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';

We need to start counting how many students we have.

DataFrameas

df1

variable

-- COUNTING TOTAL RECORDS IN DATASET
SELECT COUNT(*) AS Total_records
FROM students

Then, we separate the students between International and Domestic, and then clean the data, ignoring all null values from the sample.

DataFrameas

df2

variable

-- COUNTING TOTAL RECORDS FOR EACH STUDENT TYPE
-- IGNORING NULL VALUES
SELECT inter_dom, COUNT(*) AS Total_records
FROM students
WHERE inter_dom IN ('Inter', 'Dom')
GROUP BY inter_dom

We calculate the min, max and avg value of the scores of the international students, which is the one we want to measure.

DataFrameas

df3

variable

-- DIAGNOSIS SCORES FOR INTERNATIONAL STUDENTS
SELECT inter_dom
		, MIN(todep) AS min_phq
		, MAX(todep) AS max_phq
		, ROUND(MIN(todep),2) AS avg_phq
		, MIN(tosc) AS min_sc
		, MAX(tosc) AS max_sc
		, ROUND(MIN(tosc),2) AS avg_sc
		, MIN(toas) AS min_as
		, MAX(toas) AS max_as
		, ROUND(MIN(toas),2) AS avg_as
FROM students
WHERE inter_dom IN ('Inter')
GROUP BY inter_dom

Then, we present the avarage of each group of students by the year they're staying.

DataFrameas

df

variable

-- IMPACT IN LENGHT STAY
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

DataFrameas

df9

variable

-- STAY (YEARS) VS AVG_PHQ
SELECT stay
	    , ROUND(AVG(todep),2) AS avg_phq
--		, ROUND(AVG(tosc),2) AS avg_scs
--		, ROUND(AVG(toas),2) AS avg_as
FROM students
WHERE inter_dom = 'Inter' 
GROUP BY stay
ORDER BY stay DESC

DataFrameas

df4

variable

-- STAY (YEARS) VS AVG_SCS
SELECT stay
--	    , ROUND(AVG(todep),2) AS avg_phq
		, ROUND(AVG(tosc),2) AS avg_scs
--		, ROUND(AVG(toas),2) AS avg_as
FROM students
WHERE inter_dom = 'Inter' 
GROUP BY stay
ORDER BY stay DESC

DataFrameas

df5

variable

-- STAY (YEARS) VS AVG_AS
SELECT stay
--	    , ROUND(AVG(todep),2) AS avg_phq
--		, ROUND(AVG(tosc),2) AS avg_scs
		, ROUND(AVG(toas),2) AS avg_as
FROM students
WHERE inter_dom = 'Inter' 
GROUP BY stay
ORDER BY stay DESC

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