Confronting Multicollinearity in Ecological Multiple Regression
The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response...
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| Veröffentlicht in: | Ecology (Durham) 2003-11, Vol.84 (11), p.2809-2815 |
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| container_title | Ecology (Durham) |
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| creator | Graham, Michael H. |
| description | The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r ≥ 0.28 or r2≥ 0.08), causing (1) inaccurate model parameterization, (2) decreased statistical power, and (3) exclusion of significant predictor variables during model creation. Then, using real ecological data, I demonstrated the utility of various statistical techniques for enhancing the reliability and interpretation of ecological multiple regression in the presence of multicollinearity. |
| doi_str_mv | 10.1890/02-3114 |
| format | Article |
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Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r ≥ 0.28 or r2≥ 0.08), causing (1) inaccurate model parameterization, (2) decreased statistical power, and (3) exclusion of significant predictor variables during model creation. 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Then, using real ecological data, I demonstrated the utility of various statistical techniques for enhancing the reliability and interpretation of ecological multiple regression in the presence of multicollinearity.</description><subject>Animal, plant and microbial ecology</subject><subject>Biological and medical sciences</subject><subject>confounding factors</subject><subject>Ecology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects. Techniques</subject><subject>Methods and techniques (sampling, tagging, trapping, modelling...)</subject><subject>multicollinearity</subject><subject>multiple regression</subject><subject>principal components regression</subject><subject>sequential regression</subject><subject>Statistical Report</subject><subject>structural equation modeling</subject><issn>0012-9658</issn><issn>1939-9170</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNpdkE9LAzEQxYMoWKv4BTwUQW-rM8lmNzmJlPoHFEG8eAoxzZaUNKnJFum3N7VFwblkyPzem-QRcopwhULCNdCKIdZ7ZICSyUpiC_tkAIC0kg0Xh-Qo5zmUwloMyM04hi7F0LswGz2vfO9M9N4Fq5Pr1yMXRpNyEWfOaL-dL70dvdpZsjm7GI7JQad9tie7c0je7iZv44fq6eX-cXz7VM0ZtLL6aKYCSseZ4ch0A0ZKmAIyahiyjtYSDTZdI0CaVnbS8KlG7FBw05qGsyG53NouU_xc2dyrhcvGeq-DjausUFIqBG8KeP4PnMdVCuVpipZ4ygK6cbvYQTqXf3VJB-OyWia30GmtkLOmRZCFq7fcl_N2_TcHtUlaAVWbpNVk_E4BmKgRqfiRnW1l89zH9CtjdS0lp-wbG0J6Sw</recordid><startdate>200311</startdate><enddate>200311</enddate><creator>Graham, Michael H.</creator><general>Ecology Society of America</general><general>Ecological Society of America</general><scope>IQODW</scope><scope>7QG</scope><scope>7SN</scope><scope>7SS</scope><scope>7ST</scope><scope>7T7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>RC3</scope><scope>SOI</scope></search><sort><creationdate>200311</creationdate><title>Confronting Multicollinearity in Ecological Multiple Regression</title><author>Graham, Michael H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j3079-b6d8030753c513a60c990d0132c313f2491c16f6809c79f9c5da11f185c7c653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Animal, plant and microbial ecology</topic><topic>Biological and medical sciences</topic><topic>confounding factors</topic><topic>Ecology</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects. Techniques</topic><topic>Methods and techniques (sampling, tagging, trapping, modelling...)</topic><topic>multicollinearity</topic><topic>multiple regression</topic><topic>principal components regression</topic><topic>sequential regression</topic><topic>Statistical Report</topic><topic>structural equation modeling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Graham, Michael H.</creatorcontrib><collection>Pascal-Francis</collection><collection>Animal Behavior Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Ecology (Durham)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Graham, Michael H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Confronting Multicollinearity in Ecological Multiple Regression</atitle><jtitle>Ecology (Durham)</jtitle><date>2003-11</date><risdate>2003</risdate><volume>84</volume><issue>11</issue><spage>2809</spage><epage>2815</epage><pages>2809-2815</pages><issn>0012-9658</issn><eissn>1939-9170</eissn><coden>ECGYAQ</coden><abstract>The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regression is commonly used in such cases to test the individual effects of many explanatory variables on a continuous response, the inherent collinearity (multicollinearity) of confounded explanatory variables encumbers analyses and threatens their statistical and inferential interpretation. Using numerical simulations, I quantified the impact of multicollinearity on ecological multiple regression and found that even low levels of collinearity bias analyses (r ≥ 0.28 or r2≥ 0.08), causing (1) inaccurate model parameterization, (2) decreased statistical power, and (3) exclusion of significant predictor variables during model creation. 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| source | Wiley Online Library - AutoHoldings Journals; Jstor Complete Legacy |
| subjects | Animal, plant and microbial ecology Biological and medical sciences confounding factors Ecology Fundamental and applied biological sciences. Psychology General aspects. Techniques Methods and techniques (sampling, tagging, trapping, modelling...) multicollinearity multiple regression principal components regression sequential regression Statistical Report structural equation modeling |
| title | Confronting Multicollinearity in Ecological Multiple Regression |
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