A Conceptual and Empirical Examination of Justifications for Dichotomization
Despite many articles reporting the problems of dichotomizing continuous measures, researchers still commonly use this practice. The authors' purpose in this article was to understand the reasons that people still dichotomize and to determine whether any of these reasons are valid. They contact...
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Veröffentlicht in: | Psychological methods 2009-12, Vol.14 (4), p.349-366 |
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description | Despite many articles reporting the problems of dichotomizing continuous measures, researchers still commonly use this practice. The authors' purpose in this article was to understand the reasons that people still dichotomize and to determine whether any of these reasons are valid. They contacted 66 researchers who had published articles using dichotomized variables and obtained their justifications for dichotomization. They also contacted 53 authors of articles published in
Psychological Methods
and asked them to identify any situations in which they believed dichotomized indicators could perform better. Justifications provided by these two groups fell into three broad categories, which the authors explored both logically and with Monte Carlo simulations. Continuous indicators were superior in the majority of circumstances and never performed substantially worse than the dichotomized indicators, but the simulations did reveal specific situations in which dichotomized indicators performed as well as or better than the original continuous indictors. The authors also considered several justifications for dichotomization that did not lend themselves to simulation, but in each case they found compelling arguments to address these situations using techniques other than dichotomization. |
doi_str_mv | 10.1037/a0016956 |
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Psychological Methods
and asked them to identify any situations in which they believed dichotomized indicators could perform better. Justifications provided by these two groups fell into three broad categories, which the authors explored both logically and with Monte Carlo simulations. Continuous indicators were superior in the majority of circumstances and never performed substantially worse than the dichotomized indicators, but the simulations did reveal specific situations in which dichotomized indicators performed as well as or better than the original continuous indictors. The authors also considered several justifications for dichotomization that did not lend themselves to simulation, but in each case they found compelling arguments to address these situations using techniques other than dichotomization.</description><identifier>ISSN: 1082-989X</identifier><identifier>EISSN: 1939-1463</identifier><identifier>DOI: 10.1037/a0016956</identifier><identifier>PMID: 19968397</identifier><language>eng</language><publisher>Washington, DC: American Psychological Association</publisher><subject>Academic Discourse ; Biological and medical sciences ; Classification ; Empirical Research ; Faculty Publishing ; Fundamental and applied biological sciences. Psychology ; Humans ; Justification ; Models, Psychological ; Monte Carlo Method ; Monte Carlo Methods ; Predictor Variables ; Psychology - methods ; Psychology - statistics & numerical data ; Psychology. Psychoanalysis. Psychiatry ; Psychology. Psychophysiology ; Psychometrics. Statistics. Methodology ; Researchers ; Simulation ; Statistical Analysis ; Statistical Measurement ; Statistical Variables ; Statistics. Mathematics</subject><ispartof>Psychological methods, 2009-12, Vol.14 (4), p.349-366</ispartof><rights>2009 American Psychological Association</rights><rights>2015 INIST-CNRS</rights><rights>(c) 2009 APA, all rights reserved.</rights><rights>2009, American Psychological Association</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a457t-13820f26340f535f7218389721ec2dd47539a75ef3b1c090144ad44421324e143</citedby><orcidid>0000-0001-9751-6051 ; 0000-0003-3546-0093</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906,30981</link.rule.ids><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=EJ865290$$DView record in ERIC$$Hfree_for_read</backlink><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22200004$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19968397$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Maxwell, Scott E</contributor><creatorcontrib>DeCoster, Jamie</creatorcontrib><creatorcontrib>Iselin, Anne-Marie R</creatorcontrib><creatorcontrib>Gallucci, Marcello</creatorcontrib><title>A Conceptual and Empirical Examination of Justifications for Dichotomization</title><title>Psychological methods</title><addtitle>Psychol Methods</addtitle><description>Despite many articles reporting the problems of dichotomizing continuous measures, researchers still commonly use this practice. The authors' purpose in this article was to understand the reasons that people still dichotomize and to determine whether any of these reasons are valid. They contacted 66 researchers who had published articles using dichotomized variables and obtained their justifications for dichotomization. They also contacted 53 authors of articles published in
Psychological Methods
and asked them to identify any situations in which they believed dichotomized indicators could perform better. Justifications provided by these two groups fell into three broad categories, which the authors explored both logically and with Monte Carlo simulations. Continuous indicators were superior in the majority of circumstances and never performed substantially worse than the dichotomized indicators, but the simulations did reveal specific situations in which dichotomized indicators performed as well as or better than the original continuous indictors. The authors also considered several justifications for dichotomization that did not lend themselves to simulation, but in each case they found compelling arguments to address these situations using techniques other than dichotomization.</description><subject>Academic Discourse</subject><subject>Biological and medical sciences</subject><subject>Classification</subject><subject>Empirical Research</subject><subject>Faculty Publishing</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Humans</subject><subject>Justification</subject><subject>Models, Psychological</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo Methods</subject><subject>Predictor Variables</subject><subject>Psychology - methods</subject><subject>Psychology - statistics & numerical data</subject><subject>Psychology. Psychoanalysis. Psychiatry</subject><subject>Psychology. Psychophysiology</subject><subject>Psychometrics. Statistics. 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The authors' purpose in this article was to understand the reasons that people still dichotomize and to determine whether any of these reasons are valid. They contacted 66 researchers who had published articles using dichotomized variables and obtained their justifications for dichotomization. They also contacted 53 authors of articles published in
Psychological Methods
and asked them to identify any situations in which they believed dichotomized indicators could perform better. Justifications provided by these two groups fell into three broad categories, which the authors explored both logically and with Monte Carlo simulations. Continuous indicators were superior in the majority of circumstances and never performed substantially worse than the dichotomized indicators, but the simulations did reveal specific situations in which dichotomized indicators performed as well as or better than the original continuous indictors. The authors also considered several justifications for dichotomization that did not lend themselves to simulation, but in each case they found compelling arguments to address these situations using techniques other than dichotomization.</abstract><cop>Washington, DC</cop><pub>American Psychological Association</pub><pmid>19968397</pmid><doi>10.1037/a0016956</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0001-9751-6051</orcidid><orcidid>https://orcid.org/0000-0003-3546-0093</orcidid></addata></record> |
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subjects | Academic Discourse Biological and medical sciences Classification Empirical Research Faculty Publishing Fundamental and applied biological sciences. Psychology Humans Justification Models, Psychological Monte Carlo Method Monte Carlo Methods Predictor Variables Psychology - methods Psychology - statistics & numerical data Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Psychometrics. Statistics. Methodology Researchers Simulation Statistical Analysis Statistical Measurement Statistical Variables Statistics. Mathematics |
title | A Conceptual and Empirical Examination of Justifications for Dichotomization |
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