Question

A quasi-experiment was conducted to compare men's and women's attitudes about extramarital affairs. Men and women who are married were recruited to complete a survey about their attitudes. The researchers then compared scores on the survey for men and women. The appropriate statistical test to analyze the data in this study is ______

Answer 1

Answer:

An independent samples t-test

Step-by-step explanation:

We have two different groups and we want to test if the scores are equal or not , so the best appropiate mthod would be an independent t test. Here we show the steps to conduct this test.

Data given and notation  

$\bar X_{1}$ represent the mean for group men  

$\bar X_{2}$ represent the mean for group women  

Assuming these values for the remaining data:

$s_{1}$ represent the sample standard deviation for men

$s_{2}$ represent the sample standard deviation for women

$n_{1}$ sample size for the group men  

$n_{2}$ sample size for the group women  

t would represent the statistic (variable of interest)

$p_v$ represent the p value  

Concepts and formulas to use  

Suppose that we need to conduct a hypothesis in order to check if the mean are equal or not, the system of hypothesis would be:  

H0:$\mu_{1} = \mu_{2}$  

H1:$\mu_{1} \neq \mu_{2}$  

For this case is better apply a t test to compare means since we don't know the population deviations, and the statistic is given by:  

$z=\frac{\bar X_{1}-\bar X_{2}}{\sqrt{\frac{s^2_{1}}{n_{1}}+\frac{s^2_{2}}{n_{2}}}}$ (1)  

t-test: Is used to compare group means. Is one of the most common tests and is used to determine whether the means of two groups are equal to each other.  

Calculate the statistic  

We just need to replace in formula (1) and find the calculated value.    

Find the critical value

In order to find the critical value we need to take in count that we are conducting a two tailed test, and we need a significance level provided in order to find the critical region

Statistical decision

If our calculates value $t_{calculated}>t_{critical}$ or $t_{calculated}<t_{critical}$ we reject the null hypothesis. In other case we FAIL to reject the null hypothesis.