Titlebook: Quantitative Psychology; The 82nd Annual Meet Marie Wiberg,Steven Culpepper,Dylan Molenaar Conference proceedings 2018 Springer Internation

Loathe

HPA533

An Alternative View on the NEAT Design in Test Equating,s that are used to build the equating transformation. The results show that the uncertainty about the score probability distributions, reflected on the width of the bounds, can be very large, and can thus have a big impact on equating.

愛管閑事

Duodenitis

BOLUS

Three-Way Generalized Structured Component Analysis,s criterion to estimate parameters. An alternating least squares algorithm is developed to minimize this criterion. We conduct a simulation study to evaluate the performance of three-way GSCA. We also apply three-way GSCA to real data to demonstrate its empirical usefulness.

indifferent

Modeling Accidental Mistakes in Multistage Testing: A Simulation Study, not have this probability. Comparison of latent trait estimates accuracy and the impact on the item selection process of the MST (Routing) between these two models were made. Results shows that both models had similar performance with slightly differences depending on the procedures to simulate the responses.

Hot-Flash

An Evaluation of Rater Agreement Indices Using Generalizability Theory,es that ranged from below 0.4 to over 0.8. The impact of the underlying score distributions, the number of score categories, rater/prompt variability, and rater/prompt assignment on these indices was also investigated.

CHYME

,How to Select the Bandwidth in Kernel Equating—An Evaluation of Five Different Methods,e-out cross-validation. Although the bandwidth methods generally were similar in terms of equated scores, there were potentially important differences in the upper part of the score scale where critical admission decisions are typically made.

grovel

對待

On the Bias in Eigenvalues of Sample Covariance Matrix,lly ignored in practice. This article further shows that, under some regulatory conditions, the order of the bias term is ./.. Thus, when . is large, the bias term is no longer negligible even when . is large.