Item missingness
Presence and amount of missing data for each variable, separated by class of missingness (i.e., system missingness (NA), used missing codes and jump codes). The higher the number or percentage of missing values, the lower the data quality.
Note:
1. “ADDED: SysMiss” or “Sysmiss N” refers to NA’s (i.e., system-indicated missing values);
2. The percentage of all the columns in the table (except Measurements N (%)) are calculated over the Observation N. The percentage of Measurement N is calculated by dividing the value (Measurement N) by the difference between the number of observations and the number of jumps (Observation N - Jumps N).
Item Nonresponse-rate
The higher the rate, the lower the data quality. For further information, see here
Refusal-rate (Segment level)
The higher the rate, the lower the data quality. For further information, see here
Segment missingness
Check for patterns in missing segments, i.e., presence and abundance of groups of variables completely missing per observation unit. For example, if a patient decided to not join one examination, all the variables in that segment will be empty.
dataquieR 2.1.0