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| mann whitney u assumptions: Encyclopedia of Research Design Neil J. Salkind, 2010-06-22 To request a free 30-day online trial to this product, visit www.sagepub.com/freetrial Research design can be daunting for all types of researchers. At its heart it might be described as a formalized approach toward problem solving, thinking, and acquiring knowledge—the success of which depends upon clearly defined objectives and appropriate choice of statistical tools, tests, and analysis to meet a project′s objectives. 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. Key Features Covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research Addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences Provides summaries of advantages and disadvantages of often-used strategies Uses hundreds of sample tables, figures, and equations based on real-life cases Key Themes Descriptive Statistics Distributions Graphical Displays of Data Hypothesis Testing Important Publications Inferential Statistics Item Response Theory Mathematical Concepts Measurement Concepts Organizations Publishing Qualitative Research Reliability of Scores Research Design Concepts Research Designs Research Ethics Research Process Research Validity Issues Sampling Scaling Software Applications Statistical Assumptions Statistical Concepts Statistical Procedures Statistical Tests Theories, Laws, and Principles Types of Variables Validity of Scores The Encyclopedia of Research Design is the perfect instrument for new learners as well as experienced researchers to explore both the original and newest branches of the field. |
| mann whitney u assumptions: An Introduction to Statistical Analysis in Research Kathleen F. Weaver, Vanessa C. Morales, Sarah L. Dunn, Kanya Godde, Pablo F. Weaver, 2017-09-05 Provides well-organized coverage of statistical analysis and applications in biology, kinesiology, and physical anthropology with comprehensive insights into the techniques and interpretations of R, SPSS®, Excel®, and Numbers® output An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences develops a conceptual foundation in statistical analysis while providing readers with opportunities to practice these skills via research-based data sets in biology, kinesiology, and physical anthropology. Readers are provided with a detailed introduction and orientation to statistical analysis as well as practical examples to ensure a thorough understanding of the concepts and methodology. In addition, the book addresses not just the statistical concepts researchers should be familiar with, but also demonstrates their relevance to real-world research questions and how to perform them using easily available software packages including R, SPSS®, Excel®, and Numbers®. Specific emphasis is on the practical application of statistics in the biological and life sciences, while enhancing reader skills in identifying the research questions and testable hypotheses, determining the appropriate experimental methodology and statistical analyses, processing data, and reporting the research outcomes. In addition, this book: • Aims to develop readers’ skills including how to report research outcomes, determine the appropriate experimental methodology and statistical analysis, and identify the needed research questions and testable hypotheses • Includes pedagogical elements throughout that enhance the overall learning experience including case studies and tutorials, all in an effort to gain full comprehension of designing an experiment, considering biases and uncontrolled variables, analyzing data, and applying the appropriate statistical application with valid justification • Fills the gap between theoretically driven, mathematically heavy texts and introductory, step-by-step type books while preparing readers with the programming skills needed to carry out basic statistical tests, build support figures, and interpret the results • Provides a companion website that features related R, SPSS, Excel, and Numbers data sets, sample PowerPoint® lecture slides, end of the chapter review questions, software video tutorials that highlight basic statistical concepts, and a student workbook and instructor manual An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences is an ideal textbook for upper-undergraduate and graduate-level courses in research methods, biostatistics, statistics, biology, kinesiology, sports science and medicine, health and physical education, medicine, and nutrition. The book is also appropriate as a reference for researchers and professionals in the fields of anthropology, sports research, sports science, and physical education. KATHLEEN F. WEAVER, PhD, is Associate Dean of Learning, Innovation, and Teaching and Professor in the Department of Biology at the University of La Verne. The author of numerous journal articles, she received her PhD in Ecology and Evolutionary Biology from the University of Colorado. VANESSA C. MORALES, BS, is Assistant Director of the Academic Success Center at the University of La Verne. SARAH L. DUNN, PhD, is Associate Professor in the Department of Kinesiology at the University of La Verne and is Director of Research and Sponsored Programs. She has authored numerous journal articles and received her PhD in Health and Exercise Science from the University of New South Wales. KANYA GODDE, PhD, is Assistant Professor in the Department of Anthropology and is Director/Chair of Institutional Review Board at the University of La Verne. The author of numerous journal articles and a member of the American Statistical Association, she received her PhD in Anthropology from the University of Tennessee. PABLO F. WEAVER, PhD, is Instructor in the Department of Biology at the University of La Verne. The author of numerous journal articles, he received his PhD in Ecology and Evolutionary Biology from the University of Colorado. |
| mann whitney u assumptions: Testing Statistical Assumptions in Research J. P. Verma, Abdel-Salam G. Abdel-Salam, 2019-03-04 Comprehensively teaches the basics of testing statistical assumptions in research and the importance in doing so This book facilitates researchers in checking the assumptions of statistical tests used in their research by focusing on the importance of checking assumptions in using statistical methods, showing them how to check assumptions, and explaining what to do if assumptions are not met. Testing Statistical Assumptions in Research discusses the concepts of hypothesis testing and statistical errors in detail, as well as the concepts of power, sample size, and effect size. It introduces SPSS functionality and shows how to segregate data, draw random samples, file split, and create variables automatically. It then goes on to cover different assumptions required in survey studies, and the importance of designing surveys in reporting the efficient findings. The book provides various parametric tests and the related assumptions and shows the procedures for testing these assumptions using SPSS software. To motivate readers to use assumptions, it includes many situations where violation of assumptions affects the findings. Assumptions required for different non-parametric tests such as Chi-square, Mann-Whitney, Kruskal Wallis, and Wilcoxon signed-rank test are also discussed. Finally, it looks at assumptions in non-parametric correlations, such as bi-serial correlation, tetrachoric correlation, and phi coefficient. An excellent reference for graduate students and research scholars of any discipline in testing assumptions of statistical tests before using them in their research study Shows readers the adverse effect of violating the assumptions on findings by means of various illustrations Describes different assumptions associated with different statistical tests commonly used by research scholars Contains examples using SPSS, which helps facilitate readers to understand the procedure involved in testing assumptions Looks at commonly used assumptions in statistical tests, such as z, t and F tests, ANOVA, correlation, and regression analysis Testing Statistical Assumptions in Research is a valuable resource for graduate students of any discipline who write thesis or dissertation for empirical studies in their course works, as well as for data analysts. |
| mann whitney u assumptions: Introduction to Nonparametric Statistics for the Biological Sciences Using R Thomas W. MacFarland, Jan M. Yates, 2016-07-06 This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach. |
| mann whitney u assumptions: Intermediate Statistics Using SPSS Herschel Knapp, 2017-09-14 What statistical test should I use for this kind of data? How do I set up the data? What parameters should I specify when ordering the test? How do I interpret the results? Herschel Knapp′s friendly and approachable guide to real-world statistics answers these questions. Intermediate Statistics Using SPSS is not about abstract statistical theory or the derivation or memorization of statistical formulas–it is about applied statistics. With jargon-free language and clear processing instructions, this text covers the most common statistical functions–from basic to more advanced. Practical exercises at the conclusion of each chapter offer students an opportunity to process viable data sets, write cohesive abstracts in APA style, and build a thorough comprehension of the statistical process. Students will learn by doing with this truly practical approach to statistics. |
| mann whitney u assumptions: A Conceptual Guide to Statistics Using SPSS Elliot T. Berkman, Steven P. Reise, 2011-04-12 Bridging an understanding of Statistics and SPSS. The text is written in a user-friendly language and illustrates concepts that would otherwise be confusing to beginning students and those with limited computer skills. -Justice Mbizo, University of West Florida This unique text helps students develop a conceptual understanding of a variety of statistical tests by linking the ideas learned in a statistics class from a traditional statistics textbook with the computational steps and output from SPSS. Each chapter begins with a student-friendly explanation of the concept behind each statistical test and how the test relates to that concept. The authors then walk through the steps to compute the test in SPSS and the output, clearly linking how the SPSS procedure and output connect back to the conceptual underpinnings of the test. By drawing clear connections between the theoretical and computational aspects of statistics, this engaging text aids students′ understanding of theoretical concepts by teaching them in a practical context. |
| mann whitney u assumptions: Testing Statistical Assumptions in Research J. P. Verma, Abdel-Salam G. Abdel-Salam, 2019-03-04 Comprehensively teaches the basics of testing statistical assumptions in research and the importance in doing so This book facilitates researchers in checking the assumptions of statistical tests used in their research by focusing on the importance of checking assumptions in using statistical methods, showing them how to check assumptions, and explaining what to do if assumptions are not met. Testing Statistical Assumptions in Research discusses the concepts of hypothesis testing and statistical errors in detail, as well as the concepts of power, sample size, and effect size. It introduces SPSS functionality and shows how to segregate data, draw random samples, file split, and create variables automatically. It then goes on to cover different assumptions required in survey studies, and the importance of designing surveys in reporting the efficient findings. The book provides various parametric tests and the related assumptions and shows the procedures for testing these assumptions using SPSS software. To motivate readers to use assumptions, it includes many situations where violation of assumptions affects the findings. Assumptions required for different non-parametric tests such as Chi-square, Mann-Whitney, Kruskal Wallis, and Wilcoxon signed-rank test are also discussed. Finally, it looks at assumptions in non-parametric correlations, such as bi-serial correlation, tetrachoric correlation, and phi coefficient. An excellent reference for graduate students and research scholars of any discipline in testing assumptions of statistical tests before using them in their research study Shows readers the adverse effect of violating the assumptions on findings by means of various illustrations Describes different assumptions associated with different statistical tests commonly used by research scholars Contains examples using SPSS, which helps facilitate readers to understand the procedure involved in testing assumptions Looks at commonly used assumptions in statistical tests, such as z, t and F tests, ANOVA, correlation, and regression analysis Testing Statistical Assumptions in Research is a valuable resource for graduate students of any discipline who write thesis or dissertation for empirical studies in their course works, as well as for data analysts. |
| mann whitney u assumptions: Nonparametric Statistics for Non-Statisticians Gregory W. Corder, Dale I. Foreman, 2011-09-20 A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences. Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSS® screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields. Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to: Test data for normality and randomness Use the Wilcoxon signed rank test to compare two related samples Apply the Mann-Whitney U test to compare two unrelated samples Compare more than two related samples using the Friedman test Employ the Kruskal-Wallis H test to compare more than two unrelated samples Compare variables of ordinal or dichotomous scales Test for nominal scale data A detailed appendix provides guidance on inputting and analyzing the presented data using SPSS®, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice. Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications. |
| mann whitney u assumptions: Behavioral Research and Analysis Max Vercruyssen, Hal W. Hendrick, 2011-10-19 Now in its fourth edition, Behavioral Research and Analysis: An Introduction to Statistics within the Context of Experimental Design presents an overview of statistical methods within the context of experimental design. It covers fundamental topics such as data collection, data analysis, interpretation of results, and communication of findings. New in the Fourth Edition: Extensive improvements based on suggestions from those using this book in the classroom Statistical procedures that have been developed and validated since the previous edition Each chapter in the body now contains relevant key words, chapter summaries, key word definitions, and end of chapter exercises (with answers) Revisions to include recent changes in the APA Style Manual When looking for a book for their own use, the authors found none that were totally suitable. They found books that either reviewed the basics of behavioral research and experimental design but provided only cursory coverage of statistical methods or they provided coverage of statistical methods with very little coverage of the research context within which these methods are used. No single resource provided coverage of methodology, statistics, and communication skills. In a classic example of necessity being the mother of invention, the authors created their own. This text is ideal for a single course that reviews research methods, essential statistics through multi-factor analysis of variance, and thesis (or major project) preparation without discussion of derivation of equations, probability theory, or mathematic proofs. It focuses on essential information for getting a research project completed without prerequisite math or statistics training. It has been revised many times to help students at a variety of academic levels (exceptional high school students, undergraduate honors students, masters students, doctoral students, and post-doctoral fellows) across varied academic disciplines (e.g., human factors and ergonomics, behavioral and social sciences, natural sciences, engineering, exercise and sport sciences, business and management, industrial hygiene and safety science, health and medical sciences, and more). Illustrating how to plan, prepare, conduct, and analyze an experimental or research report, the book emphasizes explaining statistical procedures and interpreting obtained results without discussing the derivation of equations or history of the method. Destined to spend more time on your desk than on the shelf, the book will become the single resource you reach for again and again when conducting scientific research and reporting it to the scientific community. |
| mann whitney u assumptions: Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition David J. Sheskin, 2020-06-09 Following in the footsteps of its bestselling predecessors, the Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition provides researchers, teachers, and students with an all-inclusive reference on univariate, bivariate, and multivariate statistical procedures.New in the Fifth Edition:Substantial updates and new material th |
| mann whitney u assumptions: Handbook of Parametric and Nonparametric Statistical Procedures David J. Sheskin, 2003-08-27 Called the bible of applied statistics, the first two editions of the Handbook of Parametric and Nonparametric Statistical Procedures were unsurpassed in accessibility, practicality, and scope. Now author David Sheskin has gone several steps further and added even more tests, more examples, and more background information-more than 200 pages of n |
| mann whitney u assumptions: Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs Edgar Brunner, Arne C. Bathke, Frank Konietschke, 2019-07-15 This book explains how to analyze independent data from factorial designs without having to make restrictive assumptions, such as normality of the data, or equal variances. The general approach also allows for ordinal and even dichotomous data. The underlying effect size is the nonparametric relative effect, which has a simple and intuitive probability interpretation. The data analysis is presented as comprehensively as possible, including appropriate descriptive statistics which follow a nonparametric paradigm, as well as corresponding inferential methods using hypothesis tests and confidence intervals based on pseudo-ranks. Offering clear explanations, an overview of the modern rank- and pseudo-rank-based inference methodology and numerous illustrations with real data examples, as well as the necessary R/SAS code to run the statistical analyses, this book is a valuable resource for statisticians and practitioners alike. |
| mann whitney u assumptions: Research Methodology and Statistical Methods Morgan Shields, 2019-08-13 The objective is to indicate instructors that the use of research standards can make them more successful in their activity of advancing learning. The fundamental point is that we don't need to quit educating to do investigate; explore is something we can do while instructing and on the off chance that we do great research, we will improve the situation educating. Research methodology and statistics is a reference direct which offers a legitimate and thorough diagram of key terms and ideas in the regions of research and statistics as concerns the field of connected etymology. The book is expected as an asset to depict the importance and utilization of different ideas, approaches, methods, plans, strategies, instruments, sorts, and procedures of connected semantics look into in a productive and open style. A few sections identifying with measurable parts of research are likewise utilized in order to help the specialist in the effective definition, examination, and execution of the exploration outline and convey the same towards its consistent end. |
| mann whitney u assumptions: Illustrating Statistical Procedures: Finding Meaning in Quantitative Data Ray W. Cooksey, 2020-05-14 This book occupies a unique position in the field of statistical analysis in the behavioural and social sciences in that it targets learners who would benefit from learning more conceptually and less computationally about statistical procedures and the software packages that can be used to implement them. This book provides a comprehensive overview of this important research skill domain with an emphasis on visual support for learning and better understanding. The primary focus is on fundamental concepts, procedures and interpretations of statistical analyses within a single broad illustrative research context. The book covers a wide range of descriptive, correlational and inferential statistical procedures as well as more advanced procedures not typically covered in introductory and intermediate statistical texts. It is an ideal reference for postgraduate students as well as for researchers seeking to broaden their conceptual exposure to what is possible in statistical analysis. |
| mann whitney u assumptions: Essential First Steps to Data Analysis Carol S. Parke, 2012-12-13 Carol S. Parke's Essential First Steps to Data Analysis: Scenario-Based Examples Using SPSS provides instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Such preparation may involve data management and manipulation tasks, data organization, structural changes to the data files, or conducting preliminary analysis. Twelve research-based scenarios are used to present the content. Each scenario tells the story of a researcher who thoroughly examined their data and the decisions they made along the way. The scenario begins with a description of the researcher's study and his/her data file(s), then describes the issues the researcher must address, explains why they are important, shows how SPSS was used to address the issues and prepare data, and shares the researcher's reflections and any additional decision-making. Finally, each scenario ends with the researcher's written summary of the procedures and outcomes from the initial data preparation or analysis. |
| mann whitney u assumptions: Understanding and Using Statistics in Psychology Jeremy Miles, Philip Banyard, 2007-04-06 `There are few people who can write about research methods in a lively and engaging way, but Miles and Banyard are amongst them. As well as being an exceptionally clear introduction to research methods, it is full of amusing asides and anecdotes that make you want to read more. A hugely enjoyable book′ - Dr Andy Field, University of Sussex Understanding and Using Statistics in Psychology takes the fear out of psychological statistics to help students understand why statistics are carried out, how to choose the best test and how to carry out the tests and understand them. Taking a non-technical approach, it encourages the reader to understand why a particular test is being used and what the results mean in the context of a psychological study, focusing on meaning and understanding rather than mindless numerical calculation. Key features include: - A light and accessible style - Descriptions of the most commonly used statistical tests and the principles that underlie them - Real world examples to aid the understanding of why statistics are valuable - Boxes on common errors, tips and quotes - Test yourself questions The perfect introductory resource, Understanding and Using Statistics in Psychology will guide any student new to statistics effortlessly through the process of test selection and analysis. |
| mann whitney u assumptions: Multivariate Nonparametric Methods with R Hannu Oja, 2010-03-25 This book offers a new, fairly efficient, and robust alternative to analyzing multivariate data. The analysis of data based on multivariate spatial signs and ranks proceeds very much as does a traditional multivariate analysis relying on the assumption of multivariate normality; the regular L2 norm is just replaced by different L1 norms, observation vectors are replaced by spatial signs and ranks, and so on. A unified methodology starting with the simple one-sample multivariate location problem and proceeding to the general multivariate multiple linear regression case is presented. Companion estimates and tests for scatter matrices are considered as well. The R package MNM is available for computation of the procedures. This monograph provides an up-to-date overview of the theory of multivariate nonparametric methods based on spatial signs and ranks. The classical book by Puri and Sen (1971) uses marginal signs and ranks and different type of L1 norm. The book may serve as a textbook and a general reference for the latest developments in the area. Readers are assumed to have a good knowledge of basic statistical theory as well as matrix theory. Hannu Oja is an academy professor and a professor in biometry in the University of Tampere. He has authored and coauthored numerous research articles in multivariate nonparametrical and robust methods as well as in biostatistics. |
| mann whitney u assumptions: SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics Daniel J. Denis, 2018-09-25 Enables readers to start doing actual data analysis fast for a truly hands-on learning experience This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research Assuming only minimal, prior knowledge of statistics, SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics is an excellent “how-to” book for undergraduate and graduate students alike. This book is also a welcome resource for researchers and professionals who require a quick, go-to source for performing essential statistical analyses and data management tasks. |
| mann whitney u assumptions: Discovering Statistics with Clarity Pasquale De Marco, 2025-04-18 In a world awash with data, statistics has emerged as an indispensable tool for navigating the complexities of information. From scientific research to business analytics, statistics empowers us to uncover hidden patterns, make informed decisions, and gain a deeper understanding of the world around us. Discovering Statistics with Clarity is your comprehensive guide to unlocking the power of statistics. Written in a clear and engaging style, this book takes you on a journey through the fundamental concepts of statistics, from probability and hypothesis testing to correlation and regression. With real-world examples and step-by-step explanations, you'll gain a solid foundation in statistical thinking and analysis. Delve into the realm of non-parametric statistics, where you'll learn to handle data that doesn't conform to traditional assumptions. Explore advanced statistical techniques, such as factor analysis and time series analysis, which equip you to tackle complex data challenges. Discover the art of data visualization, transforming raw data into compelling visuals that illuminate insights and trends. But statistics is not just about numbers and formulas. It's also about ethical considerations. This book delves into the responsible use of statistics, ensuring that you wield this powerful tool with integrity and respect for data privacy. You'll also gain practical guidance on choosing the right statistical software and navigating the world of data analysis with confidence. Whether you're a student seeking to master statistical concepts, a researcher seeking to analyze data, or a professional seeking to make data-driven decisions, Discovering Statistics with Clarity is your trusted companion. Embark on this statistical adventure today and unlock the clarity and insights that statistics can bring to your life and work. If you like this book, write a review on google books! |
| mann whitney u assumptions: Statistics for Criminal Justice and Criminology in Practice and Research Jack Fitzgerald, Jerry Fitzgerald, 2013-01-17 Statistics for Criminal Justice and Criminology in Practice and Research is an engaging and comprehensive introduction to the study of basic statistics for students pursuing careers as practitioners or researchers in both Criminal Justice and Criminology programs. This student-friendly text shows how to calculate a variety of descriptive and inferential statistics, recognize which statistics are appropriate for particular data analysis situations, and perform hypothesis tests using inferential statistics. But it is much more than a cook book. It encourages readers to think critically about the strengths and limitations of the statistics they are calculating, as well as how they may be misapplied and misleading. Examples of statistics and statistical analyses are drawn from the worlds of the practitioner as well as the policymaker and researcher. Students will also gain a clear understanding of major ethical issues in conducting statistical analyses and reporting results, as well as insight into the realities of the life of researchers and practitioners as they use statistics and statistical analyses in their day-to-day activities. |
| mann whitney u assumptions: Inferential Statistics Pasquale De Marco, 2025-03-08 In the realm of data analysis, inferential statistics emerges as a guiding light, illuminating the path from limited observations to broader truths about the world around us. This comprehensive and engaging book invites you on an intellectual journey through the captivating world of statistical inference, empowering you to make informed decisions, draw meaningful conclusions, and uncover hidden patterns in the vast ocean of information that surrounds us. As you delve into the pages of this book, you will embark on an exploration of the fundamental concepts and diverse applications of inferential statistics. You will discover the power of probability distributions, the art of hypothesis testing, and the elegance of estimation techniques. Along the way, you will encounter the giants of statistical thought, from frequentists to Bayesians, and witness the evolution of statistical methods over time. Unravel the Mysteries of Statistical Inference Begin your journey with an exploration of the very essence of inferential statistics, delving into its core principles and uncovering the philosophical underpinnings that guide its practice. Embark on a comparative voyage, contrasting the frequentist and Bayesian approaches to statistical inference, highlighting their strengths and limitations, and exploring the scenarios where each approach shines. Master the Art of Hypothesis Testing and Estimation Confront the thrilling quest of hypothesis testing, the process of making inferences about a population based on sample data. Confront Type I and Type II errors, the perils of statistical inference, and learn to navigate the delicate balance between rejecting false null hypotheses and avoiding false positives. Discover the art of estimation, the process of using sample data to estimate population parameters, and harness the power of confidence intervals, the statistical fences that capture the true population parameter with a specified level of confidence. Uncover the Secrets of Correlation and Regression Venture into the captivating world of correlation and regression, where you will uncover the secrets of linear relationships between variables. Learn to measure the strength of these relationships using correlation coefficients, construct linear regression models to predict one variable from another, and navigate the complexities of multiple regression, where multiple variables dance together in a statistical tango. Explore the Frontiers of Experimental Design and Advanced Techniques Journey to the frontiers of experimental design, where you will learn the art of crafting studies that yield reliable and meaningful data. Explore the principles of randomization and replication, ensuring the validity and reliability of your findings, and delve into the intricacies of sample size determination, balancing the need for precision with the constraints of time and resources. Inferential Statistics: Unveiling Truths from Data is your trusted guide to the captivating world of statistical inference. With its clear explanations, engaging examples, and thought-provoking exercises, this book will empower you to make sense of data, uncover hidden truths, and make informed decisions in an increasingly data-driven world. If you like this book, write a review! |
| mann whitney u assumptions: How to Use Pasw Statistics Brian. C. Cronk, Brian C. Cronk, 2020-07-24 • Designed for use by novice computer users, this text begins with the basics, such as starting SPSS, defining variables, and entering and saving data. • All major statistical techniques covered in beginning statistics classes are included: · descriptive statistics · graphing data · prediction and association · parametric inferential statistics · nonparametric inferential statistics · statistics for test construction • Each section starts with a brief description of the statistic that is covered and important underlying assumptions, which help students select appropriate statistics. • Each section describes how to interpret results and express them in a research report after the data are analyzed. For example, students are shown how to phrase the results of a significant and an insignificant t test. • More than 200 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. • A glossary of statistical terms is included, which makes a handy reference for students who need to review the meanings of basic statistical terms. • Practice exercises throughout the book give students stimulus material to use as they practice to achieve mastery of the program. • Thoroughly field-tested; your students are certain to appreciate this book. |
| mann whitney u assumptions: TEXT BOOK OF BIOSTATISTICS AND RESEARCH METHODOLOGY Prof. (Dr.) Gajendra Kumar Saraswat , Mr. Sandeep Kumar Jhamb , Dr. Vijay Kumar , Dr. Nirmala Devi , Dr. Saheli Pradhan, 2025-03-18 The Textbook of Biostatistics and Research Methodology is a comprehensive guide designed for students, researchers, and professionals in pharmaceutical and biomedical sciences. It provides fundamental concepts and practical applications of statistical methods used in research and industry. The book begins with measures of central tendency, covering mean, median, and mode with pharmaceutical examples, helping readers understand data distribution in research. It then explores measures of dispersion, including range and standard deviation, which are crucial for analyzing variability in drug formulations and clinical studies. A dedicated section on correlation explains Karl Pearson’s coefficient and multiple correlation techniques, providing real-world pharmaceutical applications. The regression analysis chapter covers curve fitting, least squares method, and multiple regression, aiding in predictive modeling of drug responses. The book delves into probability distributions, including binomial, normal, and Poisson distributions, along with sampling techniques, hypothesis testing, and standard error concepts used in pharmaceutical research. Parametric tests, such as t-tests, ANOVA, and least significance difference methods, are thoroughly explained for comparing sample groups in clinical trials. For non-parametric analysis, tests like the Wilcoxon Rank Sum Test, Mann-Whitney U Test, Kruskal-Wallis Test, and Friedman Test are covered, offering alternatives for non-normally distributed data. The introduction to research methodology discusses the importance of experimental design, plagiarism, and ethical research practices. The book also covers graphical data representation through histograms, pie charts, cubic graphs, response surface plots, and contour plots, enhancing statistical analysis visualization. The methodology design chapter includes sample size determination, data presentation, and protocol development for cohort and clinical studies. |
| mann whitney u assumptions: Understanding and Conducting Research in the Health Sciences Christopher J. L. Cunningham, Bart L. Weathington, David J. Pittenger, 2013-06-06 A comprehensive introduction to behavioral and social science research methods in the health sciences Understanding and Conducting Research in the Health Sciences is designed to develop and facilitate the ability to conduct research and understand the practical value of designing, conducting, interpreting, and reporting behavioral and social science research findings in the health science and medical fields. The book provides complete coverage of the process behind these research methods, including information-gathering, decision formation, and results presentation. Examining the application of behavioral and social science research methodologies within the health sciences, the book focuses on implementing and developing relevant research questions, collecting and managing data, and communicating various research perspectives. An essential book for readers looking to possess an understanding of all aspects of conducting research in the health science field, Understanding and Conducting Research in the Health Sciences features: Various research designs that are appropriate for use in the health sciences, including single-participant, multi-group, longitudinal, correlational, and experimental designs Step-by-step coverage of single-factor and multifactor studies as well as single-subject and nonexperimental methods Accessible chapter explanations, real-world examples, and numerous illustrations throughout Guidance regarding how to write about research within the formatting styles of the American Medical Association and the American Psychological Association The book is an excellent educational resource for healthcare and health service practitioners and researchers who are interested in conducting and understanding behavioral and social science research done within the health sciences arena. The book is also a useful resource for students taking courses in the fields of medicine, public health, epidemiology, biostatistics, and the health sciences. |
| mann whitney u assumptions: The Process Of Research Methodology Dr. Rajesh Verma, Prof. Mrs. Aayushi Bansal, Pl. Arundhatee Mishra, Dr. Vijyata, The Process of Research Methodology is a way to figure out how to solve a research problem in a planned way. It is the study of the methodology of scientific inquiry. It is the standard operating method through which researchers describe, evaluate, and anticipate phenomena. The goal is to provide the research work plan. Methods, materials, scientific instruments, and approaches pertinent to the problem's resolution are all covered. According to the author of this book, research methods include everything and everything utilized to carry out research. This term alludes to the means through which researchers carry out their investigations. Although the goal of research is to uncover the hidden and undiscovered truth or to establish (verify) an established fact, the purpose of research is to find answers to problems via the application of the scientific knowledge and techniques. This book covers important issues in the area of research methodology, including introduction to research, study planning with sample design, and data collecting techniques with emphasis on the value of data analysis. The book goes on to discuss the key aspects of hypothesis formulation in the research and test technique, including examples from the fields of science and business. |
| mann whitney u assumptions: Advanced Educational Statistics. - Reference Book Prin. Dr. Sushma Thirekar, 2019-09-02 Unlock the complexities of educational statistics with 'Statistical Mastery in Education' by Professor Sarah Brown. A comprehensive guide for educators navigating advanced statistical concepts in educational research. |
| mann whitney u assumptions: Mastering Statistics: Analytical Approaches for Decision-Makers Pasquale De Marco, 2025-05-22 In [*Mastering Statistics: Analytical Approaches for Decision-Makers*], discover the power of statistics to transform data into actionable insights. This comprehensive guidebook takes you on a journey through the fundamental principles of statistics, empowering you with the knowledge and skills to make sense of complex data and make informed decisions. Delve into the world of probability, the foundation of statistical inference, and explore concepts such as probability rules, conditional probability, and Bayes' theorem. Understand the behavior of random variables and probability distributions, and gain insights into the variability and uncertainty inherent in data. Move on to hypothesis testing, a cornerstone of statistical analysis, and learn how to formulate hypotheses, conduct tests, and interpret results to draw meaningful conclusions from data. Discover the power of regression analysis, a technique used to uncover relationships between variables, and master the art of model selection and evaluation. Explore analysis of variance (ANOVA), a statistical method for comparing multiple groups, and gain insights into the differences between groups. Delve into nonparametric statistics, a set of methods used when assumptions of normality or equal variances are not met, and discover the applications of Bayesian statistics, a powerful approach that incorporates prior knowledge and beliefs into statistical analysis. Learn to communicate statistical results effectively, both orally and in writing, and navigate the ethical considerations associated with statistical analysis. With clear explanations, engaging examples, and real-world case studies, this book is your ultimate guide to mastering statistics and unlocking the value of data. If you like this book, write a review on google books! |
| mann whitney u assumptions: Research Methods for the Behavioral and Social Sciences Bart L. Weathington, Christopher J. L. Cunningham, David J. Pittenger, 2017-09-13 A comprehensive introduction to research methods and best practices for designing,conducting, interpreting, and reporting findings This text is designed to develop in students a passion for conducting research and an understanding of the practical value of systematic information- gathering and decision-making. It features step-by-step coverage of the research process including research design, statistical considerations, and guidance on writing up and presenting results. Recognized leaders in the field—authors Bart Weathington, Christopher Cunningham, and David Pittenger—present: Introductions to multiple research designs—including single-participant, multi-group, longitudinal, correlational, and experimental designs—accompanied by examples Bibliographic research and methods for appropriate sampling Identifying, developing, and evaluating reliable and valid approaches to measurement The issues and steps common to all single-factor and multifactor studies, as well as single-subject and nonexperimental methods How to summarize research in writing that conforms to the editorial guidelines of the American Psychological Association A comprehensive review of research methods and the statistical concepts that support them, Research Methods for the Behavioral and Social Sciences offers the best techniques for studying behavior and social phenomena. |
| mann whitney u assumptions: Quantitative Research Methods in Translation and Interpreting Studies Christopher Mellinger, Thomas Hanson, 2016-08-25 Quantitative Research Methods in Translation and Interpreting Studies encompasses all stages of the research process that include quantitative research methods, from conceptualization to reporting. In five parts, the authors cover: • sampling techniques, measurement, and survey design; • how to describe data; • how to analyze differences; • how to analyze relationships; • how to interpret results. Each part includes references to additional resources and extensive examples from published empirical work. A quick reference table for specific tests is also included in the appendix. This user-friendly guide is the essential primer on quantitative methods for all students and researchers in translation and interpreting studies. Accompanying materials are available online, including step-by-step walkthroughs of how analysis was conducted, and extra sample data sets for instruction and self study: https://www.routledge.com/9781138124967. Further resources for Translation and Interpreting Studies are available on the Routledge Translation Studies Portal: http://cw.routledge.com/textbooks/translationstudies. |
| mann whitney u assumptions: R for Basic Biostatistics in Medical Research Anand Srinivasan, Archana Mishra, Praveen Kumar-M, 2024-12-02 The scientific community at the global level is fast becoming aware of the rising use of open-source tools such as R and Python for data analysis. Unfortunately, in spite of the awareness, the conversion of the intrigue to the practical knowledge in utilization of the open-source tools for routine day-to-day data analysis is seriously lacking both among physicians and medical scientists. This book enables physician-scientists to understand the complexity of explaining a programming/ data-analytic language to a healthcare professional and medical scientist. It simplifies and explains how R can be used in medical projects and routine office works. It also talks about the methodologies to convert the knowledge to practice. The book starts with the introduction to the structure of R programming language in the initial chapters, followed with explanations of utilizing R in the basics of data analysis like data importing and exporting, operations on a data frame, parametric and non-parametric tests, regression, sample size calculation, survival analysis, receiver operator characteristic analysis (ROC) and techniques of randomization. Each chapter provides a brief introduction to the involved statistics, for example, dataset, working codes, and a section explaining the codes. In addition to it, a chapter has been dedicated to describing the ways to generate plots using R. This book primarily targets health care professionals and medical/life-science researchers in general. |
| mann whitney u assumptions: The Research Process in Nursing Kate Gerrish, Judith Lathlean, 2015-01-29 Comprehensive and thorough in scope, The Research Process in Nursing 7th edition provides everything you could want to know about research methods. This established textbook reflects the significant advances in nursing research and the importance of evidence-based practice, and provides an invaluable resource for both the novice and the more experienced researcher. It includes practical information and advice on: How to find and critique the evidence How to choose the right approach How to collect data How to make sense of the data How to put research into practice Special features: A clear, explicit and easy to understand text which links theory with practical steps in the research process. Examples provided allow the reader to apply a variety of research concepts to theoretical learning and professional practice. Incorporates chapters, research examples, and policy from a range of international countries, including Canada, Australia, USA and Hong Kong. Provides detailed discussions around each example, which clearly link theory with practice Easy to read for novice researchers and undergraduate nursing students, but at the same time provides sufficient depth and detail to be of value to experienced researchers and practitioners. |
| mann whitney u assumptions: Statistics for Data Scientists Maurits Kaptein, Edwin van den Heuvel, 2022-02-02 This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science. |
| mann whitney u assumptions: IBM SPSS for Introductory Statistics George A. Morgan, Nancy L. Leech, Gene W. Gloeckner, Karen C. Barrett, 2012-09-10 Designed to help students analyze and interpret research data using IBM SPSS, this user-friendly book, written in easy-to-understand language, shows readers how to choose the appropriate statistic based on the design, and to interpret outputs appropriately. The authors prepare readers for all of the steps in the research process: design, entering and checking data, testing assumptions, assessing reliability and validity, computing descriptive and inferential parametric and nonparametric statistics, and writing about outputs. Dialog windows and SPSS syntax, along with the output, are provided. Three realistic data sets, available on the Internet, are used to solve the chapter problems. The new edition features: Updated to IBM SPSS version 20 but the book can also be used with older and newer versions of SPSS. A new chapter (7) including an introduction to Cronbach’s alpha and factor analysis. Updated Web Resources with PowerPoint slides, additional activities/suggestions, and the answers to even-numbered interpretation questions for the instructors, and chapter study guides and outlines and extra SPSS problems for the students. The web resource is located www.routledge.com/9781848729827 . Students, instructors, and individual purchasers can access the data files to accompany the book at www.routledge.com/9781848729827 . IBM SPSS for Introductory Statistics, Fifth Edition provides helpful teaching tools: All of the key IBM SPSS windows needed to perform the analyses. Complete outputs with call-out boxes to highlight key points. Flowcharts and tables to help select appropriate statistics and interpret effect sizes. Interpretation sections and questions help students better understand and interpret the output. Assignments organized the way students proceed when they conduct a research project. Examples of how to write about outputs and make tables in APA format. Helpful appendices on how to get started with SPSS and write research questions. An ideal supplement for courses in either statistics, research methods, or any course in which SPSS is used, such as in departments of psychology, education, and other social and health sciences. This book is also appreciated by researchers interested in using SPSS for their data analysis. |
| mann whitney u assumptions: Statistical Reasoning in the Behavioral Sciences Bruce M. King, Patrick J. Rosopa, Edward W. Minium, 2018-04-24 Cited by more than 300 scholars, Statistical Reasoning in the Behavioral Sciences continues to provide streamlined resources and easy-to-understand information on statistics in the behavioral sciences and related fields, including psychology, education, human resources management, and sociology. Students and professionals in the behavioral sciences will develop an understanding of statistical logic and procedures, the properties of statistical devices, and the importance of the assumptions underlying statistical tools. This revised and updated edition continues to follow the recommendations of the APA Task Force on Statistical Inference and greatly expands the information on testing hypotheses about single means. The Seventh Edition moves from a focus on the use of computers in statistics to a more precise look at statistical software. The “Point of Controversy” feature embedded throughout the text provides current discussions of exciting and hotly debated topics in the field. Readers will appreciate how the comprehensive graphs, tables, cartoons and photographs lend vibrancy to all of the material covered in the text. |
| mann whitney u assumptions: How to Use SPSS® Brian C. Cronk, 2019-09-24 How to Use SPSS® is designed with the novice computer user in mind and for people who have no previous experience using SPSS. Each chapter is divided into short sections that describe the statistic being used, important underlying assumptions, and how to interpret the results and express them in a research report. The book begins with the basics, such as starting SPSS, defining variables, and entering and saving data. It covers all major statistical techniques typically taught in beginning statistics classes, such as descriptive statistics, graphing data, prediction and association, parametric inferential statistics, nonparametric inferential statistics and statistics for test construction. More than 270 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. The book includes a glossary of statistical terms and practice exercises. A complete set of online resources including video tutorials and output files for students, and PowerPoint slides and test bank questions for instructors, make How to Use SPSS® the definitive, field-tested resource for learning SPSS. New to this edition: Now in full color with additional screenshots Fully updated to the reflect SPSS version 26 (and prior versions) Changes in nonparametric tests Model View incorporated Data and real output are now available for all Phrasing Results sections – eliminating hypothetical output or hypothetical data |
| mann whitney u assumptions: Pharmaceutical Statistics David S. Jones, 2002 Pharmaceutical Statistics is a new publication on basic statistics, specifically written for pharmacy students. It contains chapters on basic concepts such as types of data, graphical representation of data, distribution and standard deviation. More advanced, frequently used, statistical techniques such as ANOVA and the chi-squared test are also discussed using pharmaceutical examples. Pharmaceutical Statistics is essential reading for all pharmacy students and will also be of interest to those working in the pharmaceutical industry. |
| mann whitney u assumptions: The Stress-strength Model and Its Generalizations Samuel Kotz, I?A?n Petrovich Lumel'skii?, Marianna Pensky, 2003 This important book presents developments in a remarkable field ofinquiry in statistical/probability theory the stressOCostrengthmodel.Many papers in the field include the enigmatic wordsP(XY) or something similar in thetitle. |
| mann whitney u assumptions: Statistical Applications for the Behavioral and Social Sciences K. Paul Nesselroade, Jr., Laurence G. Grimm, 2018-11-01 An updated edition of a classic text on applying statistical analyses to the social sciences, with reviews, new chapters, an expanded set of post-hoc analyses, and information on computing in Excel and SPSS Now in its second edition,Statistical Applications for the Behavioral and Social Sciences has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas. The authors cover descriptive statistics and z scores, the theoretical underpinnings of inferential statistics, z and t tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory. This important resource: Contains information regarding the use of statistical software packages; both Excel and SPSS Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the social sciences Puts renewed emphasis on presentation of data and findings using the APA format Includes supplementary material consisting of a set of kick-start quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, Statistical Applications for the Behavioral and Social Sciences, Second Edition continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences. |
| mann whitney u assumptions: Survey Scales Robert L. Johnson, Grant B. Morgan, 2016-07-05 Synthesizing the literature from the survey and measurement fields, this book explains how to develop closed-response survey scales that will accurately capture such constructs as attitudes, beliefs, or behaviors. It provides guidelines to help applied researchers or graduate students review existing scales for possible adoption or adaptation in a study; create their own conceptual framework for a scale; write checklists, true-false variations, and Likert-style items; design response scales; examine validity and reliability; conduct a factor analysis; and document the instrument development and its technical quality. Advice is given on constructing tables and graphs to report survey scale results. Concepts and procedures are illustrated with Not This/But This examples from multiple disciplines. User-Friendly Features *End-of-chapter exercises with sample solutions, plus annotated suggestions for further reading. *Not This/But This examples of poorly written and strong survey items. *Chapter-opening overviews and within-chapter summaries. *Glossary of key concepts. *Appendix with examples of parametric and nonparametric procedures for group comparisons. |
| mann whitney u assumptions: Business Statistics Ken Black, 2009-12-02 Help your students see the light. With its myriad of techniques, concepts and formulas, business statistics can be overwhelming for many students. They can have trouble recognizing the importance of studying statistics, and making connections between concepts. Ken Black's fifth edition of Business Statistics: For Contemporary Decision Making helps students see the big picture of the business statistics course by giving clearer paths to learn and choose the right techniques. Here's how Ken Black helps students see the big picture: Video Tutorials-In these video clips, Ken Black provides students with extra learning assistance on key difficult topics. Available in WileyPLUS. Tree Taxonomy Diagram-Tree Taxonomy Diagram for Unit 3 further illustrates the connection between topics and helps students pick the correct technique to use to solve problems. New Organization-The Fifth Edition is reorganized into four units, which will help professor teach and students see the connection between topics. WileyPLUS-WilePLUS provides everything needed to create an environment where students can reach their full potential and experience the exhilaration of academic success. In addition to a complete online text, online homework, and instant feedback, WileyPLUS offers additional Practice Problems that give students the opportunity to apply their knowledge, and Decision Dilemma Interactive Cases that provide real-world decision-making scenarios. Learn more at www.wiley.co,/college/wileyplus. |
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