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.

Here is a data description of the columns.

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' AS students;

DataFrameas

df4

variable

-- Find the total count of students in database --
SELECT COUNT(*) as total_records
FROM students;

DataFrameas

df3

variable

-- How many students are international, domestic, null? --
SELECT inter_dom, COUNT(*) AS number_of_students
FROM students
GROUP BY inter_dom;

It looks like there are 201 students with an international status in this database.

DataFrameas

df2

variable

--Find summary statistics of each diagnostic test--
SELECT ROUND(AVG(todep), 2) AS avg_phq, 
		MIN(todep) AS min_phq, 
		MAX(todep) AS max_phq,
		ROUND(AVG(tosc), 2) AS avg_scs,
		MIN(tosc) AS min_scs,
		MAX(tosc) AS max_scs,
		ROUND(AVG(toas), 2) AS avg_as,
		MIN(toas) AS min_as,
		MAX(toas) AS max_as
FROM students;

DataFrameas

df1

variable

--Narrow down the results to only focus on international students--
SELECT ROUND(AVG(todep), 2) AS avg_phq, 
		MIN(todep) AS min_phq, 
		MAX(todep) AS max_phq,
		ROUND(AVG(tosc), 2) AS avg_scs,
		MIN(tosc) AS min_scs,
		MAX(tosc) AS max_scs,
		ROUND(AVG(toas), 2) AS avg_as,
		MIN(toas) AS min_as,
		MAX(toas) AS max_as
FROM students
WHERE inter_dom = 'Inter';

Comparing this query result back to the previous one that did not filter for only international students, it looks like the average scores of the ASISS test are higher for international students than domestic.

DataFrameas

df5

variable

--What is the amount of students for each amount of years stay?--

SELECT stay, COUNT(*) AS number_of_inter_students
FROM students
WHERE inter_dom = 'Inter'
GROUP BY stay
ORDER BY stay DESC;

DataFrameas

df

variable

--How does the length of stay impact the average diagnostic score?--
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;

There might be a correlation between the length of stay for international students and the PHQ-9 diagnostic test. As length of stay increases, the average PHQ-9 test also increases. However further investigating the sample size of each length of stay, there may not be enough significant data to make an appropriate conclusion.