Number of predictors and multicollinearity: What are their effects on error and bias in regression?
The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate...
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| Published in | Communications in statistics. Simulation and computation Vol. 48; no. 1; pp. 27 - 38 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia
Taylor & Francis
02.01.2019
Taylor & Francis Ltd |
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| Online Access | Get full text |
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| Abstract | The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate multicollinearity is unrelated to Type I error, but increases Type II error. Investigation of bias suggests that multicollinearity increases the variability in parameter bias, while leading to overall underestimation of parameters. Collinearity also increases VIF. In the case of all diagnostics however, increasing the number of predictors interacts with multicollinearity to compound observed problems. |
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| AbstractList | The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate multicollinearity is unrelated to Type I error, but increases Type II error. Investigation of bias suggests that multicollinearity increases the variability in parameter bias, while leading to overall underestimation of parameters. Collinearity also increases VIF. In the case of all diagnostics however, increasing the number of predictors interacts with multicollinearity to compound observed problems. |
| Author | Acharya, Parul Xu, Lihua Sivo, Stephen A. Lavery, Matthew Ryan |
| Author_xml | – sequence: 1 givenname: Matthew Ryan orcidid: 0000-0002-4208-7277 surname: Lavery fullname: Lavery, Matthew Ryan email: mlavery@bgsu.edu organization: Educational Foundations, College of Education and Human Development, Bowling Green State University, Leadership & Policy – sequence: 2 givenname: Parul surname: Acharya fullname: Acharya, Parul organization: College of Education and Health Professions, Columbus State University – sequence: 3 givenname: Stephen A. surname: Sivo fullname: Sivo, Stephen A. organization: Department of Educational and Human Sciences, College of Education and Human Performance, University of Central Florida – sequence: 4 givenname: Lihua surname: Xu fullname: Xu, Lihua organization: Department of Educational and Human Sciences, College of Education and Human Performance, University of Central Florida |
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| SubjectTerms | Bias Collinearity Computer simulation Error analysis Monte Carlo simulation Monte Carlo simulation study Multicollinearity Multiple regression Parameters Regression analysis Statistical methods |
| Title | Number of predictors and multicollinearity: What are their effects on error and bias in regression? |
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