"Rotator OLAP Analyzer" is the solution that allows you to analyze your surveys in a intelligent, efficient and productive way. "Rotator" is based on the OLAP technology (Online Analytical Processing), which gives an special treatment to those data that is multidimensional in nature, which is difficult to be modeled on traditional flat tools. As an example, below there is a drawing of a cube, in which you have three dimensions: Political party, Sex and County. Rotator internally creates this cubic structures to allow the analysis of combined dimensions: Party by Sex, Party by County, County by Sex and so on. Cubes created by Rotator may have n-dimensions and are completely transparent to the end user, who normally focuses on the information rather than on how the data has been structured within the system.
The component "Rotator Analyzer OLAP" was designed to fairly meet the analytical needs of typical surveys, so it totally covers the basic descriptive statistics, with emphasis on the analysis of proportions, i.e. counts and relative frequencies in all forms and combinations. It includes descriptive statistics such as average, minimum and maximum, standard deviation, mode, quartiles and percentiles and some statistical analysis of the normal curve. Thus, the Rotator OLAP Analyzer covers more than 90% of the requirements of the data analysis required by most market and opinion research companies. For those users interested in multivariate analysis and other forms of advanced statistics, such as cluster analysis, Anova, linear and multiple regression analysis, discriminant analysis, factor analysis, predictive statistics and data mining based on the data collected with Rotator, the system allows them to export the data to commercial formats and standards software packages such as Excel, SPSS and CSV, so that they can perform these multivariate analysis on these specialized statistical packages.
Based on the above said, our suite of tools for modeling and analyzing quantitative studies, is not intended to compete with traditional statistic packages, such as SAS, SPSS, R, etc., our main focus is on complementing the weaknesses of these tools, particularly in terms of modelling the data, data capturing and quality control of the different processes of the survey, as well as the substantial reduction of technical and human errors. This includes the design of the questionnaire for all collection methods and the complete systematization of the survey.