Contents
What kinds of variables are appropriate to use in factor analysis?
Linearity: Factor analysis is also based on linearity assumption. Non-linear variables can also be used. After transfer, however, it changes into linear variable. Interval Data: Interval data are assumed.
What is a continuous variable in Stata?
Although to Stata a variable is a variable, it is helpful to distinguish among three conceptual types: • A continuous variable measures something. The term “continuous” here is deliberately broad and includes variables that are discrete by convention (ages in years) or by definition (counts of people).
How are categorical variables handled in Stata data?
Stata handles categorical variables as factor variables; see [U] 11.4.3 Factor variables. Categorical variables refer to the variables in your data that take on categorical values, variables such as sex, group, and region.
Can you do confirmatory factor analysis on binary variables?
Let’s say that you have a dataset with a bunch of binary variables. Further, you believe that these binary variables reflect underlying and unobserved continuous variables. You don’t want to compute your confirmatory factor analysis (CFA) directly on the binary variables.
How can I do Cfa with binary variables?
The tetrachoric correlations are much closer to the original correlations among the continuous variables than the correlations among the binary values. For comparison purposes we will compute a CFA on the original continuous data. Next, we will create the SSD dataset and compute the CFA on the tetrachoric correlations.
How can I generate polychoric correlations in Stata?
In Stata we can generate a matrix of polychoric correlations using the user-written command polychoric. You can find and install the polychoric command by typing search polychoric in the Stata command window and following the directions the screen.