[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291219-105":59,"doc-detail-291219-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learningdriven-adaptive-testing-an-application-for-the-mmpi-assessment","Machine Learning–Driven Adaptive Testing: An Application for the MMPI Assessment","","Examines the effectiveness of machine learning classification algorithms to address limitations of traditional approaches for building computerized adaptive testing versions of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Focuses on the neurotic-area scales—hypochondria, depression, and hysteria—administered electronically to 383 nonclinical participants. Results show a model tree–based classifier (ML-MT) produced accurate scores while substantially reducing item administration (85.99%–93.78% savings) with only 2.5–3.3 point differences from full-length scales, outperforming the countdown algorithm and retaining validity via comparable correlations with other MMPI-2 scales.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learningdriven-adaptive-testing-an-application-for-the-mmpi-assessment/291219/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learningdriven-adaptive-testing-an-application-for-the-mmpi-assessment/291219.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study target in MMPI-2 adaptive testing?","Question",{"text":113,"@type":114},"It targets the limitations of traditional methods used to develop computerized adaptive versions, largely related to the MMPI-2’s test length.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which MMPI-2 scales are used in the application and evaluation?",{"text":118,"@type":114},"The study focuses on three neurotic-area scales: hypochondria, depression, and hysteria.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the ML-MT approach compare with the countdown algorithm?",{"text":122,"@type":114},"The ML-MT algorithm is reported to be significantly more efficient and accurate, with large item savings and scores close to full-length results.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291219,1790379288,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Wiley  \nHuman Behavior and Emerging Technologies Volume 2025, Article ID 5146188, 11 pages [https://doi.org/10.1155/hbe2/5146188](https://doi.org/10.1155/hbe2/5146188)  \nResearch Article  \nMachine Learning–Driven Adaptive Testing: An Application for the MMPI Assessment  \nDaiana Colledani , 1 Egidio Robusto ,2 and Pasquale Anselmi 2  \n1Department of Psychology, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy  \n2Department of Philosophy, Sociology, Education and Applied Psychology, University of Padua, Padua, Italy  \nCorrespondence should be addressed to Pasquale Anselmi; [pasquale.anselmi@unipd.it](pasquale.anselmi@unipd.it)  \nReceived 10 April 2024; Revised 27 January 2025; Accepted 15 March 2025  \nAcademic Editor: Mirko Duradoni  \nCopyright © 2025 Daiana Colledani et al. Human Behavior and Emerging Technologies published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nThis paper aims to examine the effectiveness of machine learning classification algorithms as a strategy to overcome the limitations associated with traditional methods for developing computerized adaptive versions of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) . The focus is on the three scales in the neurotic area of the instrument, namely, hypochondria, depression, and hysteria, which were administered electronically to a nonclinical sample of 383 participants. The findings indicate that a machine learning classifier based on a model tree (ML-MT) algorithm effectively handled the complex MMPI-2 scales, yielding accurate scores while noticeably reducing item administration. In particular, the ML-MT algorithm achieved item savings between 85.99% and 93.78% and produced scores that differed from those of the full-length scales by only 2.5–3.3 points. Compared to the countdown algorithm, the ML-MT algorithm proved to be significantly moreefficient and accurate. Furthermore, the ML-MT scores retained their validity, as indicated by correlations with other MMPI-2 scales that were comparable to those obtained with the full-length scales (the average difference between the correlations was less than 0.10) . These findings support the potential of the ML-MT algorithm as an effective method for adaptive assessment in the context of the MMPI instruments and other psychometric tools.  \nKeywords: CAT; countdown algorithm; decision tree; M5P; machine learning classifier algorithms; MMPI-2; model tree;  \nregression tree  \n1. Introduction  \nThe Minnesota Multiphasic Personality Inventory (MMPI)  \n[1] and its revisions (e.g., the MMPI-2, [2]; the MMPI-2-Restructured Form, MMPI-2-RF, [3]; and the MMPI-3,[4, 5]) hold a pivotal position in psychological assessment and constitute an essential resource for delineating personality and offering psychopathological insight. The MMPI instruments (i.e., the MMPI and its revisions) are currently used all over the world in clinical, forensic, psychiatric, and organizational settings, where they are highly valued and appreciated [6–11] . The global acceptance of the MMPI instruments and their prominence in scientific research and clinical practice are due to their rigorous development, which is grounded in extensive observations of patients in real psychiatric and clinical settings [12] . These instruments repre-  \nsent the culmination of decades of collaborative refinement among leading researchers who have collectively contributed to strengthening their validity [8] . Moreover, the extensive literature surrounding them has stimulated the development of rich and valuable interpretative methods that provide detailed and vivid personality profiles through careful analysis of scale scores [12, 13] .  \nDespite their widespread acceptance and extensive use, the MMPI instruments exhibit a weakness associated with their length [14, 15] . To","cbCaim377f6gagGO","https://ap.wps.com/l/cbCaim377f6gagGO","pdf",795916,11,"English","# Introduction\n## MMPI instruments and their role\n## Limitations due to test length\n## Attempts to shorten MMPI-2 and later revisions\n## Rationale for the study","[{\"question\":\"What problem does the study target in MMPI-2 adaptive testing?\",\"answer\":\"It targets the limitations of traditional methods used to develop computerized adaptive versions, largely related to the MMPI-2’s test length.\"},{\"question\":\"Which MMPI-2 scales are used in the application and evaluation?\",\"answer\":\"The study focuses on three neurotic-area scales: hypochondria, depression, and hysteria.\"},{\"question\":\"How does the ML-MT approach compare with the countdown algorithm?\",\"answer\":\"The ML-MT algorithm is reported to be significantly more efficient and accurate, with large item savings and scores close to full-length results.\"}]","Machine Learning–Driven Adaptive Testing: An Application for the MMPI Assessment | PDF",1789643768,28]