Title: Conventions for dealing with missing data
Course: UGQuants (SSPS) > Section: Univariate Analysis

Conventions for dealing with missing data

Defining data as missing is usually an active decision on the part of the researcher, the objective of which is to allow us to exclude from our analysis any cases that take missing values. Before doing so we usually assign values and value labels as we would with other categories of data. For example, it is common to use the values 8 or 88 to represent categories ‘don’t know’, ‘can’t choose’, or ‘no opinion’; and to use 9 or 99 to represent ‘NA’ (which can variously stand for ‘not answered’, ‘not available’, or ‘not applicable’). We might choose to use 88 rather than 8, or 99 rather than 9 where we have a variable that already has, e.g., 10 or more ‘valid’ values. For variables that are not relevant to some cases, we often use the value -1. Using negative values for missing data also comes in handy where we have variables with a great many possible positive values (such as age or income).

In all such instances it is imperative that we define the values in question as missing in the software package we are using. This is a common error: labelling a value as ‘missing’ and/or giving it a value code such as -1 will not make any difference unless you also define the value as missing.