![]() This means your results may not be generalizable outside of your study because your data come from an unrepresentative sample. Missing data are problematic because, depending on the type, they can sometimes cause sampling bias. Their data are MNAR because their health outcomes are worse, so your final dataset may only include healthy individuals, and you miss out on important data. Attrition bias means that some participants are more likely to drop out than others.įor example, in long-term medical studies, some participants may drop out because they become more and more unwell as the study continues. In longitudinal studies, attrition bias can be a form of MNAR data. ![]() Your sample may not end up being representative of your population. This type of missing data is important to look for because you may lack data from key subgroups within your sample. Some participants with low incomes avoid reporting their holiday spending amounts because they are low. Example: MNAR dataIn the new dataset, you also notice that there are fewer low values. Missing not at randomĭata missing not at random (MNAR) are missing for reasons related to the values themselves. Instead, some younger adults may be less inclined to reveal their holiday spending amounts for unrelated reasons (e.g., more protective of their privacy). It’s unlikely that the missing data are missing because of the specific values themselves. You notice that there are more missing values for adults aged 18–25 than for other age groups.īut looking at the observed data for adults aged 18–25, you notice that the values are widely spread. Example: MAR dataYou repeat your data collection with a new group. The likelihood of a data point being missing is related to another observed variable but not to the specific value of that data point itself. ![]() This type of missing data systematically differs from the data you’ve collected, but it can be fully accounted for by other observed variables. Missing at randomĭata missing at random (MAR) are not actually missing at random this term is a bit of a misnomer. When data are missing due to equipment malfunctions or lost samples, they are considered MCAR. In practice, it’s hard to meet this assumption because “true randomness” is rare. Therefore, you conclude that the missing values aren’t related to any specific holiday spending amount range.ĭata are often considered MCAR if they seem unrelated to specific values or other variables. However, you note that you have data points from a wide distribution, ranging from low to high values. Some people started answering your survey but dropped out or skipped a question. Example: MCAR dataYou note that there are a few missing values in your holiday spending dataset. ![]() These MCAR data are also unrelated to other unobserved variables. The missing values are randomly distributed, so they can come from anywhere in the whole distribution of your values. When data are missing completely at random (MCAR), the probability of any particular value being missing from your dataset is unrelated to anything else. You survey adults on how much they spend annually on gifts for family and friends in dollar amounts. Missing data systematically differ from the observed values.Įxample: Research projectYou collect data on end-of-year holiday spending patterns. Missing data are not randomly distributed but they are accounted for by other observed variables. Missing data are randomly distributed across the variable and unrelated to other variables. There are three main types of missing data. The reason for the missing data is important to consider, because it helps you determine the type of missing data and what you need to do about it. Missing data are errors because your data don’t represent the true values of what you set out to measure.
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