Click the “ Output Variable” name box and type a name for your new dichotomous variable.
Select the variable you want to recode, and then click the arrow, which moves the variable name into the box labeled “ Numeric Variable.” This opens a window that displays the variables in your data set.
Click the “ Transform” menu at the top of the SPSS data sheet, then select “ Recode Into Different Variable,” because you will transform the categorical variable into one or more dichotomous or dummy variables. A person with a zero in these categories would be counted as independent). 
A person who identifies as one of these would be coded a “1” in the data set. For example, a categorical variable on political affiliation with three categories - Democrat, Republican and Independent - would be dummy coded into two dichotomous variables, such as Democrat and Republican. (Note the number of categories, remembering that dummy coding transforms a variable with “n” categories into “n-1” categories.
Select the categorical variable that you want to dummy code. Let’s walk through the steps! Dummy Coding Step by Step This process is known as “dummy coding.” IBM SPSS makes dummy coding an unpretentious practice. An example would be employed and unemployed. These steps involve coding a categorical variable into multiple dichotomous variables, in which variables take the value of “1” or zero.įor clarity, a dichotomous variable is defined as a variable that splits or groups data into 2 distinct categories. Including these variables in an analytical model requires special steps to ensure the results can be interpreted properly. Statistical Analysis often include s variables in which the numbers represent qualitative categories (such as gender, ethnicity or political affiliation). If we consider a morning latte example, we might note the following: The Independent_Dicotomicvariable, on the other hand, is not statistically significant in order to predict the model (0.125> 0.05).With some guidance, you can craft a data platform that is right for your organization’s needs and gets the most return from your data capital. The p-value associated with the explanatory variable ‘Independent_Continuous’ is statistically significant at 5% (0.019 0) inclines the probability of occurrence towards the value defined as 1 for the dependent variable of dichotomous response. The p-value associated with the explanatory variable ‘AGE’ is statistically significant (0.058 0) inclines the probability of occurrence towards the value defined as 1 for the dichotomous response dependent variable. With the Hosmer-Lemeshow test, the logistic model is considered potable (0.631> 0.05), and explains 53.9% of the variability from the Nagelkerke R2 value, with these 3 variables becoming part of the same. Recode into different variables with SPSS, variable with 6 categories is recoded into 5 dummies variables, taking as reference the first category or a concret category… We proceed to generate 2 new dummies variables, using the Transform: Recode into different variables. 
If the individual belongs to the Primary category, we give 0 value to each of these 2 dummies created. Then by having 3 categories in the variable level of studies, we generate 2 variables, Secondary and University. The coding scheme that is used to create, and therefore, recode into a dummy a categorical or ordinal variable with more than 2 categories ( example: Level of Studies), taking into account that the reference category is “Primary Studies”, so that it can become part of the binary logistic regression it is of the form, that is, 2 dummy variables are generated for 3 categories, 3 for 4 categories, the omitted category remaining as reference variable: To be able to introduce a nominal variable of this type, which has 3 categories (or more), in the model we must resort to the “dummy” categorization, which consists of the generation of dummy dichotomous variables for the different categories of the variable.
0 kommentar(er)
