Project: Analyzing Students' Mental Health

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 view the data in students
SELECT * 
FROM students;

DataFrameas

df1

variable

SELECT COUNT(*)
FROM public.students

DataFrameas

df2

variable

SELECT COUNT(*) AS total_count, 
       SUM(public.students.todep) AS total_sum, 
       AVG(public.students.todep) AS average, 
       MIN(public.students.todep) AS min_value, 
       MAX(public.students.todep) AS max_value
FROM public.students;

DataFrameas

df4

variable

SELECT COUNT(*) AS total_count, 
       SUM(public.students.tosc) AS total_sum, 
       AVG(public.students.tosc) AS average, 
       MIN(public.students.tosc) AS min_value, 
       MAX(public.students.tosc) AS max_value
FROM public.students;

DataFrameas

df5

variable

SELECT COUNT(*) AS total_count, 
       SUM(public.students.apd) AS total_sum, 
       AVG(public.students.apd) AS average, 
       MIN(public.students.apd) AS min_value, 
       MAX(public.students.apd) AS max_value
FROM public.students;

DataFrameas

df6

variable

SELECT COUNT(*) AS total_count, 
       SUM(public.students.ahome) AS total_sum, 
       AVG(public.students.ahome) AS average, 
       MIN(public.students.ahome) AS min_value, 
       MAX(public.students.ahome) AS max_value
FROM public.students;

DataFrameas

df7

variable

SELECT inter_dom, COUNT(*) AS count_inter
FROM public.students
WHERE inter_dom = 'inter'
GROUP BY inter_dom;

DataFrameas

df8

variable

SELECT public.students.inter_dom, COUNT(public.students.inter_dom) AS count_per_category
FROM public.students
GROUP BY public.students.inter_dom;

DataFrameas

df9

variable

SELECT COUNT(public.students.inter_dom) AS count_inter
FROM public.students
WHERE public.students.inter_dom = 'inter';

DataFrameas

df10

variable

SELECT 
    stay,
    COUNT(CASE WHEN inter_dom = 'inter' THEN 1 END) AS count_int,
    ROUND(AVG(CASE WHEN inter_dom = 'inter' THEN todep END), 2) AS average_phq,
    ROUND(AVG(CASE WHEN inter_dom = 'inter' THEN tosc END), 2) AS average_scs,
    ROUND(AVG(CASE WHEN inter_dom = 'inter' THEN toas END), 2) AS average_as
FROM 
    students
GROUP BY 
    stay
ORDER BY 
    stay DESC
LIMIT 9;

DataFrameas

df

variable

-- Find the number of international students and their average scores by length of stay, in descending order of length of stay
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
WHERE inter_dom = 'Inter'
GROUP BY stay
ORDER BY stay DESC;