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CLINICAL ISSUES

Machine learning item selection for short scale construction: A proof-of-concept using the SIMS

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Pages 1371-1388 | Received 05 Apr 2022, Accepted 12 Aug 2022, Published online: 26 Aug 2022

References

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Author. https://doi.org/10.1176/appi.books.9780890425596
  • Boone, K. B. (2007). A reconsideration of the Slick et al. (1999) criteria for malingered neurocognitive dysfunction. In K. B. Boone (Ed.), Assessment of feigned cognitive impairment: A neuropsychological perspective (pp. 29–49). Guilford.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Carr, T., Moss, T., & Harris, D. (2005). The DAS24: A short form of the Derriford Appearance Scale DAS59 to measure individual responses to living with problems of appearance. British Journal of Health Psychology, 10(Pt 2), 285–298. https://doi.org/10.1348/135910705X27613
  • Clegg, C., Fremouw, W., & Mogge, N. (2009). Utility of the Structured Inventory of Malingered Symptomatology (SIMS) and the Assessment of Depression Inventory (ADI) in screening for malingering among outpatients seeking to claim disability. Journal of Forensic Psychiatry & Psychology, 20(2), 239–254. https://doi.org/10.1080/14789940802267760
  • Cumming, G. (2008). Replication and p intervals: P values predict the future only vaguely, but confidence intervals do much better. Perspectives on Psychological Science, 3(4), 286–300. https://doi.org/10.1111/j.1745-6924.2008.00079.x
  • Dandachi-FitzGerald, B., Ponds, R. W. H. M., & Merten, T. (2013). Symptom validity and neuropsychological assessment: A survey of practices and beliefs of neuropsychologists in six European countries. Archives of Clinical Neuropsychology, 28(8), 771–783. https://doi.org/10.1093/arclin/act073
  • De Marchi, B., & Balboni, G. (2018). Detecting malingering mental illness in forensics: Known-group comparison and simulation design with MMPI-2, Sims and Nim. PeerJ. 6, e5259. https://doi.org/10.7717/peerj.5259
  • Derogatis, L. R., Lipman, R. S., & Covi, L. (1973). SCL-90: An outpatient psychiatric rating scale–Preliminary report. Psychopharmacology Bulletin, 9(1), 13–28.
  • Dwork, C., Feldman, V., Hardt, M., Pitassi, T., Reingold, O., & Roth, A. (2015). STATISTICS. The reusable holdout: Preserving validity in adaptive data analysis. Science (New York, N.Y.), 349(6248), 636–638. https://doi.org/10.1126/science.aaa9375
  • Edelen, M. O., & Reeve, B. B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16(S1), 5–18. https://doi.org/10.1007/s11136-007-9198-0
  • Frank, E., Mark, A., Hall, M. A., & Witten, I. H. (2016). The WEKA Workbench: Online appendix for “Data mining”: Practical machine learning tools and techniques (5th ed.). Morgan Kaufmann.
  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
  • Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
  • Karabulut, E. M., Özel, S. A., & İbrikçi, T. (2012). A comparative study on the effect of feature selection on classification accuracy. Procedia Technology, 1, 323–327. https://doi.org/10.1016/j.protcy.2012.02.068
  • Kocak, B., Kus, E. A., & Kilickesmez, O. (2021). How to read and review papers on machine learning and artificial intelligence in radiology: A survival guide to key methodological concepts. European Radiology, 31(4), 1819–1830. https://doi.org/10.1007/s00330-020-07324-4
  • Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273–324. https://doi.org/10.1016/S0004-3702(97)00043-X
  • Kruyen, P. M., Emons, W. H. M., & Sijtsma, K. (2013). On the shortcomings of shortened tests: A literature review. International Journal of Testing, 13(3), 223–248. https://doi.org/10.1080/15305058.2012.703734
  • Malcore, S. A., Schutte, C., Van Dyke, S. A., & Axelrod, B. N. (2015). The Development of a Reduced-Item Structured Inventory of Malingered Symptomatology (SIMS). Psychological Injury and Law, 8(2), 95–99. https://doi.org/10.1007/s12207-015-9214-6
  • Martin, P. K., Schroeder, R. W., & Odland, A. P. (2015). Neuropsychologists’ validity testing beliefs and practices: A survey of North American professionals. The Clinical Neuropsychologist, 29(6), 741–776. https://doi.org/10.1080/13854046.2015.1087597
  • Mazza, C., Orrù, G., Burla, F., Monaro, M., Ferracuti, S., Colasanti, M., & Roma, P. (2019). Indicators to distinguish symptom accentuators from symptom producers in individuals with a diagnosed adjustment disorder: A pilot study on inconsistency subtypes using SIMS and MMPI-2-RF. PLoS One, 14(12), e0227113. https://doi.org/10.1371/journal.pone.0227113
  • Merckelbach, H. (2003). Diagnostic accuracy of the Structured Inventory of Malingered Symptomatology (SIMS) in detecting instructed malingering. Archives of Clinical Neuropsychology, 18(2), 145–152. https://doi.org/10.1016/S0887-6177(01)00191-3
  • Monaro, M., Gamberini, L., Zecchinato, F., & Sartori, G. (2018a). False identity detection using complex sentences. Frontiers in Psychology, 9, 283. https://doi.org/10.3389/fpsyg.2018.00283
  • Monaro, M., Toncini, A., Ferracuti, S., Tessari, G., Vaccaro, M. G., De Fazio, P., Pigato, G., Meneghel, T., Scarpazza, C., & Sartori, G. (2018b). The detection of malingering: A new tool to identify made-up depression. Frontiers in Psychiatry, 9, 249. https://doi.org/10.3389/fpsyt.2018.00249
  • Orrù, G., Mazza, C., Monaro, M., Ferracuti, S., Sartori, G., & Roma, P. (2021). The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment. Psychological Injury and Law, 14(1), 46–57. https://doi.org/10.1007/s12207-020-09389-4
  • Orrù, G., Gemignani, A., Ciacchini, R., Bazzichi, L., & Conversano, C. (2020a). Machine learning increases diagnosticity in psychometric evaluation of alexithymia in fibromyalgia. Frontiers in Medicine, 6, 319. https://doi.org/10.3389/fmed.2019.00319
  • Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020b). Machine learning in psychometrics and psychological research. Frontiers in Psychology, 10, 2970. https://doi.org/10.3389/fpsyg.2019.02970
  • Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012). Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review. Neuroscience and Biobehavioral Reviews, 36(4), 1140–1152. https://doi.org/10.1016/j.neubiorev.2012.01.004
  • Pace, G., Orrù, G., Monaro, M., Gnoato, F., Vitaliani, R., Boone, K. B., Gemignani, A., & Sartori, G. (2019). Malingering detection of cognitive impairment with the b test is boosted using machine learning. Frontiers in Psychology, 10, 1650. https://doi.org/10.3389/fpsyg.2019.01650
  • Paunonen, S. V., & Jackson, D. N. (1985). The validity of formal and informal personality assessments. Journal of Research in Personality, 19(4), 331–342. https://doi.org/10.1016/0092-6566(85)90001-7
  • Prunas, A., Sarno, I., Preti, E., Madeddu, F., & Perugini, M. (2012). Psychometric properties of the Italian version of the SCL-90-R: A study on a large community sample. European Psychiatry, 27(8), 591–597. https://doi.org/10.1016/j.eurpsy.2010.12.006
  • Rabin, L. A., Paolillo, E., & Barr, W. B. (2016). Stability in Test-Usage Practices of Clinical Neuropsychologists in the United States and Canada Over a 10-Year Period: A Follow-Up Survey of INS and NAN Members. Archives of Clinical Neuropsychology, 31(3), 206–230. https://doi.org/10.1093/arclin/acw007
  • Roma, P., Giromini, L., Burla, F., Ferracuti, S., Viglione, D. J., & Mazza, C. (2020). Ecological Validity of the Inventory of Problems-29 (IOP-29): an Italian Study of Court-Ordered, Psychological Injury Evaluations Using the Structured Inventory of Malingered Symptomatology (SIMS) as Criterion Variable. Psychological Injury and Law, 13(1), 57–65. https://doi.org/10.1007/s12207-019-09368-4
  • Shura, R. D., Ord, A. S., & Worthen, M. D. (2021). Structured inventory of malingered symptomatology: A psychometric review. Psychological Injury and Law. 15, 64–78. https://doi.org/10.1007/s12207-021-09432-y
  • Smith, G. P., & Burger, G. K. (1997). Detection of malingering: Validation of the Structured Inventory of Malingered Symptomatology (SIMS). Journal of the American Academy on Psychiatry and Law, 25, 180–183.
  • Smith, G. T., McCarthy, D. M., & Anderson, K. G. (2000). On the sins of short-form development. Psychological Assessment, 12(1), 102–111. https://doi.org/10.1037/1040-3590.12.1.102
  • Spencer, R. J., Gradwohl, B. D., & Kordovski, V. M. (2021). Initial Validation of Short Forms of the SIMS for Neuropsychological Evaluations. Psychological Injury and Law, 14(1), 37–45. https://doi.org/10.1007/s12207-020-09394-7
  • van Impelen, A., Merckelbach, H., Jelicic, M., & Merten, T. (2014). The Structured Inventory of Malingered Symptomatology (SIMS): A systematic review and meta-analysis. The Clinical Neuropsychologist, 28(8), 1336–1365. https://doi.org/10.1080/13854046.2014.984763
  • Van Schoor, N. M., Knol, D. L., Glas, C. A. W., Ostelo, R. W. J. G., Leplège, A., Cooper, C., Johnell, O., & Lips, P. (2006). Development of the Qualeffo–31, an osteoporosis-specific quality-of-life questionnaire. Osteoporosis International, 17(4), 543–551. https://doi.org/10.1007/s00198-005-0024-7
  • Widows, M. R., & Smith, G. P. (2005). SIMS: Structured Inventory of Malingered Symptomatology: Professional manual. PAR.
  • Wisdom, N. M., Callahan, J. L., & Shaw, T. G. (2010). Diagnostic utility of the Structured Inventory of Malingered Symptomatology to detect malingering in a forensic sample. Archives of Clinical Neuropsychology, 25(2), 118–125.
  • Yarkoni, T. (2010). The abbreviation of personality, or how to measure 200 personality scales with 200 items. Journal of Research in Personality, 44(2), 180–198. https://doi.org/10.1016/j.jrp.2010.01.002
  • Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393

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