Göran Grimvall is determined to help mere mortals understand how scientists get to the kernel of perplexing problems. Entertaining and enlightening, his latest book uses examples from sports, literature, and nature—as well as from the varied worlds of science—to illustrate how scientists make sense of and explain the world around us. Grimvall's fun-to-read essays and easy-to-follow examples detail how order-of-magnitude estimation, extreme cases, dimensional analysis, and other modeling methods work. They also reveal how nonscientists absorb these concepts and use them at home, school, and work. Grimvall's simple, elegant explanations will help you tap into your inner scientist. Read this book and enjoy your own "Aha!" moment.

Since 1986, the British government, faced with dwindling budgets and growing calls for public accountability, has sought to assess the value of scholarly work in the nation’s universities. Administrators have periodically evaluated the research of most full-time academics employed in British universities, seeking to distribute increasingly scarce funding to those who use it best. How do such attempts to quantify the worth of knowledge change the nature of scholarship? Juan Pablo Pardo-Guerra examines the effects of quantitative research evaluations on British social scientists, arguing that the mission to measure academic excellence resulted in less diversity and more disciplinary conformity. Combining interviews and original computational analyses, The Quantified Scholar provides a compelling account of how scores, metrics, and standardized research evaluations altered the incentives of scientists and administrators by rewarding forms of scholarship that were closer to established disciplinary canons. In doing so, research evaluations amplified publication hierarchies and long-standing forms of academic prestige to the detriment of diversity. Slowly but surely, they reshaped academic departments, the interests of scholars, the organization of disciplines, and the employment conditions of researchers. Critiquing the effects of quantification on the workplace, this book also presents alternatives to existing forms of evaluation, calling for new forms of vocational solidarity that can challenge entrenched inequality in academia.

Quantifying the User Experience: Practical Statistics for User Research, Second Edition, provides practitioners and researchers with the information they need to confidently quantify, qualify, and justify their data. The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. It addresses questions users face every day, including, Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on their first attempt? How long will it take users to purchase products on the website? This book provides a foundation for statistical theories and the best practices needed to apply them. The authors draw on decades of statistical literature from human factors, industrial engineering, and psychology, as well as their own published research, providing both concrete solutions (Excel formulas and links to their own web-calculators), along with an engaging discussion on the statistical reasons why tests work and how to effectively communicate results. Throughout this new edition, users will find updates on standardized usability questionnaires, a new chapter on general linear modeling (correlation, regression, and analysis of variance), with updated examples and case studies throughout. - Completely updated to provide practical guidance on solving usability testing problems with statistics for any project, including those using Six Sigma practices - Includes new and revised information on standardized usability questionnaires - Includes a completely new chapter introducing correlation, regression, and analysis of variance - Shows practitioners which test to use, why they work, and best practices for application, along with easy-to-use Excel formulas and web-calculators for analyzing data - Recommends ways for researchers and practitioners to communicate results to stakeholders in plain English

We live in a world where seemingly everything can be measured. We rely on indicators to translate social phenomena into simple, quantified terms, which in turn can be used to guide individuals, organizations, and governments in establishing policy. Yet counting things requires finding a way to make them comparable. And in the process of translating the confusion of social life into neat categories, we inevitably strip it of context and meaning—and risk hiding or distorting as much as we reveal. With The Seductions of Quantification, leading legal anthropologist Sally Engle Merry investigates the techniques by which information is gathered and analyzed in the production of global indicators on human rights, gender violence, and sex trafficking. Although such numbers convey an aura of objective truth and scientific validity, Merry argues persuasively that measurement systems constitute a form of power by incorporating theories about social change in their design but rarely explicitly acknowledging them. For instance, the US State Department’s Trafficking in Persons Report, which ranks countries in terms of their compliance with antitrafficking activities, assumes that prosecuting traffickers as criminals is an effective corrective strategy—overlooking cultures where women and children are frequently sold by their own families. As Merry shows, indicators are indeed seductive in their promise of providing concrete knowledge about how the world works, but they are implemented most successfully when paired with context-rich qualitative accounts grounded in local knowledge.

Quantification forms a significant aspect of cross-linguistic research into both sentence structure and meaning. This book surveys research in quantification starting with the foundational work in the 1970s. It paints a vivid picture of generalized quantifiers and Boolean semantics. It explains how the discovery of diverse scope behaviour in the 1990s transformed the view of quantification, and how the study of the internal composition of quantifiers has become central in recent years. It presents different approaches to the same problems, and links modern logic and formal semantics to advances in generative syntax. A unique feature of the book is that it systematically brings cross-linguistic data to bear on the theoretical issues, covering French, German, Dutch, Hungarian, Russian, Japanese, Telugu (Dravidian), and Shupamem (Grassfield Bantu) and points to formal semantic literature involving quantification in around thirty languages.

In Quantified, Whitworth draws lessons from the world's most tech-savvy, high-impact organizations to show how we can make real gains for the environment. The principles of his approach, dubbed quantified conservation, will be familiar to any thriving entrepreneur: situational awareness, bold outcomes, innovation and technology, data and analytics, and gain-focused investment. As President of The Freshwater Trust, Whitworth has put quantified conservation into practice, pioneering the model of a "do-tank" that is dramatically changing how rivers can get restored across the United States. The stories in Quantified highlight the most precious of resources--water--but they apply to any environmental effort. Whether in the realm of policy, agriculture, business, or philanthropy, Whitworth is charting a new course for conservation.

Measurements and experiments are made each and every day, in fields as disparate as particle physics, chemistry, economics and medicine, but have you ever wondered why it is that a particular experiment has been designed to be the way it is. Indeed, how do you design an experiment to measure something whose value is unknown, and what should your considerations be on deciding whether an experiment has yielded the sought after, or indeed any useful result? These are old questions, and they are the reason behind this volume. We will explore the origins of the methods of data analysis that are today routinely applied to all measurements, but which were unknown before the mid-19th Century. Anyone who is interested in the relationship between the precision and accuracy of measurements will find this volume useful. Whether you are a physicist, a chemist, a social scientist, or a student studying one of these subjects, you will discover that the basis of measurement is the struggle to identify the needle of useful data hidden in the haystack of obscuring background noise.

This book presents an approach to quantifying consciousness and its various states. It represents over ten years of work in developing, test ing, and researching the use of relatively simple self-report question naires in the retrospective assessment of subjective or phenomenologi cal experience. While the simplicity of the method allows for subjective experience to be reliably and validly assessed across various short stim ulus conditions, the flexibility of the approach allows the cognitive psy chologist, consciousness researcher, and mental health professional to quantify and statistically assess the phenomenological variables associ ated with various stimulus conditions, altered-state induction tech niques, and clinical procedures. The methodology allows the cognitive psychologist and mental health professional to comprehensively quantify the structures and pat terns of subjective experience dealing with imagery, attention, affect, volitional control, internal dialogue, and so forth to determine how these phenomenological structures might covary during such stimulus conditions as free association, a sexual fantasy, creative problem solving, or a panic attack. It allows for various phenomenological pro cesses to be reported, quantified, and statistically assessed in a rather comprehensive fashion that should help shed greater understanding on the nature of mind or consciousness.

This research attempts to evaluate nitrification treatment performance in combined carbon/nitrogen municipal wastewater reactors using traditional physical/chemical methods and modern molecular techniques. Bench scale activated sludge reactors were operated at different SRTs under varying DO levels and temperatures over a 21-month period. Real-time PCR assays were used to determine cell concentrations of total bacterial 16S rDNA, a gross measure of biomass content, the amoA gene, a measure of ammonia-oxidizing bacteria (AOB), and the Nitrospira 16S rDNA gene, a measure of nitrite-oxidizing bacteria (NOB). As expected, gravimetric biomass and total bacterial 16S rDNA levels increased with increasing SRT. Ammonia oxidation rates and N. oligotropha-type AOB concentrations did not follow similar trends with respect to changes in SRT, temperature, and DO nor were they highly correlated. The concentration of available nitrite and SRT were positively correlated with Nitrospira cell densities, while DO concentration and temperature were negatively correlated with NOB levels. The percentage of the total population comprised of AOB and NOB obtained with the real-time PCR assays were compared to predicted values estimated from design equations using typical kinetic parameters. While the percentages of NOB measured using the real-time PCR assay corresponded very well with the predicted values, the measured percentages of AOB were much lower than those estimated from the design equations, suggesting that N. oligotropha-type AOB were not the dominant ammonia-oxidizing species in these reactors. This publication can be purchased and downloaded via Pay Per View on Water Intelligence Online - click on the Pay Per View icon below

This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.