Categorical Predictor/Dummy Variables using SPSS
Categorical Predictor/Dummy Variables
Categorical variables are those variables where the values of a particular variable are in the form of discrete categories. For example Job Rank (Junior, Middle, Senior), Country of Origin (Pakistan, USA, Spain), or Income Level (Low, Middle, High). In order to add a categorical variable as a predictor in the regression analysis, the categories should be converted into a dummy variable. Each category represents a variable.
Dummy variables (also called indicator variables) are categorical variables that have only two values, 0 and 1. For example, if we would like to use Job rank as a categorical predictor variable in our study, we can create two dummy variables i-e Junior and Middle (the third category serves as a reference category). Each of the two categories (Junior and Middle) will have the value 0 and 1. Where 1 would represent that the respondent belongs to the category and 0 denotes otherwise.
The concept of dummy variables and how to use them in SPSS regression is explained in detail in the following video.
Step by Step guide on how to use Categorical Predictor/Dummy Variables in SPSS
Additional SPSS Tutorials
- Binary Logistic Regression Analysis in SPSS
- Crosstabulation and Chi-Square Test using SPSS
- Data Screening and Handling Missing Data using SPSS
- How to Check Linear Relationship in SPSS
- How to Perform Exploratory Factor Analysis using SPSS
- How to Perform One Way ANOVA
- How to Run, Interpret, and Report Descriptive Statistics using SPSS
- Identifying and Correcting Data Entry Errors in SPSS
- Independent Samples T-Test using SPSS
- Mann Whitney U Test using SPSS
- Partial Correlation Analysis using SPSS
- Pearson Correlation Analysis using SPSS
- Regression Analysis using SPSS: Concept, Interpretation, Reporting
- Transform Continuous Variables into Categorical Variables using SPSS