Why is it most appropriate to compute a chi-square when comparing frequency of gender (male/female) and smoking status (smoker/nonsmoker)?
1. Gender and smoking status are both nominal-level variables in this example, and a chi-square requires nominal-level data.
2. Gender and smoking status, as defined in this case, are both ordinal-level data, and a chi-square requires ordinal-level data.
3. Chi-square is used in any research question where the independent variable is grouped according to gender.
4. An ANOVA is the most commonly used test with nominal/nominal-level data.
1
Rationale 1: Chi-square is probably the most commonly used statistic with nominal-level data.
Rationale 2: Chi-square requires nominal-level data, which are the type of data presented in this scenario.
Rationale 3: A t test for independent samples can be calculated with gender as the grouping or independent variable and an interval level score as the dependent variable.
Rationale 4: ANOVA requires nominal-level independent or group data but interval- or ratio-level data for the dependent variable.
Global Rationale:
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