An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm

Author:

Shin Tacksoo,Long Jeffrey D.,Davison Mark L.

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics and Probability

Reference142 articles.

1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Sage.

2. Aittokallio, T. (2009). Dealing with missing values in large-scale studies: Microarray data imputation and beyond. Briefings in Bioinformatics, 2(2), 253–264.

3. Ajzen, I. (1987). Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social psychology. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 20, pp. 1–63). Academic Press.

4. Algina, J., & Moulder, B. C. (2001). A note on estimating the Jöreskog-Yang model for latent variable interaction using LISREL 8.3. Structural Equation Modeling, 8, 40–52.

5. Alkasawneh, Pan, & Green. (2007) Multiple imputation for missing data. A caution tale. Sociological Methods and Research, 28(3), 301–309.

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