Imputing Missing Data: A Comparison of Methods for Social Work Researchers.
A study compared six methods of data imputation used to handle the problem of missing research data: listwise deletion; mean substitution; hotdecking; regression imputation, sometimes referred to as conditional mean imputation; and single implicate and multiple implicate data sets. Results suggest...
| Publicado en: | Social Work Research Vol. 30; no. 1; pp. 19 - 32 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
National Association of Social Workers
March 2006
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | A study compared six methods of data imputation used to handle the problem of missing research data: listwise deletion; mean substitution; hotdecking; regression imputation, sometimes referred to as conditional mean imputation; and single implicate and multiple implicate data sets. Results suggest that single and multiple implicate methods produce more accurate values than the other methods. The methods are compared and recommendations for dealing with missing data are presented. |
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