Marie Wiberg

Generalized Kernel Equating is a comprehensive guide for statisticians, psychometricians, and educational researchers aiming to master test score equating. This book introduces the Generalized Kernel Equating (GKE) framework, providing the necessary tools and methodologies for accurate and fair score comparisons.The book presents test score equating as a statistical problem and covers all commonly used data collection designs. It details the five steps of the GKE framework: presmoothing, estimating score probabilities, continuization, equating transformation, and evaluating the equating transformation. Various presmoothing strategies are explored, including log-linear models, item response theory models, beta4 models, and discrete kernel estimators. The estimation of score probabilities when using IRT models is described and Gaussian kernel continuization is extended to other kernels such as uniform, logistic, epanechnikov and adaptive kernels. Several bandwidth selection methods are described. The kernel equating transformation and variants of it are defined, and both equating-specific and statistical measures for evaluating equating transformations are included. Real data examples, guiding readers through the GKE steps with detailed R code and explanations are provided. Readers are equipped with an advanced knowledge and practical skills for implementing test score equating methods.

This book includes presentations given at the 88th annual meeting of the Psychometric Society, held in Maryland, USA on July 24–28, 2023. The proceeding covers a diverse set of psychometric topics. The topics include, but are not limited to item response theory, cognitive diagnostic models, Bayesian estimation, validity and reliability issues, and several applications within different fields. The authors are from all over the world, they work in different psychometrics areas, as well as having diverse professional and academic experiences.

Mänsklighetens samlade kunskap kan beskrivas som ett stort pussel. Bitar av nytt vetande bygger på och hakar i den tidigare samlade förståelsen om världen vi existerar i. Kunskap från olika vetenskapsområden bygger upp motiven som tillsammans bildar pusslets helhet. Små pusselbitar, till synes isolerade kan leda till stora genombrott när de plötsligt får ett sammanhang från ett angränsande område. I denna essäsamling har ledamöter i Sveriges unga akademi samlat texter om sina olika vetenskapsfält. Liksom kalejdoskopet rymmer boken infallsvinklar från många delar av forskningsfronten och bildar tillsammans en helhet som är något större än varje enskild del. En gemensam nämnare i skildringarna av forskarnas vardag är den otroliga nyfikenhet som driver fram ny kunskap. Ett öppet sinne och ett förutsättningslöst sökande, är närmast en naturlag för att skapa nytt vetande. Det kan tyckas lockande som beslutsfattare att rikta satsningar på forskning mot strategiska mål. Men utan den grundläggande forskning som dag efter dag och år efter år bedrivs kommer aldrig nya mönster att framträda. Vi kan inte säkert veta idag var avgörande genombrott väntar. Lösningar på dagens utmaningar kan som så många gånger förr framträda på oväntat håll. Ledamöterna inom Sveriges unga akademi representerar ett brett urval av olika vetenskapsområden. Under akademins möten träffas de och får möjlighet till en djupare inblick i respektive fälts stora utmaningar. Genom dessa tvärvetenskapliga möten kan nya frågeställningar formuleras och utforskas genom den bildliga vridningen av kalejdoskopets tub. Därmed finns det redan en rik källa av kunskap att ösa ur när stora samhällsutmaningar tornar upp sig och mänskligheten kan stå lite mer förberedd.

This book describes how to use test equating methods in practice. The non-commercial software R is used throughout the book to illustrate how to perform different equating methods when scores data are collected under different data collection designs, such as equivalent groups design, single group design, counterbalanced design and non equivalent groups with anchor test design. The R packages equate, kequate and SNSequate, among others, are used to practically illustrate the different methods, while simulated and real data sets illustrate how the methods are conducted with the program R. The book covers traditional equating methods including, mean and linear equating, frequency estimation equating and chain equating, as well as modern equating methods such as kernel equating, local equating and combinations of these. It also offers chapters on observed and true score item response theory equating and discusses recent developments within the equating field. More specifically it covers the issue of including covariates within the equating process, the use of different kernels and ways of selecting bandwidths in kernel equating, and the Bayesian nonparametric estimation of equating functions. It also illustrates how to evaluate equating in practice using simulation and different equating specific measures such as the standard error of equating, percent relative error, different that matters and others.

This book describes how to use test equating methods in practice. The non-commercial software R is used throughout the book to illustrate how to perform different equating methods when scores data are collected under different data collection designs, such as equivalent groups design, single group design, counterbalanced design and non equivalent groups with anchor test design. The R packages equate, kequate and SNSequate, among others, are used to practically illustrate the different methods, while simulated and real data sets illustrate how the methods are conducted with the program R. The book covers traditional equating methods including, mean and linear equating, frequency estimation equating and chain equating, as well as modern equating methods such as kernel equating, local equating and combinations of these. It also offers chapters on observed and true score item response theory equating and discusses recent developments within the equating field. More specifically it covers the issue of including covariates within the equating process, the use of different kernels and ways of selecting bandwidths in kernel equating, and the Bayesian nonparametric estimation of equating functions. It also illustrates how to evaluate equating in practice using simulation and different equating specific measures such as the standard error of equating, percent relative error, different that matters and others.