Enhancing the Detection of Social Desirability Bias Using Machine Learning: A Novel Application of Person-Fit Indices.
Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random...
| Publicado en: | Educational & Psychological Measurement Vol. 84; no. 6; pp. 1107 - 1138 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Sage Publications Inc.
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=180676615&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 180676615 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Dec2024 vid: 84 iid: 6 pid: 344 pub: Sage Publications Inc. artinfo: ui: 180676615 10.1177/00131644241255109 ppf: 1107 ppct: 31 formats: tig: atl: Enhancing the Detection of Social Desirability Bias Using Machine Learning: A Novel Application of Person-Fit Indices. aug: au: Nazari, Sanaz Leite, Walter L. Huggins-Manley, A. Corinne affil: University of California, San Diego, La Jolla, USA University of Florida, Gainesville, USA su: Undergraduates Social skills Analysis of variance Random forest algorithms Statistical models Scale analysis (Psychology) Statistical significance Receiver operating characteristic curves Logistic regression analysis Descriptive statistics Research bias Simulation methods in education Research methodology Machine learning Data analysis software Evaluation sug: subj: Undergraduates Social skills Analysis of variance Random forest algorithms Statistical models Scale analysis (Psychology) Statistical significance Receiver operating characteristic curves Logistic regression analysis Descriptive statistics Research bias Simulation methods in education Research methodology Machine learning Data analysis software Evaluation keyword: area under the curve machine learning classifiers person-fit indices social desirability bias area under the curve machine learning classifiers person-fit indices social desirability bias ab: Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect SDB by considering several person-fit indices as features or predictors in the machine learning methods. The results of a Monte Carlo simulation study showed that for a single feature, applying person-fit indices directly and logistic regression led to similar classification results. However, the random forest classifier improved the classification of biased and unbiased responses substantially. Classification was improved in both logistic regression and random forest by considering multiple features simultaneously. Moreover, cross-validation indicated stable area under the curves (AUCs) across machine learning classifiers. A didactical illustration of applying random forest to detect SDB is presented. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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