Construct identification in the neuropsychological battery: What are we measuring?

Area of Science:

• Neuropsychology
• Psychometrics
• Cognitive Neuroscience

Background:

• Harmonizing neuropsychological data is challenging due to a lack of consensus on essential and invariant constructs.
• The National Neuropsychology Network (NNN) dataset offers a valuable resource for addressing these challenges.

Purpose of the Study:

• To evaluate the invariance of neuropsychological constructs across healthy and clinical populations.
• To identify optimal measurement models for key neuropsychological constructs using data from the NNN.

Main Methods:

• Confirmatory factor analysis was used on data from 5,000 NNN participants and standardization samples.
• Four instruments were analyzed: WAIS-IV, WMS-IV, CVLT3, and D-KEFS.
• Measurement invariance and factor score relations to demographics/clinical characteristics were examined.

Main Results:

• Optimal factor models for patients generally mirrored those from standardization samples, including task-specific factors.
• Novel Recognition-Familiarity (WMS-IV) and Inhibition-Switching (D-KEFS) factors were identified from NNN data.
• Strong to strict factorial invariance was observed across samples, with expected differences in factor means and variances.

Conclusions:

• Factor models developed in healthy groups demonstrated good fit in patient populations.
• The identified invariant Recognition-Familiarity and Inhibition-Switching factors may hold clinical utility.
• Findings support the development of evidence-based, efficient, and cross-group valid neuropsychological measurements.