JOURNAL ARTICLE

B - 143 A Comparison of Structured Inventory of Malingered Symptomology (SIMS) Performance to Multiple Validity Measures in a Criminal Forensic Sample.

  • Published In: Archives of Clinical Neuropsychology, 2024, v. 39, n. 7. P. 1250 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hernandez, Katherine A; Ikonomou, Vasilios C; Campbell, Ivan A; Wagaman, Bailey N; Sullivan, Sydney; Ton-Loy, Adan; Kockler, Timothy; Hill, Benjamin D 3 of 3

Abstract

This article examines the performance of the Structured Inventory of Malingered Symptomatology (SIMS) compared to multiple validity measures in a criminal forensic sample undergoing neuropsychological evaluations. Analyzing data from 119 cases, the study found that 36% of individuals failed the SIMS total score yet passed other validity tests, resulting in a 64% overall concordance rate between SIMS and other measures such as the MMPI-2-RF, WAIS-IV Reliable Digit Span, and the Word Memory Test. The SIMS affective (AF) subscale showed better alignment with other validity measures than the low intelligence (LI) subscale, which performed near chance levels. The findings suggest that SIMS results do not strongly correlate with performance validity tests (PVTs) or symptom validity tests (SVTs) focused on response consistency, indicating classification overlap and potential limitations in generalizability due to the high failure rate of PVT/SVTs in this forensic sample.

Additional Information

  • Source:Archives of Clinical Neuropsychology. 2024/10, Vol. 39, Issue 7, p1250
  • Document Type:Article
  • Subject Area:Applied Sciences
  • Publication Date:2024
  • ISSN:0887-6177
  • DOI:10.1093/arclin/acae067.304
  • Accession Number:184163595
  • Copyright Statement:Copyright of Archives of Clinical Neuropsychology is the property of Oxford University Press / USA 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.)

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