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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, 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 NameDescription
inter_domTypes of students (international or domestic)
japanese_cateJapanese language proficiency
english_cateEnglish language proficiency
academicCurrent academic level (undergraduate or graduate)
ageCurrent age of student
stayCurrent length of stay in years
todepTotal score of depression (PHQ-9 test)
toscTotal score of social connectedness (SCS test)
toasTotal score of acculturative stress (ASISS test)
Spinner
DataFrameas
students
variable
-- Run this code to view the data in students

SELECT *
FROM students;
Spinner
DataFrameas
df
variable
-- Cleaning the data with NULL Values
    SELECT 
        inter_dom, 
        region, 
        gender, 
        academic, 
        age, 
        age_cate, 
        stay, 
        stay_cate, 
        japanese, 
        japanese_cate, 
        english, 
        english_cate, 
        intimate, 
        religion, 
        suicide, 
        dep
    FROM students
    WHERE inter_dom IS NOT NULL 
      AND region IS NOT NULL 
      AND gender IS NOT NULL 
      AND academic IS NOT NULL 
      AND age IS NOT NULL 
      AND age_cate IS NOT NULL 
      AND stay IS NOT NULL 
      AND stay_cate IS NOT NULL 
      AND japanese IS NOT NULL 
      AND japanese_cate IS NOT NULL 
      AND english IS NOT NULL 
      AND english_cate IS NOT NULL 
      AND intimate <> ''    -- intimate have some empty so we use '<>'
      AND religion IS NOT NULL 
      AND suicide IS NOT NULL 
      AND dep IS NOT NULL

;
Spinner
DataFrameas
df
variable
-- Calculate average depression score based on suicide consideration
SELECT 
inter_dom,
stay_cate,
stay,
    gender, 
    academic,
	japanese_cate, 
    english_cate,
    AVG(CASE 
            WHEN suicide = 'Yes' THEN 1
            WHEN suicide = 'No' THEN 0
            ELSE NULL  -- Exclude nulls from average calculation
        END) AS average_depression_score,
		avg(case 
            when intimate = 'Yes' Then 1
            When intimate = 'No' Then 0
            Else Null 
       End) as avg_social_connectedness,
AVG(stay) AS avg_len_of_stay,
	AVG(CASE 
            WHEN japanese_cate = 'High' THEN 3
            WHEN japanese_cate = 'Average' THEN 2
            WHEN japanese_cate = 'Low' THEN 1
            ELSE NULL  -- Exclude NULLs from average calculation
        END) AS average_japanese_proficiency,
    AVG(CASE 
            WHEN english_cate = 'High' THEN 3
            WHEN english_cate = 'Average' THEN 2
            WHEN english_cate = 'Low' THEN 1
            ELSE NULL  -- Exclude NULLs from average calculationIS NOT NULL THEN english_cate
        END) AS average_english_proficiency
FROM 
    students
Where inter_dom IS NOT NULL 
AND stay_cate IS NOT NULL
      AND region IS NOT NULL 
      AND gender IS NOT NULL 
      AND academic IS NOT NULL
	  AND japanese_cate IS NOT NULL
      AND english_cate IS NOT NULL 
	  AND stay IS NOT NULL
	  
GROUP BY inter_dom, gender, academic, stay_cate, japanese_cate, 
    english_cate, stay

ORDER BY inter_dom, gender, academic, stay_cate, japanese_cate, 
    english_cate;
Current Type: Line
Current X-axis: avg_social_connectedness
Current Y-axis: average_depression_score
Current Color: None

Depression Scores vs. Social Connectedness

Current Type: Bar
Current X-axis: stay_cate
Current Y-axis: average_depression_score
Current Color: stay_cate

Depression Scores vs. Length of Stay

Current Type: Bar
Current X-axis: japanese_cate
Current Y-axis: average_depression_score
Current Color: japanese_cate

Japanese Language Proficiency vs. Depression Scores

Current Type: Bar
Current X-axis: english_cate
Current Y-axis: average_depression_score
Current Color: english_cate

English Language Proficiency vs. Depression Scores

Analysis & Findings

The query results offer valuable insights into several key areas of student mental health:

  • Depression Scores: A comparative analysis of average depression scores between international and domestic students, taking into account additional demographic factors, reveals variations in mental health risk.

  • Social Connectedness: Examines students’ average social connectedness scores to assess its relationship with depression levels, providing a clearer view of its impact on mental health.

  • Length of Stay: Investigates how varying lengths of stay affect mental health, particularly in terms of depression and acculturative stress, to identify patterns related to adjustment periods.

  • Language Proficiency: Analyzes the influence of Japanese and English language proficiency on mental health outcomes, considering how language skills may mitigate or heighten mental health challenges.

Conclusion

This analysis provides stakeholders with a nuanced understanding of mental health challenges among international students, identifying critical focus areas for targeted interventions. The insights gained can guide the development of support systems that promote the well-being and integration of international students, fostering a healthier university experience.