 The data cleaning process will actively search for errors in a planned way. Data screening will include searching for four types of oddities: outliers including inconsistencies, strange patterns in distributions, and unexpected analysis results. Screening methods will include browsing of data tables after sorting, printouts of variables not passing consistency checks, graphical exploration of distribution, frequency distributions and cross-tabulations and summary statistics. Once errors have been identified, we will go to previous stages of the data flow to see whether the value is consistently the same. If no error is found in the data entry phase, we will question the interviewer about what may have happened. If possible, we will contact the interviewee for repeated measurement. 