1.Step: Create a hypothesis: Those who are married are more satisfied with their financial situation of the household than those who are divorced.

Note: When you are writing your own research then hereby you have to check the assumptions of the regression and you might have to filter your data for a certain group of people. In this example, I did not filter the data.

2.Step: Create the Dummy Variables: Recode the categorical variables into dummy variables. Transform – Create Dummy variables.

Slide the variable that you want to recode as a dummy variable into the “Create Dummy Variables for:” box and give a name to your new dummy variable in the “Root Names (One Per Selected Variable)” box. In this case: marital_status

Note: Here it is important to mentions that this method is not always a good choice. If your main goal is to recode the variables in a different way then you have to use the Transform – Recode into Different Variables. Example: if you have 5 categories and you want to recode 1-4 to 1 and 5 to 0, then use Recode into Different Variables.

3. Step: Select the omitted category. I have selected the “married” as a reference category since in my hypothesis I want to test if those who are married are significantly more satisfied with the financial situation of their household than those who are divorced.

Note: The hypothesis has to be in line with the variables introduced in the regression. The omitted category is the one to which you want to compare the other categories to. In this case, we want to compare those who are divorced to those who are married, so the married category is the omitted category.

4. Step Run the regression: Analyze – Regression – Linear

Dependent: satisfaction

Independent: living together as married, divorced, separated, widowed, single

You only want to analyze the valid answers, so the dummy variables including “don’t know” and similar ones do not have to be put in the regression. When you have a categorical variable with more than two categories then you have to leave one category out, that is the omitted category, in this case, “married”.

5.Step: Interpret the Coefficients table: b0, b1, b2, … check the p values

b0: On average married people score 6.004 in terms of satisfaction.

b1: At first glance, those who are living together as married score 0.022 higher in terms of the satisfaction with the financial situation of their household than those who are married. Since the significance level is above 0,05 this result might be due by chance. So, we cannot state that those who are living together are more satisfied with their financial situation than those who are married. (p=0,512)

b2: Those who are divorced score 0,729 lower than those who are married in terms of satisfaction with financial household. (p<0,001)

b3: Those who are separated score 0,594 lower than those who are married in terms of satisfaction with the financial situation of their household. (p<0,001)

b4: Those who are widowed score 0,686 lower than those who are married in terms of satisfaction with the financial situation of their household. (p<0,001)

b5: At first glance, it seems like those who are single score 0,023 higher than those who are married in terms of satisfaction with the financial situation of their household, but because the p value is below 0,05 this result might be due by chance, so we cannot accept this statement. (p<0,001) Thus, we cannot state that those who are single are more satisfied with the financial situation of their household than those who are married. (p=0,236)

6.Step: Write down: Did the result support or refute your hypothesis?

In this section always repeat your previous hypothesis and state if the result supports or refutes your hypothesis. At the end of the sentence write the level of the significance. (p value).

In conclusion, the result supports my hypothesis stating that “Those who are married are more satisfied with their financial situation of the household than those who are divorced.” (p<0,001)

We got to this conclusion because of the b2 coefficient and its p value. So, the coefficient of b2 (Divorced) is – 0,729, which means that those who are divorced score – 0,729 lower in terms of satisfaction than those who are married and this result is significant (p<0,05), so we did not get this result by chance.