McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are measured before and after a treatment or in matched-pair study designs.
Assumptions of McNemar's Test
For McNemar's test to produce valid results, the following assumptions must be met:
Applicability and Conditions
McNemar's test is particularly suited for the following situations:
McNemar's test is a valuable tool for analyzing paired nominal data, particularly in medical and psychological research, where pre-post designs and matched-pair studies are commonly used. By understanding and meeting the assumptions of the test, researchers can apply McNemar's test to draw reliable conclusions about differences in proportions between two related groups.
From Chapter 13:
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