Question Types
EDSL provides a comprehensive set of question types for surveys. All questions require question_name (a valid Python identifier) and question_text.
Core Question Types
QuestionFreeText
Open-ended text responses without constraints.
python
from edsl import QuestionFreeText
q = QuestionFreeText(
question_name="feedback",
question_text="What do you think about our service?"
)
QuestionMultipleChoice
Single selection from a predefined list of options.
python
from edsl import QuestionMultipleChoice
q = QuestionMultipleChoice(
question_name="color",
question_text="What is your favorite color?",
question_options=["Red", "Blue", "Green", "Yellow"]
)
QuestionCheckBox
Multiple selections from a predefined list (checkbox-style).
python
from edsl import QuestionCheckBox
q = QuestionCheckBox(
question_name="features",
question_text="Which features do you use? (Select all that apply)",
question_options=["Feature A", "Feature B", "Feature C", "Feature D"],
min_selections=1, # Optional: minimum selections required
max_selections=3 # Optional: maximum selections allowed
)
QuestionNumerical
Numeric responses with optional min/max bounds.
python
from edsl import QuestionNumerical
q = QuestionNumerical(
question_name="age",
question_text="How old are you?",
min_value=0, # Optional: minimum allowed value
max_value=120 # Optional: maximum allowed value
)
QuestionYesNo
Simple binary yes/no question (derived from MultipleChoice).
python
from edsl import QuestionYesNo
q = QuestionYesNo(
question_name="consent",
question_text="Do you agree to participate in this survey?"
)
# Options are automatically ["Yes", "No"]
QuestionLinearScale
Linear scale with customizable range and endpoint labels.
python
from edsl import QuestionLinearScale
q = QuestionLinearScale(
question_name="satisfaction",
question_text="How satisfied are you with our service?",
question_options=[1, 2, 3, 4, 5], # Scale values
option_labels={1: "Very Unsatisfied", 5: "Very Satisfied"} # Endpoint labels
)
QuestionLikertFive
Standard 5-point Likert scale (agree/disagree).
python
from edsl import QuestionLikertFive
q = QuestionLikertFive(
question_name="statement_agree",
question_text="I find the product easy to use."
)
# Options: Strongly disagree, Disagree, Neutral, Agree, Strongly agree
QuestionList
Response as a list of items.
python
from edsl import QuestionList
q = QuestionList(
question_name="top_movies",
question_text="List your top 3 favorite movies.",
max_list_items=3 # Optional: maximum items allowed
)
QuestionRank
Ranking/ordering items by preference.
python
from edsl import QuestionRank
q = QuestionRank(
question_name="priority",
question_text="Rank these features by importance (1 = most important):",
question_options=["Speed", "Security", "Price", "Support"],
num_selections=4 # How many items to rank
)
QuestionMatrix
Grid-based responses with rows (items) and columns (options).
python
from edsl import QuestionMatrix
q = QuestionMatrix(
question_name="product_ratings",
question_text="Rate each product on the following attributes:",
question_items=["Product A", "Product B", "Product C"], # Rows
question_options=["Poor", "Fair", "Good", "Excellent"], # Columns
option_labels=None # Optional labels for options
)
QuestionBudget
Allocating a fixed budget across multiple options.
python
from edsl import QuestionBudget
q = QuestionBudget(
question_name="time_allocation",
question_text="How would you allocate 100 hours across these activities?",
question_options=["Work", "Exercise", "Leisure", "Sleep"],
budget_sum=100 # Total that allocations must sum to
)
QuestionDict
Response as key-value pairs (structured data).
python
from edsl import QuestionDict
q = QuestionDict(
question_name="contact_info",
question_text="Provide your contact information:",
answer_keys=["name", "email", "phone"] # Required keys in response
)
QuestionExtract
Extracting specific information from text.
python
from edsl import QuestionExtract
q = QuestionExtract(
question_name="entities",
question_text="Extract all company names from the following text: {{ text }}",
answer_template={"companies": "List of company names"}
)
QuestionDropdown
BM25-powered search through large option sets.
python
from edsl import QuestionDropdown
q = QuestionDropdown(
question_name="country",
question_text="Select your country:",
question_options=["Afghanistan", "Albania", ..., "Zimbabwe"] # Large list
)
Derived/Special Question Types
QuestionMultipleChoiceWithOther
Multiple choice with an "Other" option for custom responses.
python
from edsl import QuestionMultipleChoiceWithOther
q = QuestionMultipleChoiceWithOther(
question_name="source",
question_text="How did you hear about us?",
question_options=["Google", "Friend", "Advertisement"],
other_option_label="Other (please specify)"
)
QuestionCheckboxWithOther
Checkbox with an "Other" option for custom responses.
python
from edsl import QuestionCheckboxWithOther
q = QuestionCheckboxWithOther(
question_name="interests",
question_text="What are your interests?",
question_options=["Sports", "Music", "Reading"],
other_option_label="Other"
)
QuestionTopK
Select top K items from a list.
python
from edsl import QuestionTopK
q = QuestionTopK(
question_name="favorites",
question_text="Select your top 3 favorite items:",
question_options=["A", "B", "C", "D", "E"],
k=3
)
QuestionFunctional
Python function-based question (not sent to LLM - computed locally).
python
from edsl import QuestionFunctional
def compute_sum(scenario, agent):
numbers = scenario.get("numbers", [])
return sum(numbers)
q = QuestionFunctional(
question_name="total",
question_text="Calculate the sum",
func=compute_sum
)
QuestionPydantic
Use custom Pydantic models as response schemas.
python
from edsl import QuestionPydantic
from pydantic import BaseModel
class PersonInfo(BaseModel):
name: str
age: int
occupation: str
q = QuestionPydantic(
question_name="person",
question_text="Describe a person:",
pydantic_model=PersonInfo
)
QuestionMarkdown
Responses with markdown formatting.
python
from edsl import QuestionMarkdown
q = QuestionMarkdown(
question_name="formatted_response",
question_text="Write a formatted response with headers and lists."
)
Common Parameters
All questions support these common parameters:
| Parameter | Description |
|---|---|
question_name | Unique identifier (valid Python identifier) |
question_text | The question text (supports Jinja2 templating) |
answering_instructions | Optional custom instructions for the LLM |
question_presentation | Optional custom presentation template |
Quick Reference
| Type | Use Case | Key Parameter |
|---|---|---|
QuestionFreeText | Open-ended responses | - |
QuestionMultipleChoice | Single selection | question_options |
QuestionCheckBox | Multiple selections | question_options |
QuestionNumerical | Numbers | min_value, max_value |
QuestionYesNo | Binary yes/no | - |
QuestionLinearScale | Numeric scale | question_options, option_labels |
QuestionLikertFive | 5-point agree/disagree | - |
QuestionList | List of items | max_list_items |
QuestionRank | Ordering | question_options, num_selections |
QuestionMatrix | Grid/table | question_items, question_options |
QuestionBudget | Budget allocation | question_options, budget_sum |
QuestionDict | Key-value pairs | answer_keys |
QuestionExtract | Extract from text | answer_template |
QuestionDropdown | Large option sets | question_options |