Chi-Square Test of the Association Between restaurant type and spending per tourist Categories in culinary tourism restaurants

Project info

• Field of studyTourism and Hospitality
• LevelMaster's thesis (S2)
• Method typeInferential Statistics
• Estimated build time± 5 working days
• Toolsscipy.stats chi2_contingency, pandas.crosstab
• PriceUS$25 - US$147

Research goal

Test the association between restaurant type and categories of spending per tourist in culinary tourism restaurants with a Chi-Square test on a contingency table.

Variables

Example result

• 29.714Chi-square
• <0.001Chi-square p-value
• 4Degrees of freedom
• 0.198Cramer's V
• 42Min expected count
• 378Sample size N

The chi-square test between restaurant type and spending per tourist levels shows an association (chi2 = 29.714, df = 4, p < 0.001) with a Cramer's V of 0.198.

Computed from illustrative synthetic data, not from a real study.

About Chi-Square Test

The Chi-Square test examines the association between two categorical variables in a contingency table, or the fit of a distribution to expected proportions. It suits categorical survey data such as gender and response choice.

Steps

1. 1Build a contingency table from two categorical variables
2. 2Compute expected frequencies and check the condition (at least 5)
3. 3Run the chi-square test of independence
4. 4Obtain the statistic, degrees of freedom, and p-value
5. 5Compute effect size (Cramer's V)
6. 6Plot stacked bars or a residual heatmap

Assumptions to check

• Observations are independent
• Expected frequency in each cell is at least 5 (otherwise use Fisher's exact test)
• Data are counts, not percentages

Reading and reporting the results

Report chi-square, degrees of freedom, sample size, p-value, and Cramer's V, plus the cells with the largest standardised residuals. Stress that association does not imply causation.

Example Python code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

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

rng = np.random.default_rng(27)
n = 300
restaurant_type = rng.choice(["Male", "Female"], n)
p_yes = np.where(restaurant_type == "Female", 0.65, 0.45)
spending_per_tourist = np.where(rng.random(n) < p_yes, "Agree", "Disagree")

tab = pd.crosstab(restaurant_type, spending_per_tourist)
chi2, p, dof, exp = st.chi2_contingency(tab)
print(tab)
print("chi2 =", round(chi2, 3), "dof =", dof, "p =", round(p, 4))
print("Min expected frequency:", round(exp.min(), 2))

v = np.sqrt(chi2 / (tab.values.sum() * (min(tab.shape) - 1)))
print("Cramer V:", round(v, 3))
resid = (tab.values - exp) / np.sqrt(exp)
print("Standardised residuals:\n", resid.round(2))

tab.plot(kind="bar", stacked=True)
plt.ylabel("Count")
plt.show()

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions