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| explanatory variable in observational study: 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) |
| explanatory variable in observational study: 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. |
| explanatory variable in observational study: Measuring Racial Discrimination National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Methods for Assessing Discrimination, 2004-07-24 Many racial and ethnic groups in the United States, including blacks, Hispanics, Asians, American Indians, and others, have historically faced severe discriminationâ€pervasive and open denial of civil, social, political, educational, and economic opportunities. Today, large differences among racial and ethnic groups continue to exist in employment, income and wealth, housing, education, criminal justice, health, and other areas. While many factors may contribute to such differences, their size and extent suggest that various forms of discriminatory treatment persist in U.S. society and serve to undercut the achievement of equal opportunity. Measuring Racial Discrimination considers the definition of race and racial discrimination, reviews the existing techniques used to measure racial discrimination, and identifies new tools and areas for future research. The book conducts a thorough evaluation of current methodologies for a wide range of circumstances in which racial discrimination may occur, and makes recommendations on how to better assess the presence and effects of discrimination. |
| explanatory variable in observational study: OpenIntro Statistics David Diez, Christopher Barr, Mine Çetinkaya-Rundel, 2015-07-02 The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources. |
| explanatory variable in observational study: Handbook of EHealth Evaluation Francis Yin Yee Lau, Craig Kuziemsky, 2016-11 To order please visit https://onlineacademiccommunity.uvic.ca/press/books/ordering/ |
| explanatory variable in observational study: Statistical Methods for Overdispersed Count Data Jean-Francois Dupuy, 2018-11-19 Statistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementation. All methods are illustrated on datasets arising in the field of health economics. As several tools have been developed to tackle over-dispersed and zero-inflated data (such as adjustment methods and zero-inflated models), this book covers the topic in a comprehensive and interesting manner. - Includes reading on several levels, including methodology and applications - Presents the state-of-the-art on the most recent zero-inflated regression models - Contains a single dataset that is used as a common thread for illustrating all methodologies - Includes R code that allows the reader to apply methodologies |
| explanatory variable in observational study: Introductory Business Statistics 2e Alexander Holmes, Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| explanatory variable in observational study: Commercial Motor Vehicle Driver Fatigue, Long-Term Health, and Highway Safety National Academies of Sciences, Engineering, and Medicine, Transportation Research Board, Division of Behavioral and Social Sciences and Education, Board on Human-Systems Integration, Committee on National Statistics, Panel on Research Methodologies and Statistical Approaches to Understanding Driver Fatigue Factors in Motor Carrier Safety and Driver Health, 2016-09-12 There are approximately 4,000 fatalities in crashes involving trucks and buses in the United States each year. Though estimates are wide-ranging, possibly 10 to 20 percent of these crashes might have involved fatigued drivers. The stresses associated with their particular jobs (irregular schedules, etc.) and the lifestyle that many truck and bus drivers lead, puts them at substantial risk for insufficient sleep and for developing short- and long-term health problems. Commercial Motor Vehicle Driver Fatigue, Long-Term Health and Highway Safety assesses the state of knowledge about the relationship of such factors as hours of driving, hours on duty, and periods of rest to the fatigue experienced by truck and bus drivers while driving and the implications for the safe operation of their vehicles. This report evaluates the relationship of these factors to drivers' health over the longer term, and identifies improvements in data and research methods that can lead to better understanding in both areas. |
| explanatory variable in observational study: Handbook of Survival Analysis John P. Klein, Hans C. van Houwelingen, Joseph G. Ibrahim, Thomas H. Scheike, 2016-04-19 Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians |
| explanatory variable in observational study: 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. |
| explanatory variable in observational study: Practical Sampling Techniques, Second Edition Ranjan K. Som, 1995-09-13 Second Edition offers a comprehensive presentation of scientific sampling principles and shows how to design a sample survey and analyze the resulting data. Demonstrates the validity of theorems and statements without resorting to detailed proofs. |
| explanatory variable in observational study: The Behavioral and Social Sciences National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Committee on Basic Research in the Behavioral and Social Sciences, 1988-02-01 This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research. |
| explanatory variable in observational study: Introduction to Educational Research W. Newton Suter, 2012 W. Newton Suter argues that what is important in a changing education landscape is the ability to think clearly about research methods, reason through complex problems and evaluate published research. He explains how to evaluate data and establish its relevance. |
| explanatory variable in observational study: Biostatistics for Medical and Biomedical Practitioners Julien I. E. Hoffman, 2015-09-03 Biostatistics for Practitioners: An Interpretative Guide for Medicine and Biology deals with several aspects of statistics that are indispensable for researchers and students across the biomedical sciences. The book features a step-by-step approach, focusing on standard statistical tests, as well as discussions of the most common errors. The book is based on the author's 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields. - Discusses how to use the standard statistical tests in the biomedical field, as well as how to make statistical inferences (t test, ANOVA, regression etc.) - Includes non-standards tests, including equivalence or non-inferiority testing, extreme value statistics, cross-over tests, and simple time series procedures such as the runs test and Cusums - Introduces procedures such as multiple regression, Poisson regression, meta-analysis and resampling statistics, and provides references for further studies |
| explanatory variable in observational study: , |
| explanatory variable in observational study: An Introduction to Medical Statistics Martin Bland, 2015-07-23 Now in its Fourth Edition, An Introduction to Medical Statistics continues to be a 'must-have' textbook for anyone who needs a clear logical guide to the subject. Written in an easy-to-understand style and packed with real life examples, the text clearly explains the statistical principles used in the medical literature. Taking readers through the common statistical methods seen in published research and guidelines, the text focuses on how to interpret and analyse statistics for clinical practice. Using extracts from real studies, the author illustrates how data can be employed correctly and incorrectly in medical research helping readers to evaluate the statistics they encounter and appropriately implement findings in clinical practice. End of chapter exercises, case studies and multiple choice questions help readers to apply their learning and develop their own interpretative skills. This thoroughly revised edition includes new chapters on meta-analysis, missing data, and survival analysis. |
| explanatory variable in observational study: 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. |
| explanatory variable in observational study: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| explanatory variable in observational study: Regression Analysis Richard A. Berk, 2004 PLEASE UPDATE SAGE INDIA AND SAGE UK ADDRESSES ON IMPRINT PAGE. |
| explanatory variable in observational study: Reference Manual on Scientific Evidence , 1994 |
| explanatory variable in observational study: Student Solution Manual for The Practice of Statistics in the Life Sciences Brigitte Baldi, 2011-02-04 This is an introduction to the uses and applications of statistics in the life sciences with a data analysis approach. The book provides step-by-step solutions along with summaries of the key concepts needed to solve the problems. |
| explanatory variable in observational study: The Basic Practice of Statistics David S. Moore, 2010 This is a clear and innovative overview of statistics which emphasises major ideas, essential skills and real-life data. The organisation and design has been improved for the fifth edition, coverage of engaging, real-world topics has been increased and content has been updated to appeal to today's trends and research. |
| explanatory variable in observational study: Multiple Imputation and its Application James Carpenter, Michael Kenward, 2012-12-21 A practical guide to analysing partially observeddata. Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences.Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods. This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials. Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation. Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application. |
| explanatory variable in observational study: A Guide to Statistical Thinking Third Edition SCC Fifth Edition James E. Mays, 2005-01-15 |
| explanatory variable in observational study: Experimental and Quasi-experimental Designs for Generalized Causal Inference William R. Shadish, Thomas D. Cook, Donald Thomas Campbell, 2002 Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions. |
| explanatory variable in observational study: Information Computing and Applications Baoxiang Liu, Chunlai Chai, 2011-12-02 This book constitutes the refereed proceedings of the Second International Conference on Information Computing and Applications, ICICA 2010, held in Qinhuangdao, China, in October 2011. The 97 papers presented were carefully reviewed and selected from numerous submissions. They are organized in topical sections on computational economics and finance, computational statistics, mobile computing and applications, social networking and computing, intelligent computing and applications, internet and Web computing, paralelle and distributed computing, and system simulation and computing. |
| explanatory variable in observational study: Statistics Robin H. Lock, Patti Frazer Lock, Kari Lock Morgan, Eric F. Lock, Dennis F. Lock, 2020-10-13 Statistics: Unlocking the Power of Data, 3rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications. Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions. Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text. A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches. |
| explanatory variable in observational study: Visual and Statistical Thinking Edward R. Tufte, 1997 |
| explanatory variable in observational study: Introducing Linguistic Research Svenja Voelkel, Franziska Kretzschmar, 2021-09-09 Over the past decade, conducting empirical research in linguistics has become increasingly popular. The first of its kind, this book provides an engaging and practical introduction to this exciting versatile field, providing a comprehensive overview of research aspects in general, and covering a broad range of subdiscipline-specific methodological approaches. Subfields covered include language documentation and descriptive linguistics, language typology, corpus linguistics, sociolinguistics and anthropological linguistics, cognitive linguistics and psycholinguistics, and neurolinguistics. The book reflects on the strengths and weaknesses of each single approach and on how they interact with one-another across the study of language in its many diverse facets. It also includes exercises, example student projects and recommendations for further reading, along with additional online teaching materials. Providing hands-on experience, and written in an engaging and accessible style, this unique and comprehensive guide will give students the inspiration they need to develop their own research projects in empirical linguistics. |
| explanatory variable in observational study: Foundations of Statistics for Data Scientists Alan Agresti, Maria Kateri, 2021-11-22 Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on why it works as well as how to do it. Compared to traditional mathematical statistics textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into Data Analysis and Applications and Methods and Concepts. Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises. |
| explanatory variable in observational study: Workshop Statistics Allan J. Rossman, Beth L. Chance, 2011-10-25 Allan Rossman's 4th Edition of Workshop Statistics: Discovery with Data is enhanced from previous issues with more focus and emphasis on collaborative learning. It further requires student observation, and integrates technology for gathering, recording, and synthesizing data. The text offers more flexibility in selecting technology tools for classrooms primarily using technologies other than graphing calculators or Fathom Dynamic Data software. Furthermore, it presents more standards for teaching statistics in an innovative, investigative, and accessible as well as provides in-depth guidance and resources to support active learning of statistics and includes updated real data sets with everyday applications in order to promote statistical literacy. |
| explanatory variable in observational study: Multivariate Dependencies D.R. Cox, Nanny Wermuth, 1996-03-01 Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences. This book sets out both the general concepts and the more technical statistical issues involved in analysis and interpretation. Numerous illustrative examples are described in outline and four studies are discussed in some detail. The use of graphical representations of dependencies and independencies among the features under study is stressed, both to incorporate available knowledge at the planning stage of an analysis and to summarize aspects important for interpretation after detailed statistical analysis is complete. This book is aimed at research workers using statistical methods as well as statisticians involved in empirical research. |
| explanatory variable in observational study: The Practice of Statistics Daren S. Starnes, Dan Yates, David S. Moore, 2010-12-17 View a Panopto recording of textbook author Daren Starnes detailing ten reasons the new fourth edition of The Practice of Statistics is the right choice for the AP* Statistics course. Watch instructor video reviews here. Available for your Fall 2010 Course! Request Sample Chapter 3 here. The most thorough and exciting revision to date, The Practice of Statistics 4e is a text that fits all AP* Statistics classrooms. Authors Starnes, Yates and Moore drew upon the guidance of some of the most notable names in AP* and their students to create a text that fits today’s classroom. The new edition comes complete with new pedagogical changes, including built-in AP* testing, four-step examples, section summaries, “Check Your Understanding” boxes and more. The Practice of Statistics long stands as the only high school statistics textbook that directly reflects the College Board course description for AP* Statistics. Combining the data analysis approach with the power of technology, innovative pedagogy, and a number of new features, the fourth edition will provide you and your students with the most effective text for learning statistics and succeeding on the AP* Exam. |
| explanatory variable in observational study: Basic Statistics with R Stephen C. Loftus, 2021-02-20 Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area. In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines. - Features real-data to give students an engaging practice to connect with their areas of interest - Evolves from basic problems that can be worked by hand to the elementary use of opensource R software - Offers a direct, clear approach highlighted by useful visuals and examples |
| explanatory variable in observational study: Observational Studies Paul R. Rosenbaum, 2013-06-29 An observational study is an empirical investigation of the effects of treatments, policies, or exposures. It differes from an experiment in that the investigator cannot control the assignments of treatments to subjects. Scientists across a wide range of disciplines undertake such studies, and the aim of this book is to provide a sound statistical account of the principles and methods for the design and analysis of observational studies. Readers are assumed to have a working knowledge of basic probability and statistics, but otherwise the account is reasonably self-contained. Throughout there are extended discussions of actual observational studies to illustrate the ideas discussed. These are drawn from topics as diverse as smoking and lung cancer, lead in children, nuclear weapons testing, and placement programs for students. As a result, many researchers involved in observational studes will find this an invaluable companion to their work. |
| explanatory variable in observational study: Research Methods for Social Work Antoinette Y. Farmer, G. Lawrence Farmer, 2020-01-08 Research Methods for Social Work: A Problem-Based Approach is a comprehensive introduction to methods instruction that engages students innovatively and interactively. Using a problem-based learning (PBL) approach, authors Antoinette Y. Farmer and G. Lawrence Farmer utilize case examples to achieve a level of application which builds readers’ confidence in methodology and reinforces their understanding of research across all levels of social work practice. These case examples, along with critical thinking questions, research tips, and step-by-step problem-solving methods, will improve student mastery and help them see why research is relevant. With the guidance of this new and noteworthy textbook, readers will transform into both knowledgeable consumers of research and skilled practitioners who can effectively address the needs of their clients through research. Included with this title: The password-protected Instructor Resource Site (formally known as SAGE Edge) offers access to all text-specific resources, including a test bank and editable, chapter-specific PowerPoint® slides. |
| explanatory variable in observational study: Statistics Ann E. Watkins, Richard L. Scheaffer, George W. Cobb, 2011 Statistics, 2nd Edition teaches statistics with a modern, data-analytic approach that uses graphing calculators and statistical software. It allows more emphasis to be put on statistical concepts and data analysis rather than following recipes for calculations. This gives readers a more realistic understanding of both the theoretical and practical applications of statistics, giving them the ability to master the subject. |
| explanatory variable in observational study: Intermediate Statistical Investigations Nathan Tintle, Beth L. Chance, Karen McGaughey, Soma Roy, Todd Swanson, Jill VanderStoep, 2020-09-09 Intermediate Statistical Investigations provides a unified framework for explaining variation across study designs and variable types, helping students increase their statistical literacy and appreciate the indispensable role of statistics in scientific research. Requiring only a single introductory statistics course as a prerequisite, the program uses the immersive, simulation-based inference approach for which the author team is known.Students engage with various aspects of data collection and analysis using real examples and clear explanations designed to strengthen multivariable understanding and reinforce first-course concepts. Each chapter contains in-depth exercises which follow a consistent six-step statistical exploration and investigation method (ask a research question, design a study, explore the data, draw inferences, formulate conclusions, and look back and ahead) enabling students to assess a variety of concepts in a single assignment. Challenging questions based on research articles strengthen critical reading skills, fully worked examples demonstrate essential concepts and methods, and engaging visualizations illustrate key themes of explained variation. End-of-chapter investigations use real data from popular culture and published research studies in a variety of disciplines, exposing students to various applications of statistics in the real world. Throughout the text, user-friendly Rossman Chance web applets allow students to conduct the simulations and analyses covered in the book. |
| explanatory variable in observational study: Statistics Goteti Bala Krishnamurty, Patricia Kasovia-Schmitt, David J. Ostroff, 1995 This textbook is intended for use in introductory biostatistics courses for health science, nursing, and biology students. It deals with research designs used for collecting data, methods for summarizing data, and testing hypotheses in health and related fields. The emphasis is on illustrating how statistics are generated and used by practitioners in health fields and interpreting crucial aspects of journal articles. Concepts are stressed rather than the usual computational methods. Every major concept is accompanied by an exercise and correct answers, and these form an integral part of the text. |
| explanatory variable in observational study: Understanding Statistics in the Behavioral Sciences Roger Bakeman, Byron F. Robinson, 2005-03-23 Understanding Statistics in the Behavioral Sciences is designed to help readers understand research reports, analyze data, and familiarize themselves with the conceptual underpinnings of statistical analyses used in behavioral science literature. The authors review statistics in a way that is intended to reduce anxiety for students who feel intimidated by statistics. Conceptual underpinnings and practical applications are stressed, whereas algebraic derivations and complex formulas are reduced. New ideas are presented in the context of a few recurring examples, which allows readers to focus more on the new statistical concepts than on the details of different studies. The authors' selection and organization of topics is slightly different from the ordinary introductory textbook. It is motivated by the needs of a behavioral science student, or someone in clinical practice, rather than by formal, mathematical properties. The book begins with hypothesis testing and then considers how hypothesis testing is used in conjunction with statistical designs and tests to answer research questions. In addition, this book treats analysis of variance as another application of multiple regression. With this integrated, unified approach, students simultaneously learn about multiple regression and how to analyze data associated with basic analysis of variance and covariance designs. Students confront fewer topics but those they do encounter possess considerable more power, generality, and practical importance. This integrated approach helps to simplify topics that often cause confusion. Understanding Statistics in the Behavioral Sciences features:*Computer-based exercises, many of which rely on spreadsheets, help the reader perform statistical analyses and compare and verify the results using either SPSS or SAS. These exercises also provide an opportunity to explore definitional formulas by altering raw data or terms within a formula and immediately see the consequences thus providing a deeper understanding of the basic concepts. *Key terms and symbols are boxed when first introduced and repeated in a glossary to make them easier to find at review time. *Numerous tables and graphs, including spreadsheet printouts and figures, help students visualize the most critical concepts. This book is intended as a text for introductory behavioral science statistics. It will appeal to instructors who want a relatively brief text. The book's active approach to learning, works well both in the classroom and for individual self-study. |
EXPLANATORY Definition & Meaning - Merriam-Webster
The meaning of EXPLANATORY is serving to explain. How to use explanatory in a sentence.
EXPLANATORY | English meaning - Cambridge Diction…
explanatory note There are explanatory notes with the diagram. The kit comes complete with an explanatory …
153 Synonyms & Antonyms for EXPLANATORY - Thesaurus.c…
Find 153 different ways to say EXPLANATORY, along with antonyms, related words, and example …
EXPLANATORY definition and meaning | Collins English Dict…
Explanatory statements or theories are intended to make people understand something by describing it or giving …
EXPLANATORY Definition & Meaning - Dictionary.com
Explanatory definition: serving to explain.. See examples of EXPLANATORY used in a sentence.
EXPLANATORY Definition & Meaning - Merriam-Webster
The meaning of EXPLANATORY is serving to explain. How to use explanatory in a sentence.
EXPLANATORY | English meaning - Cambridge Dictionary
explanatory note There are explanatory notes with the diagram. The kit comes complete with an explanatory instruction leaflet. The explanatory texts by the curators are very informative. …
153 Synonyms & Antonyms for EXPLANATORY - Thesaurus.com
Find 153 different ways to say EXPLANATORY, along with antonyms, related words, and example sentences at Thesaurus.com.
EXPLANATORY definition and meaning | Collins English Dictionary
Explanatory statements or theories are intended to make people understand something by describing it or giving the reasons for it. [ formal ] These statements are accompanied by a …
EXPLANATORY Definition & Meaning - Dictionary.com
Explanatory definition: serving to explain.. See examples of EXPLANATORY used in a sentence.
Explanatory - definition of explanatory by The Free Dictionary
explanatory - serving or intended to explain or make clear; "explanatory notes"; "an explanatory paragraph"
explanatory - Wiktionary, the free dictionary
Feb 20, 2025 · explanatory (comparative more explanatory, superlative most explanatory) Intended to serve as an explanation. Below the diagram is an explanatory text. (of a person) …
explanatory adjective - Definition, pictures, pronunciation and …
Definition of explanatory adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
explanatory, adj. & n. meanings, etymology and more | Oxford …
What does the word explanatory mean? There are three meanings listed in OED's entry for the word explanatory. See ‘Meaning & use’ for definitions, usage, and quotation evidence. How …
Explanatory - Definition, Meaning & Synonyms - Vocabulary.com
Use the adjective explanatory when you're talking about a clarification or an explanation. An abstract artwork in a gallery that consists of an old sneaker might require an explanatory note, …
