Decreasing Adolescent Drug Use and School Failure:

Behavioral analyses of 3 adolescents show that 3 months of targeted family problem-solving training can decrease drug use and school failure by the end of a 1 1/4 year follow-up while control behaviors remain stable. Some findings, however, reflected incomplete understanding of the controlling variables: (a) lengthy delays before behavioral improvement, (b) recurrences of the problem behavior and subsequent recoveries during follow-up, and (c) correlated changes in the home and school environments. It is suggested that systematic study of relevant variables in the intervention could reduce behavioral variability and further increase understanding of adolescent drug use.     

Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform Information

The standard error (SE) stopping rule, which terminates a computer adaptive test (CAT) when the SE is less than a threshold, is effective when there are informative questions for all trait levels. However, in domains such as patient-reported outcomes, the items in a bank might all target one end of the trait continuum (e.g., negative symptoms), and the bank may lack depth for many individuals. In such cases, the predicted standard error reduction (PSER) stopping rule will stop the CAT even if the SE threshold has not been reached and can avoid administering excessive questions that provide little additional information. By tuning the parameters of the PSER algorithm, a practitioner can specify a desired tradeoff between accuracy and efficiency. Using simulated data for the Patient-Reported Outcomes Measurement Information System Anxiety and Physical Function banks, we demonstrate that these parameters can substantially impact CAT performance. When the parameters were optimally tuned, the PSER stopping rule was found to outperform the SE stopping rule overall, particularly for individuals not targeted by the bank, and presented roughly the same number of items across the trait continuum. Therefore, the PSER stopping rule provides an effective method for balancing the precision and efficiency of a CAT.     

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.