Project Background
Does going to university in a different country affect your mental health? A Japanese international university surveyed its students in 2018 and published a study the following year that was approved by several ethical and regulatory boards.
The study found that international students have a higher risk of mental health difficulties than the general population, and that social connectedness (belonging to a social group) and acculturative stress (stress associated with joining a new culture) are predictive of depression.
Explore the students data using PostgreSQL to find out if you would come to a similar conclusion for international students and see if the length of stay is a contributing factor.
Here is a data description of the columns you may find helpful.
Column Descriptions
| Field Name | Description |
|---|---|
inter_dom | Types of students (international or domestic) |
japanese_cate | Japanese language proficiency |
english_cate | English language proficiency |
academic | Current academic level (undergraduate or graduate) |
age | Current age of student |
stay | Current length of stay in years |
todep | Total score of depression (PHQ-9 test) |
tosc | Total score of social connectedness (SCS test) |
toas | Total score of acculturative stress (ASISS test) |
Project Instructions
Explore and analyze the students data to see how the study reached its conclusions and gain a better understanding of it. This project will take you through some exploratory analysis before investigating a specific factor for international students. The final query is the only part of your code that will be tested.
Your project will follow these exploratory steps:
- Start by counting all of the records in the data, then all records per student type to see how the records are categorized and scored.
- Filter the data to see how it differs between the student types.
- Find the summary statistics of the diagnostic tests for all students using aggregate functions, rounding the test scores to two decimal places, remembering to use aliases.
- Repeat this to summarize the data for international students only.
Your final query:
- See if length of stay impacts the average diagnostic scores rounded to two decimal places for international students, and order the results by descending order of the length of stay.
- The final output of your query with aliases will have a total nine observation rows and four columns: stay, average_phq, average_scs, and average_as, in that order.
Note: Creating new cells in workspace will rename the DataFrame. Make sure that your final solution uses the name df.
Code
-- Run this code to save the CSV file as students
SELECT *
FROM 'students.csv';-- Count the number of records in the dataset to confirm expected number of records, which is 286
SELECT COUNT(*) AS total_records
FROM students;-- See number of records per student type
SELECT inter_dom, COUNT(*) AS count_inter_dom
FROM students
GROUP BY inter_dom;-- Filter students for 'Inter' student type
SELECT *
FROM students
WHERE inter_dom = 'Inter';-- Filter students for 'Dom' student type
SELECT *
FROM students
WHERE inter_dom = 'Dom';-- Filter students for 'null' student type
SELECT *
FROM students
WHERE inter_dom IS NULL;-- Find summary statistics for PHQ-9 test
SELECT
ROUND(MIN(todep),2) AS min_phq,
ROUND(MAX(todep),2) AS max_phq,
ROUND(AVG(todep),2) AS avg_phq
FROM students;-- Find summary statistics for SCS test
SELECT
ROUND(MIN(tosc),2) AS min_scs,
ROUND(MAX(tosc),2) AS max_scs,
ROUND(AVG(tosc),2) AS avg_scs
FROM students;