Title: Implications of missing data for analysis
Course: UGQuants (SSPS) > Section: Univariate Analysis

Implications of missing data for analysis

Defining values as 'missing' affects the presentation of data in a frequency table: this allows us to view the distribution of data for a variable both including and excluding those cases that take values defined as missing. If we choose the latter option, then we would describe those cases not included in our table as missing cases. When we present our data in this way we must always record how many cases are missing and why.

There are good reasons for doing this:

 | Missing cases inevitably reduce the overall number of cases on which our findings are based. Where there is a substantial number of missing cases this might reduces the confidence we can invest in our conclusions
 | Large numbers of missing cases may suggest a potential problem with a variable that might reduce our confidence in the data even further. For example, perhaps a survey question asked for an unreasonable level of recall, or was so poorly worded that most people could not understand the question well enough to answer. So knowing why as well as how many cases have been defined as missing is important.

If we are not transparent about missing data then this may give rise to misleading or perhaps even dishonest presentation of data. This is perhaps best exemplified initially through reference to market research and advertising. For example, consider the implications of the difference between the following two (real) advertising slogans for tinned cat food:

1: 'Whiskas: 8 out of 10 owners said their cats preferred it’

1: 'Whiskas: 8 out of 10 owners who expressed a preference said their cats preferred it’

Image courtesy of www.architectures.danlockton.co.uk/2012/02/09/if/

Slogan 2 alerts us to the possibility that (the earlier) slogan 1 might have been based on data where many cat owners (perhaps even the vast majority) did not believe their cats had any preference for any particular brand of cat food. Apparently, the slogan was changed only after a complaint to the Advertising Standards Authority.

Even with the second slogan it is unclear just how many owners actually expressed a preference: we are not told what proportion of cat owners were classified as 'missing' because they thought the question irrelevant!

We can apply a similar argument to data from social and political research. Consider the following examples:

Missing data can also affect key measures for continuous as well as categorical data. In the 2010 ESS, the mean number of months respondents were unemployed during the last 3 years was approximately 2. But if we exclude those who were never unemployed, this changes drastically to more than 11 months.

Missing cases can also have profound effects when our analysis includes more than one variable. In these instances, any cases that take a value defined as missing for any of the variables included in our analysis will be excluded from the analysis. Once more, this obviously has the effect of reducing the overall number of cases in our analysis, and again we should always be transparent about the number and nature of such missing cases.