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Developing learning slope scores for the repeatable battery for the assessment of neuropsychological status.

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Title: Developing learning slope scores for the repeatable battery for the assessment of neuropsychological status.
Authors: Spencer, Robert J. (AUTHOR), Gradwohl, Brian D. (AUTHOR), Williams, Trevor F. (AUTHOR), Kordovski, Victoria M. (AUTHOR), Hammers, Dustin B. (AUTHOR)
Source: Applied Neuropsychology: Adult. Jul/Aug2022, Vol. 29 Issue 4, p584-590. 7p. 5 Charts.
Subjects: Psychology of veterans, Learning, Psychometrics, Neuropsychological tests, Educational attainment
Abstract: Initial learning and learning slope are often acknowledged as important qualitative aspects of learning, but the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) contains discrete indices for neither. The traditional method of calculating learning slope involves a difference score between the last trial and first trial, which is referred to as raw learning score (RLS). However, this method does not account for initial Trial One performance and produces a ceiling effect that penalizes efficient first learners. We propose an alternative method of calculating learning score that accounts for initial learning performance, called learning ratio (LR), and we compared the psychometric and predictive properties of these methods. Performances from the List Learning and Story Memory subtests were used to create the indices, and composite learning scores were calculated by combining List Learning and Story Memory. The sample included 289 military veterans (mean age = 65.9 [12.6], education = 13.3 [2.4]), most of whom were male, undergoing neuropsychological assessments that included the RBANS. Results indicated that LR demonstrated superior correlations with criterion measures of memory when compared with RLS, and the LR composite score better discriminated between those with and without a neurocognitive diagnosis, AUC = 0.81 (0.76-0.87), than the RLS composite, AUC = 0.70 (0.64-0.76). We concluded that scores from the RBANS can be computed for initial learning and learning slope and that the LR method of calculating learning is superior to RLS in this older veteran sample. [ABSTRACT FROM AUTHOR]
Copyright of Applied Neuropsychology: Adult is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Developing learning slope scores for the repeatable battery for the assessment of neuropsychological status.
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  Data: <searchLink fieldCode="JN" term="%22Applied+Neuropsychology%3A+Adult%22">Applied Neuropsychology: Adult</searchLink>. Jul/Aug2022, Vol. 29 Issue 4, p584-590. 7p. 5 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Psychology+of+veterans%22">Psychology of veterans</searchLink><br /><searchLink fieldCode="DE" term="%22Learning%22">Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Neuropsychological+tests%22">Neuropsychological tests</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+attainment%22">Educational attainment</searchLink>
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  Data: Initial learning and learning slope are often acknowledged as important qualitative aspects of learning, but the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) contains discrete indices for neither. The traditional method of calculating learning slope involves a difference score between the last trial and first trial, which is referred to as raw learning score (RLS). However, this method does not account for initial Trial One performance and produces a ceiling effect that penalizes efficient first learners. We propose an alternative method of calculating learning score that accounts for initial learning performance, called learning ratio (LR), and we compared the psychometric and predictive properties of these methods. Performances from the List Learning and Story Memory subtests were used to create the indices, and composite learning scores were calculated by combining List Learning and Story Memory. The sample included 289 military veterans (mean age = 65.9 [12.6], education = 13.3 [2.4]), most of whom were male, undergoing neuropsychological assessments that included the RBANS. Results indicated that LR demonstrated superior correlations with criterion measures of memory when compared with RLS, and the LR composite score better discriminated between those with and without a neurocognitive diagnosis, AUC = 0.81 (0.76-0.87), than the RLS composite, AUC = 0.70 (0.64-0.76). We concluded that scores from the RBANS can be computed for initial learning and learning slope and that the LR method of calculating learning is superior to RLS in this older veteran sample. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Neuropsychology: Adult is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/23279095.2020.1791870
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              Text: Jul/Aug2022
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