The Chi-square Test is one of the most widely used statistical techniques for analyzing categorical data. It helps researchers, students, and businesses determine whether there is a significant relationship between variables or whether observed results differ from expected outcomes. From academic research to market surveys and quality control, the chi-square test plays a critical role in data-driven decision-making.
At Kinza Ashraf, we provide professional statistical analysis services using industry-standard software like SPSS and Microsoft Excel, ensuring accuracy, clarity, and compliance with academic and professional standards.
What Is a Chi-square Test?
It is a non-parametric statistical test used to compare observed frequencies with expected frequencies. It answers questions such as:
Is there a relationship between two categorical variables?
Does the observed data fit a theoretical distribution?
Are differences between groups statistically significant or due to chance?
Because it does not rely on assumptions of normal distribution, the chi-square test is especially useful for categorical and nominal data.
Types of Chi-square Tests
1. Chi-square Test of Independence
This test checks whether two categorical variables are related.
Example use case:
Gender vs. product preference
Education level vs. employment status
2. Chi-square Goodness of Fit Test
This test determines whether observed data matches an expected distribution.
Example use case:
Customer preferences compared to equal distribution
Observed defects vs. expected defect rates
Chi Square Table Explained
The chi square table is used to find the critical value of the chi-square statistic based on:
Degrees of freedom (df)
Significance level (usually 0.05)
If the calculated chi-square value is greater than the table value, the result is statistically significant, and the null hypothesis is rejected.
In practice, professionals often rely on SPSS, Excel, or a chi square calculator, but understanding the chi square table is still important for interpretation and reporting.
How to Find Expected Frequency
One of the most common questions students ask is how to find expected frequency in a chi-square test.
The formula is:
Expected Frequency = (Row Total × Column Total) ÷ Grand Total
Hypothetical Example
Suppose a survey of 200 people records gender and preference for Online vs. Offline shopping.
| Online | Offline | Total | |
|---|---|---|---|
| Male | 70 | 30 | 100 |
| Female | 50 | 50 | 100 |
| Total | 120 | 80 | 200 |
Expected frequency for Male & Online:
(100 × 120) ÷ 200 = 60
Expected frequency for Female & Offline:
(100 × 80) ÷ 200 = 40
These expected values are then compared with observed values in the chi-square formula.
Chi-square Test Formula
The chi-square statistic is calculated as:
χ² = Σ (Observed − Expected)² ÷ Expected
Each cell’s value is computed, then summed to get the final chi-square value.
Because manual calculation can be time-consuming and error-prone, we recommend using SPSS, Excel, or a calculator for accuracy.
Chi Square Calculator
A chi square calculator automates calculations by:
Computing expected frequencies
Calculating chi-square values
Providing p-values
However, calculators alone do not interpret results or format reports. That’s where our professional service adds value.
Using Chi-square Test in SPSS
SPSS is one of the most trusted tools for statistical analysis.
Steps in SPSS
Enter categorical data in rows and columns
Go to Analyze → Descriptive Statistics → Crosstabs
Select variables
Click Statistics and choose Chi-square
Run the test
SPSS provides:
Chi-square value
Degrees of freedom
P-value
Assumptions check
Our team ensures correct interpretation and reporting according to academic standards.
Using Chi-square Test in Excel
Excel is widely used for basic statistical analysis.
Steps in Excel
Create a contingency table
Calculate expected frequencies
Use the
CHISQ.TEST()functionInterpret the p-value
Excel is suitable for simpler projects, while SPSS is preferred for academic research. We support both platforms, depending on your requirements.
Hypothetical Example
Scenario
A company wants to know if customer satisfaction is related to service type.
| Service Type | Satisfied | Not Satisfied | Total |
|---|---|---|---|
| Online | 45 | 15 | 60 |
| In-store | 30 | 10 | 40 |
| Total | 75 | 25 | 100 |
After calculating expected frequencies and applying the chi-square formula, suppose:
χ² = 0.00
p-value = 1.00
Interpretation
Since p > 0.05, there is no significant relationship between service type and satisfaction.
Our service includes:
Clear hypothesis testing
Professional interpretation
Tables and explanations ready for submission
Why Choose Our Analysis Service?
Accurate calculations using SPSS and Excel
Proper use of chi square table and p-values
Clear explanation of results
Suitable for assignments, theses, and business reports
Plagiarism-free reports (if required)
FAQs
1. What is the purpose of a Chi-square Test?
This test checks whether observed data differs significantly from expected data or whether two categorical variables are related.
2. When should I use a Chi-square Test?
Use it when your data is categorical (nominal or ordinal) and you want to test relationships or distributions.
3. Can I use a calculator instead of SPSS?
Yes, but calculators only provide numbers. SPSS and Excel allow proper reporting, assumptions checking, and interpretation.
4. What software do you use for Chi-square Test analysis?
We use SPSS and Microsoft Excel, depending on project requirements.
5. How to place order for Chi-square Test service?
Placing an order is simple:
Contact us with your dataset or research question
Specify whether you need SPSS or Excel output
Confirm deadline and requirements
Receive professionally analyzed results with interpretation
Conclusion
The Chi-square Test is a powerful statistical tool for analyzing categorical data and testing relationships. Whether you need help understanding the chi square table, using a chi square test calculator, or performing analysis in SPSS or Excel, professional support ensures accuracy and confidence.
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