We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Multicollinearity in Path Analysis: A Simple Method to Reduce Its Effects.
- Authors
Olivoto, Tiago; de Souza, Velci Q.; Nardino, Maicon; Carvalho, Ivan R.; Ferrari, Maurício; de Pelegrin, Alan J.; Szareski, Vinícius J.; Schmidt, Denise
- Abstract
Some data arrangement methods often used may mask correlation coefficients among explanatory traits, increasing multicollinearity in multiple regression analysis. This study was performed to determine if the harmful effects of multicollinearity might be reduced in the estimation of the X'X correlation matrix among explanatory traits. For this, data on 45 treatments (15 maize [Zea mays L.] hybrids sown in three places) were used. Three path analysis methods (traditional, with k inclusion, and traditional with trait exclusion) were tested in two scenarios: with X'X matrix estimated with all sampled observations (ASO, n = 900) and with the X'X matrix estimated with the average values of each plot (AVP, n = 180). The condition number (CN) was reduced from 3395 to 2004 when the matrix was estimated with all observations. On average, the factors that inflate the variance of regression coefficients were increased by 61% in the AVP scenario. The addition of the k coefficient reduced the CN to 85.40 and 51.17 for the ASO and AVP scenarios, respectively. Exclusion of multicollinearity-generating traits was more effective in the ASO than the AVP scenario, resulting in CNs of 29.62 and 63.66, respectively. The largest coefficient of determination (0.977) and the smallest noise (0.150) were obtained in the ASO scenario after the exclusion of the multicollinearity-generating traits. The use of all sampled observations does not mask the individual variances and reduces the magnitude of the correlations among explanatory traits in 90% of cases, improving the accuracy of biological studies involving path analysis.
- Subjects
MULTICOLLINEARITY; PATH analysis (Statistics); MULTIPLE regression analysis
- Publication
Agronomy Journal, 2017, Vol 109, Issue 1, p131
- ISSN
0002-1962
- Publication type
Article
- DOI
10.2134/agronj2016.04.0196