Do not Be Afraid of Missing Data: Modern Approaches to Handle Missing Information
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| 著者: | , , |
|---|---|
| フォーマット: | artículo original |
| 状態: | Versión publicada |
| 出版日付: | 2015 |
| その他の書誌記述: | 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. |
| 国: | Portal de Revistas UCR |
| 機関: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| 言語: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/18812 |
| オンライン･アクセス: | https://revistas.ucr.ac.cr/index.php/actualidades/article/view/18812 |
| キーワード: | missing data 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. |