- Standard question types
- Advanced question types
- Multiple choice question type
- Text question- comment box
- Matrix multi-point scales question type
- Rank order question
- Smiley-rating question
- Image question type
- Date and time question type
- CAPTCHA question type
- Net Promoter Score question type
- Van Westendorp's price sensitivity question
- Choice modelling questions
- Side-By-Side matrix question
- Homunculus question type
- Randomizer
- Block rotation using randomization
- Predictive answer options
- Presentation text questions
- Multiple choice: select one
- Multiple choice: select many

- Answer type
- Reorder questions
- Question tips
- Text box next to question
- Text question settings
- Adding other option
- Matrix question settings
- Image rating question settings
- Scale options for numeric slider question
- Constant sum question settings
- Budget scale question settings
- Setting default answer option
- Exclusive option for multiple choice questions
- Making a question required - validation
- Bulk validation
- Remove validation message
- Question separators
- Question code
- Page breaks in survey
- Survey introduction with acceptance checkbox

- Compound or delayed branching
- Dynamic quota control
- Dynamic text or comment boxes
- Extraction logic
- Show or hide question logic
- Dynamic show or hide
- Scoring logic
- Net promoter scoring model
- Delayed branching using custom scripting
- Piping text
- Survey chaining
- Looping logic
- Branching to terminate survey
- Logic operators
- Branching - Skip Logic
- Compound Branching

Optimal designs are a class of experimental designs that are optimal with respect to some statistical criterion. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum-variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation.

What are D-Optimal Designs?

Conjoint Analysis D-Optimal Design is a design based on D-Optimality for the Conjoint Analysis (Discrete Choice) question. In general, D-Optimality is a concept that uses a desired set of experiments to optimize or investigate a studied object. It seeks to minimize |(X'X)−1|, or equivalently maximize the determinant of the information matrix X'X of the design.

How to setup D-Optimal Design?

- Go to:
**Edit Conjoint Question >> Settings** - Select Design Type as
**DOptimal**. - Select
**versions**. - Click on
**Start**. - Click on
**Save Settings**.

What is Version option while generating D-Optimal Design?

Here specify how many versions of the D-Optimal design would you like to generate. If multiple versions are selected, a large D-optimal design with total tasks equal to (the number of tasks specified) X ( the number of versions specified) will be generated. While taking survey each respondent will get as many tasks as specified in the question but these will be randomly chosen from large D-optimal design generated earlier.

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Task Count - Conjoint (Discrete Choice)