Data Presentation with SPSS Explained provides students with all the information they need to conduct small scale analysis of research projects using SPSS and present their results appropriately in their reports. Quantitative data can be collected in the form of a questionnaire, survey or experimental study. This book focuses on presenting this data clearly, in the form of tables and graphs, along with creating basic summary statistics. Data Presentation with SPSS Explained uses an example survey that is clearly explained step-by-step throughout the book. This allows readers to follow the procedures, and easily apply each step in the process to their own research and findings. No prior knowledge of statistics or SPSS is assumed, and everything in the book is carefully explained in a helpful and user-friendly way using worked examples. This book is the perfect companion for students from a range of disciplines including psychology, business, communication, education, health, humanities, marketing and nursing - many of whom are unaware that this extremely helpful program is available at their institution for their use.

A new and important contribution to the re-emergent field of comparative anthropology, this book argues that comparative ethnographic methods are essential for more contextually sophisticated accounts of a number of pressing human concerns today. The book includes expert accounts from an international team of scholars, showing how these methods can be used to illuminate important theoretical and practical projects. Illustrated with examples of successful inter-disciplinary projects, it highlights the challenges, benefits, and innovative strategies involved in working collaboratively across disciplines. Through its focus on practical methodological and logistical accounts, it will be of value to both seasoned researchers who seek practical models for conducting their own cutting-edge comparative research, and to teachers and students who are looking for first-person accounts of comparative ethnographic research.

Reliable data analysis lies at the heart of scientific research, helping you to figure out what your data is really telling you. Yet the analysis of data can be a stumbling block for even the most experienced researcher - and can be a particularly daunting prospect when analyzing your own data for the first time. Drawing on the author's extensive experience of supporting project students, Scientific Data Analysis is a guide for any science undergraduate or beginning graduate who needs to analyse their own data, and wants a clear, step-by-step description of how to carry out their analysis in a robust, error-free way. With video content generated by the author to dovetail with the printed text, the resource not only describes the principles of data analysis and the strategies that should be adopted for a successful outcome but also shows you how to carry out that analysis - with the videos breaking down the process of analysis into easy-to-digest chunks. With guidance on the use of Minitab, SPSS and Excel, Scientific Data Analysis doesn't just support the use of one particular software package: it is the ideal guide to carrying out your own data analysis regardless of the software you have chosen. Online Resource Centre: The

Like the three editions that preceded it, this new edition targets markets in health care practice and educational settings. It addresses practicing nurses and nursing students, together with nursing leadership and nursing faculty. It speaks to nursing informatics specialists and-in a departure from earlier editions of this title-to all nurses, regardless of their specialty, extending its usefulness as a text as noted below. In recognition of the evolving electronic health information environment and of interdisciplinary health care teams, the book is designed to be of interest to members of other health care professions (quality officers, administrators, etc.) as well as health information technology professionals (in health care facilities and in industry). The book will include numerous relevant case studies to illustrate the theories and principles discussed, making it an ideal candidate for use within nursing curricula (both undergraduate and graduate), as well as continuing education and staff development programs. This book honors the format established by the first three editions by including a content array and questions to guide the reader. This 4th edition also includes numerous brief case studies that help to illustrate the theories and practices described within the various chapters. Most of these "mini-cases" are provided by members of professional nursing organizations that comprise the TIGER Initiative. These mini-cases are listed in the front matter and highlighted via formatting throughout the text.

"This comprehensive, practical, user-friendly book provides a wealth of data analysis strategies that are essential for any qualitative research. It is a must-have tool book for moving from data analysis to writing for publication!" -Guofang Li, University of British Columbia, Canada Miles, Huberman, and Saldana's Qualitative Data Analysis: A Methods Sourcebook is the authoritative text for analyzing and displaying qualitative research data. The Fourth Edition maintains the analytic rigor of previous editions while showcasing a variety of new visual display models for qualitative inquiry. Graphics are added to the now-classic matrix and network illustrations of the original co-authors. Five chapters have been substantially revised, and the appendix's annotated bibliography includes new titles in research methods. Graduate students and established scholars from all disciplines will find this resource an innovative compendium of ideas for the representation and presentation of qualitative data. As the authors demonstrate, when researchers "think display," their analyses of social life capture the complex and vivid processes of the people and institutions studied.

Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. * In-depth discussion of data analysis for scientists and engineers * Coverage of both frequentist and Bayesian approaches to data analysis * Extensive look at analysis techniques for time-series data and images * Detailed exploration of linear and nonlinear modeling of data * Emphasis on error analysis * Instructor's manual (available only to professors)

"Modeling with Data" fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

"Modeling with Data" will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Drawing on his extensive experience of qualitative research, Douglas Ezzy reviews approaches to data analysis in established research traditions including ethnography, phenomenology and symbolic interactionism, alongside the newer approaches informed by cultural studies and feminism. He explains the difference between inductive, deductive and abductive theory building, provides a guide to computer-assisted analysis and outlines techniques such as journal writing, team meetings and participant reviews.

Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.

How do planetary scientists analyze and interpret data from laboratory, telescopic, and spacecraft observations of planetary surfaces? What elements, minerals, and volatiles are found on the surfaces of our Solar System's planets, moons, asteroids, and comets? This comprehensive volume answers these topical questions by providing an overview of the theory and techniques of remote compositional analysis of planetary surfaces. Bringing together eminent researchers in Solar System exploration, it describes state-of-the-art results from spectroscopic, mineralogical, and geochemical techniques used to analyze the surfaces of planets, moons, and small bodies. The book introduces the methodology and theoretical background of each technique, and presents the latest advances in space exploration, telescopic and laboratory instrumentation, and major new work in theoretical studies. This engaging volume provides a comprehensive reference on planetary surface composition and mineralogy for advanced students, researchers, and professional scientists.

Learn how to analyse and manage evolutionary and sequential user behaviours in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behaviour, and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing.

Whilst a great deal of progress has been made in recent decades, concerns persist about the course of the social sciences. Progress in these disciplines is hard to assess and core scientific goals such as discovery, transparency, reproducibility, and cumulation remain frustratingly out of reach. Despite having technical acumen and an array tools at their disposal, today's social scientists may be only slightly better equipped to vanquish error and construct an edifice of truth than their forbears - who conducted analyses with slide rules and wrote up results with typewriters. This volume considers the challenges facing the social sciences, as well as possible solutions. In doing so, we adopt a systemic view of the subject matter. What are the rules and norms governing behavior in the social sciences? What kinds of research, and which sorts of researcher, succeed and fail under the current system? In what ways does this incentive structure serve, or subvert, the goal of scientific progress?

'A necessary book for our times. But also just great fun' Saul Perlmutter, Nobel Laureate The world is awash in bullshit, and we're drowning in it. Politicians are unconstrained by facts. Science is conducted by press release. Start-up culture elevates hype to high art. These days, calling bullshit is a noble act. Based on a popular course at the University of Washington, Calling Bullshit gives us the tools to see through the obfuscations, deliberate and careless, that dominate every realm of our lives. In this lively guide, biologist Carl Bergstrom and statistician Jevin West show that calling bullshit is crucial to a properly functioning social group, whether it be a circle of friends, a community of researchers, or the citizens of a nation. Through six rules of thumb, they help us recognize bullshit whenever and wherever we encounter it - even within ourselves - and explain it to a crystal-loving aunt or casually racist grandfather.

This invaluable addition to any data scientist's library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice oriented approach to explaining basic principles in the ever-expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Key features * Data science and statistical analysis for the business professional * Numerous instantly familiar real-world use cases * Keys to effective data presentations * Modeling and analysis techniques like boosting, regularized regression, and quadratic discriminant analysis Audience While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science. About the technology Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day Nina Zumel and John Mount are co-founders of Win-Vector LLC, a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com.

The social sciences are becoming datafied. The questions once considered the domain of sociologists are now answered by data scientists operating on large datasets and breaking with methodological tradition, for better or worse. The traditional social sciences, such as sociology or anthropology, are under the double threat of becoming marginalized or even irrelevant, both from new methods of research which require more computational skills and from increasing competition from the corporate world which gains an additional advantage based on data access. However, unlike data scientists, sociologists and anthropologists have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data therefore needs Thick Data. This book presents the available arsenal of new methods and tools for studying society both quantitatively and qualitatively, opening ground for the social sciences to take the lead in analysing digital behaviour. It shows that Big Data can and should be supplemented and interpreted through thick data as well as cultural analysis. Thick Big Data is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and to successfully build mixed-methods approaches.

A nuts-and-bolts guide to conducting your own professional-quality surveys without paying professional fees. How can you gauge public support for a cause or test the market for a product or service? What are the best methods for validating opinions for use in a paper or dissertation? A well-documented survey is the answer. But what if you don’t have thousands of dollars to commission one? No problem. How to Conduct Your Own Survey gives you everything you need to do it yourself! Without any prior training, you can learn expert techniques for conducting accurate, low-cost surveys. In step-by-step, down-to-earth language, Priscilla Salant and Don A. Dillman give you the tools you need to:

• Determine which type of survey is best for you
• Estimate the cost of your survey
• Conduct mail, telephone, and face-to-face surveys
• Draw accurate samples
• Write effective questionnaires
• Compile and report results
• Avoid common survey errors
• Find reliable outside assistance
• And much more

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

Network thinking and network analysis are rapidly expanding features of ecological research. Network analysis of ecological systems include representations and modelling of the interactions in an ecosystem, in which species or factors are joined by pairwise connections. This book provides an overview of ecological network analysis including generating processes, the relationship between structure and dynamic function, and statistics and models for these networks. Starting with a general introduction to the composition of networks and their characteristics, it includes details on such topics as measures of network complexity, applications of spectral graph theory, how best to include indirect species interactions, and multilayer, multiplex and multilevel networks. Graduate students and researchers who want to develop and understand ecological networks in their research will find this volume inspiring and helpful. Detailed guidance to those already working in network ecology but looking for advice is also included.

Humberto Barreto gives professors a simple way to teach fundamental concepts for any undergraduate macroeconomics course using Microsoft Excel (R) with Excel workbooks and add-ins and videos freely available on his university website. The Excel files are designed to be used by students with any textbook, and have been used many times by the author in his own teaching. Each Excel workbook contains links to short screencasts, around five to ten minutes, that show the cursor and typing as the file is manipulated with narration that walks the student through the steps needed to complete a task. The book shows professors a simple way to present macroeconomic models and incorporate data into their courses.

You can't get anywhere in your statistics course without grasping statistical significance. it's often seen as difficult but is actually a straightforward concept everyone can-and should-understand. Do your results mean something-or not? How can you measure it? Breaking it down into three building blocks, this Little Quick Fix shows students how to master: hypothesis testing normal distribution p values Students will learn how to understand the concept and also how to explain it for maximum effect in their essays and lab reports. Good for results-this is also a secret weapon for critical thinking. Little Quick Fix titles provide quick but authoritative answers to the problems, hurdles, and assessment points students face in the research course, project proposal, or design-whatever their methods learning is. Lively, ultra-modern design; full-colour, each page a tailored design. An hour's read. Easy to dip in and out of with clear navigation enables the reader to find what she needs-quick. Direct written style gets to the point with clear language. Nothing needs to be read twice. No fluff. Learning is reinforced through a 2-minute overview summary; 3-second summaries with super-quick Q&A DIY tasks create a work plan to accomplish a task, do a self-check quiz, solve a problem, get students to what they need to show their supervisor. Checkpoints in each section make sure students are nailing it as they go and support self-directed learning. How do I know I'm done? Each Little Quick Fix wraps up with a final checklist that allows the reader to self-assess they've got what they need to progress, submit, or ace the test or task.

The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine.

A human disaster is defined as a hazardous event that overwhelms the capacity of the local community to respond to the needs of the affected population. Medical and public health responses aim to provide care efficiently and promptly but all too often, responses are hampered by recurring mistakes. Analysing the factors at play such as the scale and frequency of disasters and the variety of challenges they present, is central to developing more effective response plans. However the complexity of disasters often precludes reliable data collection, hampering the accuracy of the results, conclusions and recommendations required to improve responses. Disaster Evaluation Research: A field guide presents a new approach to the study of disaster by incorporating a mixed-methods research approach. This practical manual provides a range of reliable methods, robust approaches and proven techniques for the gathering and analyzing of data. Written by leading evaluation scientists with a wealth of experience, the authors present their 'EIGHT Step Model' for disaster evaluation studies. This framework applies evaluation science to disaster responses, helping scientists to select key stakeholders effectively, write evaluation questions, use logic models and mixed-methods research design, prepare sampling plans, collect and analyse data, and prepare a final report. This guide also features useful tools for carrying out evaluations including; evaluation questions, indicators and data sources, resources, and questionnaires used in past evaluation studies. Using a clear, accessible and step-by-step style this practical manual is easy to use in the field and essential reading for medical and public health professionals involved in disaster preparedness and response, humanitarian relief workers, policy analysts, evaluation scientists and epidemiologists.

More students study management and organization studies than ever, the number of business schools worldwide continues to rise, and more management research is being published in a greater number of journals than could have been imagined twenty years ago. Dennis Tourish looks beneath the surface of this progress to expose a field in crisis and in need of radical reform. He identifies the ways in which management research has lost its way, including a remoteness from the practical problems that managers and employees face, a failure to replicate key research findings, poor writing, endless obscure theorizing, and an increasing number of research papers being retracted for fraud and other forms of malpractice. Tourish suggests fundamental changes to remedy these issues, enabling management research to become more robust, more interesting and more valuable to society. A must read for academics, practising managers, university administrators and policy makers within higher education.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Whilst a great deal of progress has been made in recent decades, concerns persist about the course of the social sciences. Progress in these disciplines is hard to assess and core scientific goals such as discovery, transparency, reproducibility, and cumulation remain frustratingly out of reach. Despite having technical acumen and an array tools at their disposal, today's social scientists may be only slightly better equipped to vanquish error and construct an edifice of truth than their forbears - who conducted analyses with slide rules and wrote up results with typewriters. This volume considers the challenges facing the social sciences, as well as possible solutions. In doing so, we adopt a systemic view of the subject matter. What are the rules and norms governing behavior in the social sciences? What kinds of research, and which sorts of researcher, succeed and fail under the current system? In what ways does this incentive structure serve, or subvert, the goal of scientific progress?