Missing data
Missing Data
Missing data (pronunciation: /ˈmɪsɪŋ ˈdeɪtə/) refers to the absence of data values in a dataset, which can occur for a variety of reasons.
Etymology
The term "missing data" is derived from the English words "missing", meaning not present or included when expected or required, and "data", which refers to facts and statistics collected together for reference or analysis.
Types of Missing Data
There are three main types of missing data:
- Missing Completely at Random (MCAR): This occurs when the probability of missing values in a variable is the same for all samples.
- Missing at Random (MAR): This occurs when the probability of missing values in a variable depends on the information available in other variables but not on the variable itself.
- Not Missing at Random (NMAR): This occurs when the probability of missing values in a variable depends on the value of the variable itself.
Handling Missing Data
There are several methods for handling missing data, including:
- Listwise deletion: This method involves removing the entire record if any single value is missing.
- Pairwise deletion: This method involves removing the specific missing value, but not the entire record.
- Imputation: This method involves replacing the missing data with substituted values.
Related Terms
- Data Imputation: The process of replacing missing data with substituted values.
- Data Cleaning: The process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database.
- Data Analysis: The process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
External links
- Medical encyclopedia article on Missing data
- Wikipedia's article - Missing data
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