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This definitive resource covers every aspect of teaching in higher education. Particular emphasis is placed on the two main concerns facing professors in North America and the UK today; the growing emphasis on the quality of college teaching and, more specifically, the increasingly demanding nature of the profession, whereby lecturers are forced to teach more students on a greater number of courses. Pons' practical advice is supported throughout by research evidence.
Utilizing the latest software, this book presents the essentialstatistical procedures for drawing valuable results from data inthe social sciences. Mobilizing interesting real-world examples from the field ofeducation, Understanding Educational Statistics Using MicrosoftExcel and SPSS supplies a seamless presentation that identifiesvaluable connections between statistical applications and researchdesign. Class-tested to ensure an accessible presentation, the bookcombines clear, step-by-step explanations and the use of softwarepackages that are accessible to both the novice and professionalalike to present the fundamental statistical practices fororganizing, understanding, and drawing conclusions from educationalresearch data. The book begines with an introduction to descriptive andinferential statistics and then proceeds to acquaint readers withthe various functions for working with quantitative data in theMicrosoft Excel environment, such as spreadsheet navigation;sorting and filtering; and creating pivot tables. Subsequentchapters treat the procedures that are commonly-employed whenworking with data across various fields of social science research,including: Single-sample tests Repeated measure tests Independent t-tests One way ANOVA and factorial ANOVA Correlation Bivariate regression Chi square Multiple regression Individual chapters are devoted to specific procedures, eachending with a lab exercise that highlights the importance of thatprocedure by posing a research question, examining the questionthrough its application in Excel and SPSS, and concluding with abrief research report that outlines key findings drawn from theresults. Real-world examples and data from modern educationalresearch are used throughout the book, and a related Web sitefeatures additional data sets, examples, and labs, allowing readersto reinforce their comprehension of the material. Bridging traditional statistical topics with the latest softwareand applications in the field of education, UnderstandingEducational Statistics Using Microsoft Excel and SPSS is anexcellent book for courses on educational research methods andintroductory statistics in the social sciences at theupper-undergraduate and graduate levels. It also serves as avaluable resource for researchers and practitioners in the fieldsof education, psychology, and the social sciences who require astatistical background to work with data in their everydaywork.
In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology. Through examples using real data sets, you’ll discover how to characterize complex multivariate data in: Studies involving genetic databases Patterns in the progression of diseases and disabilities Combinations of topics covered by text documents Political ideology or electorate voting patterns Heterogeneous relationships in networks, and much more The handbook spans more than 20 years of the editors’ and contributors’ statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.
Research in learning and behavioral disabilities, employing a variety of methods and techniques, has provided information relevant to practitioners. This volume discusses, applies and evaluates different methodological approaches to learning and behavioral disorder research; and serves as a reference to educators, researchers, and others.
Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.
The final report of the National Research Council's (NRC) Panel on Statistical Methods for Testing and Evaluating Defense Systems (National Research Council, 1998) was intended to provide broad advice to the U.S. Department of Defense (DoD) on current statistical methods and principles that could be applied to the developmental and operational testing and evaluation of defense systems. To that end, the report contained chapters on the use of testing as a tool of system development; current methods of experimental design; evaluation methods; methods for testing and assessing reliability, availability, and maintainability; software development and testing; and validation of modeling and simulation for use in operational test and evaluation. While the examination of such a wide variety of topics was useful in helping DoD understand the breadth of problems for which statistical methods could be applied and providing direction as to how the methods currently used could be improved, there was, quite naturally, a lack of detail in each area. To address the need for further detail, two DoD agencies-the Office of the Director of Operational Test and Evaluation and the Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics-asked the NRC's Committee on National Statistics to initiate a series of workshops on statistical issues relevant to defense acquisition. The aim of each workshop is to inform DoD about the methods that represent the statistical state of the art and, through interactions of the statistical and defense communities, explore their relevance for DoD application.

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