Questionnaire Dataset for Data Analyst Projects

• responses.questionnaire_id →references questionnaires.questionnaire_idmany to one: each Responses row points to one Questionnaires row
• questions.questionnaire_id →references questionnaires.questionnaire_idmany to one: each Questions row points to one Questionnaires row
• responses.question_id →references questions.question_idmany to one: each Responses row points to one Questions row
• responses.respondent_id →references respondents.respondent_idmany to one: each Responses row points to one Respondents row

1. Retrieve the title and status of all questionnaires created in the last year.

   Filter questionnaires where creation_date is within the last 12 months from the current date.

   tables: questionnaires

2. List all questions belonging to the 'Feedback' category, ordered by their appearance in the questionnaire.

   Join questions and questionnaires tables, filter by questionnaire category, and order by order_in_questionnaire.

   tables: questions, questionnaires

3. For each questionnaire, count the number of active questions.

   Join questions and questionnaires tables, filter for active questionnaires, and group by questionnaire to count questions.

   tables: questions, questionnaires

4. Find the average rating for 'Rating' type questions across all 'Active' questionnaires.

   Join all three tables, filter for 'Rating' question types and 'Active' questionnaires, then calculate the average of response_rating.

   tables: responses, questions, questionnaires

5. Identify respondents who have completed more than 5 questionnaires.

   Join responses and respondents tables, group by respondent, and count distinct questionnaires completed.

   tables: responses, respondents

Table names match the SQLite file and the SQL script.

SELECT q.question_text FROM questions q JOIN questionnaires qs ON q.questionnaire_id = qs.questionnaire_id WHERE qs.category = 'Feedback' ORDER BY q.order_in_questionnaire LIMIT 20;

SELECT qs.title, COUNT(q.question_id) AS active_question_count FROM questionnaires qs JOIN questions q ON qs.questionnaire_id = q.questionnaire_id WHERE qs.status = 'Active' GROUP BY qs.title LIMIT 20;

SELECT AVG(r.response_rating) AS average_rating FROM responses r JOIN questions q ON r.question_id = q.question_id JOIN questionnaires qs ON q.questionnaire_id = qs.questionnaire_id WHERE q.question_type = 'Rating' AND qs.status = 'Active' LIMIT 20;

SELECT res.respondent_id, COUNT(DISTINCT resp.questionnaire_id) AS questionnaires_completed FROM respondents res JOIN responses resp ON res.respondent_id = resp.respondent_id GROUP BY res.respondent_id HAVING questionnaires_completed > 5 LIMIT 20;

Excel

Each table can be loaded into its own sheet. Use XLOOKUP to join tables based on common IDs (e.g., questionnaire_id, respondent_id). Power Query can also be used for more complex joins. Create a PivotTable from the responses sheet to summarize data.

Power BI

Model the data using a star schema: 'questionnaires' and 'respondents' as dimension tables, and 'responses' as the fact table. Create relationships from 'questionnaires.questionnaire_id' to 'responses.questionnaire_id' and from 'respondents.respondent_id' to 'responses.respondent_id'. Create measures for average response rating and count of responses.

SQL

Load the SQLite or SQL relational download. Primary keys are defined for each table (e.g., questionnaire_id in 'questionnaires'). Use JOIN clauses to link tables, for example, joining 'responses' to 'questions' on 'question_id' to analyze question-specific data.

Python

Use pandas to load CSV files into DataFrames. Merge DataFrames using common columns (e.g., 'questionnaire_id') to combine data from different tables. Analyze response patterns and respondent demographics.

• CSV (zip)One CSV file per table, zipped
• Excel workbookOne worksheet per table
• SQLite databaseA ready-to-query database file with every table
• Parquet (zip)One Parquet file per table, zipped
• SQL scriptCREATE TABLE with primary and foreign keys, then INSERTs
• CSVA single CSV file
• JSONA single JSON file

• Excel Practice Datasets: Sample Data for Skill Development