Multicollinearity problems and ridge regression in sociological models

The consequences of omitting relevant variables in regression analysis in order to cope with even moderate levels of multicollinearity can produce severe difficulties in subsequent theoretical interpretation of social science data. Use of ridge regression, a form of biased linear estimation, is explored. Application of ridge regression to a substantive sociological model is given to illustrate ridge regression's strengths as well as its limitations.