EDT is a Python-based tool to construct D-efficient designs for Discrete Choice Experiments. EDT combines enough flexibility to construct from simple 2-alternative designs with few attributes, to more complex settings that may involve conditions between attributes.
While EDT designs are based on the widely-used D-efficiency criterion (see Kuhfeld, 2005), it differs from other free- or open-source efficient design tools (such as idefix for R) on the use of a Random Swapping Algorithm based on the work of Quan, Rose, Collins and Bliemer (2011), obtaining significant speed improvements to reach an optimal design, to a level that competes with well-known paid software such as NGene.
The main features of EDT are:
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Allows to customize each attribute in terms of:
- Attribute Levels
- Continuous or Dummy coding (Effects coding is work-in-progress)
- Assignement of prior parameters
- Attribute names
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Designs with constraints: EDT allows to define conditions over different attribute levels.
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Designs with blocks.
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Designs with alternative-specific constants (ASC).
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Multiple stopping criteria (Fixed number of iterations, iterations without improvement or fixed time).
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Allows to export the output design in an Excel file.
Any contributions to EDT are welcome via this Git, or to the email joseignaciohernandezh at gmail dot com.
- Kuhfeld, W. F. (2005). Experimental design, efficiency, coding, and choice designs. Marketing research methods in SAS: Experimental design, choice, conjoint, and graphical techniques, 47-97.
- Quan, W., Rose, J. M., Collins, A. T., & Bliemer, M. C. (2011). A comparison of algorithms for generating efficient choice experiments.