Support #390
closed
how to deal with inapplicable values
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Description
Dear support team
a lot of the variables in the Understanding Society dataset has a high percentage of inapplicable values. For example, the highest qualification variable, w_qfhigh, has around 85-90% of inapplicable values. How should I deal with the inapplicable values? Is inapplicable values the same as missing values? why is a variable like the highest qualification has so many inapplicable values? I would assume most of the people in the sample has some kind of education. Any other inputs on the questions will be much appreciated.
Thank you Jun
Updated by Gundi Knies over 9 years ago
- Category set to Data documentation
- Assignee set to Gundi Knies
- % Done changed from 0 to 90
Dear Jun,
we differentiate between a number of different sources of missingness. The full listing is reported in Table 19 of the Understanding Society User Guide (2014).
You will see in the questionnaire routing that _qfhigh is only asked when a respondent first enters the study, hence the large number of [-8 missing] from Wave 2 onward. Differences between _qfhigh and _qfhigh_dv are explained in the variable level descriptions of the respective variables. _qfhigh_dv is a derived variable and the variable level description includes references to which variables are used in its construction. See, for example:
Hope this helps,
Gundi
Updated by Gundi Knies over 9 years ago
- Status changed from New to Resolved
- % Done changed from 90 to 100
- Private changed from No to Yes