Support #390

how to deal with inapplicable values

Added by Jun Liu about 7 years ago. Updated almost 7 years ago.

Gundi Knies
Data documentation
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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 

Updated by Gundi Knies about 7 years ago

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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,


Updated by Gundi Knies about 7 years ago

  • Status changed from New to Resolved
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Updated by Gundi Knies about 7 years ago

  • Private changed from Yes to No

Updated by Gundi Knies about 7 years ago

  • Status changed from Resolved to Closed

Updated by Gundi Knies almost 7 years ago

  • Target version set to X M

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