How does missing data cause bias?
Missing data present various problems. First, the absence of data reduces statistical power, which refers to the probability that the test will reject the null hypothesis when it is false. Second, the lost data can cause bias in the estimation of parameters. Third, it can reduce the representativeness of the samples.
What type of bias is introduced by missing data?
selection bias
Although missing data clearly lead to a loss of information and hence reduced statistical power, a more insidious consequence is that this lack of data may introduce selection bias, which could potentially invalidate the entire study.
Is missing data information bias?
Missing data can be a major cause of information bias, where certain groups of people are more likely to have missing data. An example where differential recording may occur is in smoking data within medical records. The bias was more likely when the exposure is dichotomized.
Can missing value imputation cause bias?
Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness.
Why is missing data such a difficult problem when modeling?
Missing data can be treacherous because it is difficult to identify the problem. This means that in the end, you may not have enough data to perform the analysis. For example, you could not run a factor analysis on just a few cases.
Can a nmar model explain the missing data?
With NMAR, valid statistical inferences can only be obtained by correctly modeling the mechanism for the missing data. Including variables that help explain probability of missing data makes MAR more reasonable.
How are nmar and Mar similar and different?
NMAR is like MAR in that the missingness is related to what is happening in your study, but differs in that the data that are related to the missingness is included in the data that are missing.
What does MCAR mean in Bayesian data analysis?
MCAR means that the probability of a missing response (denoted as R) is unrelated to anything of interest in the research question. For example, for the left graph in Figure 2, Z maybe some haphazard events such as interviewers accidentally erase responses for some people, which we believe to be unrelated to participants’ ages or voting intentions.
Which is an example of missing data in Mar?
The plot on the bottom left panel of Figure 1 is an example, with the missing cases being grayed out. As can be seen, when data are MAR, the distributions of X are different for groups with and without missing Y values. Also, the distributions of the observed Y values differ systematically from the complete data.