Factor structure of the CNS Vital Signs computerized cognitive battery in youth with neurological diagnoses

Computerized cognitive batteries, such as CNS Vital Signs (CNSVS), can provide valuable information in clinical and research settings. However, psychometric properties, especially in children and adolescents, remain relatively understudied. The aim of this study was to investigate the factor structure of CNSVS in children and adolescents with neurological diagnoses.

Participants with neurological diagnoses (N = 280) age 7–19 years were assessed as part of their clinical care at a tertiary hospital. All participants received the full CNSVS computerized cognitive battery, which contains seven subtests designed to measure attention, executive functioning, psychomotor speed, and memory. Principal components analyses were used to examine factor structure.

Scores from CNSVS subtests loaded onto a three-component solution and accounted for 46% of the variance. The three components were deemed to best represent (1) speed, (2) memory, and (3) inhibition, with subtest scores loading differently than the original 11 primary and secondary domain scores would have suggested.

Although the CNSVS program generates numerous primary and secondary domain scores, a three-component solution represents a more parsimonious approach to interpreting performance on the CNSVS in youth with neurological diagnoses. Confirmation of this factor solution in other samples is warranted.     

A diagnostic tree model for polytomous responses with multiple strategies

Constructed-response items have been shown to be appropriate for cognitively diagnostic assessments because students’ problem-solving procedures can be observed, providing direct evidence for making inferences about their proficiency. However, multiple strategies used by students make item scoring and psychometric analyses challenging. This study introduces the so-called two-digit scoring scheme into diagnostic assessments to record both students’ partial credits and their strategies. This study also proposes a diagnostic tree model (DTM) by integrating the cognitive diagnosis models with the tree model to analyse the items scored using the two-digit rubrics. Both convergent and divergent tree structures are considered to accommodate various scoring rules. The MMLE/EM algorithm is used for item parameter estimation of the DTM, and has been shown to provide good parameter recovery under varied conditions in a simulation study. A set of data from TIMSS 2007 mathematics assessment is analysed to illustrate the use of the two-digit scoring scheme and the DTM.