Electrical impedance tomography is a non-invasive method for imaging the electrical conductivity of an object from electrode measurements on its surface. The underlying mathematical problem is highly nonlinear, severely ill-posed, and several model parameters are usually not known accurately. Despite the strong nonlinearity, iterative Newton-type methods are widely used to tackle the problem numerically. This work presents and analyzes tailored transformations for the conductivity and for electrode parameters which are favourable in two regards: they remove the constrainedness of the unknown parameters and simultaneously decrease the nonlinearity of the underlying problem. We study the impact of various transformations on the nonlinearity of the problem and demonstrate improved speed of convergence for Newton-type methods while avoiding local minima in the solution space. The presented transformations can conveniently be incorporated into existing iterative solvers as they improve stability and do not require hand-tuned regularization parameters or line-search strategies, thereby bridging a gap between a variety of established conductivity estimation methods and practical applications.