1.Step: Create a hypothesis: Those who have a very good health status are significantly more satisfied with their financial situation of household than those who have a good health status.
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: health_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 “very good” as a reference category because in my hypothesis I want to test if those who perceive their health status as very good are significantly more satisfied than those who perceive their health status as good.
4. Step Run the regression: Analyze – Regression – Linear
Independent: Good, Fair, Poor
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, “very good”.
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 the good health status to the very good health status, so the very good health status is the omitted category.
5.Step: Interpret the Coefficients table: b0, b1, b2, … check the p values
In order to make the example more simple in the following, I will refer to the satisfaction of the financial situation of the household as satisfaction and the subjective state of health as health status. (In social sciences and during the assignments you should always refer to the exact terms, since these terms do not measure the same things.)
At the end of every statement write down in parenthesis at which level is your finding significant. If the p value is equal to 0,000 then we usually write p<0,001, if the p value is not equal to 0,000, but it is less than 0,05 then write (p<0,05) and if the p value is above 0,05 write the exact value of the p, example: (p=0,457).
On average those whose health status is very good score 6.566 in terms of satisfaction. (p<0,001)
On average those whose health status is good score 0,499 less than those whose health status is very good in terms of satisfaction. (p<0,001)
On average those whose health status is fair score 1,195 less than those whose health status is very good in terms of satisfaction. (p<0,001)
On average those whose health status is poor score 2,232 less than those whose health status is very good in terms of satisfaction.. (p<0,001)
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 have a very good health status are significantly more satisfied with their financial situation of household than those who have a good health status.” (p<0,001)
We got to this conclusion because of the b1 coefficient and its p value. So, the coefficient of b1 (Good) is – 0,499, which means that those whose health status is good score 0,499 less in terms of satisfaction than those whose health status is very good and this result is significant (p<0,05), so we did not get this result by chance.UP