Project: Analyzing Students' Mental Health in SQL

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.

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)

Title: Analyzing Student Data: Demographics and Diagnostic Scores

Summary: This SQL code analyzes data from a CSV file named 'students.csv', focusing on various student demographics and their diagnostic scores. The code performs several tasks:

It counts the total number of records in the CSV file.
It counts the number of distinct student types (domestic or international) and groups them accordingly.
It filters and orders the data by student type.
It calculates summary statistics for diagnostic scores (PHQ, SCS, AS) specifically for international students.
It examines the impact of the length of stay on average diagnostic scores for international students by grouping and ordering the data by the length of stay.

DataFrameas

students

variable

-- Run this code to save the CSV file as students
SELECT * 
FROM 'students.csv';

DataFrameas

df

variable

-- Copy data from CSV into the table (assuming table already exists)
SELECT
  COUNT(*) AS num_records
FROM 'students.csv';

-- Count records per student type
SELECT
  inter_dom AS student_type,
  COUNT(DISTINCT inter_dom) AS count_of_student
FROM 'students.csv'
GROUP BY inter_dom;

-- Filter data by student types
SELECT *
FROM 'students.csv'
ORDER BY inter_dom;

-- Summary statistics for all students (focusing on international students)
SELECT
  ROUND(AVG(todep), 2) AS average_phq,
  ROUND(AVG(tosc), 2) AS average_scs,
  ROUND(AVG(toas), 2) AS average_as
FROM 'students.csv'
WHERE inter_dom = 'Inter';

-- Impact of length of stay on average diagnostic scores for international students
SELECT
  stay,
  COUNT(*) AS count_int,
  ROUND(AVG(todep), 2) AS average_phq,
  ROUND(AVG(tosc), 2) AS average_scs,
  ROUND(AVG(toas), 2) AS average_as
FROM 'students.csv'
WHERE inter_dom = 'Inter'
GROUP BY stay
ORDER BY stay DESC;