Answer Keys¶

This page contains solutions for all exercises and quizzes in the curriculum. Use it to check your work after completing each module.

Module 1: What Is Metadata?¶

Exercise solutions¶

Exercise 1: Three things a new person would need to know:

What each column means (what is "age"? In years? Months?)
Who was surveyed (students? Adults? Everyone?)
When the data was collected

Exercise 2: From the DDI Alliance website: "The Data Documentation Initiative (DDI) is an international standard for describing data from the social, behavioral, economic, and health sciences."

Quiz answers¶

(B) Information about data. Metadata describes your data: what it contains, who collected it, and how.
(C) Without it, people cannot understand what the data means. Without documentation, nobody knows what the numbers mean.
(B) Findable, Accessible, Interoperable, Re-usable. These four principles guide how data should be shared. DDI metadata helps you meet all four.
(B) A Python tool that creates, reads, updates, and validates DDI documents. ddi-l handles the XML so you can focus on your data.

Module 2: Set Up Your Environment¶

Exercise solutions¶

Exercise 1: The last line of pip install output should show "Successfully installed ddi-l-..." (the version may vary).

Exercise 2:

import ddi_l as ddi

print(ddi.__version__)
# Output: 0.1.0 (or the current version)

Exercise 3: The number of commands varies by version. Typically 4-6 commands are listed.

Quiz answers¶

(b) 3.11. ddi-l requires Python 3.11 or newer.
(a) ddi --help. This shows all available CLI commands.
(a) Downloads and installs the ddi-l package. pip is Python's package installer.

Module 3: Create Your First Study¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="Student Well-Being Survey", agency="university.edu")
q1 = doc.add_question(text="What is your age?")
q2 = doc.add_question(text="How would you rate your health?")
q3 = doc.add_question(text="How many hours do you sleep per night?")
doc.save("well-being.xml")
print(f"Questions: {len(doc.questions)}")

Output: Questions: 3

Exercise 2: Open well-being.xml in a text editor. Look for <r:Content>What is your age?</r:Content> inside the XML.

Exercise 3:

from ddi_l.models.base import InternationalString

q1.question_texts.append(InternationalString(text="Quel est votre âge ?", lang="fr"))
doc.save("well-being.xml")

Open the XML. You should see both xml:lang="en" and xml:lang="fr" entries for the first question.

Quiz answers¶

(B) A Document object. The Document holds your study and all its contents.
(B) Adds a question to the study. The question is stored inside a QuestionScheme in the DataCollection module.
(C) Writes the DDI document to an XML file. The file uses proper DDI namespace prefixes.
(B) Append an InternationalString with lang="fr". Both language versions are stored in the same question item.
(B) len(doc.questions). The questions property returns a list.

Module 4: Variables and Questions¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="Student Well-Being Survey", agency="university.edu")
q1 = doc.add_question(text="What is your age?")
q2 = doc.add_question(text="How would you rate your health?")
q3 = doc.add_question(text="How many hours do you sleep per night?")

doc.add_variable(name="Age", question=q1)
doc.add_variable(name="HealthRating", question=q2)
doc.add_variable(name="SleepHours", question=q3)

print(f"Variables: {len(doc.variables)}")

Output: Variables: 3

Exercise 2:

for v in doc.variables:
    print(v.identifier)

Each identifier is a UUID like a1b2c3d4-....

Exercise 3: Save and compare. The XML file is now larger because it contains both QuestionScheme and VariableScheme sections.

Quiz answers¶

(a) Links the variable to that question. This creates a DDI reference element.
(a) doc.variables. Returns a list of all Variable objects.
(a) Column. A variable is like a column in a spreadsheet.

Module 5: Concepts and Universes¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="Health Survey", agency="health.gc.ca")

q1 = doc.add_question(text="What is your age?")
q2 = doc.add_question(text="What is your weight?")
q3 = doc.add_question(text="How often do you exercise per week?")
q4 = doc.add_question(text="How many fruit servings do you eat per day?")

demo = doc.add_concept(name="Demographics")
activity = doc.add_concept(name="Physical Activity")
nutrition = doc.add_concept(name="Nutrition")

doc.add_universe(name="Adults aged 18+ in Canada")

doc.add_variable(name="Age", question=q1, concept=demo)
doc.add_variable(name="Weight", question=q2, concept=demo)
doc.add_variable(name="ExerciseFrequency", question=q3, concept=activity)
doc.add_variable(name="FruitServings", question=q4, concept=nutrition)

print(f"Questions: {len(doc.questions)}")
print(f"Variables: {len(doc.variables)}")
print(f"Concepts: {len(doc.concepts)}")
print(f"Universes: {len(doc.universes)}")

Output:

Questions: 4
Variables: 4
Concepts: 3
Universes: 1

Quiz answers¶

(a) An abstract idea that a variable measures. For example, "Age" the concept is measured by "Age" the variable.
(a) The group of people or things being studied. For example, "Adults aged 18+ in Canada".
(a) Pass concept= when calling add_variable(). This creates a reference link in the DDI document.

Module 6: From CSV/Excel to DDI¶

Exercise solutions¶

Exercise 1:

import csv
import ddi_l as ddi

with open("survey_sample.csv") as f:
    reader = csv.DictReader(f)
    columns = reader.fieldnames

# The question each column records, worded the way respondents saw it
QUESTIONS = {
    "age": "How old are you?",
    "gender": "What is your gender?",
    "income": "What was your total income last year, before taxes?",
    "education_level": "What is the highest level of education you have completed?",
}

doc = ddi.new_study(title="Household Survey", agency="research.org")
for col in columns:
    wording = QUESTIONS.get(col)
    # A column nobody was asked about, such as respondent_id, gets no question
    q = doc.add_question(text=wording) if wording else None
    doc.add_variable(name=col, question=q)

doc.save("household-survey.xml")
print(f"Variables: {len(doc.variables)}")

Output: Variables: 5

Exercise 2 (pandas):

import pandas as pd
import ddi_l as ddi

df = pd.read_csv("survey_sample.csv")
doc = ddi.new_study(title="Household Survey", agency="research.org")
for col in df.columns:
    doc.add_variable(name=col)
doc.save("household-survey-pandas.xml")
print(f"Variables: {len(doc.variables)}")

Output: Variables: 5

Exercise 3 (enriched):

doc.add_concept(name="Demographics")
doc.add_concept(name="Socioeconomic Status")
doc.add_universe(name="Canadian households")
issues = doc.validate()
print(f"Valid: {not issues}")
doc.save("household-survey-enriched.xml")

Quiz answers¶

(a) A list of column names. reader.fieldnames gives you the header row from the CSV.
(b) df.columns. This returns the column names from the DataFrame.
(a) A DDI XML file with one variable per column. The script reads column names and creates DDI metadata.
(a) Because column names alone are not useful metadata. Concepts, universes, and code lists add meaning.

Module 7: Code Lists¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi
from ddi_l.models.logicalproduct import Category

doc = ddi.new_study(title="Census", agency="statcan.gc.ca")

doc.add_code_list(name="Gender Codes")
doc.add_item(Category, name="Male")
doc.add_item(Category, name="Female")
doc.add_item(Category, name="Other")

doc.add_code_list(name="Employment Status")
doc.add_item(Category, name="Employed")
doc.add_item(Category, name="Unemployed")
doc.add_item(Category, name="Retired")
doc.add_item(Category, name="Student")

doc.add_code_list(name="Housing Type")
doc.add_item(Category, name="House")
doc.add_item(Category, name="Apartment")
doc.add_item(Category, name="Other")

print(f"Code lists: {len(doc.code_lists)}")
print(f"Categories: {len(doc.items(Category))}")

Output:

Code lists: 3
Categories: 10

Exercise 2:

for cat in doc.items(Category):
    print(cat.identifier)

Exercise 3:

from ddi_l.models.datacollection import Instrument

doc.add_item(Instrument, name="Census Form")

Quiz answers¶

(b) A set of pre-defined answer choices. Like "Male / Female / Other" for gender.
(b) doc.add_item(Category, name="..."). The add_item method works for any DDI type.
(c) doc.items(Category). Returns all categories in the document.
(c) They make answers consistent across surveys. Everyone uses the same codes.

Module 8: Questionnaire Flows¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="National Health Survey", agency="health.gc.ca")
q_age = doc.add_question(text="What is your age?")
q_gender = doc.add_question(text="What is your gender?")
q_employed = doc.add_question(text="Are you currently employed?")
q_occupation = doc.add_question(text="What is your occupation?")
q_health = doc.add_question(text="How would you rate your general health?")
q_smoke = doc.add_question(text="Do you smoke?")
print(f"Questions: {len(doc.questions)}")

Output: Questions: 6

Exercise 2:

from ddi_l.models.datacollection import QuestionConstruct, Sequence

for q in doc.questions:
    doc.add_item(QuestionConstruct, name="Ask", question_reference=q.to_reference())

doc.add_item(Sequence, name="Section A - Demographics")
doc.add_item(Sequence, name="Section B - Employment")
doc.add_item(Sequence, name="Section C - Health")

print(f"QuestionConstructs: {len(doc.items(QuestionConstruct))}")
print(f"Sequences: {len(doc.items(Sequence))}")

Output:

QuestionConstructs: 6
Sequences: 3

Exercise 3:

from ddi_l.models.datacollection import IfThenElse

doc.add_item(IfThenElse, name="Age gate for employment")
doc.add_item(IfThenElse, name="Occupation routing")
print(f"IfThenElse: {len(doc.items(IfThenElse))}")

Output: IfThenElse: 2

Exercise 4:

from ddi_l.models.datacollection import StatementItem, Instrument

doc.add_item(Sequence, name="Main Survey Flow")
doc.add_item(StatementItem, name="Welcome")
doc.add_item(Instrument, name="Health Survey Instrument")

print(f"Sequences: {len(doc.items(Sequence))}")
print(f"StatementItems: {len(doc.items(StatementItem))}")
print(f"Instruments: {len(doc.items(Instrument))}")

Output:

Sequences: 4
StatementItems: 1
Instruments: 1

Quiz answers¶

(b) Sequence. Groups steps in order, like a section.
(c) IfThenElse with a condition on age. Routes the respondent.
(b) It wraps a question for use in a Sequence. Separates content from flow.
(d) Loop. Repeats a section for each item in a list.
(c) List all questions and create QuestionConstructs. Then build the flow around them.

Module 9: Data Lineage¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="National Health Survey", agency="health.gc.ca")

q_age = doc.add_question(text="What is your age?")
q_gender = doc.add_question(text="What is your gender?")
q_employed = doc.add_question(text="Are you currently employed?")
q_occupation = doc.add_question(text="What is your occupation?")
q_health = doc.add_question(text="How would you rate your general health?")
q_smoke = doc.add_question(text="Do you smoke?")

v_age = doc.add_variable(name="age", question=q_age)
v_gender = doc.add_variable(name="gender", question=q_gender)
v_employed = doc.add_variable(name="employed", question=q_employed)
v_occupation = doc.add_variable(name="occupation", question=q_occupation)
v_health = doc.add_variable(name="health_rating", question=q_health)
v_smoke = doc.add_variable(name="smoker", question=q_smoke)

print(f"Questions: {len(doc.questions)}")
print(f"Variables: {len(doc.variables)}")

Output:

Questions: 6
Variables: 6

Exercise 2:

from ddi_l.models.logicalproduct import Category
from ddi_l.models.base import Reference

cl = doc.add_code_list(name="Age Group Codes")
doc.add_item(Category, name="0-15")
doc.add_item(Category, name="16-24")
doc.add_item(Category, name="25-44")
doc.add_item(Category, name="45-64")
doc.add_item(Category, name="65+")

v_age_group = doc.add_variable(name="age_group", question=q_age)
v_age_group.source_variable_references = [
    Reference(agency="health.gc.ca", identifier=v_age.identifier, version="1"),
]

print(f"Code lists: {len(doc.code_lists)}")
print(f"Variables: {len(doc.variables)}")

Output:

Code lists: 1
Variables: 7

Exercise 3:

v_emp_code = doc.add_variable(name="employment_status_code", question=q_employed)
v_emp_code.source_variable_references = [
    Reference(agency="health.gc.ca", identifier=v_employed.identifier, version="1"),
]

v_health_score = doc.add_variable(name="health_score", question=q_health)
v_health_score.source_variable_references = [
    Reference(agency="health.gc.ca", identifier=v_health.identifier, version="1"),
]

print(f"Variables: {len(doc.variables)}")

Output: Variables: 9

Exercise 4:

v_master_ag = doc.add_variable(name="age_group_master", question=q_age)
v_master_ag.source_variable_references = [
    Reference(agency="health.gc.ca", identifier=v_age_group.identifier, version="1"),
]

v_master_emp = doc.add_variable(name="employment_status_master", question=q_employed)
v_master_emp.source_variable_references = [
    Reference(agency="health.gc.ca", identifier=v_emp_code.identifier, version="1"),
]

v_master_hs = doc.add_variable(name="health_score_master", question=q_health)
v_master_hs.source_variable_references = [
    Reference(agency="health.gc.ca", identifier=v_health_score.identifier, version="1"),
]

print(f"Total variables: {len(doc.variables)}")

Output: Total variables: 12

Quiz answers¶

(b) Which other variables a derived variable was computed from.
(c) Use doc.add_variable(name=..., question=q).
(c) Both collection variables and derived variables.
(c) 2: one for height and one for weight.
(b) Trace any variable back to the original question and data.

Module 10: Data Linkage¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi
from ddi_l.models.base import Reference

doc = ddi.new_study(
    title="Canadian Community Health Survey 2021",
    agency="statcan.gc.ca",
)
q_key = doc.add_question(text="Anonymized linkage key")
q_health = doc.add_question(text="How would you rate your general health?")

survey_key = doc.add_variable(name="anon_id", question=q_key)
survey_health = doc.add_variable(name="health_rating", question=q_health)
survey_key.set_property("linkage_role", "key")

print(f"Survey variables: {len(doc.variables)}")
print(f"anon_id linkage_role: {survey_key.get_property('linkage_role')}")

Output:

Survey variables: 2
anon_id linkage_role: key

Exercise 2:

admin = doc.add_study(title="Hospital Admissions Register 2021")
admin_ds = doc.study(admin.identifier)

admin_key = admin_ds.add_variable(name="anon_id")
admin_visits = admin_ds.add_variable(name="hospital_visits")
admin_key.set_property("linkage_role", "key")

cmp = doc.add_comparison(name="Survey-to-Admin Microdata Linkage 2021")
key_match = cmp.correspondence(
    commonality="Anonymized personal identifier common to both sources.",
    weight=1.0,
)
cmp.add_variable_map(
    survey_key.to_reference(),
    admin_key.to_reference(),
    correspondence=key_match,
)

print(f"Comparisons: {len(doc.comparisons)}")

Output: Comparisons: 1

Exercise 3:

cmp.set_property("linkage_method", "deterministic")
cmp.set_property("match_rate", "0.94")

print(cmp.get_property("linkage_method"))

Output: deterministic

Exercise 4:

linked = doc.add_variable(name="health_by_hospital_use")
linked.source_variable_references = [
    Reference(agency="statcan.gc.ca", identifier=survey_health.identifier, version="1"),
    Reference(agency="statcan.gc.ca", identifier=admin_visits.identifier, version="1"),
]

print(f"Linked variable sources: {len(linked.source_variable_references)}")

Output: Linked variable sources: 2

Exercise 5 (bonus: probabilistic linkage):

prob = doc.add_comparison(name="Probabilistic Linkage")

dob = doc.add_variable(name="date_of_birth")
sex = doc.add_variable(name="sex")
postal = doc.add_variable(name="postal_code")
for v in (dob, sex, postal):
    v.set_property("linkage_role", "matching")

weighted = prob.correspondence(
    commonality="Agreement across date of birth, sex, and postal code.",
    weight=0.85,
)

for v in (dob, sex, postal):
    print(f"{v.names[0].text}: {v.get_property('linkage_role')}")

Output:

date_of_birth: matching
sex: matching
postal_code: matching

Quiz answers¶

(b) Combining records from two or more sources that refer to the same unit.
(b) The variable common to both sources that connects matching records.
(a) Deterministic matches on an exact key; probabilistic weighs agreement across several quasi-identifiers.
(b) Put both sources in source_variable_references.
(b) To protect confidentiality. Personal identifiers are removed so the linked file is anonymized.

Module 11: Properties, Find, and Validate¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="Household Survey", agency="research.org")
q1 = doc.add_question(text="What is your age?")
q2 = doc.add_question(text="What is your gender?")
q3 = doc.add_question(text="What is your income?")

q3.set_property("sensitivity", "high")
print(q3.properties)

Output: {'sensitivity': 'high'}

Exercise 2:

doc.add_variable(name="Age", question=q1)
v = doc.variables[0]
print(v.identifier)

Exercise 3:

doc.remove(q1.identifier)
print(f"Questions after removal: {len(doc.questions)}")

Output: Questions after removal: 2

Exercise 4:

issues = doc.validate()
if not issues:
    print("Document is valid!")

Quiz answers¶

(a) Attaches a custom key-value pair to the item. Stored as a UserAttributePair in the XML.
(a) item.properties. Returns a dict of all key-value pairs.
(a) The item object. Returns None if not found.
(a) Checks the document against the DDI schema. Returns a list of issues (empty if valid).
(a) Deletes the item from the document. Returns True if found.

Module 12: Custom Fields¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="Household Survey", agency="survey.gc.ca")
q_income = doc.add_question(text="What is your household income?")
income = doc.add_variable(name="income", question=q_income)

study = doc.study_unit
study.set_property("myorg:retention_policy", "destroy after 7 years")
study.set_property("myorg:security_class", "Protected B")

print(study.properties)

Output:

{'myorg:retention_policy': 'destroy after 7 years', 'myorg:security_class': 'Protected B'}

Exercise 2:

income.set_property("myorg:source_system", "CRM-2024")
q_income.set_property("myorg:steward", "Survey Methods")


def items_with_custom_fields(doc, prefix="myorg:"):
    count = 0
    for collection in (doc.questions, doc.variables):
        for item in collection:
            if any(key.startswith(prefix) for key in item.properties):
                count += 1
    return count


print(f"Items with custom fields: {items_with_custom_fields(doc)}")

Output: Items with custom fields: 2

Exercise 3:

from ddi_l.models.base import UserID

income.user_ids.append(UserID(value="CAT-000734", type_of_user_id="InternalCatalogue"))

uid = income.user_ids[0]
print(f"{uid.type_of_user_id}: {uid.value}")

Output: InternalCatalogue: CAT-000734

Exercise 4:

doc.save("household-survey.xml")

reopened = ddi.open_ddi("household-survey.xml")
print(reopened.variables[0].get_property("myorg:source_system"))

Output: CRM-2024

Exercise 5 (bonus: controlled vocabulary):

from uuid import uuid4

from ddi_l.models.logicalproduct import Category, CodeItem

# Build the controlled vocabulary. Each allowed value needs a Category (its
# meaning) AND a Code in the list that references it; a Category alone is not
# in any list. Each Code gets its own UUID4-based URN.
quality_codes = doc.add_code_list(name="Quality Flag Codes")
for value in ("validated", "provisional", "suppressed"):
    category = doc.add_item(Category, name=value)
    quality_codes.codes.append(
        CodeItem(
            agency=quality_codes.agency,
            identifier=str(uuid4()),
            version=quality_codes.version,
            value=value,
            category=category.to_reference(),
        )
    )

# A field whose value is drawn from the vocabulary, plus a field that
# references the code list defining the allowed values
income.set_property("myorg:quality_flag", "validated")
income.set_property("myorg:quality_flag_codes", quality_codes)

print(income.get_property("myorg:quality_flag"))
print(income.get_property("myorg:quality_flag_codes").startswith("urn:ddi:"))

Output:

validated
True

Objects stay attached

Variables you hold remain the objects the document serializes, before and after save().

Quiz answers¶

(b) Because DDI is an open, extensible standard with a built-in extension point.
(b) UserAttributePair. set_property writes an AttributeKey/AttributeValue pair.
(b) They survive: they are written to the DDI XML and read straight back.
(a) When the value identifies the item in another system. Use a custom property when it describes the item.
(b) To keep your fields distinct so they never collide with another organization's fields.

Module 13: Update and Version¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.new_study(title="Survey v1", agency="lab.org")
q1 = doc.add_question(text="What is your age?")
q2 = doc.add_question(text="What is your gender?")
q3 = doc.add_question(text="How often do you exercise?")
doc.add_variable(name="Age", question=q1)
doc.add_variable(name="Gender", question=q2)
doc.add_variable(name="Exercise", question=q3)
doc.save("survey-v1.xml")

Exercise 2:

doc = ddi.open_ddi("survey-v1.xml")
q4 = doc.add_question(text="How many hours do you sleep?")
doc.add_variable(name="Sleep", question=q4)
doc.remove(doc.questions[0].identifier)

Exercise 3:

from ddi_l.models.base import VersionRationale, InternationalString

study = doc.study_unit
study.increment_minor_version()
study.version_rationales.append(
    VersionRationale(
        descriptions=[
            InternationalString(text="Added sleep quality question for wave 2")
        ]
    )
)
study.version_responsibility = "Survey Design Team"

Exercise 4:

issues = doc.validate()
doc.save("survey-v2.xml")
print(f"Version: {study.version}")
for r in study.version_rationales:
    for d in r.descriptions:
        print(f"Rationale: {d.text}")

Output:

Version: 1.1
Rationale: Added sleep quality question for wave 2

Quiz answers¶

(a) Changes the version from 1.0.0 to 1.1.0. Minor version bumps are for additions and small changes.
(a) Why a change was made. A text explanation stored in the DDI document.
(a) Who made the change. A text field identifying the responsible person or team.
(a) Major for big changes, minor for additions. Use major when the structure changes significantly, minor when you add or tweak.

Module 14: Open and Modify Files¶

Exercise solutions¶

Exercise 1:

import ddi_l as ddi

doc = ddi.open_ddi("survey-v1.xml")
print(f"Questions: {len(doc.questions)}")
print(f"Variables: {len(doc.variables)}")

Exercise 2:

q_new1 = doc.add_question(text="What is your education level?")
q_new2 = doc.add_question(text="What is your marital status?")
doc.add_variable(name="Education", question=q_new1)
doc.add_variable(name="MaritalStatus", question=q_new2)
issues = doc.validate()
doc.save("survey-updated.xml")

Exercise 3:

doc2 = ddi.open_ddi("survey-updated.xml")
print(f"Questions: {len(doc2.questions)}")
print(f"Variables: {len(doc2.variables)}")

The counts should be 2 higher than Exercise 1.

Quiz answers¶

(a) Opens and parses a DDI XML file into a Document. You can then read and modify it.
(a) Checks the file against the DDI schema during loading. Any errors are reported immediately.
(a) No, existing content is preserved. ddi-l keeps unknown XML in other_elements so nothing is lost.

Module 15: CLI Validation¶

Exercise solutions¶

Exercise 1:

ddi validate survey-v1.xml

Output: Document is valid.

Exercise 2:

ddi to-json survey-v1.xml --indent 2 > survey.json
wc -l survey.json

The number of lines depends on the document size.

Exercise 3:

ddi roundtrip survey-v1.xml --output survey-roundtrip.xml
ls -la survey-v1.xml survey-roundtrip.xml

The file sizes should be similar.

Quiz answers¶

(a) Checks the file against the DDI schema. Prints "Document is valid." or a JSON error list.
(a) The file is valid. Exit code 0 means success in Unix.
(a) Converts DDI XML to JSON format. Useful for web APIs and analytics systems.

Module 16: Capstone Projects¶

Sample solution: Track A (Student)¶

Uses the sample dataset from the capstone page: thesis-data.csv.

import csv
import ddi_l as ddi
from ddi_l.models.base import VersionRationale, InternationalString

# Read CSV
with open("thesis-data.csv") as f:
    columns = csv.DictReader(f).fieldnames

# Create v1.0
doc = ddi.new_study(title="Thesis Dataset", agency="university.edu")
# How each column was asked. StudentID is assigned and AnxietyScore is computed
# from a questionnaire, so neither gets a question of its own.
QUESTIONS = {
    "Age": "How old are you?",
    "Gender": "What is your gender?",
    "YearOfStudy": "What year of your program are you in?",
    "Program": "Which program are you enrolled in?",
    "StudyHours": "On a typical day, how many hours do you study?",
    "SleepHours": "On a typical night, how many hours do you sleep?",
    "WorkHours": "How many hours a week do you work for pay?",
    "ExerciseDays": "On how many days last week did you exercise?",
    "StressLevel": "On a scale of 1 to 10, how stressed have you felt this term?",
    "SoughtSupport": "Have you sought support from campus services this term?",
}
for col in columns:
    wording = QUESTIONS.get(col)
    q = doc.add_question(text=wording) if wording else None
    v = doc.add_variable(name=col, question=q)
    v.set_property("source", "Primary survey data")

doc.add_concept(name="Demographics")
doc.add_concept(name="Academic Performance")
doc.add_concept(name="Well-Being")
doc.add_universe(name="Undergraduate students at University X")

doc.save("thesis-v1.xml")
issues = doc.validate()
print(f"v1 valid: {not issues}")

# Update to v1.1
doc = ddi.open_ddi("thesis-v1.xml")
q_new = doc.add_question(text="What is the student's GPA?")
v_new = doc.add_variable(name="GPA", question=q_new)

study = doc.study_unit
study.increment_minor_version()
study.version_rationales.append(
    VersionRationale(
        descriptions=[InternationalString(text="Added GPA variable for analysis")]
    )
)
study.version_responsibility = "Thesis Author"
doc.save("thesis-v1.1.xml")
print(f"v1.1 valid: {not doc.validate()}")

Reflection questions¶

These are open-ended. There are no right or wrong answers. Use them for discussion or written feedback.