer Science #10474)
by Élise Lavoué Hendrik Drachsler Katrien Verbert Julien Broisin Mar Pérez-SanagustínThis book constitutes the proceedings of the 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, held in Tallinn, Estonia, in September 2017.The 24 full papers, 23 short papers, 6 demo papers, and 22 poster papers presented in this volume were carefully reviewed and selected from 141 submissions.The theme for the 12th EC-TEL conference on Data Driven Approaches in Digital Education' aims to explore the multidisciplinary approaches thateectively illustrate how data-driven education combined with digital education systems can look like and what are the empirical evidences for the use of datadriven tools in educational practices.
Data-Driven Decision Making and Dynamic Planning: A School Leader's Guide
by Paul PreussThis book will help you understand how to integrate data-based decisions into the daily work of the school. It is a practical and relevant handbook for converting data into wise decision-making and planning. It will give you the skills to successfully make data-based decisions, measure student learning and program effectiveness, evaluate student progress, use data to improve instruction, integrate a "Dynamic Planning" process into the daily operation of your school.
Data-Driven Decision Making and Dynamic Planning
by Paul PreussThis book will help you understand how to integrate data-based decisions into the daily work of the school. It is a practical and relevant handbook for converting data into wise decision-making and planning. It will give you the skills to successfully make data-based decisions, measure student learning and program effectiveness, evaluate student progress, use data to improve instruction, integrate a "Dynamic Planning" process into the daily operation of your school.
Data-Driven Decision-Making in Schools: Lessons From Trinidad
by J. Yamin-AliYamin-Ali shows how schools can undertake responsible decision-making through gathering and evaluating data, using as examples six fully developed case studies that shed light on common questions of school culture and student life, including student stress, subject selection, and the role of single-sex classes.
Data-Driven Design for Computer-Supported Collaborative Learning: Design Matters (Lecture Notes in Educational Technology)
by Lanqin ZhengThis book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design collaborative learning activities based on data-driven design approaches, while the second shares illustrative examples of computer-supported collaborative learning activities. In turn, the third and last section demonstrates how to evaluate design quality and the fidelity of enactment based on design-centered research.The book features several examples of innovative data-driven design approaches to optimizing collaborative learning activities; highlights innovative CSCL activities in authentic learning environments; demonstrates how learning analytics can be used to optimize CSCL design; and discusses the design-centered research approach to evaluating the alignment between design and enactment in CSCL. Given its scope, it will be of interest to a broad readership including researchers, educators, practitioners, and students in the field of collaborative learning, as well as the rapidly growing community of people who are interested in optimizing learning performance with CSCL.