Implementation Criteria for the Shotgun Stochastic Parameter Estimation Algorithm
The use of rank ordered data is widespread and commonplace in fields of research from marketing to biology and medicine. Complexity and the mathematical rigour required for the analysis of rank ordered data has resulted in the use of parametric methods for data analysis. There have been conflicting views on the validity of the results produced by parametric methods. Developments in stochastic methods have allowed researchers the opportunity to bypass the complexity and rigour associated with rank ordered data analysis by trading accuracy for simplified solutions. The cost of computing has been the limiting factor for the use of these techniques. With the recent decline in computing cost and increasing accessibility more researchers are now turning their attention back to these alternatives. Stacey (2006) introduced one such method known as the Shotgun Stochastic Parameter Estimation Algorithm, to determine the mean and variance of rank ordered data. This study determined the reliability of the algorithm by developing guidelines that ensured consistency in the results. This was achieved by observing the output as key variables were changed. Another important contribution of the algorithm was its ability to analyse partially ranked data. This research investigated the behaviour of the SSPEA with partially ranked data and quantified the error introduced including possible solutions to reduce the error. The data used for the study was obtained from Grant (2010) for some test cases and simulated internally for others. Similar to the incorrect application of parametric methods to rank ordered data; this research proved that the algorithms true potential is realised by correct choice of simulation software and efficient implementation. A set of guidelines and key considerations is produced that will assist researchers utilising this algorithm to prevent potential pitfalls in the data analysis. The study has demonstrated that the algorithm can be used with confidence to analyse rank ordered data provided these guidelines are followed and considerations noted.
Rank ordered data, Stochastic methods