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espanolIntroduccion. La imputacion multiple (IM) es uno de los metodos mas recomendados para sustituir valores perdidos en datos de investigacion. Este articulo se dedica al manejo de los valores perdidos en MES, analizando dos bases de datos de Psicologia Educativa y a recomendaciones para investigadores orientados a las aplicaciones. Metodo. Presentamos dos muestras de estudiantes (N = 589 y N = 621, respectivamente) de estudios anteriores que se dedicaron al autoconcepto, a las metas de aprendizaje y de evitacion, y al rendimiento en un tutorial de siete pasos. En los datos de las dos muestras producimos artificilamente un 20 y un 40 por ciento de valores perdidos. Luego analizamos estos datos utilizando (1) eliminacion de los casos (listwise) y modelos de ecuaciones estructurales (MES), (2) maxima verosimilitud con informacion completa (MVIC) con MES, y (3) IM con MES e agrupamiento de datos (Pooling). Por lo tanto los resultados proceden de un diseno de 2 x 3 x 3 condiciones. Resultados. Replicamos investigaciones anteriores para ilustrar una manera practica de combinar IM con MES e Pooling. Imputando valores multiples en las dos muestras, podemos confirmar la estructura supesta de la MES. Discusion. Recomendamos anadir variables para aclarar el mecanismo de datos perdidos,sobre todo para variables dependentes que se refieren a la motivacion. Estos tipos de variablespodrian indicar que los valores perdidos en variables dependentes estan en correlacion convariables independentes (por ejemplo interes) o con la variable dependente en si (por ejemplo falta de motivacion, con independicia de interes). EnglishIntroduction. Multiple imputation (MI) is one of the most highly recommended methods for replacing missing values in research data. The scope of this paper is to demonstrate missing data handling in SEM by analyzing two modified data examples from educational psychology, and to give practical recommendations for applied researchers. Method. We provide two examples (N = 589 and N = 621, respectively) based on previous studies of students’ self-concepts, mastery goals and performance avoidance goals, and a 7-step tutorial. Then, we produced 20% and 40% missing data under three missing mechanisms by these complete, genuine data sets. The resulting datasets were then analyzed by (1) listwise deletion and structural equation models (SEM), (2) full information maximum likelihood (FIML) with SEM, and (3) MI combined with SEM and pooling. Thus, the results stem from 2 × 3 × 3 conditions. Results. Previous research was replicated by illustrating a practical way to combine MI with SEM and pooling. The assumed factor structure was depicted in both examples with multiplyimputed values applied. Discussion. We suggest adding variables to clarify the missing data mechanism, especially for dependent variables as motivation. Such variables might indicate whether missing values in dependent variables are correlated with independent variables (e.g., interest) or the dependent variable itself (e.g. lack of motivation independently of interest).

Handling Missing Data in Structural Equation Models in R. A Replication Study for Applied Researchers