No abstract

LibQUAL+TM data to date have not been subjected to the modern measurement theory called polytomous item response theory (IRT). The data interpreted here were collected from 42,090 participants who completed the "American English" version of the 22 core LibQUAL+TM items, and 12,552 participants from Australia and Europe who completed the "British English" version of the 22 core LibQUAL+TM items. Results suggest that the LibQUAL+TM protocol has psychometric integrity, that American English participants tend to be somewhat less critical of library service quality, and that students and faculty have fairly similar views of library service quality.

The AAIDD has promulgated various models of adaptive behavior, including its 1992 model stressing 10 adaptive skills and its 2002 model that highlighted three conceptual domains. In previous studies on the Adaptive Behavior Assessment System-II (ABAS-II), researchers found support for a model including both 10 adaptive skills and three conceptual domains. To extend this review, we examined gender-invariant structure of adaptive behavior using the ABAS-II Parent Form, Ages 5-21, to answer four questions: Do the skill areas in this measure display the same pattern of factor loadings and the same factor loadings? Are intercepts of the observed skill areas equal? Do skill areas measure the corresponding factors with the same accuracy? Results show a similar one-factor structure for males and females.

For a testing program with frequent administrations, it is important to understand and monitor the stability and fluctuation of test performance across administrations. Different methods have been proposed for this purpose. This study explored the potential of using multilevel analysis to understand and monitor examinees' test performance across administrations based on their background information. Based on the data of 330,091 examinees' test scores and their background information collected from 254 administrations of an English‐speaking test, the study found: (a) at the individual examinee level, examinees' background had statistically significant relationships with their test performance, and the relationships varied across administrations; however, the prediction of individuals' test scores based on their background variables was not strong, and (b) at the administration level, group composition had strong relationships with administration means; the prediction of administration means based on group composition variables was fairly strong. The results suggest that multilevel analysis has potential application in understanding and monitoring test performance across administrations by identifying statistical relationships between examinees' characteristics and their test performance at both individual and administration levels.

No abstract