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Assessing Negative Response Bias with the Inventory of Problems-29 (IOP-29): a Quantitative Literature Review

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Abstract

This article reviews published, journal articles informing on the conditions of use, strengths, weaknesses, and optimal cut scores of the Inventory of Problems-29 (IOP-29; Viglione & Giromini, 2020). To provide more accurate information on the convergent and incremental validity, hit rates, and optimal cut scores of the IOP-29, in addition to reviewing all published IOP-29 studies, we also retrieved the datasets associated with each of those studies and performed some additional analyses. Taken together, the findings presented in this quantitative literature review indicate that (a) the IOP-29 correlates more strongly with other symptom validity tests (SVTs) than with other performance validity tests (PVTs), (b) the IOP-29 yields incremental validity when used together with other validity checks, (c) its classification accuracy compares favorably to that of other established tools, and (d) its suggested cut scores perform similarly well across various diagnoses and contexts. When considering the 3777 IOP-29 protocols included in the statistical analyses comparing credible (k = 16) versus noncredible (k = 17) presentations, the standard IOP-29 cut score of False Disorder probability Score ≥ .50 yielded a weighted mean sensitivity of .86 (weighted SD = .07; range: .63–.96) at a weighted mean specificity of .92 (weighted SD = .06; range: .79–1.00). The weighted mean Cohen’s d was 3.02 (weighted SD = .98; range: 1.48–5.31), and the weighted mean AUC was .95 (weighted SD = .04; range: .83–1.00). These excellent statistics, however, could be inflated by the fact that almost all of the examined studies used a simulation research paradigm.

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Notes

  1. These 29 items were also included in the previous 165-item version of the test.

  2. To calculate the weighted mean correlation, a positive sign was assigned to correlations in the expected direction and a negative sign was assigned to correlations in the non-expected direction. For instance, the IOP-29 is supposed to correlate negatively with the TOMM and positively with the SIMS; as such, if the IOP-29 correlated r =  − .50 with the TOMM and r = .30 with the SIMS, the average correlation would be r = .40.

  3. To avoid circularity, studies used to develop the algorithm of the FDS were excluded from this review.

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Authors and Affiliations

  1. Department of Psychology, University of Turin, Turin, Italy

    Luciano Giromini

  2. Alliant International University, San Diego, CA, USA

    Donald J. Viglione

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  1. Luciano Giromini
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Correspondence to Luciano Giromini.

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Luciano Giromini and Donald J. Viglione declare that they own a share in the corporate (LLC) that possesses the rights to Inventory of Problems.

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Giromini, L., Viglione, D.J. Assessing Negative Response Bias with the Inventory of Problems-29 (IOP-29): a Quantitative Literature Review. Psychol. Inj. and Law 15, 79–93 (2022). https://doi.org/10.1007/s12207-021-09437-7

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