Skid resistance is an important parameter for road safety and therefore it is essential to monitor the skid resistance of pavements. In the Netherlands, skid resistance is measured with either the RWS Skid Resistance Tester -measuring the longitudinal friction coefficient- or the Seitenkraft-Messverfahren (SKM) -measuring the sideway friction coefficient. The latter is nowadays the preferred measurement device by Rijkswaterstaat. Skid resistance depends much on the vehicle speed: the higher the speed, the lower the skid resistance. Furthermore, the texture of the surface influences the speed dependency. Because it is not always possible to measure the skid resistance at target speeds set by Rijkswaterstaat, there is a demand for a speed conversion model for the skid resistance measured with the SKM. The objective of this research is therefore formulated as follows: the development of a speed conversion model for the wet skid resistance, measured with the SKM at different speeds, taking into account the macrotexture of the road surface. The used dataset consists of 718 sections of 100 metre, measured at the Dutch road network. Measurements were performed at 10 different roads with different pavement layers: porous asphalt, concrete pavements, dense pavements and stone mastic asphalt. The mean profile depth of the pavements varies between 0.21 and 1.80 mm. The performed measuring speeds were 40, 60 and 80 km/h, and for few sections 30 km/h. Three regression methods were performed. Firstly, a multiple linear regression was performed. The datapoints consisted of combinations of two measurements at different speeds on identical 100 metre sections. The second method estimated per 100 metre section a zero speed intercept which was used as a reference point. The third method used multilevel modelling and includes a hierarchical structure. Concluded was that the multiple linear regression on speed combinations is inappropriate for the objective of this research, because of two reasons: the datapoints are dependent on each other, and information is lost by splitting the 100 metre sections into datapoints with combinations of two measurements. In the second method, problems arise with estimating the zero speed intercept. The third method is most appropriate for this research and the three-level structure fits best on the dataset. The first level contains the individual measurements on the 100 metre sections, performed at different measuring speeds. The second level consists of the 100 metre sections and the third level consist of the roads on which the measurements took place. The standard error of the model on the training data is 0.032 whereas the average change in skid resistance for two datapoints is 0.053. This average change includes conversions over a speed difference from 10 to 40 km/h. From a sensitivity analysis of the macrotexture it was concluded that if no macrotexture can be measured, it is advised to use a different model in which no macrotexture is included. Recommendations for further research include among others registering more accurately the type and age of the measured pavements and extending the dataset with measurements performed at low measuring speeds and on curved sections. Furthermore, it is recommended to perform a more comprehensive outlier analysis and to optimise the hierarchical structure.