The Chi-Square Test of Independence Essay
The chi-square (?2) test is one of the nonparametric inferential statistical tests that are used in nursing research. The test evaluates the relationship that exists between two categorical classes of data. In the event that the test shows no relationship, then it means the two classes of data are independent. This is the null hypothesis of the test (Creswell & Creswell, 2018; Statistics Solutions, 2022). Some of the research questions that can be answered by the chi-square statistic are: The Chi-Square Test of Independence Essay
Is there a statistically significant relationship between the gender of the nurse practitioner and malpractice claims made by patients?
Is there a relationship between the frequency of patient falls and the age of the patient?
Is there a relationship between the number of days admitted (length of hospital stay) and the incidence of hospital-acquired infections? The Chi-Square Test of Independence Essay
The Level of Measurement
The level of measurement with the chi-square statistic is at the nominal level of measurement. It is from the bivariate table that cross-tabulation is possible to allow for the comparison of the nominal variables. Testing for independence then involves comparing the observed response pattern from the cells with the expected pattern were the variables to be actually independent. After calculation the chi-square value arrived at is then compared with a critical value from the chi-square distribution table (BMJ, 2022; Creswell & Creswell, 2018; Shi et al., 2018; Statistics Solutions, 2022). It is at this point that it will be discovered by the researcher if there is a difference or similarity in the expected and observed cell counts.
The critical value is also referred to as the hypothesized value. As a nonparametric test, there are no assumptions that are made concerning the population at all. The degrees of freedom (d.f.) calculated from the bivariate table as well as the level of significance used are the two statistical parameters that are used to help compare the calculated value and the critical or hypothesized value (BMJ, 2022; Creswell & Creswell, 2018; Shi et al., 2018; Statistics Solutions, 2022). The Chi-Square Test of Independence Essay
The Formula
The statistical formula for the calculation of the chi-square statistic is ?2 = S [(O-E)2/E]. In its simplest form, a two-way contingency table will be used to calculate the chi-square statistic. The level of significance to be used has to be decided before the contingency table is populated and the chi-square statistic is calculated. For the most valid and reliable results, a significance level (a) of 5% is usually employed. The null hypothesis will state that there is no relationship while the alternative hypothesis will state that there is a relationship between the two categorical variables or data. Then the decision criterion will be to reject the null hypothesis is the calculated chi-square statistic is higher in value than the critical value or hypothesized value (BMJ, 2022; Creswell & Creswell, 2018; Shi et al., 2018; Statistics Solutions, 2022). If the comparison agrees with the decision criterion, then it will mean that indeed there is a statistically significant relationship existing between the two variables in question. The Chi-Square Test of Independence Essay
The chi-square test of independence can be applied to nursing research to draw inferences about variables and disease phenomena. It has been stated that the test of independence gauges if there is any positive relationship between two sets of data. An example of how this statistical test can be used in nursing is to determine if the body mass index (BMI) value is related to the number of comorbidities a person suffers from. That is, does a higher BMI mean more present comorbidities such as type II diabetes, hypertension, and hyperlipidemia? The Chi-Square Test of Independence Essay
References
British Medical Journal [BMJ] (2022). The chi squared tests. https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/8-chi-squared-tests
Creswell, J.W., & Creswell, J.D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches, 5th ed. Sage Publications, Inc.
Shi, D., DiStefano, C., McDaniel, H.L., & Jiang, Z. (2018). Examining chi-square test statistics under conditions of large model size and ordinal data. Structural Equation Modeling: A Multidisciplinary Journal, 25(6), 924?945. https://doi.org/10.1080/10705511.2018.1449653
Statistics Solutions (2022). Using chi-square statistic in research. https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/using-chi-square-statistic-in-research/ The Chi-Square Test of Independence Essay
