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| control variables in science: Theory-Based Data Analysis for the Social Sciences Carol S. Aneshensel, 2013 This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of third variables to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions. |
| control variables in science: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science. |
| control variables in science: Principles of Animal Research for Graduate and Undergraduate Students Mark A. Suckow, Kay Stewart, 2016-11-16 Principles of Animal Research is the first publication to offer a broad look at animal research science for a student, early researcher, or technician. Offering guidance for all aspects of the research experience, including the research and development of a thesis, model selection, experimental design, IACUC protocol preparation, and animal husbandry and technical procedural needs, the book is a necessary addition to every student, technician, and researcher's education. - Provides background material for students to understand the broader backdrop against which animal research is undertaken - Includes ethical and regulatory information - Covers commonly used animal models and the process to choose a model for biomedical research |
| control variables in science: The Book of Why Judea Pearl, Dana Mackenzie, 2018-05-15 A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence Correlation is not causation. This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why. |
| control variables in science: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide Agency for Health Care Research and Quality (U.S.), 2013-02-21 This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov) |
| control variables in science: Encyclopedia of Research Design Neil J. Salkind, 2010-06-22 Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases.--Publisher's description. |
| control variables in science: How to Build Social Science Theories Pamela J. Shoemaker, James William Tankard, Jr., Dominic L. Lasorsa, 2003-12-10 Click ′Additional Materials′ to read the foreword by Jerald Hage As straightforward as its title, How to Build Social Science Theories sidesteps the well-traveled road of theoretical examination by demonstrating how new theories originate and how they are elaborated. Essential reading for students of social science research, this book traces theories from their most rudimentary building blocks (terminology and definitions) through multivariable theoretical statements, models, the role of creativity in theory building, and how theories are used and evaluated. Authors Pamela J. Shoemaker, James William Tankard, Jr., and Dominic L. Lasorsa intend to improve research in many areas of the social sciences by making research more theory-based and theory-oriented. The book begins with a discussion of concepts and their theoretical and operational definitions. It then proceeds to theoretical statements, including hypotheses, assumptions, and propositions. Theoretical statements need theoretical linkages and operational linkages; this discussion begins with bivariate relationships, as well as three-variable, four-variable, and further multivariate relationships. The authors also devote chapters to the creative component of theory-building and how to evaluate theories. How to Build Social Science Theories is a sophisticated yet readable analysis presented by internationally known experts in social science methodology. It is designed primarily as a core text for graduate and advanced undergraduate courses in communication theory. It will also be a perfect addition to any course dealing with theory and research methodology across the social sciences. Additionally, professional researchers will find it an indispensable guide to the genesis, dissemination, and evaluation of social science theories. |
| control variables in science: The Sourcebook for Teaching Science, Grades 6-12 Norman Herr, 2008-08-11 The Sourcebook for Teaching Science is a unique, comprehensive resource designed to give middle and high school science teachers a wealth of information that will enhance any science curriculum. Filled with innovative tools, dynamic activities, and practical lesson plans that are grounded in theory, research, and national standards, the book offers both new and experienced science teachers powerful strategies and original ideas that will enhance the teaching of physics, chemistry, biology, and the earth and space sciences. |
| control variables in science: The SAGE Encyclopedia of Communication Research Methods Mike Allen, 2017-04-11 Communication research is evolving and changing in a world of online journals, open-access, and new ways of obtaining data and conducting experiments via the Internet. Although there are generic encyclopedias describing basic social science research methodologies in general, until now there has been no comprehensive A-to-Z reference work exploring methods specific to communication and media studies. Our entries, authored by key figures in the field, focus on special considerations when applied specifically to communication research, accompanied by engaging examples from the literature of communication, journalism, and media studies. Entries cover every step of the research process, from the creative development of research topics and questions to literature reviews, selection of best methods (whether quantitative, qualitative, or mixed) for analyzing research results and publishing research findings, whether in traditional media or via new media outlets. In addition to expected entries covering the basics of theories and methods traditionally used in communication research, other entries discuss important trends influencing the future of that research, including contemporary practical issues students will face in communication professions, the influences of globalization on research, use of new recording technologies in fieldwork, and the challenges and opportunities related to studying online multi-media environments. Email, texting, cellphone video, and blogging are shown not only as topics of research but also as means of collecting and analyzing data. Still other entries delve into considerations of accountability, copyright, confidentiality, data ownership and security, privacy, and other aspects of conducting an ethical research program. Features: 652 signed entries are contained in an authoritative work spanning four volumes available in choice of electronic or print formats. Although organized A-to-Z, front matter includes a Reader’s Guide grouping entries thematically to help students interested in a specific aspect of communication research to more easily locate directly related entries. Back matter includes a Chronology of the development of the field of communication research; a Resource Guide to classic books, journals, and associations; a Glossary introducing the terminology of the field; and a detailed Index. Entries conclude with References/Further Readings and Cross-References to related entries to guide students further in their research journeys. The Index, Reader’s Guide themes, and Cross-References combine to provide robust search-and-browse in the e-version. |
| control variables in science: Research Methods and Statistics Janie H. Wilson, Shauna W. Joye, 2016-07-21 This innovative text offers a completely integrated approach to teaching research methods and statistics by presenting a research question accompanied by the appropriate methods and statistical procedures needed to address it. Research questions and designs become more complex as chapters progress, building on simpler questions to reinforce student learning. Using a conversational style and research examples from published works, this comprehensive book walks readers through the entire research process and includes ample pedagogical support for SPSS, Excel, and APA style. |
| control variables in science: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| control variables in science: Science And Human Behavior B.F Skinner, 2012-12-18 The psychology classic—a detailed study of scientific theories of human nature and the possible ways in which human behavior can be predicted and controlled—from one of the most influential behaviorists of the twentieth century and the author of Walden Two. “This is an important book, exceptionally well written, and logically consistent with the basic premise of the unitary nature of science. Many students of society and culture would take violent issue with most of the things that Skinner has to say, but even those who disagree most will find this a stimulating book.” —Samuel M. Strong, The American Journal of Sociology “This is a remarkable book—remarkable in that it presents a strong, consistent, and all but exhaustive case for a natural science of human behavior…It ought to be…valuable for those whose preferences lie with, as well as those whose preferences stand against, a behavioristic approach to human activity.” —Harry Prosch, Ethics |
| control variables in science: Verbal Behavior Burrhus Frederic Skinner, 1957 |
| control variables in science: Saving Women's Lives National Research Council, Institute of Medicine, Policy and Global Affairs, Board on Science, Technology, and Economic Policy, National Cancer Policy Board, Committee on New Approaches to Early Detection and Diagnosis of Breast Cancer, 2005-03-18 The outlook for women with breast cancer has improved in recent years. Due to the combination of improved treatments and the benefits of mammography screening, breast cancer mortality has decreased steadily since 1989. Yet breast cancer remains a major problem, second only to lung cancer as a leading cause of death from cancer for women. To date, no means to prevent breast cancer has been discovered and experience has shown that treatments are most effective when a cancer is detected early, before it has spread to other tissues. These two facts suggest that the most effective way to continue reducing the death toll from breast cancer is improved early detection and diagnosis. Building on the 2001 report Mammography and Beyond, this new book not only examines ways to improve implementation and use of new and current breast cancer detection technologies but also evaluates the need to develop tools that identify women who would benefit most from early detection screening. Saving Women's Lives: Strategies for Improving Breast Cancer Detection and Diagnosis encourages more research that integrates the development, validation, and analysis of the types of technologies in clinical practice that promote improved risk identification techniques. In this way, methods and technologies that improve detection and diagnosis can be more effectively developed and implemented. |
| control variables in science: Balancing Power without Weapons Ashley Thomas Lenihan, 2018-03-22 This book focuses on the non-military military means through which states intervene to balance the economic and military power of other states. Also available as Open Access. |
| control variables in science: Experimental and Quasi-Experimental Designs for Research Donald T. Campbell, Julian C. Stanley, 2015-09-03 We shall examine the validity of 16 experimental designs against 12 common threats to valid inference. By experiment we refer to that portion of research in which variables are manipulated and their effects upon other variables observed. It is well to distinguish the particular role of this chapter. It is not a chapter on experimental design in the Fisher (1925, 1935) tradition, in which an experimenter having complete mastery can schedule treatments and measurements for optimal statistical efficiency, with complexity of design emerging only from that goal of efficiency. Insofar as the designs discussed in the present chapter become complex, it is because of the intransigency of the environment: because, that is, of the experimenter’s lack of complete control. |
| control variables in science: The SAGE Dictionary of Statistics Duncan Cramer, Dennis Howitt, 2004-06-09 The SAGE Dictionary of Statistics provides students and researchers with an accessible and definitive resource to use when studying statistics in the social sciences, reading research reports and undertaking data analysis. |
| control variables in science: Methods of Meta-Analysis John E Hunter, Frank L. Schmidt, 2004-04-07 Covering the most important developments in meta-analysis from 1990 to 2004, this text presents new patterns in research findings as well as updated information on existing topics. |
| control variables in science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| control variables in science: Mad Margaret Experiments with the Scientific Method Eric Mark Braun, 2019-05-01 I'm Mad Margaret, and I'm not really mad, I'm just crazy about science! When a friend tells me he has a sneezing problem, I help him use the scientific method to solve it. I'm no Einstein, but I can tell you all about the scientific method! |
| control variables in science: Guidelines for the Care and Use of Mammals in Neuroscience and Behavioral Research National Research Council, Division on Earth and Life Studies, Institute for Laboratory Animal Research, Committee on Guidelines for the Use of Animals in Neuroscience and Behavioral Research, 2003-08-22 Expanding on the National Research Council's Guide for the Care and Use of Laboratory Animals, this book deals specifically with mammals in neuroscience and behavioral research laboratories. It offers flexible guidelines for the care of these animals, and guidance on adapting these guidelines to various situations without hindering the research process. Guidelines for the Care and Use of Mammals in Neuroscience and Behavioral Research offers a more in-depth treatment of concerns specific to these disciplines than any previous guide on animal care and use. It treats on such important subjects as: The important role that the researcher and veterinarian play in developing animal protocols. Methods for assessing and ensuring an animal's well-being. General animal-care elements as they apply to neuroscience and behavioral research, and common animal welfare challenges this research can pose. The use of professional judgment and careful interpretation of regulations and guidelines to develop performance standards ensuring animal well-being and high-quality research. Guidelines for the Care and Use of Mammals in Neuroscience and Behavioral Research treats the development and evaluation of animal-use protocols as a decision-making process, not just a decision. To this end, it presents the most current, in-depth information about the best practices for animal care and use, as they pertain to the intricacies of neuroscience and behavioral research. |
| control variables in science: The SAGE Encyclopedia of Social Science Research Methods Michael Lewis-Beck, Alan E Bryman, Tim Futing Liao, 2004 Featuring over 900 entries, this resource covers all disciplines within the social sciences with both concise definitions & in-depth essays. |
| control variables in science: Key Concepts in Migration David Bartram, Maritsa V Poros, Pierre Monforte, 2014-04-14 Demonstrates that the study of international migration has really come of age. From acculturation to undocumented immigration, the authors consider more than three dozen concepts at the heart of migration studies. Clearly written in a highly readable style, the book is a valuable resource for students and scholars alike. - Nancy Foner, City University of New York This very useful and authoritative compendium explicates thirty-eight concepts central to analysis of international migration. It is accessible to undergraduate students and even can enrich graduate courses. It nicely complements books like The Age of Migration or Exceptional People. Concision is a virtue! - Mark J. Miller, University of Delaware This book provides lucid and intuitive explanations of the most important migration concepts as used in classrooms, among policymakers, and in popular and academic discourse. Arguing that there is a clear need for a better public understanding of migration, it sets out to clarify the field by exploring relevant concepts in a direct and engaging way. Each concept: Includes an easy to understand definition Provides real-world examples Gives suggestions for further reading Is carefully cross-referenced to other related concepts It is an ideal resource for undergraduate and post-graduate students studying migration in sociology, politics, development and throughout the social sciences, as well as scholars in the field and practitioners in governmental and non-governmental organizations. |
| control variables in science: A Framework for K-12 Science Education National Research Council, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on a Conceptual Framework for New K-12 Science Education Standards, 2012-02-28 Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments. |
| control variables in science: Quasi-Experimental Research Designs Bruce A. Thyer, 2012-02-16 The role of group research designs to evaluate social work practice -- Pre-experimental group research designs -- Quasi-experimental group research designs -- Time-series research designs -- Evaluating and reporting quasi-experimental studies. |
| control variables in science: Complex Dynamics Vladimir G. Ivancevic, Tijana T. Ivancevic, 2010-11-30 Readers learn to predict and control low- and high-dimensional as well as continuous- and discrete-time nonlinear systems dynamics in complex variables. In the final chapter, all the previously developed methods are used to present the Holy Grail of modern physical and cosmological science, the search for the theory of everything and the true cosmological dynamics. |
| control variables in science: All About Earth Sara Lynn Latta, 2019-05-01 Science-based, clear instructions guide readers through experiments pertaining to Earth, including its rotation in space, seasons, gravity, and matter. An engaging way to support the Next Generation Science Standards. Experiments are simple, accessible, and something that could be used at a science fair, in the classroom, or at home. Exploring the planet has never been simpler. |
| control variables in science: Applied Structural Equation Modeling Using Amos Joel E. Collier, 2020-06-02 This is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences. |
| control variables in science: Living Earth Suzanne Garbe, 2015-08 Illustrated instructions for experiments pertaining to life on Earth, including photosynthesis, bacteria, minerals, and fossils-- |
| control variables in science: Statistics David Freedman, Robert Pisani, Roger Purves, 2009 Statistics is written in clear, everyday language, without the equations that sometimes baffle non-mathematical readers. The goal is teaching students how to think about statistical issues. |
| control variables in science: The Transforming Principle Maclyn McCarty, 1986 Forty years ago, three medical researchers--Oswald Avery, Colin MacLeod, and Maclyn McCarty--made the discovery that DNA is the genetic material. With this finding was born the modern era of molecular biology and genetics. |
| control variables in science: What is Scientific Inquiry? Types, Variables and Controls in Scientific Investigation Explained | Grade 6-8 Life Science Baby Professor, 2024-04-15 Embark on a journey through the fascinating world of scientific inquiry with this enlightening resource, perfect for middle school educators. Explore the fundamental aspects of the scientific process, including the crucial distinction between observation and experimentation and the vital steps of gathering data and drawing conclusions. Learn about the nuances of descriptive, comparative, and experimental investigations, each dissected for their unique approaches and impacts on scientific understanding. This book is an invaluable tool for inspiring young minds to ask critical questions, engage in rigorous investigations, and appreciate the iterative nature of science. Equip your students with the skills to distinguish science from pseudoscience and understand various research methods' limitations and strengths. |
| control variables in science: Handbook of EHealth Evaluation Francis Yin Yee Lau, Craig Kuziemsky, 2016-11 To order please visit https://onlineacademiccommunity.uvic.ca/press/books/ordering/ |
| control variables in science: Assessment of Competencies in Educational Contexts Eckhard Klieme, Detlev Leutner, Johannes Hartig, 2008 Covers theoretical, psychometric, and practical issues related to the assessment of competencies in educational and vocational settings. This book is suitable for researchers interested in theoretical and psychometric background of assessment, and for readers interested in practical aspects of computer-based assessment and evaluation. |
| control variables in science: R Programming for Data Science Roger D. Peng, 2012-04-19 Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox. |
| control variables in science: Causal Inference Miquel A. Hernan, James M. Robins, 2019-07-07 The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data. |
| control variables in science: Data Science Live Book Pablo Casas, 2018-03-16 This book is a practical guide to problems that commonly arise when developing a machine learning project. The book's topics are: Exploratory data analysis Data Preparation Selecting best variables Assessing Model Performance More information on predictive modeling will be included soon. This book tries to demonstrate what it says with short and well-explained examples. This is valid for both theoretical and practical aspects (through comments in the code). This book, as well as the development of a data project, is not linear. The chapters are related among them. For example, the missing values chapter can lead to the cardinality reduction in categorical variables. Or you can read the data type chapter and then change the way you deal with missing values. You¿ll find references to other websites so you can expand your study, this book is just another step in the learning journey. It's open-source and can be found at http://livebook.datascienceheroes.com |
| control variables in science: Research Methods in Human Development Paul C. Cozby, Patricia E. Worden, Daniel W. Kee, 1989 For undergradute social science majors. A textbook on the interpretation and use of research. Annotation copyright Book News, Inc. Portland, Or. |
| control variables in science: Exploring Research Neil J. Salkind, 2017 An informative and unintimidating look at the basics of research in the social and behavioural sciences. It makes research methods accessible for students - describing how to collect and analyse data and providing thorough instruction on how to prepare and write a research proposal and manuscript. |
| control variables in science: The Design of Animal Experiments Michael Festing, Philip Overend, Mario Cortina Borja, Manuel Berdoy, 2016-12-14 Where there is no alternative to the use of animals in biomedical research, it is important that experiments are well designed and correctly analysed in order to minimise pain and maximize the chance of getting scientifically valid results. Experiments that use too few animals may fail to pick up biologically important effects, while those who use them incorrectly or wastefully may get invalid results while subjecting the animals to unnecessary pain, distress or lasting harm. The Design of Animal Experiments is intended for all research scientists who use laboratory animals, with the aim of helping them to design their own experiments more effectively and/or to improve their ability to communicate with professional statisticians when necessary. It covers all randomised controlled experimental designs likely to be needed in laboratory animal research, with worked examples showing how they can be statistically analysed. It suggests the more widespread use of randomised block designs and shows how both males and females can be included in an experiment without the need to increase the total number of animals by using factorial designs. It also includes guidance on the choice of experimental animals. The book covers the learning outcomes of Module 10 and part (ii) of Module 11 of education and training under Directive 2010/63/EU. |
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