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In the second edition of her critically acclaimed book The Dreamkeepers, Gloria Ladson-Billings revisits the eight teachers who were profiled in the first edition and introduces us to new teachers who are current exemplars of good teaching. She shows that culturally relevant teaching is not a matter of race, gender, or teaching style. What matters most is a teacher's efforts to work with the unique strengths a child brings to the classroom. A brilliant mixture of scholarship and storytelling, The Dreamkeepers challenges us to envision intellectually rigorous and culturally relevant classrooms that have the power to improve the lives of not just African American students, but all children. This new edition also includes questions for reflection

With more than $3.7 billion in assets and annual revenue of $800 million, the Nature Conservancy has generated staggering growth that would be the envy of any business. Incorporated in 1951 by a small circle of concerned ecologists, the Conservancy has grown financially into the world's largest environmental organization. It has one million members–up from 500,000 in 1990–and 3,500 employees operating in 50 states and 28 countries across the world. Nature's Keepers offers readers an inspirational leadership tale and management chronicle, as it goes behind the scenes and details the inner workings of the Nature Conservancy. Highlighting the efforts of nine extraordinary leaders, Nature's Keepers examines the organization's culture and management, strategy and decisions, and courageous and ingenious individuals who have dedicated their lives to conservation. Author Bill Birchard reveals how the Conservancy's sometimes controversial business practices–entrepreneurial approaches to preserving ecosystems while meeting human needs–have earned the praise of management gurus such as Peter Drucker. The Conservancy's way of operating, though not free of failings, is both widely emulated in the nonprofit community and greatly respected by business scholars and CEOs nationwide.

A user-friendly, hands-on guide to recognizing and conducting proper research techniques in data collection Offering a unique approach to numerical research methods, Analyzing Quantitative Data: An Introduction for Social Researchers presents readers with the necessary statistical applications for carrying out the key phases of conducting and evaluating a research project. The book guides readers through the steps of data analysis, from organizing raw data to utilizing descriptive statistics and tests of significance, drawing valid conclusions, and writing research reports. The author successfully provides a presentation that is accessible and hands-on rather than heavily theoretical, outlining the key quantitative processes and the use of software to successfully draw valid conclusions from gathered data. In its discussion of methods for organizing data, the book includes suggestions for coding and entry into spreadsheets or databases while also introducing commonly used descriptive statistics and clarifying their roles in data analysis. Next, inferential statistics is explored in-depth with explanations of and instructions for performing chi-square tests, t-tests, analyses of variance, correlation and regression analyses, and a number of advanced statistical procedures. Each chapter contains explanations of when to use the tests described, relevant formulas, and sample computations. The book concludes with guidance on extracting meaningful conclusions from statistical tests and writing research reports that describe procedures and analyses. Throughout the book, Statistical Resources for SPSS® sections provide fundamental instruction for using SPSS® to obtain the results presented. Where necessary, the author provides basic theoretical explanations for distributions and background information regarding formulas. Each chapter concludes with practice problems, and a related website features derivations of the book's formulas along with additional resources for performing the discussed processes. Analyzing Quantitative Data is an excellent book for social sciences courses on data analysis and research methods at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians and practitioners working in the fields of education, medicine, business and public service who analyze, interpret, and evaluate data in their daily work.

This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.