Question

We learned this week that a chi-square analysis has requirements for variables that are unique from the other analyses we've considered. For instance, the variables must be exhaustive, so they must include all possible answers. For this reason, something like yes and no are valid since those would be the only two options, unless you had someone who couldn't answer it for various reasons. Similarly, it needs to be mutually exclusive, so someone couldn't be in two categories at the same time. This should make sense as you want to see if there's a relationship between the variables and you need to compare the expected counts with the actual counts. This is a very unique kind of test as you're only able to see categories - you wouldn't be able to tell if someone was at the high or low end of a category, for instance. Therefore, while it provides a quick and easy way to see if there are general differences, you don't necessarily know how extreme the situations are. Do you feel it's more important to have the quick and easy categorical results, or have the more detailed numerical results, and why?

Answer 1

Answer:

The Chi Square measurement is usually utilized for testing connections between absolute factors. The invalid speculation of the Chi-Square test is that no relationship exists on the clear cut factors in the populace; they are independent.

How does the Chi-Square statistic work?

Calculating the Chi-Square statistic and comparing it against a critical value from the Chi-Square distribution allows the researcher to assess whether the observed cell counts are significantly different from the expected cell counts.

The calculation of the Chi-Square statistic is quite straight-forward and intuitive:

where

fe = the expected frequency if NO relationship existed between the variables

fo = the observed frequency (the observed counts in the cells)

Here

In such cases, increasingly point by point numerical outcomes are significant as one of the fundamental assumption  of Chi-Square is that the measurement  of estimate of the considerable number of factors is nominal  or ordinal. Along these lines, if an value  is at the high or low end of a class then Chi Square Test should  not be utilized for such cases.