Combining text mining of long constructed responses and item-based measures: A hybrid test design to screen for posttraumatic stress disorder (PTSD)

Qiwei He*, Bernard P. Veldkamp, Cees A.W. Glas, Stéphanie M. van den Berg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

This article introduces a new hybrid intake procedure developed for posttraumatic stress disorder (PTSD) screening, which combines an automated textual assessment of respondents’ self-narratives and item-based measures that are administered consequently. Text mining technique and item response modeling were used to analyze long constructed response (i.e., self-narratives) and responses to standardized questionnaires (i.e., multiple choices), respectively. The whole procedure is combined in a Bayesian framework where the textual assessment functions as prior information for the estimation of the PTSD latent trait. The purpose of this study is twofold: first, to investigate whether the combination model of textual analysis and item-based scaling could enhance the classification accuracy of PTSD, and second, to examine whether the standard error of estimates could be reduced through the use of the narrative as a sort of routing test. With the sample at hand, the combination model resulted in a reduction in the misclassification rate, as well as a decrease of standard error of latent trait estimation. These findings highlight the benefits of combining textual assessment and item-based measures in a psychiatric screening process. We conclude that the hybrid test design is a promising approach to increase test efficiency and is expected to be applicable in a broader scope of educational and psychological measurement in the future.

Original languageEnglish
Article number2358
JournalFrontiers in psychology
Volume10
Issue numberOCT
DOIs
Publication statusPublished - 22 Oct 2019

Keywords

  • Bayesian framework
  • Item response theory
  • Posttraumatic stress disorder
  • Self-narratives
  • Text mining

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