Keywords
maximum likelihood estimation
full-information maximum likelihood
multiple imputation
planned missingness
psychometrics.
datos perdidos
máxima verosimilitud con información completa
imputación múltiple
diseños de datos perdidos
psicometría.
How to Cite
Abstract
Most of the social and educational data have missing observations due to either attrition or nonresponse.Missing data methodology has improved dramatically in recent years, and popular computer programs as well as software now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, many researchers still rely on old imputation techniques that can create biased analysis. This article provides conceptual introductions to the patterns of missing data. In line with that, this article introduces how to handle and analyze the missing information based on modern mechanisms of full-information maximum likelihood (FIML) and multiple imputation (MI). An introduction about planned missing designs is also included and new computational tools like Quark function, and semTools package are also mentioned. The authors hope that this paper encourages researchers to implement modern methods for analyzing missing data.References
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