Ordering & Assignment

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Experiment Ordering

• Each experiment card shows a numbered badge indicating its position
• Use the up arrow (˄) and down arrow (˅) to move an experiment earlier or later in the sequence
• Participants see experiments as horizontally scrollable cards on the run page
• The active experiment is highlighted and clicking a card selects it

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Dependencies

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Setting Dependencies

1. Click Edit on an experiment card
2. Open the Ordering Options disclosure at the bottom of the editor
3. Under Required Experiments (Dependencies), check the experiments that must be completed first
4. Only experiments that come before the current one in the list can be selected as dependencies

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How Dependencies Work at Runtime

• Experiments with unmet dependencies show a lock icon and cannot be selected
• A locked experiment displays the message “Complete the required experiments first”
• Once all required experiments are completed, the lock is removed and the experiment becomes clickable
• Experiments with no dependencies are always available

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Validation Rules

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Auto-Trigger

1. Click Edit on an experiment card
2. Open the Ordering Options disclosure
3. Check Auto-trigger next experiment

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Completion Tracking

• Completing an experiment marks it with a green checkmark
• Dependent experiments are unlocked as their prerequisites are met
• Auto-trigger fires immediately after completion if enabled

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Assignment Editor

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How It Works

1. You write a Python script in the assignment editor
2. You map onboarding question responses to script input variables
3. When a participant joins, the script runs with their onboarding answers as inputs
4. The script output determines which experiment the participant sees

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Configuration

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Variable Mapping

1. Select a source — an onboarding question
2. Enter a placeholder name — the variable name used in the script
3. The participant’s answer to that question is injected as the variable’s value when the script runs

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Example: Random Assignment

import random

# No variables needed — purely random
condition = random.choice(["control", "treatment"])
print(condition)

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Example: Age-Based Assignment

# Variable mapping: onboarding question "How old are you?" → age
age = int(age)  # injected from onboarding response

if age < 25:
    print("young_adult_protocol")
elif age < 65:
    print("adult_protocol")
else:
    print("older_adult_protocol")

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Example: Counterbalancing

import hashlib

# Variable mapping: onboarding question "participant ID" → participant_id
hash_val = int(hashlib.md5(participant_id.encode()).hexdigest(), 16)
condition = "A" if hash_val % 2 == 0 else "B"
print(condition)

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How Assignment Data Is Used

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Tips

• Use the up/down arrows to set experiment order, then add dependencies to enforce that order at runtime
• Enable auto-trigger on sequential experiments to create a guided flow
• Experiments without any ordering options configured behave exactly as before — all are available, no locks
• Use assignment scripts for between-subjects designs where different groups see different experiments
• Combine with onboarding questions to collect the variables your assignment script needs
• Test your assignment script thoroughly before publishing — incorrect assignment logic can compromise your study design