… the strengths of meta-analysis and structural equation modeling. Meta-analysis is useful in …
, whereas structural equation modeling allows researchers to test different theoretical models. …

… Chapter 12 is also a new contribution with Whitney Moore where we discuss various finite
mixture models (eg, latent profile/class models) to look for unknown heterogeneity (ie, unique …

… incorporated into the imputation regression model through a … 10 called this strategy multiple
imputation with missing … general strategy “sequential regression multiple imputation with …

… The choice between the two measurement models just mentioned should be based on …
dealing with missing data, including multiple imputation and full information maximum likelihood (…

… was included in the imputation model. Only MIte was … multiple imputation in the inverse
probability of treatment weighting context, MIte with the outcome included in the imputation model …

… When using multiple imputation, users often want to know how many imputations they need.
An old … In this section, we derive formulas for the number of imputations that are required for …

… scores calculation where the method fails when the two … Multiple Imputation (MI), defined
a conceptual framework for characterizing missing data that allows to distinguish various …

… of different imputation methods depends on the structure of the … sizes, our simulations show
that the imputation of up to 90% of … We conclude that multiple imputation of missing variance …

… Multiple imputation has been shown to be a useful strategy to handle missing data problems
and account for the uncertainty of imputation… We present multiple imputation methods based …

… Missing data handling Typical default: listwise deletion (ie, loss of information) Superior
alternative: multiple imputation prior to model estimation Typical default: automatically handled …