Document Type
Article
Abstract
Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This paper explores this question by using simulated three-form planned missing data to assess analytic model convergence, parameter estimate bias, standard error bias, mean squared error (MSE), and relative efficiency (RE).Three models were examined: a one-time point, cross-sectional model with 3 constructs; a two-time point model with 3 constructs at each time point; and a three-time point, mediation model with 3 constructs over three time points. Both full-information maximum likelihood (FIML) and multiple imputation (MI) were used to handle the missing data. Models were found to meet convergence rate and acceptable bias criteria with FIML at smaller sample sizes than with MI.
Disciplines
Applied Statistics | Kinesiology | Statistical Models
Recommended Citation
Jia, F., Moore, E. W. G., Kinai, R., Crowe, K. S., Schoemann, A. M., & Little, T. D. (2014). Planned missing data designs with small sample sizes: How small is too small? International Journal of Behavioral Development, 38 (5), 435-452. doi: 10.1177/0165025414531095
Comments
Fan Jia, E. Whitney G. Moore, Richard Kinai, Kelly S. Crowe, Alexander M. Schoemann, Todd D. Little, Planned missing data designs with small sample sizes: How small is too small?, International Journal of Behavioral Development (38, 5) pp. 435-452. Copyright © 2014 The authors. Reprinted by permission of SAGE Publications. doi: 10.1177/0165025414531095