Abstract
The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.
Highlights
The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings
Several research practices still prevailed from a decade ago, namely, not explicitly acknowledging the presence of missing data, not describing the particular approach used in dealing with missing data, and not testing assumptions associated with missing data methods. These findings suggest that researchers in educational psychology have not fully embraced principled missing data methods in research
When the normality assumption was violated, Enders (2001) reported that (1) full information maximum likelihood (FIML) provided unbiased estimates across different missing rates, sample sizes, and distribution shapes, as long as the missing mechanism was missing completely at random (MCAR) or missing at random (MAR), but (2) FIML resulted in negatively biased SE estimates and an inflated model rejection rate
Summary
The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. The literature on missing data methods often suggests including additional variables into a statistical model in order to make the missing data mechanism ignorable (Collins et al 2001; Graham 2003; Rubin 1996). Multiple Imputation (MI) MI is a principled missing data method that provides valid statistical inferences under the MAR condition
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