Project 14: Analyzing Student’s Mental Health using SQL

Published by

Ram Rallabandi

December 23, 2022

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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. Explore the students data using PostgreSQL to determine if this is true and see if the length of stay is a contributing factor.

Here is a data description of the fields you may find helpful. The full dataset is in one table with 50 fields and, according to the survey, 268 records. Each row is a student.

Field Name	Description
inter_dom	Types of students
japanese_cate	Japanese language proficiency
english_cate	English language proficiency
academic	Current academic level
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)

In [13]:

-- Exploring Students Data 
SELECT *
FROM students;

Out[13]:

	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	internet	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	24.0	4.0	5.0	Long	3.0	Average	5.0	High		Yes	No	No	No	0.0	Min	34.0	23.0	9.0	11.0	8.0	11.0	2.0	27.0	91.0	5.0	5.0	6.0	3.0	2.0	1.0	4.0	1.0	3.0	4.0	NaN	Yes	Yes	Yes	No	No	No	No	No	No	No	No
1	Inter	SEA	Male	Grad	28.0	5.0	1.0	Short	4.0	High	4.0	High		No	No	No	No	2.0	Min	48.0	8.0	7.0	5.0	4.0	3.0	2.0	10.0	39.0	7.0	7.0	7.0	4.0	4.0	4.0	4.0	1.0	1.0	1.0	NaN	Yes	Yes	Yes	No	No	No	No	No	No	No	No
2	Inter	SEA	Male	Grad	25.0	4.0	6.0	Long	4.0	High	4.0	High	Yes	Yes	No	No	No	2.0	Min	41.0	13.0	4.0	7.0	6.0	4.0	3.0	14.0	51.0	3.0	3.0	3.0	1.0	1.0	2.0	1.0	1.0	1.0	1.0	NaN	No	No	No	No	No	No	No	No	No	No	No
3	Inter	EA	Female	Grad	29.0	5.0	1.0	Short	2.0	Low	3.0	Average	No	No	No	No	No	3.0	Min	37.0	16.0	10.0	10.0	8.0	6.0	4.0	21.0	75.0	5.0	5.0	5.0	5.0	5.0	2.0	2.0	2.0	4.0	4.0	NaN	Yes	Yes	Yes	Yes	Yes	No	No	No	No	No	No
4	Inter	EA	Female	Grad	28.0	5.0	1.0	Short	1.0	Low	3.0	Average	Yes	No	No	No	No	3.0	Min	37.0	15.0	12.0	5.0	8.0	7.0	4.0	31.0	82.0	5.0	5.0	5.0	2.0	5.0	2.0	5.0	5.0	4.0	4.0	NaN	Yes	Yes	Yes	No	Yes	No	Yes	Yes	No	No	No
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
281					NaN	NaN	NaN		NaN		NaN							NaN		NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	128	140
282					NaN	NaN	NaN		NaN		NaN							NaN		NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	137	131
283					NaN	NaN	NaN		NaN		NaN							NaN		NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	66	202
284					NaN	NaN	NaN		NaN		NaN							NaN		NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	61	207
285					NaN	NaN	NaN		NaN		NaN							NaN		NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	30	238

286 rows × 50 columns

In [30]:

-- All records count by student type
SELECT inter_dom, COUNT(*) FROM students
WHERE stay IS NOT NULL
GROUP BY inter_dom;

Out[30]:

	inter_dom	count
0	Inter	201
1	Dom	67

In [28]:

-- Filter the data to see how it differs between the student types
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 stay IS NOT NULL AND inter_dom = 'Inter'
GROUP BY stay
ORDER BY stay DESC;

Out[28]:

	stay	average_phq	average_scs	average_as
0	10	13.00	32.00	50.00
1	8	10.00	44.00	65.00
2	7	4.00	48.00	45.00
3	6	6.00	38.00	58.67
4	5	0.00	34.00	91.00
5	4	8.57	33.93	87.71
6	3	9.09	37.13	78.00
7	2	8.28	37.08	77.67
8	1	7.48	38.11	72.80

2 responses to “Project 14: Analyzing Student’s Mental Health using SQL”

BluntPathway

March 2, 2023

I just found your blog, I am looking forward to you next posts, I came across it because I have been learning python and data anlysis tools in general. Keep blogging.

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1. Ram Rallabandi
  
  March 6, 2023
  
  Glad to know that you are enjoying the posts. Will do my best to post content regularly
  
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