Kai R Larsen, Roland M Mueller, Dario Bonaretti, Diana Fischer-Preßler, James Jim Burleson, Nimisha Singh, Jeffrey Parsons, Jean-Charles Pillet, Lan Sang, Zhu Drew Zhang
Survey-based research in information systems requires valid scales to advance theory, and the discipline has developed rigorous procedures to assess scale validity. In principle, these procedures ensure that scales consist of clear indicators and faithfully represent the focal construct. However, the focus on the psychometric properties of scales has overshadowed the role of lexical and semantic elements in the validation process, leading to invalid scales. This overemphasis on psychometric properties will persist unless researchers have a systematic approach to analyzing the properties of indicators and share the outcome of such analyses in formats that can be peer-reviewed, critiqued, or corroborated by other researchers. Thus, the psychometric community needs a shared language and method to uncover the properties of indicators and identify validity problems that psychometric analysis fails to detect. Drawing on ontology development methods, we propose the Indicator Terminology for Explanation and Measurement (ITEM) Ontology, consisting of four high-level hierarchies of entities: objects, measurables, qualifiers, and response sets, each almost always found within an individual indicator. We develop an approach, a codebook, and a website for applying ITEM to psychometric indicators. Common approaches to ontology evaluation are then used to evaluate its expressiveness, utility, importance, accessibility, suitability, and external validity. We find that the ITEM Ontology is highly generative in that it can be used to address several previously unsolvable problems in survey science, polling, and theory testing.
{"title":"The ITEM Ontology: A Tool to Elucidate the Anatomy of Psychometric Indicators.","authors":"Kai R Larsen, Roland M Mueller, Dario Bonaretti, Diana Fischer-Preßler, James Jim Burleson, Nimisha Singh, Jeffrey Parsons, Jean-Charles Pillet, Lan Sang, Zhu Drew Zhang","doi":"10.1287/isre.2023.0257","DOIUrl":"10.1287/isre.2023.0257","url":null,"abstract":"<p><p>Survey-based research in information systems requires valid scales to advance theory, and the discipline has developed rigorous procedures to assess scale validity. In principle, these procedures ensure that scales consist of clear indicators and faithfully represent the focal construct. However, the focus on the psychometric properties of scales has overshadowed the role of lexical and semantic elements in the validation process, leading to invalid scales. This overemphasis on psychometric properties will persist unless researchers have a systematic approach to analyzing the properties of indicators and share the outcome of such analyses in formats that can be peer-reviewed, critiqued, or corroborated by other researchers. Thus, the psychometric community needs a shared language and method to uncover the properties of indicators and identify validity problems that psychometric analysis fails to detect. Drawing on ontology development methods, we propose the Indicator Terminology for Explanation and Measurement (ITEM) Ontology, consisting of four high-level hierarchies of entities: objects, measurables, qualifiers, and response sets, each almost always found within an individual indicator. We develop an approach, a codebook, and a website for applying ITEM to psychometric indicators. Common approaches to ontology evaluation are then used to evaluate its expressiveness, utility, importance, accessibility, suitability, and external validity. We find that the ITEM Ontology is highly generative in that it can be used to address several previously unsolvable problems in survey science, polling, and theory testing.</p>","PeriodicalId":92212,"journal":{"name":"Information systems research : ISR","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01Epub Date: 2017-04-12DOI: 10.1287/isre.2016.0676
Xiao-Bai Li, Jialun Qin
Health information technology has increased accessibility of health and medical data and benefited medical research and healthcare management. However, there are rising concerns about patient privacy in sharing medical and healthcare data. A large amount of these data are in free text form. Existing techniques for privacy-preserving data sharing deal largely with structured data. Current privacy approaches for medical text data focus on detection and removal of patient identifiers from the data, which may be inadequate for protecting privacy or preserving data quality. We propose a new systematic approach to extract, cluster, and anonymize medical text records. Our approach integrates methods developed in both data privacy and health informatics fields. The key novel elements of our approach include a recursive partitioning method to cluster medical text records based on the similarity of the health and medical information and a value-enumeration method to anonymize potentially identifying information in the text data. An experimental study is conducted using real-world medical documents. The results of the experiments demonstrate the effectiveness of the proposed approach.
{"title":"Anonymizing and Sharing Medical Text Records.","authors":"Xiao-Bai Li, Jialun Qin","doi":"10.1287/isre.2016.0676","DOIUrl":"https://doi.org/10.1287/isre.2016.0676","url":null,"abstract":"<p><p>Health information technology has increased accessibility of health and medical data and benefited medical research and healthcare management. However, there are rising concerns about patient privacy in sharing medical and healthcare data. A large amount of these data are in free text form. Existing techniques for privacy-preserving data sharing deal largely with structured data. Current privacy approaches for medical text data focus on detection and removal of patient identifiers from the data, which may be inadequate for protecting privacy or preserving data quality. We propose a new systematic approach to extract, cluster, and anonymize medical text records. Our approach integrates methods developed in both data privacy and health informatics fields. The key novel elements of our approach include a recursive partitioning method to cluster medical text records based on the similarity of the health and medical information and a value-enumeration method to anonymize potentially identifying information in the text data. An experimental study is conducted using real-world medical documents. The results of the experiments demonstrate the effectiveness of the proposed approach.</p>","PeriodicalId":92212,"journal":{"name":"Information systems research : ISR","volume":"28 2","pages":"332-352"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1287/isre.2016.0676","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35939746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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