The objective is to compare semantic web service selection methods based on Quality of Service (QoS) attributes. The investigation is carried out in three phases namely: Preprocessing, discovery and QoS based selection phase. Preprocessing phase deals with registration of services in the repository. Discovery phase deals with retrieval of functionally similar services using matchmaking techniques. The third phase focuses on QoS based selection using three methods namely Analytical Hierarchical Processing (AHP), Logical Scoring Preference with Ordered Weighted Averaging (LSP and OWA) and Fuzzy with Technique for Order Preference by Similarity to an Ideal Solution (Fuzzy Topsis). Further this paper analyzes the selection methods based on range of user preference criteria and compares them using Analysis of variance (ANOVA). It is a statistical hypothesis testing used for making decisions using data. Another objective of this paper is to propose a new approach to improve the performance of service selection using Iterative MapReduce. QWS dataset has been used for analyzing above mentioned three methods. Experimental results show that the Fuzzy Topsis scheme outperforms the other web service selection methods.