Rubin (1976, and elsewhere) claimed that there are three kinds of “missingness”: missing completely at random; missing at random; and missing not at random. He gave examples of each. The article that now follows takes an opposing view by arguing that almost all missing data are missing not at random
Modern missing data techniques, such as full information maximum likelihood (FIML) and multiple impu...
The classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581-592, 1976),...
often make use of tests and questionnaires to obtain data. When such instruments are applied, some p...
The terminology describing missingness mechanisms is confusing. In particular the meaning of ‘missin...
The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missin...
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice...
Missing data (a) reside at threemissing data levels of analysis (item-, construct-, and person-level...
We provide conceptual introductions to missingness mechanisms—missing completely at random (MCAR), m...
Abstract: When data are missing due to at most one cause from some time to next time, we can make sa...
Recent work (Seaman et al., 2013; Mealli & Rubin, 2015) attempts to clarify the not always well-unde...
Background: Missing data are common in end-of-life care studies, but there is still relatively littl...
Background: Missing data are common in end-of-life care studies, but there is still relatively littl...
BACKGROUND: Within epidemiological and clinical research, missing data are a common issue and often ...
Over the last decade a variety of models to analyse incomplete multivariate and longitudinal data ha...
Medical Research Council Clinical Trails Unit at UCL [Studentship to MS; MC EX G0800814 to JRC,TPM]
Modern missing data techniques, such as full information maximum likelihood (FIML) and multiple impu...
The classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581-592, 1976),...
often make use of tests and questionnaires to obtain data. When such instruments are applied, some p...
The terminology describing missingness mechanisms is confusing. In particular the meaning of ‘missin...
The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missin...
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice...
Missing data (a) reside at threemissing data levels of analysis (item-, construct-, and person-level...
We provide conceptual introductions to missingness mechanisms—missing completely at random (MCAR), m...
Abstract: When data are missing due to at most one cause from some time to next time, we can make sa...
Recent work (Seaman et al., 2013; Mealli & Rubin, 2015) attempts to clarify the not always well-unde...
Background: Missing data are common in end-of-life care studies, but there is still relatively littl...
Background: Missing data are common in end-of-life care studies, but there is still relatively littl...
BACKGROUND: Within epidemiological and clinical research, missing data are a common issue and often ...
Over the last decade a variety of models to analyse incomplete multivariate and longitudinal data ha...
Medical Research Council Clinical Trails Unit at UCL [Studentship to MS; MC EX G0800814 to JRC,TPM]
Modern missing data techniques, such as full information maximum likelihood (FIML) and multiple impu...
The classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581-592, 1976),...
often make use of tests and questionnaires to obtain data. When such instruments are applied, some p...
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