The miexample.do file includes Stata code illustrating implementation of the recommended multiple imputation (MI) reporting practices from the article Reporting the use of multiple imputation for missing data in higher education research, Research in Higher Education, doi: 10.1007/s11162-014-9344-9. After a brief description of the example used in the code (which uses a publicly available, downloadable dataset), a sample paragraph offers possible text for writing up results using MI to handle missing data in this example. In both the sample paragraph and the subsequent example Stata code, the recommended MI reporting practices identified in the article (Table 1) are highlighted. These recommendations are intended to help authors report MI...
Abstract. The method of multiple imputation (MI) is used increasingly for ana-lyzing datasets with m...
This book explores missing data techniques and provides a detailed and easy-to-read introduction to ...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing obse...
Stata's -mi- command can be used to perform multiple-imputation analysis, including imputation, data...
The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing obse...
Missing data are a common problem in organizational research. Missing data can occur due to attritio...
Carlin et al. (2003) illustrate the use of their Stata texttt for multiple imputations with data fro...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
A new set of tools is described for performing analyses of an ensemble of datasets that includes mul...
Higher education researchers using survey data often face decisions about handling missing data. Mul...
Multiple imputation is a recommended method for handling incomplete data problems. One of the barrie...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
BACKGROUND: Missing data are common in medical research, which can lead to a loss in statistical pow...
This article describes a substantial update to mvis, which brings it more closely in line with the f...
Abstract. The method of multiple imputation (MI) is used increasingly for ana-lyzing datasets with m...
This book explores missing data techniques and provides a detailed and easy-to-read introduction to ...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing obse...
Stata's -mi- command can be used to perform multiple-imputation analysis, including imputation, data...
The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing obse...
Missing data are a common problem in organizational research. Missing data can occur due to attritio...
Carlin et al. (2003) illustrate the use of their Stata texttt for multiple imputations with data fro...
Missing data is a common occurrence in clinical research. Missing data occurs when the value of the ...
A new set of tools is described for performing analyses of an ensemble of datasets that includes mul...
Higher education researchers using survey data often face decisions about handling missing data. Mul...
Multiple imputation is a recommended method for handling incomplete data problems. One of the barrie...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
BACKGROUND: Missing data are common in medical research, which can lead to a loss in statistical pow...
This article describes a substantial update to mvis, which brings it more closely in line with the f...
Abstract. The method of multiple imputation (MI) is used increasingly for ana-lyzing datasets with m...
This book explores missing data techniques and provides a detailed and easy-to-read introduction to ...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...