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)

DataFrameas

students

variable

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

DataFrameas

df

variable

-- Start coding here...
SELECT COUNT(*) 
FROM students

DataFrameas

df1

variable

SELECT inter_dom, COUNT(*) AS count_inter_dom
FROM students
GROUP BY inter_dom;

Hidden output

DataFrameas

df2

variable

SELECT *
FROM students
where inter_dom = 'Inter'

Hidden output

DataFrameas

df3

variable

SELECT *
FROM students
where inter_dom = 'Dom'

Hidden output

DataFrameas

df4

variable

SELECT *
FROM students
WHERE inter_dom IS NULL

DataFrameas

df5

variable

SELECT inter_dom,ROUND(MIN(todep),2) as min_phq,ROUND(MAX(todep),2) as max_phq,ROUND(AVG(todep),2) as avg_phq
FROM students
GROUP BY inter_dom;

DataFrameas

df6

variable

SELECT inter_dom,ROUND(MIN(tosc),2) as min_scs,ROUND(MAX(tosc),2) as max_scs,ROUND(AVG(tosc),2) as avg_scs
FROM students
GROUP BY inter_dom;

DataFrameas

df7

variable

SELECT inter_dom,ROUND(MIN(toas),2) as min_as, ROUND(MAX(toas),2) as max_as, ROUND(AVG(toas),2) as avg_as
FROM students
GROUP BY inter_dom;

DataFrameas

df8

variable

select inter_dom,ROUND(MIN(todep),2) as min_phq,ROUND(MAX(todep),2) as max_phq,ROUND(AVG(todep),2) as avg_phq,ROUND(MIN(tosc),2) as min_scs,ROUND(MAX(tosc),2) as max_scs,ROUND(AVG(tosc),2) as avg_scs,ROUND(MIN(toas),2) as min_as, ROUND(MAX(toas),2) as max_as, ROUND(AVG(toas),2) as avg_as
FROM students
WHERE inter_dom = 'Inter'
Group by inter_dom

DataFrameas

df9

variable

SELECT stay,
		ROUND(AVG(todep),2) as average_phq,
		ROUND(AVG(tosc),2) as average_scs,
		ROUND(AVG(toas),2) as average_as
FROM students
WHERE inter_dom = 'Inter' 
GROUP BY stay
ORDER BY stay DESC;