Handbook Of Inter Rater Reliability

The third edition of this book was very well received by researchers working in many different fields of research. The use of that text also gave these researchers the opportunity to raise questions, and express additional needs for materials on techniques poorly covered in the literature. For example, when designing an inter-rater reliability study, many researchers wanted to know how to determine the optimal number of raters and the optimal number of subjects that should participate in the experiment. Also, very little space in the literature has been devoted to the notion of intra-rater reliability, particularly for quantitative measurements. The fourth edition of this text addresses those needs, in addition to further refining the presentation of the material already covered in the third edition. Features of the Fourth Edition include: New material on sample size calculations for chance-corrected agreement coefficients, as well as for intraclass correlation coefficients. The researcher will be able to determine the optimal number raters, subjects, and trials per subject.The chapter entitled “Benchmarking Inter-Rater Reliability Coefficients” has been entirely rewritten.The introductory chapter has been substantially expanded to explore possible definitions of the notion of inter-rater reliability.All chapters have been revised to a large extent to improve their readability.

Low inter-rater reliability can jeopardize the integrity of scientific inquiries or have dramatic consequences in practice. In a clinical setting for example, a wrong drug or wrong dosage of the correct drug may be administered to patients at a hospital due to a poor diagnosis. Likewise, exam grades are considered reliable if they are determined only by the candidate's proficiency level in a particular skill, and not by the examiner's scoring method. The study of inter-rater reliability helps researchers address these issues using an approach that is methodologically sound. The 4th edition of this book covers Chance-corrected Agreement Coefficients (CAC) for the analysis of categorical ratings, as well as Intraclass Correlation Coefficients (ICC) for the analysis of quantitative ratings. The 5th edition however, is released in 2 volumes. The present volume 2, focuses on ICC methods whereas volume 1 is devoted to CAC methods. The decision to release 2 volumes was made at the request of numerous readers of the 4th edition who indicated that they are often interested in either CAC techniques or in ICC techniques, but rarely in both at a given point in time. Moreover, the large number of topics covered in this 5th edition could not be squeezed in a single book, without it becoming voluminous. Volume 2 of the Handbook of Inter-Rater Reliability 5th edition contains 2 new chapters not found in the previous editions, and updated versions of 7 chapters taken from the 4th edition. Here is a summary of the main changes from the 4th edition that you will find in this book: Chapter 2 is new to the 5th edition and covers various ways of setting up your rating dataset before analysis. Chapter 3 is introductory and an update of chapter 7 in the 4th edition. In addition to providing an overview of the book content similar to that of the 4th edition, this chapter introduces the new multivariate intraclass correlation not covered in previous editions. Chapter 4 covers intraclass correlation coefficients in one-factor models and has a separate section devoted to sample size calculations. Two approaches to sample size calculations are now offered: the statistical power approach and the confidence interval approach. Chapter 5 covers intraclass correlation coefficients under the random factorial design, which is based on a two-way Analysis of Variance model where the rater and subject factors are both random. Section 5.4 on sample size calculations has been expanded substantially. Researchers can now choose between the statistical power approach based on the Minimum Detectable Difference (MDD) and the confidence interval approach based on the target interval length. Chapter 6 covers intraclass correlation coefficients under the mixed factorial design, which is based on a two-way Analysis of Variance model where the rater factor is fixed and the subject factor random. The treatment of sample size calculations has been expanded substantially. Chapter 7 is new and covers Finn's coefficient of reliability as an alternative to the traditional intraclass correlations when they are not be applicable. Chapter 8 entitled "Measures of Association and Concordance" covers various association and concordance measures often used by researchers. It includes a discussion of Lin's concordance correlation coefficient and its statistical properties. Chapter 9 is new and covers 3 important topics: the benchmarking of ICC estimates, a graphical approach for exploring the influence of individual raters in low-agreement inter-rater reliability experiments, and the multivariate intraclass correlation. I wanted this book to be sufficiently detailed for practitioners to gain more insight into the topics, which would not be possible if the book was limited to a high-level coverage of technical concepts.

This book presents various methods for calculating the extent of agreement among raters for different types of ratings. Some of the methods initially developed for nominal-scale ratings only, are extended in this book to ordinal and interval scales as well. To ensure an adequate level of sophistication in the treatment of this topic, the precision aspects associated with the agreement coefficients are treated. New methods begin with the simple scenario of 2 raters and 2 response categories before being extended to the more complex situation of multiple raters, and multiple-level nominal, ordinal and interval scales. Cohen's Kappa coefficient is one of the most widely-used agreement coefficients among researchers, despite its tendency to yield controvertial results. Kappa and its various versions have raised concerns among practitioners and showed limitations, which are well-documented in the literature. This book discusses numerous alternatives, and proposes a new framework of analysis that allows researchers to gain further insight into the core issues related to the interpretation of the coefficients' magnitude, in addition to providing a common framework for evaluating the merit of different approaches. The author explains in a clear and intuitive fashion the motivations and assumptions underlying each technique discussed in the book. He demonstrates the benefits of using basic level statistical thinking in the design and analysis of inter-rater reliability experiments. The interpretation and limitations of various techniques are extensively discussed. From optimizing the design of the inter-rater reliability study to validating the computed agreement coefficients, the author's step-by-step approach is practical, easy to understand and will put all practitioners on the path to achieving their data quality objectives.

Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.

How can we make sense of make sense of the deluge of information in the digital age? The new science of Quantitative Ethnography dissolves the boundaries between quantitative and qualitative research to give researchers tools for studying the human side of big data: to understand not just what data says, but what it tells us about the people who created it. Thoughtful, literate, and humane, Quantitative Ethnography integrates data-mining, discourse analysis, psychology, statistics, and ethnography into a brand-new science for understanding what people do and why they do it. Packed with anecdotes, stories, and clear explanations of complex ideas, Quantitative Ethnography is an engaging introduction to research methods for students, an introduction to data science for qualitative researchers, and an introduction to the humanities for statisticians--but also a compelling philosophical and intellectual journey for anyone who wants to understand learning, culture and behavior in the age of big data.

The Population Situation Analysis (PSA) provides the basis for an integrated appraisal of the population and reproductive health dynamics and their impacts on poverty, inequality and development. By integrating a micro and macro analytical approach, the population situation analysis clarifies the interactions between individual behaviour and demographic dynamics. The Population Situation Analysis (PSA) responds to demand by countries that international cooperation should promote national capacity-building and recognize national ownership and leadership as prerequisites for development, in accordance with the principles agreed at the International Conference on Population and Development (ICPD) and the Millennium Declaration. This manual contributes to more efficient evidence-based programming, which relies on increased capacity for data generation, new databases, the consolidation of available evidence and the promotion of the use of hard data. The knowledge generated thr