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This different handling of missing observations led to the slightly different results seen across data sources.
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With such data, removing participants with missing observations from the analysis will lead to a biased sampling of the study population, and for this reason we chose multiple imputation as the approach to handle missing data.
*Level of university education: 10 missing observations in Germany and 6 missing observations in Austria.
If this is not the case, defining dummy codes for missing observations may be questionable from a theoretical point of view (Schafer & Graham [2002]) and may also lead to biased mean estimates (Rutkowski, [2011]).
Third, all relevant variables are screened for missing observations.
The last observation carried forward (LOCF) was used to analyze missing observations.
*Three missing observations.
Some variables have missing observations.
cN = 3 with missing observations.
dN = 6 with missing observations.
*Between 0 3 missing observations per period.
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