CIMPUTE= impute values for missing data |
PCA Analysis is done for Table 23 and Table 24, When there are missing values in the data, there are two options:
Data type: PRCOMP= |
CIMPUTE=Yes missing data becomes |
CIMPUTE=No
|
S or Y: Standardized residuals |
value: 0.00 |
missing data skipped |
R: Raw score residuals. Yen's Q3 |
value: 0.00 |
missing data skipped |
L: Logit residuals |
value: 0.00 |
missing data skipped |
O: Scored observations |
missing data skipped |
|
K: Observation probability |
missing data skipped |
|
H: Observation log-probability |
missing data skipped |
|
G: Observation logit-probability |
missing data skipped |
|
For more about PCA with missing data, see Principal component analysis of incomplete data – A simple solution to an old problem
Example: Using Exam5.txt (computer-adaptive test)
Help for Winsteps Rasch Measurement and Rasch Analysis Software: www.winsteps.com. Author: John Michael Linacre