Setting Up Your Data

• 1). List the dependent variables (Red, Blue, Yellow) as row headings, starting in cell A2. This means that Red is entered in cell A2, Blue in cell A3 and Yellow in cell A4.
  
  
• 2). List the independent variables (Male, Female) as column headings, starting in cell B1. This means that Male is entered in cell B1 and Female is entered in cell C1.
  
  
• 3). Assume the following dataset for Males:
  
  Red 40%
  
  Blue 60%
  
  Yellow 0%
  
  
• 4). Assume the following dataset for Females:
  
  Red 30%
  
  Blue 30%
  
  Yellow 40%
  
  
• 5). Enter the datasets in the appropriate cells. This means that the Male (column B, independent variable) dataset is entered in the appropriate Color (dependent variable) row and the Female (column C, independent variable) dataset is entered in the appropriate Color (dependent variable) row.
  
  You now have a cross-tabular frequency analysis of nominal data.
  
  

Visualizing Your Data

• 1). Use the cross-tabular table method described above, along with pie and bar charts, to assist in descriptive or graphical analysis of data in nominal form.
  
  
• 2). Use a cross-tabs methodology to apply Inferential Analysis to nominal data to compare group effects using nominal data.
  
  
• 3). Use a cross-tabs methodology in conjunction with proper Chi-Square statistical tests to apply Inferential Analysis to analyze the relationship between two nominal data variables.
  
  
• 4). Use a logistics regression to model a response (dependent variable) using predictor variables (independent variables) where only two response values are possible. Use multinomial logistic regression if the response has more than two possible values.