Geodetic measurements are commonly used for monitoring volcanic activities and crustal motions. Together with paleoseismic and other geologic observations, geodetic data are central in long-term forecast of earthquake hazards. Presence of outliers in geodetic data strongly affects least squares principle, which are extensively used for data analysis and modeling in geodesy. Thus, the positions of the geodetic points are computed as biased. Robust methods are techniques used to construct estimates describing well data majority. In this study, some robust methods and conventional tests for outliers have been tested on a number of linear and nonlinear geodetic adjustment models. The results are presented to illustrate the effectiveness of the methods. Furthermore, we discuss how the effectiveness of the methods changes depending on various key parameters for geodetic networks, i.e. the number of outliers, the magnitude of outliers, the degree of freedom, the number of observation and number of unknowns.