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

-- Count all records
SELECT COUNT(*) AS total_records
FROM students;

DataFrameas

df

variable

-- Count records per student type
SELECT inter_dom, COUNT(*) AS count_inter_dom
FROM students
GROUP BY inter_dom;

DataFrameas

df

variable

select *
from students
where inter_dom='Inter';

DataFrameas

df

variable

select *
from students
where inter_dom='Dom';

DataFrameas

df

variable

select *
from students
where (inter_dom not like 'D%') and (inter_dom not like 'I%') or (inter_dom is null);

DataFrameas

df

variable

-- 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.

select 
 	min(todep) as min_phq,
  	max(todep),2 as max_phq,
	round(avg(todep),2) as avg_phq,
 	min(tosc) as min_scs,
  	max(tosc) as max_scs,
	round(avg(tosc),2) as avg_scs,
 	min(toas) as min_as,
  	max(toas) as max_as,
	round(avg(toas),2) as avg_as,

from students;

DataFrameas

df

variable

-- Repeat this to summarize the data for international students only.

select 
 	min(todep) as min_phq,
  	max(todep),2 as max_phq,
	round(avg(todep),2) as avg_phq,
 	min(tosc) as min_tosc,
  	max(tosc) as max_tosc,
	round(avg(tosc),2) as avg_tosc,
 	min(toas) as min_toas,
  	max(toas) as max_toas,
	round(avg(toas),2) as avg_toas,
	

from students
where inter_dom='Inter';

DataFrameas

df

variable

-- 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.
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;