The chi-square test is a critical statistical measure for investigating the relationship between qualitative population variables. A population variable refers to descriptive measures of a group of elements; for example, variables related to obesity are height, weight, and age. Chi-square tests focus on the association between qualitative or categorical variables of research. The two types of chi-square tests, namely goodness of fit and test of independence are conducted in a four-step process in qualitative and quantitative research.
Chi-square testing is a systematic process involving four steps. The first phase is formulating the null and alternative hypotheses, which are statements proposing the type of relationship between the variables (Sharpe, 2015). The statistical information may either lead to the confirmation of the null hypothesis or its rejection and consequent acceptance of the alternate hypothesis. Therefore, the chi-square testing centers in the developed hypothesis. The second step in chi-square testing is to calculate the expected values. Mainly, the expected variables refer to the values of a population that would be observed that there was no relationship between the variables (Moore, 2017). Therefore, expected values are calculated for all variables involved in the study. Thirdly is calculating the chi-square statistic, which will be used as the primary basis of comparing data. Lastly, is determining the statistical significance of the chi-square test and interpreting the results.
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Although running the chi-square test of goodness fit and independence follows the same procedure, they significantly differ in their purpose. The goodness fit aims are used to determine whether the distribution of the observed population is consistent with the distribution of the expected or hypothesized population. An example of the goodness of fit test in a quantitative study is to determine whether the proportion of women with obesity is equal to the proportion of men with obesity. Conversely, the chi-square test of independence is used to determine the type of association between independent variables, for example, determining whether gender influences obesity development in the population (Moore, 2017). Through the chi-square analysis, the relationship between qualitative variables is reliably established.
References
Moore, D. S. (2017). Tests of chi-squared type. In Goodness-of-fit-techniques (pp. 63-96). Routledge.
Sharpe, D. (2015). Your chi-square test is statistically significant: now what?. Practical Assessment, Research & Evaluation , 20 .
