Please create a response to this discussion of 200 words
Week 7 Discussion
Francy Perez Rodrguez
Miami Regional University
Advanced Nursing Inquiry and Evidence Based Practice
Master of Science in Nursing. Family Nurse Practitioner
Professor: Dr. Emelio Garcia DNP, FNP-BC
February 19/2026
The Pearson ChiSquare Test in Nursing Research
The chisquare test (Pearson test) is a non-parametric statistical test that is one of the most widely used tests of two categorical variables in health and nursing research Parametric statistical tests usually rely upon having an interval scale of measurement and an assumption of normality being fulfilled, and so the chi square test is often used when the data are nominal or categorical The distribution is also useful for testing whether the frequencies or counts of outcomes between two or more groups are different without making assumptions about their distribution
The aim of a Pearson chisquare test is to determine whether one of more of the observed frequencies of one or more cells differs from what we would expect under the null hypothesis that the two groups are not associated It is effectively asking whether the distribution of the categories (eg the outcome rate) is different than random For example, to test whether there’s a difference in the rate of wound infection between groups of subjects wearing one type of bandage compared to one wearing a different type of bandage, a chi-square test would be appropriate
Customarily, chi-square tests can be divided into three categories, goodness-of-fit tests (for determining whether a categorical variable follows a population distribution), tests of independence (for determining whether two categorical variables are related) and tests of homogeneity (for determining whether a categorical variable is distributed differently between populations) The chi-square test of independence is the most commonly used chi-square test in nursing research (eg, chi-square tests to determine whether patient education level is associated with adherence to a care protocol)
The Pearson chisquare test builds a contingency table by counting the number of observed occurrences of a variable in a given category or cell It then creates a test statistic by dividing the sum of the squares of the difference between observed and expected values for the cells by the expected value and comparing that result to a chi-square distribution A statistically meaningful association is present if this pvalue is smaller than a level of importance (often termed alpha or alpha level) commonly set at <005
Although useful, the Pearson chisquare test has some limitations when used in nursing research For example, the assumptions underlying the test must be satisfied including having expected counts be large enough The test also assumes that the observations are independent, ie the outcome of one subject does not affect the outcome of another Where these assumptions are not met, Fisher’s exact test may be more appropriate
Conclusion The Pearson chisquare test is an important analytic method in nursing and healthcare research studies When appropriately used, the Pearson chi-square test allows nurses and other healthcare researchers to determine statistically meaningful relationships in categorical data and use that information to make decisions based on the probability that those associations are not due to chance
References
McHugh, M. L. (2013). The Chisquare test of independence: A nonparametric tool for nominal data. PMC.
Valarmathi, S., Hemapriya, A. S., & Sundar, J. S. (2024). Chisquare tests: A quick guide for health researchers. International Journal of Advanced
Research.
Kim, H. Y. (2017). Chisquared test and Fishers exact test.
PMC.
Requirements: 200
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