Ready-Made Survey Dataset for Analysis

• responses.survey_id →references surveys.survey_idmany to one: each Responses row points to one Surveys row
• responses.respondent_id →references respondents.respondent_idmany to one: each Responses row points to one Respondents row

1. Analyze the distribution of survey topics and the number of responses per survey.

   Join surveys and responses tables, then group by survey topic and survey ID.

   tables: surveys, responses

2. Examine the relationship between respondent demographics (country, gender) and their survey responses.

   Join respondents and responses tables, then group by country and gender.

   tables: respondents, responses

3. Track the trend of survey creation dates and response submission dates over time.

   Analyze the distribution of dates from both surveys and responses tables.

   tables: surveys, responses

4. Identify which survey questions receive the most varied answers.

   Group responses by question_text and count distinct answer_text.

   tables: responses

Table names match the SQLite file and the SQL script.

SELECT T1.topic, COUNT(T2.response_id) AS response_count
FROM surveys AS T1
JOIN responses AS T2 ON T1.survey_id = T2.survey_id
GROUP BY T1.topic
ORDER BY response_count DESC
LIMIT 5;

SELECT country, AVG(age) AS average_age
FROM respondents
GROUP BY country
ORDER BY average_age DESC
LIMIT 20;

SELECT T1.gender, T2.topic, COUNT(T3.response_id) AS response_count
FROM respondents AS T1
JOIN responses AS T3 ON T1.respondent_id = T3.respondent_id
JOIN surveys AS T2 ON T3.survey_id = T2.survey_id
GROUP BY T1.gender, T2.topic
LIMIT 20;

WITH RankedAnswers AS (
  SELECT
    question_text,
    answer_text,
    COUNT(*) AS answer_count,
    ROW_NUMBER() OVER(PARTITION BY question_text ORDER BY COUNT(*) DESC) as rn
  FROM responses
  GROUP BY question_text, answer_text
)
SELECT
  question_text,
  answer_text,
  answer_count
FROM RankedAnswers
WHERE rn = 1
LIMIT 20;

Excel

Each table can be loaded into a separate sheet. Use Power Query to join tables based on their keys (e.g., survey_id, respondent_id). Create a PivotTable on the 'responses' sheet to analyze question text and answer text distributions.

Power BI

Load all three tables. Create relationships: responses.survey_id to surveys.survey_id (many-to-one) and responses.respondent_id to respondents.respondent_id (many-to-one). Measures: Total Responses = COUNT(responses[response_id]), Average Age = AVERAGE(respondents[age]).

SQL

Load the SQLite or SQL relational download. Use foreign keys (survey_id, respondent_id) to join tables. Example join: FROM responses r JOIN surveys s ON r.survey_id = s.survey_id JOIN respondents resp ON r.respondent_id = resp.respondent_id.

• 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