[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-188347-en":3,"doc-seo-188347-105":30,"detail-sidebar-cat-1-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":11,"category_id":12,"category_name":13,"doc_title":14,"doc_description":15,"doc_content":16,"file_id":17,"file_url":18,"file_type":19,"file_size":20,"view_count":11,"is_deleted":4,"is_public":11,"is_downloadable":11,"audit_status":11,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":15,"update_tm":28,"read_time":29},188347,2336477405376,"Stanley","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",1,158,"General","The Robust Beauty of Improper Linear Models in Decision Making","Proper linear models choose weights for numerical predictors so the resulting linear composite optimally predicts a criterion, yielding advantages over clinical intuition. Evidence from research on clinical versus statistical prediction shows that even improper linear models—where predictor weights come from nonoptimal methods—can outperform clinical judgment. The discussion emphasizes the robustness of unit (equal) weighting for predicting numeric outcomes from numeric predictors and considers applications to decision making, plus technical, psychological, and ethical objections.","ROBYN M.DAWES University of Oregon  \nABSTRACT:Proper linear models are those in whichpredictor variables are given weights in such a waythat the resulting linear composite optimally predictssome criterion of interest;examples of proper linearmodels are standard regression analysis,discriminantfunction analysis,and ridge regression analysis.Re-search summarized in Paul Meehl's book on clinicalversus statistical prediction—-and a plethora of re-search stimulated in part by that book—all indicatesthat when a numerical criterion variable(e.g.,graduategrade point average)is to be predicted from numericalpredictor variables,proper linear models outperformclinical intuition.Improper linear models are those inwhich the weights of the predictor variables are ob-tained by some nonoptimal method;for example,theymay be obtained on the basis of intuition,derivedfrom simulating a clinical judge's predictions,or set tobe equal.This article presents evidence that evensuch improper linear models are superior to clinical in-tuition when predicting a numerical criterion fromnumerical predictors.In fact,unit(i.e.,equal)weight-ing is quite robust for making such predictions.Thearticle discusses,in some detail,the application of unitweights to decide what bullet the Denver Police De-partment should use.Finally,the article considerscommonly raised technical,psychological,and ethicalresistances to using linear models to make importantsocial decisions and presents arguments that couldweaken these resistances.  \nPaul Meehl's(1954)book Clinical Versus Statis-tical Prediction:A Theoretical Analysis and aReview of the Evidence appeared 25 years ago.It reviewed studies indicating that the predictionof numerical criterion variables of psychologicalinterest(e.g.,faculty ratings of graduate studentswho had just obtained a PhD)from numericalpredictor variables(e.g.,scores on the GraduateRecord Examination,grade point averages,ratingsof letters of recommendation)is better done by aproper linear model than by the clinical intuitionof people presumably skilled in such prediction.The point of this article is to review evidence thateven improper linear models may be superior toclinical predictions.  \n# The Robust Beauty of Improper Linear Modelsin Decision Making\n\nA proper linear model is one in which theweights given to the predictor variables are chosenin such a way as to optimize the relationship be-tween the prediction and the criterion.Simpleregression analysis is the most common exampleof a proper linear model;the predictor variablesare weighted in such a way as to maximize thecorrelation between the subsequent weighted com-posite and the actual criterion.  Discriminantfunction analysis is another example of a properlinear model;weights are given to the predictorvariables in such a way that the resulting linearcomposites maximize the discrepancy between twoor more groups.Ridge regression analysis,an-other example(Darlington,1978;Marquardt &Snee,1975),attempts to assign weights in sucha way that the linear composites correlate maxi-mally with the criterion of interest in a new setof data.  \nThus,there are many types of proper linearmodels and they have been used in a variety ofcontexts.One example(Dawes,1971)was pre-sented in this Journal;it nvolved the predictionof faculty ratings of graduate students.All gradu-  \nWork on this article was started at the University ofOregon and Decision Research,Inc.,Eugene,Oregon;itwas completed while I was a James McKeen Cattell Sab-batical Fellow at the Psychology Department at the Uni-versity of Michigan and at the Research Center forGroup Dynamics at the Institute for Social Research there.I thank all these institutions for their assistance,and Iespecially thank my friends at them who helped.  \nThis article is based in part on invited talks given atthe American Psychological Association(August 1977),the University of Washington(February 1978),theAachen Technological Institute(June 1978),the Univer-sity of Groeningen(June ","cbCaikZ7qr0pEOP8","https://ap.wps.com/l/cbCaikZ7qr0pEOP8","pdf",1205823,12,"English","en",105,"# The Robust Beauty of Improper Linear Models in Decision Making\n## Proper vs improper linear models\n## Evidence and robust unit weighting\n## Application to decision policies\n## Addressing technical, psychological, and ethical resistances","[{\"question\":\"What characterizes a proper linear model in prediction?\",\"answer\":\"A proper linear model selects predictor weights to optimize the predictive relationship between a weighted composite and the criterion, such as maximizing correlation or group separation.\"},{\"question\":\"How do improper linear models differ from proper ones?\",\"answer\":\"Improper linear models obtain predictor weights using nonoptimal methods, such as intuition, simulating clinical judgments, or forcing weights to be equal.\"},{\"question\":\"Why are unit (equal) weights important for decision making?\",\"answer\":\"Unit weighting is shown to be robust, meaning equal weights can still yield strong predictive performance for important social decisions when predicting a numerical criterion from numerical predictors.\"}]","The Robust Beauty of Improper Linear Models in Decision Making | 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characterizes a proper linear model in prediction?","Question",{"text":75,"@type":76},"A proper linear model selects predictor weights to optimize the predictive relationship between a weighted composite and the criterion, such as maximizing correlation or group separation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do improper linear models differ from proper ones?",{"text":80,"@type":76},"Improper linear models obtain predictor weights using nonoptimal methods, such as intuition, simulating clinical judgments, or forcing weights to be equal.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are unit (equal) weights important for decision making?",{"text":84,"@type":76},"Unit weighting is shown to be robust, meaning equal weights can still yield strong predictive performance for important social decisions when predicting a numerical criterion from numerical 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