Suppose we had conducted an ANOVA, with individuals grouped by political affiliation (Republican, Democrat, and Other), and we were interested in how satisfied they were with the current administration. Satisfaction was measured on a scale of 1-10, so it was measured on a continuous scale. Explain what changes would be required so that you could analyze the hypothesis using a chi-square test. For instance, rather than looking at test scores as a range from 0 to 100, you could change the variable to low, medium, or high. What advantages and disadvantages do you see in using this approach? Which is the better option for this hypothesis parametric approach or non-parametric approach? Why?"
Stacey Warren - Expert brainly.com
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So, because of my background, i would never use a qualitative scale: such as low, medium high. Thus, I would always choose the ANOVA which has a continuous var. As they say at the end, with a \(\chi ^2\) you need a discreet variable, such as: Very Satisfied, Not Satisfied, etc.
Okay let me give you an example but I can't obviously use it because someone else already did. The independent variable is political affiliation, and it has 3 levels. The dependent variable is satisfaction of the current administration, and it is continuous. For performing a Chi-square test on these variables, we have to break satisfaction measurement to categories like low, medium, and high. The advantage of doing chi-square is that it is easier to compute. The disadvantage is that we could not see the continuous change of difference as a function of independent variables. Also, for people fall at the boundaries between two categories of satisfaction, it will be difficult to interpret the correlation effect on this sample. To me, if a dependent variable is measured in continuous scale, we should not break the data flow. In this case, ANOVA is a better approach.
That's pretty equivalent to what I typed. Perhaps, read both what I wrote and your example again and try and come up with something.