A comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data
This study investigated the performance of multiple imputations with Expectation-Maximization (EM) algorithm and Monte Carlo Markov chain (MCMC) method in missing data imputation. We compared the accuracy of imputation based on some real data and set up two extreme scenarios and conducted both empir...
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| Veröffentlicht in: | Quality & quantity 2010-02, Vol.44 (2), p.277-287 |
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| description | This study investigated the performance of multiple imputations with Expectation-Maximization (EM) algorithm and Monte Carlo Markov chain (MCMC) method in missing data imputation. We compared the accuracy of imputation based on some real data and set up two extreme scenarios and conducted both empirical and simulation studies to examine the effects of missing data rates and number of items used for imputation. In the empirical study, the scenario represented item of highest missing rate from a domain with fewest items. In the simulation study, we selected a domain with most items and the item imputed has lowest missing rate. In the empirical study, the results showed there was no significant difference between EM algorithm and MCMC method for item imputation, and number of items used for imputation has little impact, either. Compared with the actual observed values, the middle responses of 3 and 4 were over-imputed, and the extreme responses of 1, 2 and 5 were under-represented. The similar patterns occurred for domain imputation, and no significant difference between EM algorithm and MCMC method and number of items used for imputation has little impact. In the simulation study, we chose environmental domain to examine the effect of the following variables: EM algorithm and MCMC method, missing data rates, and number of items used for imputation. Again, there was no significant difference between EM algorithm and MCMC method. The accuracy rates did not significantly reduce with increase in the proportions of missing data. Number of items used for imputation has some contribution to accuracy of imputation, but not as much as expected. |
| doi_str_mv | 10.1007/s11135-008-9196-5 |
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We compared the accuracy of imputation based on some real data and set up two extreme scenarios and conducted both empirical and simulation studies to examine the effects of missing data rates and number of items used for imputation. In the empirical study, the scenario represented item of highest missing rate from a domain with fewest items. In the simulation study, we selected a domain with most items and the item imputed has lowest missing rate. In the empirical study, the results showed there was no significant difference between EM algorithm and MCMC method for item imputation, and number of items used for imputation has little impact, either. Compared with the actual observed values, the middle responses of 3 and 4 were over-imputed, and the extreme responses of 1, 2 and 5 were under-represented. The similar patterns occurred for domain imputation, and no significant difference between EM algorithm and MCMC method and number of items used for imputation has little impact. In the simulation study, we chose environmental domain to examine the effect of the following variables: EM algorithm and MCMC method, missing data rates, and number of items used for imputation. Again, there was no significant difference between EM algorithm and MCMC method. The accuracy rates did not significantly reduce with increase in the proportions of missing data. Number of items used for imputation has some contribution to accuracy of imputation, but not as much as expected.</description><identifier>ISSN: 0033-5177</identifier><identifier>EISSN: 1573-7845</identifier><identifier>DOI: 10.1007/s11135-008-9196-5</identifier><identifier>CODEN: QQEJAV</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Accuracy ; Algorithms ; Datasets ; Estimates ; Expected values ; History, theory and methodology ; Markov analysis ; Methodology ; Methodology of the Social Sciences ; Methods ; Missing data ; Quality of Life ; Questionnaires ; Simulation ; Social Sciences ; Sociology ; Values ; Variables</subject><ispartof>Quality & quantity, 2010-02, Vol.44 (2), p.277-287</ispartof><rights>Springer Science+Business Media B.V. 2008</rights><rights>2015 INIST-CNRS</rights><rights>Springer Science+Business Media B.V. 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c377t-c37d09ac8a3c61530bb309bd39bb84ab7645b9c37fcd460e2daba3050f0a06a93</citedby><cites>FETCH-LOGICAL-c377t-c37d09ac8a3c61530bb309bd39bb84ab7645b9c37fcd460e2daba3050f0a06a93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11135-008-9196-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11135-008-9196-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27343,27923,27924,33773,33774,41487,42556,51318</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22401078$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Ting Hsiang</creatorcontrib><title>A comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data</title><title>Quality & quantity</title><addtitle>Qual Quant</addtitle><description>This study investigated the performance of multiple imputations with Expectation-Maximization (EM) algorithm and Monte Carlo Markov chain (MCMC) method in missing data imputation. 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In the simulation study, we chose environmental domain to examine the effect of the following variables: EM algorithm and MCMC method, missing data rates, and number of items used for imputation. Again, there was no significant difference between EM algorithm and MCMC method. The accuracy rates did not significantly reduce with increase in the proportions of missing data. 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comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data</title><author>Lin, Ting Hsiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c377t-c37d09ac8a3c61530bb309bd39bb84ab7645b9c37fcd460e2daba3050f0a06a93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Datasets</topic><topic>Estimates</topic><topic>Expected values</topic><topic>History, theory and methodology</topic><topic>Markov analysis</topic><topic>Methodology</topic><topic>Methodology of the Social Sciences</topic><topic>Methods</topic><topic>Missing data</topic><topic>Quality of Life</topic><topic>Questionnaires</topic><topic>Simulation</topic><topic>Social Sciences</topic><topic>Sociology</topic><topic>Values</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Ting Hsiang</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>Sociological Abstracts (pre-2017)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Social Science Database (Alumni Edition)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni 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Edition</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Sociological Abstracts (Ovid)</collection><jtitle>Quality & quantity</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Ting Hsiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data</atitle><jtitle>Quality & quantity</jtitle><stitle>Qual Quant</stitle><date>2010-02-01</date><risdate>2010</risdate><volume>44</volume><issue>2</issue><spage>277</spage><epage>287</epage><pages>277-287</pages><issn>0033-5177</issn><eissn>1573-7845</eissn><coden>QQEJAV</coden><abstract>This study investigated the performance of multiple imputations with Expectation-Maximization (EM) algorithm and Monte Carlo Markov chain (MCMC) method in missing data imputation. We compared the accuracy of imputation based on some real data and set up two extreme scenarios and conducted both empirical and simulation studies to examine the effects of missing data rates and number of items used for imputation. In the empirical study, the scenario represented item of highest missing rate from a domain with fewest items. In the simulation study, we selected a domain with most items and the item imputed has lowest missing rate. In the empirical study, the results showed there was no significant difference between EM algorithm and MCMC method for item imputation, and number of items used for imputation has little impact, either. Compared with the actual observed values, the middle responses of 3 and 4 were over-imputed, and the extreme responses of 1, 2 and 5 were under-represented. The similar patterns occurred for domain imputation, and no significant difference between EM algorithm and MCMC method and number of items used for imputation has little impact. In the simulation study, we chose environmental domain to examine the effect of the following variables: EM algorithm and MCMC method, missing data rates, and number of items used for imputation. Again, there was no significant difference between EM algorithm and MCMC method. The accuracy rates did not significantly reduce with increase in the proportions of missing data. Number of items used for imputation has some contribution to accuracy of imputation, but not as much as expected.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11135-008-9196-5</doi><tpages>11</tpages></addata></record> |
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| subjects | Accuracy Algorithms Datasets Estimates Expected values History, theory and methodology Markov analysis Methodology Methodology of the Social Sciences Methods Missing data Quality of Life Questionnaires Simulation Social Sciences Sociology Values Variables |
| title | A comparison of multiple imputation with EM algorithm and MCMC method for quality of life missing data |
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