Short scales are time-efficient for participants and cost-effective in research. However, researchers often mistakenly expect short scales to have the same reliability as long ones without considering the effect of scale length. We argue that applying a universal benchmark for alpha is problematic as the impact of low-quality items is greater on…

The subjective aspect of standard-setting is often criticized, yet data-driven standard-setting methods are rarely applied. Therefore, we applied a mixture Rasch model approach to setting performance standards across several testing programs of various sizes and compared the results to existing passing standards derived from traditional…

Population invariance is a desirable property of test equating which might not hold when significant changes occur in the test population, such as those brought about by the COVID-19 pandemic. This research aims to investigate whether equating functions are reasonably invariant when the test population is impacted by the pandemic. Based on…

Test equating requires collecting data to link the scores from different forms of a test. Problems arise when equating samples are not equivalent and the test forms to be linked share no common items by which to measure or adjust for the group nonequivalence. Using data from five operational test forms, we created five pairs of research forms for…

This article proposes a Boolean approach to representing and analyzing interobserver agreement in dichotomous coding. Building on the notion that observations are samples of a universe of observations, it submits that coding can be viewed as a process in which observers sample pieces of evidence on constructs. It distinguishes between formal and…

Commonly used machine learning applications seem to relate to big data. This article provides a gentle review of machine learning and shows why machine learning can be applied to small data too. An example of applying machine learning to screen irregularity reports is presented. In the example, the support vector machine and multinomial naïve…