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ramputras · 13 days ago

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🧠 Chi-Square Realness: Alcohol vs. Marital Status 🍷💔💍

Using data from the NESARC survey, we dug into how your relationship status might influence how much you drink. The stats? Unfiltered and telling.

📊 Key Insight:

There’s a significant association between marital status and alcohol consumption (Chi² = 392.18, p < .001).

Married individuals top the charts across all drinking levels.

Divorced/Separated drink moderately — but steadily.

Never Married lean toward lighter drinking, but mid-range isn't rare.

Widowed appear quietly in higher consumption brackets. (Coping, perhaps?)

⚖️ Pairwise Comparisons:

We broke it down further with post-hoc testing:

Married vs Never Married → Big difference (p < .001)

Married vs Divorced/Cohabiting → Still distinct (p < .01)

Never Married vs Separated → Subtle, yet significant (p ≈ .015)

Your marital status might be quietly shaping your drinking habits — whether you're toasting to love, numbing the breakup, or just navigating life solo. In case you want to try the code yourself ````````````````````````

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns import matplotlib.pyplot as plt

Load the dataset

data = pd.read_csv('nesarc.csv', low_memory=False)

Convert variables to numeric

data['S2AQ8A'] = pd.to_numeric(data['S2AQ8A'], errors='coerce') # Alcohol frequency data['MARITAL'] = pd.to_numeric(data['MARITAL'], errors='coerce') # Marital status

Subset data (assuming sub1 already exists — else, create one)

sub1 = data.copy() sub2 = sub1.copy()

Replace invalid or missing codes with NaN

sub2['S2AQ8A'].replace([99, np.nan], np.nan, inplace=True) sub2['MARITAL'].replace(9, np.nan, inplace=True)

Filter only valid values

sub2 = sub2[(sub2['S2AQ8A'].between(1, 10)) & (sub2['MARITAL'].between(1, 6))]

Create a label-friendly MARITALSTATUS column (you can use actual labels instead of scores)

marital_map = { 1: 'Married', 2: 'Cohabiting', 3: 'Widowed', 4: 'Divorced', 5: 'Separated', 6: 'Never Married' } sub2['MARITALSTATUS'] = sub2['MARITAL'].map(marital_map)

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🔍 Main Chi-Square Test

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ct_main = pd.crosstab(sub2['S2AQ8A'], sub2['MARITALSTATUS']) print("Main Contingency Table:\n", ct_main)

print("\nColumn Percentages:\n", ct_main / ct_main.sum(axis=0))

chi2, p, dof, expected = stats.chi2_contingency(ct_main) print("\nChi-square test results:") print(f"Chi2 Value: {chi2:.2f}") print(f"p-value: {p:.4f}") print(f"Degrees of Freedom: {dof}") print("Expected Counts:\n", pd.DataFrame(expected, index=ct_main.index, columns=ct_main.columns))

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📊 Visualization

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plt.figure(figsize=(12, 6)) sns.countplot(data=sub2, x='MARITALSTATUS', hue='S2AQ8A', palette='Set2') plt.title("Drinking Frequency by Marital Status") plt.xlabel("Marital Status") plt.ylabel("Count of Respondents") plt.legend(title="Drinking Frequency Code", bbox_to_anchor=(1.05, 1), loc='upper left') plt.tight_layout() plt.show()

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🔁 Optional: Pairwise Comparisons

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def chi_square_pairwise(df, group_col, compare_col, groups): sub_df = df[df[group_col].isin(groups)] ct = pd.crosstab(sub_df[compare_col], sub_df[group_col]) chi2, p, dof, expected = stats.chi2_contingency(ct) print(f"\nChi-square for {groups[0]} vs {groups[1]}") print(f"Chi2: {chi2:.2f}, p-value: {p:.4f}, DOF: {dof}") print("Contingency Table:\n", ct)

Run pairwise tests

pairwise_groups = ('Married', 'Never Married'), ('Married', 'Divorced'), ('Married', 'Cohabiting'), ('Never Married', 'Separated')

for g1, g2 in pairwise_groups: chi_square_pairwise(sub2, 'MARITALSTATUS', 'S2AQ8A', [g1, g2])

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#DataScience #MentalHealth #NESARC #Sociology #ChiSquare #AlcoholUse #Relationships #ResearchVibes

#datascience #mentalhealth #NESARC #sociology #ChiSquare #Relationships

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