## Can you use Chi-square for 3 groups?

First, when you perform Chi-square test for the three groups together, you will get general idea about the differences between groups. Then you can perform the sub-effect test between only the group that had significantly higher prevalence with the other groups.

**Can Chi-square test be used for more than two categories?**

Chi-square can also be used with more than two categories. For instance, we might examine gender and political affiliation with 3 categories for political affiliation (Democrat, Republican, and Independent) or 4 categories (Democratic, Republican, Independent, and Green Party).

**What do you do after chi square?**

Following a Chi-Square test that includes an explanatory variable with 3 or more groups, we need to subset to each possible paired comparison. When interpreting these paired comparisons, rather than setting the α-level (p-value) at 0.05, we divide 0.05 by the number of paired comparisons that we will be making.

### What would a chi square significance value of P 0.05 suggest?

What is a significant p value for chi squared? The likelihood chi-square statistic is 11.816 and the p-value = 0.019. Therefore, at a significance level of 0.05, you can conclude that the association between the variables is statistically significant.

**What is the critical value in a chi-square test?**

In general a p value of 0.05 or greater is considered critical, anything less means the deviations are significant and the hypothesis being tested must be rejected. When conducting a chi-square test, this is the number of individuals anticipated for a particular phenotypic class based upon ratios from a hypothesis.

**What are the two types of chi square tests?**

There are two main kinds of chi-square tests: the test of independence, which asks a question of relationship, such as, “Is there a relationship between student sex and course choice?”; and the goodness-of-fit test, which asks something like “How well does the coin in my hand match a theoretically fair coin?”

## What are the two types of chi-square tests?

**What is difference between chi-square and t-test?**

A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero. A chi-square test tests a null hypothesis about the relationship between two variables.

**What is variable View in SPSS?**

The Variable View tab displays information about the variables in your data. You can get to the Variable View window in two ways: In the Data Editor window, click the Variable View tab at the bottom. In the Data Editor window, in the Data View tab, double-click a variable name at the top of the column.

### What is meant by 3 way classification?

A three-way ANOVA (also called a three-factor ANOVA) has three factors (independent variables) and one dependent variable. For comparison, a two-way ANOVA has two factors (e.g. time spent studying and prior knowledge) and one dependent variable.

**How do you calculate chi square test?**

To calculate chi square, we take the square of the difference between the observed (o) and expected (e) values and divide it by the expected value. Depending on the number of categories of data, we may end up with two or more values. Chi square is the sum of those values.

**How do you run a chi square test?**

How To Run A Chi-Square Test In Minitab 1. Select Raw Data: 2. View Data Table: 3. Go to Stat > Tables > Cross Tabulation and Chi-Square: 4. Click on the following check boxes: 5. Click OK 6. Click OK again:

## When to run a chi squared test?

Use the chi-square test of independence when you have two nominal variables and you want to see whether the proportions of one variable are different for different values of the other variable. Use it when the sample size is large.

**What is an example of a chi square test?**

The most popular chi-square test is Pearson ‘s chi-squared test and is also called ‘chi-squared’ test and denoted by ‘Χ²’. A classical example of chi-square test is the test for fairness of a die where we test the hypothesis that all six possible outcomes are equally likely.