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Understanding and Evaluating Research: A Critical Guide shows students how to be critical consumers of research and to appreciate the power of methodology as it shapes the research question, the use of theory in the study, the methods used, and how the outcomes are reported. The book starts with what it means to be a critical and uncritical reader of research, followed by a detailed chapter on methodology, and then proceeds to a discussion of each component of a research article as it is informed by the methodology. The book encourages readers to select an article from their discipline, learning along the way how to assess each component of the article and come to a judgment of its rigor or quality as a scholarly report.
A clear and concise introduction and reference for anyone new to the subject of statistics.
This is an authoritative introduction to Computing Education research written by over 50 leading researchers from academia and the industry.
This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation. Features The classic graduate-level textbook on statistical inference Develops elements of statistical theory from first principles of probability Written in a lucid style accessible to anyone with some background in calculus Covers all key topics of a standard course in inference Hundreds of examples throughout to aid understanding Each chapter includes an extensive set of graduated exercises Statistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations. This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.
This practical, conceptual introduction to statistical analysis by award-winning teacher Andrew N. Christopher uses published research with inherently interesting social sciences content to help students make clear connections between statistics and real life. Using a friendly, easy-to-understand presentation, Christopher walks students through the hand calculations of key statistical tools and provides step-by-step instructions on how to run the appropriate analyses for each type of statistic in SPSS and how to interpret the output. With the premise that a conceptual grasp of statistical techniques is critical for students to truly understand why they are doing what they are doing, the author avoids overly formulaic jargon and instead focuses on when and how to use statistical techniques appropriately.
What does this p-value actually mean? And what is a significant result? This book provides a compact and comprehension-oriented introduction to inferential statistics and answers questions like these. One focus is on the logic underlying inferential statistics and hypothesis testing: Readers learn the most commonly used procedures (t-test, analysis of variance with and without repeated measures, correlation/regression) as well as the pitfalls of data analysis, and develop the understanding necessary to interpret results correctly. The individual chapters are supplemented by concrete evaluation examples from everyday research - including exemplary implementation with the programs SPSS and R. In addition to the classic methods, cross-references to current developments in psychological methodological research are also included.This book is a translation of the original German 3rd edition of Inferenzstatistik verstehen by Markus Janczyk and Roland Pfister. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation.
This book focuses on the meaning of statistical inference and estimation. Statistical inference is concerned with the problems of estimation of population parameters and testing hypotheses. Primarily aimed at undergraduate and postgraduate students of statistics, the book is also useful to professionals and researchers in statistical, medical, social and other disciplines. It discusses current methodological techniques used in statistics and related interdisciplinary areas. Every concept is supported with relevant research examples to help readers to find the most suitable application. Statistical tools have been presented by using real-life examples, removing the “fear factor” usually associated with this complex subject. The book will help readers to discover diverse perspectives of statistical theory followed by relevant worked-out examples. Keeping in mind the needs of readers, as well as constantly changing scenarios, the material is presented in an easy-to-understand form.
Research Methods and Statistics for Public and Nonprofit Administrators: A Practical Guide is a comprehensive, easy-to-read, core text that thoroughly prepares readers to apply research methods and data analysis to the professional environments of public and non-profit administration. The authors expertly incorporate original case examples to demonstrate concepts using “real actors,” facing specific scenarios, in which research methods must be applied. This unique approach—presented in language accessible to both students new to research as well as current practitioners—guides the reader in fully understanding the research options detailed throughout the text.
Descriptive and Inferential Statistics, is a book that is intended for university students of any college. You'll find theory as summaries, and exercises solved, on the following topics: Descriptive Statistics, Confidence Intervals and Test Hypothesis for means, proportions and variances for one sample, Chi Square Test, Test Hypothesis for means, proportions and variances, for two or more samples, and Regression line. Statistical software such as SPSS, Minitab, programs have been used in the resolution of problems and in some cases have been resolved by using the Excel and also manually.
Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.