Elaboration of Neuropsychological Evaluation of Children: Structural Analysis of Test Results

Abstract

Background. Modern neuropsychology is discussing the possibility of combiningqualitative and quantitative approaches in the evaluation of cognitive functions.In Russia a battery of tests called “Methods of neuropsychological assessmentfor children 6–9 years old” (Akhutina et al., 2016) has been proposed; it isbased on the Lurian approach to diagnosis and combines qualitative and quantitativeapproaches to testing. The present paper describes the development of thiscombined qualitative and quantitative assessment of various groups of cognitivefunctions in preschool and primary school children. Structural modeling enablesus to analyze a possible combination of integral indices of functions that includesthe results of both a face-to-face neuropsychological assessment and computerizedtesting.Objective. To develop a combined qualitative and quantitative neuropsychologicalassessment of children, in order to 1) check the structural reliability ofintegral indicators of various cognitive functions; and 2) confirm the correctnessof combining the results of face-to-face and computerized tests.Design. A sample of 299 children between the ages of 6 and 9 years old(111 preschoolers, 82 first graders, and 106 second graders) underwent a Lurianface-to-face neuropsychological examination adapted for 6-to-9 year-oldchildren, and five tests from the Computerized Neuropsychological Assessmentfor 6–9 Year-old Children. The five were the “Dots” test, the Schulte Tables, theCancellation test, the Corsi Tapping Block test, and the Understanding of SimilarSounding Words test. In each of the tests (face-to-face and computerized), keyparameters were identified to evaluate various cognitive functions.Results. A confirmatory factor analysis verified the composition of the neuropsychologicalindices that were based on the results of the face-to-face neuropsychologicalassessment. At the same time, when the computer test data wereadded to the model, the fit indices of the model considerably improved.