NONMETRIC DATA

(nguồn: https://kplex-project.com/wp-content/uploads/2017/04/scales.png)

• One common situation faced by researchers is the desire to utilize nonmetric independent variables
• When the dependent variable is measured as a dichotomous (0, 1) variable, either discriminant analysis or a specialized form of regression (logistic regression) is appropriate. 
• What can we do when the independent variables are nonmetric and have two or more categories? 

(Nguồn: https://2.bp.blogspot.com/)

Indicator Coding: The Most Common Format. 

• Of the two forms of dummy variable coding, the most common is indicator coding in which each category of the nonmetric variable is represented by either 1 or 0. 
• The regression coefficients for the dummy variables represent differences on the dependent variable for each group of respondents from the reference category. 
• These group differences can be assessed directly, because the coefficients are in the same units as the dependent variable.
• This form of coding is most appropriate when a logical reference group is present, such as in an experiment. 

Effects Coding

• An alternative method of dummy-variable coding is termed effects coding.
• It is the same as indicator coding except that the reference group is now given the value of -1 instead of 0 for the dummy variables. 
• Now the coefficients represent differences for any group from the mean of all groups rather than from the reference group.
• Both forms of dummy-variable coding will give the same predictive results, coefficient of determination, and regression coefficients for the continuous variables. The only differences will be in the interpretation of the dummy-variable coefficients.
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(Nguồn: https://www.researchgate.net/)

Nguồn:

• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate data analysis (8th ed.). Boston: Cengage.
• https://kplex-project.com/2017/04/05/data-scales-in-applied-statistics-are-nominal-data-poor-in-information/
• https://isme.edu.in/start-from-the-scratch-step-by-step-guidelines-for-quantitative-data-analysis-in-social-science-research/