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
LIMIT 5;

Hidden output

index	inter_dom	region	gender	academic	age	age_cate	stay	stay_cate	japanese	japanese_cate	english	english_cate	intimate	religion	suicide	dep	deptype	todep	depsev	tosc	apd	ahome	aph	afear	acs	aguilt	amiscell	toas	partner	friends	parents	relative	profess	phone	doctor	reli	alone	others	partner_bi	friends_bi	parents_bi	relative_bi	professional_bi	phone_bi	doctor_bi	religion_bi	alone_bi	others_bi	internet_bi
0	Inter	SEA	Male	Grad	24	4	5	Long	3	Average	5	High		Yes	No	No	No	0	Min	34	23	9	11	8	11	2	27	91	5	5	6	3	2	1	4	1	3	4	Yes	Yes	Yes	No	No	No	No	No	No	No	No
1	Inter	SEA	Male	Grad	28	5	1	Short	4	High	4	High		No	No	No	No	2	Min	48	8	7	5	4	3	2	10	39	7	7	7	4	4	4	4	1	1	1	Yes	Yes	Yes	No	No	No	No	No	No	No	No
2	Inter	SEA	Male	Grad	25	4	6	Long	4	High	4	High	Yes	Yes	No	No	No	2	Min	41	13	4	7	6	4	3	14	51	3	3	3	1	1	2	1	1	1	1	No	No	No	No	No	No	No	No	No	No	No
3	Inter	EA	Female	Grad	29	5	1	Short	2	Low	3	Average	No	No	No	No	No	3	Min	37	16	10	10	8	6	4	21	75	5	5	5	5	5	2	2	2	4	4	Yes	Yes	Yes	Yes	Yes	No	No	No	No	No	No
4	Inter	EA	Female	Grad	28	5	1	Short	1	Low	3	Average	Yes	No	No	No	No	3	Min	37	15	12	5	8	7	4	31	82	5	5	5	2	5	2	5	5	4	4	Yes	Yes	Yes	No	Yes	No	Yes	Yes	No	No	No

Explore and analyze the students data to see how the length of stay (stay) impacts the average mental health diagnostic scores of the International students present in the study.

Return a table with nine rows and five columns.
The five columns should be aliased as: stay, count_int, average_phq, average_scs, and average_as, in that order.
The average columns should contain the average of the todep (PHQ-9 test), tosc (SCS test), and toas (ASISS test) columns for each length of stay, rounded to two decimal places.
The count_int column should be the number of international students for each length of stay.
Sort the results by the length of stay in descending order.

DataFrameas

df

variable

-- Start coding here...
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
LIMIT 9;

Hidden output

index	stay	count_int	average_phq	average_scs	average_as
0	10	1	13	32	50
1	8	1	10	44	65
2	7	1	4	48	45
3	6	3	6	38	58.67
4	5	1	0	34	91
5	4	14	8.57	33.93	87.71
6	3	46	9.09	37.13	78
7	2	39	8.28	37.08	77.67
8	1	95	7.48	38.11	72.8