Modeling Consumer Opinion Using RIDIT and Grey Relational Analysis

In order to understand consumers, researchers are forced to gather primary data on Likert scale. Such data is usually considered as ordinal or at best interval scaled data. One key requirement in research is to identify components which have high individual contribution to understanding the research problem. Hence the concept of ranking of the components comes under consideration. Most of the ranking techniques are based on simplistic mean ranks or overtly complicated methods. In this chapter the authors highlight two techniques - Grey Relational Analysis (GRA) and RIDIT - for the purpose. In this chapter the authors explain the techniques of the two methods and then try to show the simplicity and efficiency of GRA and RIDIT algorithms in analyzing a commonly available dataset. The outcome of the GRA and RIDIT analysis is also compared with the commonly used techniques and the authors would examine if GRA and RIDIT does a better job at ranking data than the commonly used techniques