How to Randomize Treatment Conditions

Experimental conditions let you assign respondents to treatment arms, control arms, information treatments, survey branches, or study paths. In Domandata, condition sets support randomized and rule-based assignment for academic survey experiments and social science research designs.

Use conditions when different respondents should see different blocks, when an experiment needs random assignment, or when assignment depends on a previous answer or variable. For a study-level guide, see Online Survey Experiments.

1. Step 1: Build the blocks first. In Block Builder, create the blocks for the control arm, treatment arms, outcomes, and end screens.

   Step 1: Add blocks in Block Builder

2. Step 2: Open Flow. Select the Flow tab in the survey editor after the block list is mostly stable.

   Step 2: Open Flow canvas

3. Step 3: Add a condition set. Name it after the design decision it controls, such as Main Experiment, Message Treatment, or Eligibility Path.

   Step 3: Add and name a condition set

4. Step 4: Set the number of arms. Add the conditions you need and name each arm clearly, such as Control, Treatment A, and Treatment B.

   Step 4: Set condition arms

5. Step 5: Choose assignment rules. Set random percentages, stratification, or by-variable assignment before connecting the paths; check Sample Allocation Across Condition Arms if cell size matters.

   Step 5: Choose assignment rules

6. Step 6: Connect each arm. Route every condition to the block respondents should see next, including fallback paths; use Branching and Skip Logic for more complex routes.

   Step 6: Connect condition arms to blocks

• Random Assignment assigns respondents to arms using weighted-random probabilities. Use equal splits for simple experiments or custom percentages for planned unequal allocation. A Balance arm sizes toggle chooses whether that draw is even (block/balanced — guaranteed to converge on the target split as data comes in) or random (independent — matches the target only on average). See How Randomization Works.
• Stratify by variable keeps random assignment balanced within groups defined by a variable, such as country, party, gender, school, or quota status. The same Balance arm sizes within each group toggle is available per group; it claims lazily, once the blocking variable's value is known for that respondent, since the group cannot be balanced before it is known.
• By Variable assigns respondents deterministically based on a survey answer or variable that appears earlier in the flow.

Many research survey question types can feed assignment logic. Use Multiple Choice or Dropdown questions for discrete assignment, Slider or Short Answer questions for numeric ranges, and Grid Matrix questions for repeated measures. Use conjoint analysis survey design questions when the experimental task itself is a randomized preference design, or a vignette experiment when respondents should react to one profile.

Preview every major path before publishing. Confirm that each arm reaches the correct block, that random assignment percentages total 100 percent, that by-variable sources appear before the split, and that fallback arms behave as expected.

If each arm needs a target number of completed responses, add quotas in Deploy. See Set Survey Quotas. For planning expected n by arm, see Sample Allocation Across Condition Arms.

• How Randomization Works in Survey Experiments
• Vignette Experiment Example
• List Experiment Question
• Set Survey Quotas
• Export Survey Data to Stata, R, and Python