Multilevel analysis with messy data.
This paper reviews applications of the method of multiple imputation to dealing with multilevel data that have several kinds of imperfections. These are classified into two broad categories: missing values and imprecise measurement (corrupted recording). The role of the model describing the data imperfections is emphasized. With multiple imputation, these imperfections and information about the processes underlying them can be taken into account. The inferences drawn exploit all the collected information and appropriately reflect the information contained in the data.
Longford
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NT
De Montfort University, James Went Building 2-8, The Gateway, Leicester LE2 5YL, UK.
D000465:Algorithms; D003625:Data Collection / Q000592:standards; D003627:Data Interpretation, Statistical; D019992:Databases as Topic / Q000592:standards; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D011786:Quality Control; D016008:Statistical Distributions