Wednesday, 27 May 2015

Missing Data Analysis

The May Eco-Stats Lab (Friday 29th, 2pm, Bioscience level 6) will be on the missing data analysis, using the method of Multiple Imputation.

One often encounters missing data in almost all types of studies. Ecological data is also commonly subject to missing data. However, most of the statistical analysis methods are designed for complete datasets. A common way to handle missing data is to remove cases with missing values in order to obtain a complete dataset, which reduces the sample size and thus the statistical power. This approach can result in biased estimates for descriptive statistics and regression coefficients as well. An alternative approach is to impute (fill-in) the missing data by plausible values multiple times, analyse each imputed dataset separately, and then combine the results together. This method is called Multiple Imputation (MI) and was proposed by Rubin (1987).

In this lab we will explore the method of MI implemented in the mice package (van Buuren & Groothuis-Oudshoorn, 2011), which stands for multivariate imputation by chained equations. For more details see

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