How Do I Perform Chi-Square Tests in Excel Using SigmaXL?

Chi-Square Test & Association

1. Open Customer Data.xlsx. Click Sheet 1 tab. The discrete data of interest is Complaints and Customer Type, i.e., does the type of complaint differ across customer type? Formally the Null Hypothesis is that there is no relationship (or independence) between Customer Type and Complaints.


2. Click SigmaXL > Statistical Tools > Chi-Square Test & Association. Ensure that the entire data table is selected. If not, check Use Entire Data Table. Click Next.


3. Select Major-Complaint, click Rows Category >>; select Customer Type, click Columns Category >>.
   
   


4. Click OK. Results:
   
   
   
   With the p-value = 0.142 we fail to reject H0, so we do not have enough evidence to show a difference in customer complaints across customer types.
   
   Note: 9 out of 15 cells have expected counts less than 5. If more than 20% of the cells have expected counts less than 5 (or if any of the cells have an expected count less than 1), the Chi-Square approximation may be invalid. Use Chi-Square Test – Fisher’s Exact).
   
   Tip: Use Advanced Pareto Analysis and Excel’s 100% Stacked Column Chart to complement Chi-Square Analysis.
   
   Caution: When using stacked column format data with Ordinal Category variables that are text, SigmaXL will sort alphanumerically which may not result in the correct ascending order for analysis. We recommend coding text Ordinal variables as numeric (e.g., 1,2,3) or modified text (e.g., Sat_0, Sat_1).
   


5. Press F3 or click Recall SigmaXL Dialog to recall last dialog. Select Loyalty – Likely to Recommend, click Rows Category >>; select Sat-Discrete, click Columns Category >>. Click Options, check Ordinal Categories.
   
   
   
   Sat-Discrete is derived from Overall Satisfaction where a score >= 3.5 is considered a 1, and scores < 3.5 are considered a 0.


6. Click OK. Results:
   
   
   
   
   
   As expected, there is a strong positive association between Loyalty and Discrete Satisfaction. Note: Kendall-Stuart Tau-C should be used here rather than Tau-B because it is a rectangular table. Since Satisfaction leads to Loyalty, Loyalty is the Dependent variable, so Rows Dependent Somers’ D should be used rather than Cols Dependent or Symmetric.


7. In order to visualize the row and column percentages with Excel’s 100% Stacked Column Chart, we will need to modify the numeric row and column labels as shown, converting to text as shown:
   
   


8. Select cells A3:C8 of the Chi-Square sheet. Click Excel’s Insert > Insert Column or Bar Chart and select 100% Stacked Column as shown.
   
   


9. Click to create the 100% stacked column chart (uncheck the Chart Title):
   
   

Chi-Square Test (Two-Way Table Data)

1. Open the file Attribute Data.xlsx, ensure that Example 1 Sheet is active. This data is in Two –Way Table format, or pivot table format. Note that cells B2:D4 have been pre-selected.
   
   
2. Click SigmaXL > Statistical Tools > Chi-Square Test & Association – Two-Way Table Data. Note the selection of data includes the Row and Column labels (if we had Row and Column Totals these would NOT be selected). Do not check Advanced Tests and Measures of Association.
   
   
   
   See Appendix Chi-Square Tests and (Contingency) Table Associations for the Chi-Square Table Statistics formula details.


3. Click Next. The resulting output is:
   
   
   
   The p-value matches that of the 2 proportion test. Since the p-value of 0.1 is greater than .05, we fail to reject H0.


4. Now click Example 2 Sheet tab. The Yields have not changed but we have doubled the sample size. Repeat the above analysis. The resulting output is:
   
   
   
   Since the p-value is < .05, we now reject the Null Hypothesis, and conclude that Day Shift and Night Shift are significantly different. The Residuals tell us that Day Shift failures are less than expected (assuming equal proportions), and Night Shift failures are more than expected.
   
   Note, by doubling the sample size, we improved the power or sensitivity of the test.


5. Click the Example 3 Sheet tab. In this scenario we have 3 suppliers, and an additional marginal level. A random sample of 100 units per supplier is tested. The null hypothesis here is: No relationship between Suppliers and Pass/Fail/Marginal rates, but in this case we can state it as No difference across suppliers. Redoing the above analysis (for selection B2:E5) yields the following:
   
   
   
   The p-value tells us that we do not have enough evidence to show that there is a difference across the 3 suppliers.


6. Click the Example 4 Sheet tab. Here we have doubled the sample size to 200 per supplier. Note that the percentages are identical to example 3. Redoing the above analysis yields the following:
   
   
   
   With the P-Value < .05 we now conclude that there is a significant difference across suppliers. Examining the Std. (Standardized) Residuals tells us that Supplier A has fewer failures than expected (if there was no difference across suppliers), Supplier B has more marginal parts than expected and Supplier C has fewer marginal parts than expected.


7. The table row and column cell percentages can be visualized using Excel’s 100% Stacked Column Chart. Select cells A3:D6 of the Chi-Square sheet. Click Excel’s Insert > Insert Column or Bar Chart and select 100% Stacked Column as shown.
   
   


8. Click to create the 100% stacked column chart. Uncheck the Chart Title as shown.
   
   


9. The rows and columns can easily be switched by clicking Design > Switch Row/Column.
   
   
   
   


10. These charts make it easy to visualize the cell row and column percentages.

Chi-Square Test – Two-Way Table Data: Advanced Tests and Measures of Association – Nominal Categories

Checking the Nominal Categories option provides additional chi-square statistics and measures of association, including:

1. Press F3 or click Recall SigmaXL Dialog to recall last dialog. Check Nominal Categories as shown:
   
   


2. Click Next. The resulting output is:
   
   
   
   The adjusted residuals are equivalent to normal z values, so for a specified 95% confidence level, any value greater than 1.96 (or less than -1.96) is highlighted in red. This results in a slight difference in interpretation from that of the standardized residuals, but the 3 largest magnitude residuals are consistent.
   
   
   
   As noted above, the Chi-Square P-Value tells us that there is a significant difference across suppliers, in other words, there is association between Supplier and Pass/Fail/Marginal, but it does not tell us the degree or strength of that association. Cramer’s V is used for tables larger than 2x2 and from the rules-of-thumb, the 0.1 value is considered weak (or small effect).

Computing Odds Ratio and Confidence Interval for 2x2 Table

Odds Ratio and Confidence Intervals are not directly available for 2x2 Tables but can obtained using Logistic Regression. The table data must be rearranged to stacked column format and the response changed to “Event/Trial” format. Example 2 of Attribute Data.xlsx:



would be changed to:



Note, coding Shift as continuous numeric is easier to interpret and is valid because it has a range of 1.

Analyze using SigmaXL > Statistical Tools > Regression > Binary Logistic Regression. Select Response Count (Y) / Sample Size. Pass(Y) is selected as Numeric Response Count (Y), Trials (N) is Numeric Sample Size (Trials) and Shift (X) is selected as Continuous Predictors (X):



Click OK. The Odds Ratio and Confidence Limits are given as:



The shift change 1 to 2 is 0.583 times as likely to produce passed (good) product with a 95% confidence interval of 0.368 to 0.924.

Chi-Square Test – Two-Way Table Data: Advanced Tests and Measures of Association – Ordinal Categories

Checking the Ordinal Categories option provides statistics and measures of association appropriate when both row and column category variables are ordinal:

1. Open the file Attribute Data.xlsx, click Example 5 – Salary Sat Sheet tab. This data is in two-way table format and has ordinal categories: Salary in the Rows and Satisfaction Level in the Columns. Note that cells A1:E5 have been pre-selected.


2. Click SigmaXL > Statistical Tools > Chi-Square Tests > Chi-Square Test & Association – Two-Way Table Data. Note the selection of data includes the Row and Column labels (if we had Row and Column Totals these would NOT be selected). Check Nominal Categories and Ordinal Categories as shown:
   
   
   
   Tip: Even if the categories are ordinal, it is sometimes useful to select nominal categories as well for comparison purposes.


3. Click Next. The resulting output is:
   
   
   
   
   
   
   
   Note that the Chi-Square P-Value is 0.1, indicating that there is no significant association between Salary and Satisfaction when they are treated as nominal categories (although the significant result for McNemar-Bowker does show that there is lack of symmetry in the off diagonals).
   
   Since the Chi-Square P-Value is not significant, the Adjusted Residuals are not highlighted, even though some values are greater than 1.96 (and less than -1.96). This follows the concept used in ANOVA called “Fisher Protected” where one considers the significance of post-hoc tests only when the overall test is significant.
   
   Note: 4 out of 16 cells have expected counts less than 5. If more than 20% of the cells have expected counts less than 5 (or if any of the cells have an expected count less than 1), the Chi-Square approximation may be invalid, and Fisher’s Exact should be used. This will be discussed later, but for this example the Fisher’s Monte-Carlo Exact P-Value = 0.095 so does not change the interpretation of the results for the above Chi-Square analysis.
   
   When Salary and Satisfaction are treated as ordinal categories, the more powerful Concordant – Discordant and Spearman Rank Correlation P-Values clearly show that there is a significant association. The Measures of Association for Ordinal Categories show that this is positive, i.e., an increase in Salary is associated with an increase in Satisfaction. However, using the rules-of-thumb given above, we see that the association is weak.


4. The table row and column cell percentages can be visualized using Excel’s 100% Stacked Column Chart. Select cells A3:E7 of the Chi-Square sheet. Click Excel’s Insert > Insert Column or Bar Chart and select 100% Stacked Column as shown.
   
   


5. Click to create the 100% stacked column chart (uncheck the Chart Title):
   
   


6. The rows and columns can easily be switched by clicking Design > Switch Row/Column