MAKING A GROUPED-DATA FREQUENCY TABLE: DEVELOPMENT AND EXAMINATION OF THE ITERATION ALGORITHM
This study focuses on the development and examination of a new method to construct frequency tables for grouped data. This method is called the iteration algorithm in that it proceeds by successive iterations to determine the four key elements that are essential in building a grouped-data frequency distribution. The algorithm also uses five formulas and stops running as soon as the first solution is attained (for teaching purposes only). Two major interests emerged. The first interest was to evaluate how accurate the iteration algorithm is as a process. The second and main focus of this study was to assess the effectiveness of the iteration algorithm as an instructional method. The findings of the Monte Carlo simulations to address the first main interest showed that the results yielded by the iteration algorithm are comparable to those produced by a well-known statistical package. To tackle the second foremost aspect of this study, the multivariate analysis of covariance (MANCOVA) results indicated that the students expressed, on average, more positive attitudes towards the iteration algorithm than towards a traditional method in learning how to construct their own grouped-data frequency tables.
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:iteration algorithm grouped data frequency table monte carlo simulations mancova ancova
Date of Publication:01/01/2007