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McNemar’s Test is a statistical method used to analyze paired nominal data in order to determine whether there is a significant difference between two related groups or conditions. SPSS (Statistical Package for the Social Sciences) is a popular software used for data analysis. It offers various tools and tests for different types of data analysis, including McNemar’s Test. This test can be easily performed in SPSS, providing researchers with a reliable and efficient tool for analyzing paired nominal data. By inputting the appropriate data and following the necessary steps, SPSS can generate the results of the McNemar’s Test, allowing for a thorough and accurate analysis of paired nominal data.
Perform McNemar’s Test in SPSS
McNemar’s Test is used to determine if there is a statistically significant difference in proportions between paired data.
This tutorial explains how to perform McNemar’s Test in SPSS.
Example: McNemar’s Test in SPSS
Suppose researchers want to know if a certain marketing video can change people’s opinion of a particular law. They survey 25 people to find out if they do or do not support the law. Then, they show all 25 people the marketing video and survey them again once the video is over.
The following table shows the total number of people who supported the law both before and after viewing the video:
| Before Marketing Video | ||
|---|---|---|
| After Marketing Video | Do Not Support | Support |
| Do Not Support | 7 | 5 |
| Support | 3 | 10 |
To determine if there was a statistically significant difference in the proportion of people who supported the law before and after viewing the video, we can use the following steps to perform McNemar’s Test in SPSS:
Step 1: Enter the data.
First, enter the data as shown below:
Each row shows an individual’s ID, their stance before watching the marketing video, and their stance after watching the marketing video.
Step 2: Perform McNemar’s Test.
Click the Analyze tab, then Descriptive Statistics, then Crosstabs:
Drag the variable After into the box labelled Rows and the variable Before into the box labelled Columns. Then click the button labelled Statistics and make sure that the box next to McNemar is checked. Then click Continue. Then click OK.
Step 3: Interpret the results.
The first table displays the number of missing cases in the dataset. We can see that there are 0 missing cases in this example.
The second table displays a crosstab of the total number of individuals who did or did not support the law before and after watching the marketing video.
The third table shows the results of McNemar’s Test. The p-value of the test is .727. Since this value is not less than 0.05, we do not have sufficient evidence to say that the proportion of people who supported the law before and after watching the marketing video was statistically significant different.
A Note on the p-value Calculation
Given the following 2×2 table:
| Before Marketing Video | ||
|---|---|---|
| After Marketing Video | Do Not Support | Support |
| Do Not Support | A | B |
| Support | C | D |
SPSS uses the formula sing the formula (|B-C| – 1)2 / (B+C) to calculate the Chi-square test statistic.
In this example, the Chi-square test statistic is found to be (|5-3| – 1)2 / (5+3) = 1/8 = .125
The p-value corresponds to the Chi-Square test statistic with 1 degree of freedom. Using the , we find that a Chi-square value of .125 with 1 degree of freedom is equal to a p-value of .727.