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In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data. Missing data can occur because of nonresponse: no information is provided for several items or no information is provided for a whole unit. Some items are more sensitive for nonresponse than others, for example items about private subjects such as income. Dropout is a type of missingness that occurs mostly when studying development over time. In this type of study the measurement is repeated after a certain period of time. Missingness occurs when participants drop out before the test ends and one or more measurements are missing. Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry.〔Ader, H.J., Mellenbergh, G.J. 2008〕 Data often are missing in research in economics, sociology, and political science because governments choose not to, or fail to, report critical statistics. ==Types of missing data== Understanding the reasons why data are missing can help with analyzing the remaining data. If values are missing at random, the data sample may still be representative of the population. But if the values are missing systematically, analysis may be harder. For example, in a study of the relation between IQ and income, participants with an above-average IQ might tend to skip the question ‘What is your salary?’ Analysis may falsely show no association between IQ and salary, while in fact there may be a relationship. Because of these problems, methodologists routinely advise researchers to design studies to minimize the incidence of missing values.〔 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「missing data」の詳細全文を読む スポンサード リンク
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