CHI-SQUARE TEST OF INDEPENDENCE IN TREATMENT AND EMPLOYMENT
The purpose of the of the relationship between the employment levels and treatment is to enable the organization to gauge the effectiveness of the program. An insight into the clients’ changes would enable the agency to know whether the program is a success.
Non-parametric tests do not require interval measurements and can also measure variables not normally distributed compared to parametric tests. The chi-square is an example of a non-parametric test, and since the variables in the study may not be normally distributed, it was a good choice of a test design. Chi-square tests can only be used on categorical variables (actual numbers) and not continuous variables or between categorical and continuous variables (Kent State University, n.d.). The variables, in this case, were employment levels and intervention which are definite. Measurements of the variables were taken as actual numbers representing the number of people in the sample study.
The null hypothesis stated that the research variables were equal in their proportions, with variable one (employment) and variable two (intervention program) equal. The null hypothesis essentially states that there is no effect of the treatment on the employment level (Seltman, 2018). However, the alternative hypothesis states that there is a difference in the variables’ proportions, essentially meaning that the treatment effectively increases employment rates. The total sample number was sixty, with a comparison group of thirty and a program participation group of thirty people. However, fifty-nine were valid, with one missing.
The null hypothesis states that a statistically significant chi-square statistic is the basis for rejection of the null hypothesis and is achieved by p < 0.05; from the statistics, p is found to be 0.003 (StatisticsSolution, n.d.). The chi-square statistic is significant and, therefore, proves a relationship between the employment levels and the treatment. The treatment is hence considered effective in the increase of of the participants.
References
Kent State University (n.d.). SPSS TUTORIALS: CHI-SQUARE TEST OF INDEPENDENCE.
StatisticsSolution (n.d.). Chi-Square Test of Independence.
Seltman, H.J. (2018). Experimental design and analysis.
