What does R-sw Tradeoff do for MDS ?
- it allows generating a MDS design or check the properties of an existing design;
- it performs individual hierarchical Bayes (HB) estimation of MDS data;
- it performs individual count analysis of MDS data.
- no minimum number of tasks for hierarchical Bayes estimation;
- all respondents must evaluate the same number of MDS tasks and all tasks are characterized by the same number of attributes;
- the user can change the hierarchical Bayes (HB) parameters (number of Markov Chain Monte Carlo draws and the Markov Chain Monte Carlo thinning parameter) and modify the default priors to increase or decrease the bayesian shrinkage effect;
- the outcomes are individual raw utilities (coefficients) for each item included in the experimental design; they are on a scale [0:100];
- data can be easily imported and outcomes can be exported as CSV/text files.
Support, Manual and Examples:
- technical support and statistical consulting is available free of charge (within reasonable limits);
- an ‘html’ manual is provided with a detailed description of all available functions;
- full working examples are provided to help the User to become familiar with the package. These examples can be easily adapted by the User for new projects.
Note: this is a pure design and analysis tool, therefore data must be collected through an external source (such as a CAWI, CAPI or PAPI system).