In multiple regression, tolerance is used as an indicator of multicollinearity. Tolerance is estimated by 1 - R

^{2}, where R

^{2}is calculated by regressing the independent variable of interest onto the remaining independent variables included in the multiple regression analysis. All other things equal, researchers desire higher levels of tolerance, as low levels of tolerance are known to affect adversely the results associated with a multiple regression analysis.

Various recommendations for acceptable levels of tolerance have been published in the literature. Perhaps most commonly, a value of .10 is recommended as the minimum level of tolerance (e.g., Tabachnick & Fidell, 2001). However, a recommended minimum value as high as .20 has also been suggested (Menard, 1995) and a value of .25 can be seen used in the literature (e.g., Huber & Stephens, 1993). It would appear that researchers can use which ever criterion they choose to help serve their own purposes.

As a point of interest, tolerance may be said to be the opposite of the coefficient of determination. In that sense, tolerance is identical to the coefficient of alienation.

__See also__

● Variance inflation factor

● Multicollinearity

__References__

● Menard, S. (1995). Applied Logistic Regression Analysis: Sage University Series on Quantitative Applications in the Social Sciences. Thousand Oaks, CA: Sage.

● Huber, E. & Stephens, J. D. (1993). Political Parties and Public Pensions: A Quantitative Analysis,

*Acta Sociologica*,*36*, 309-325.
●Tabachnick, B. G., & Fidell, L. S. (2001). Using Multivariate Statistics (4th ed.). Boston, MA: Allyn and Bacon