Statistical Comparison of differences in user satisfaction across interface versions Through Chi-Square Test

Project info

• Field of studyInformatics and Information Systems
• LevelMaster's thesis (S2)
• Method typeInferential Statistics
• Estimated build time± 5 working days
• Toolsscipy.stats chi2_contingency, pandas.crosstab
• PriceUS$25 - US$156

Research goal

Test differences or associations in user satisfaction score across interface version, accounting for usage experience, using Chi-Square Test at the 5% significance level.

Variables

Example result

• 33.021Chi-square
• <0.001Chi-square p-value
• 4Degrees of freedom
• 0.277Cramer's V
• 23.447Min expected count
• 215Sample size N

The chi-square test between Interface version and User satisfaction score levels shows an association (chi2 = 33.021, df = 4, p < 0.001) with a Cramer's V of 0.277.

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
interface_version = rng.choice(["Male", "Female"], n)
p_yes = np.where(interface_version == "Female", 0.65, 0.45)
user_satisfaction_score = np.where(rng.random(n) < p_yes, "Agree", "Disagree")

tab = pd.crosstab(interface_version, user_satisfaction_score)
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