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

df11

variable

select count(*) as total_records,
from students;

DataFrameas

df2

variable

select inter_dom,count(*) as count_inter_dom
from 'students.csv'
group by inter_dom

DataFrameas

df1

variable

select *
from 'students.csv'
where inter_dom='Inter';

DataFrameas

df3

variable

select *
from 'students.csv'
where inter_dom='Dom';

DataFrameas

df4

variable

select min(todep) as min_phq,
	max(todep) as max_phq,
	round(avg(todep),2) as avg_phq
from 'students.csv'

DataFrameas

df5

variable

select min(tosc) as min_scs,
	max(tosc) as max_scs,
	round(avg(tosc),2) as avg_scs
from 'students.csv'

DataFrameas

df6

variable

select min(toas) as min_as,
	max(toas) as max_as,
	round(avg(toas),2) as avg_as
from 'students.csv'

DataFrameas

df7

variable

select min(todep) as min_phq,
	max(todep) as max_phq,
	round(avg(todep),2) as avg_phq
from 'students.csv'
where inter_dom='Inter';

DataFrameas

df8

variable

select min(tosc) as min_scs,
	max(tosc) as max_scs,
	round(avg(tosc),2) as avg_scs
from 'students.csv'
where inter_dom='Inter'

DataFrameas

df9

variable

select min(toas) as min_as,
	max(toas) as max_as,
	round(avg(toas),2) as avg_as
from 'students.csv'
where inter_dom='Inter';

DataFrameas

df12

variable

select * 
from 'students.csv'
where inter_dom is NULL;

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