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Nevertheless, the reconstruction artifacts caused by (i) the missing data issue (this is the missing wedge/pyramid problem) and (ii) the discretization problem, can only be attenuated through more sampling, and in some extent by computationally intensive iterative reconstruction methods.
To address missing data issues, the full information maximum likelihood (FIML, Muthén and Muthén 1998 2012) was used under the assumption of missing at random (MAR).
In addition to addressing the issue of exposure imprecision, the SEM model addresses multiple testing and missing data issues that may not be adequately considered by standard regression analyses (Budtz-Jørgensen et al. 2002).
If genotypes of markers flanking a missing data point do not match, the missing data point is left missing.
We addressed these issues by including a frailty term in the model to account for correlated responses among employees and through multiple imputation techniques to handle the missing data.
Missing data were addressed through the GEE estimating mechanism which uses all available pairs method to encompass the missing data from dropouts or intermittent missing.
The second approach employed for handling addressing missing data is through GSEM's maximum likelihood estimation procedures in Stata 13.
In both cases, however, the problem is one of addressing the issue of missing data – that is, TALIS is missing student level data available from PISA, and PISA is missing teacher level data available in TALIS.
To address this challenge, missing data were completed through informal interviews with key informants.
A special feature is the use of multiple imputation to address the issue of missing data and the inclusion of a sensitivity analysis.
We addressed the problem of missing data by using FIML estimation, which allowed us to include all available data points in the analysis.
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