{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T18:44:22Z","timestamp":1748371462725},"reference-count":37,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2013,1,10]],"date-time":"2013-01-10T00:00:00Z","timestamp":1357776000000},"content-version":"unspecified","delay-in-days":985,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Span. j. psychol."],"published-print":{"date-parts":[[2010,5]]},"abstract":"<jats:p>The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.<\/jats:p>","DOI":"10.1017\/s1138741600004042","type":"journal-article","created":{"date-parts":[[2013,3,15]],"date-time":"2013-03-15T15:51:17Z","timestamp":1363362677000},"page":"494-502","source":"Crossref","is-referenced-by-count":4,"title":["A General Equation to Obtain Multiple Cut-off Scores on a Test from Multinomial Logistic Regression"],"prefix":"10.1017","volume":"13","author":[{"given":"Rosa","family":"Bersab\u00e9","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teresa","family":"Rivas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2013,1,10]]},"reference":[{"key":"S1138741600004042_ref034","volume-title":"Categorical Data Analysis Using the SAS System","author":"Stokes","year":"2000"},{"key":"S1138741600004042_ref013","doi-asserted-by":"publisher","DOI":"10.1002\/0471722146"},{"key":"S1138741600004042_ref030","volume-title":"SAS\/STAT User's Guide, Version 8.0","year":"2000"},{"key":"S1138741600004042_ref020","doi-asserted-by":"publisher","DOI":"10.1037\/0022-0167.44.1.63"},{"key":"S1138741600004042_ref016","volume-title":"Regression models for categorical and limited dependent variables","author":"Long","year":"1997"},{"key":"S1138741600004042_ref001","doi-asserted-by":"publisher","DOI":"10.1002\/0471249688"},{"key":"S1138741600004042_ref008","volume-title":"Standard setting. 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