Don’t worry about the anchor-item setting in longitudinal learning diagnostic assessments

Abstract

Previous longitudinal assessment experiences for multidimensional continuous latent constructs suggested that the set of anchor items should be proportionally representative of the total test forms in content and statistical characteristics and that they should be loaded on every domain in multidimensional tests. In such cases, the set of items containing the unit Q-matrix, which is the smallest unit representing the whole test, seems to be the natural choice for anchor items. Two simulation studies were conducted to verify the applicability of these existing insights to longitudinal learning diagnostic assessments (LDAs). The results mainly indicated that there is no effect on the classification accuracy regardless of the unit Q-matrix in the anchor items, and even not including the anchor items has no impact on the classification accuracy. The findings of this brief study may ease practitioners’ worries regarding anchor-item settings in the practice application of longitudinal LDAs.

Approximate Invariance Testing in Diagnostic Classification Models in the Presence of Attribute Hierarchies: A Bayesian Network Approach

“…Note that, because attributes are assumed to take on a finite number of states (in our case, two), the scale of the latent attributes is fixed. Importantly, this means that there is no need to identify an anchor item since the scale of the latent variables is completely determined, i.e., a respondent either does/does not possess the attribute under investigation; see [55]. This is in contrast to measurement invariance approaches for factor analysis and item response theory, which require that the latent variables be placed on the same scale for invariance analyses and group comparisons to be meaningful (see [56] for an in-depth discussion of this issue).…”