Expectation-Maximization Approach

Expectation maximization is an effective technique that is often used in data analysis to manage missing data (for further discussion, see Schafer, 1997 & Schafer & Olsen, 1998). Indeed, expectation maximization overcomes some of the limitations of other techniques, such as mean substitution or regression substitution. These alternative techniques generate biased estimates-and, specifically, underestimate the standard errors. Expectation maximization overcomes this problem.

Expectation maximization is applicable whenever the data are missing completely at random or missing at random-but unsuitable when the data are not missing at random.

Execution using SPSS

• Choose Missing Value Analysis from the Analyze menu.

• Transfer all numerical variables that are related to the study or issue into the box labelled Quantitative Variables. Exclude irrelevant variables, such as ID.

• Transfer all categorical variables that are related to the study or issue into the box labelled Categorical Variables

• Select the EM option

• Press the EM button, and select Save completed data.

• Choose Write a new data file. Press File and type a filename.

• Open this new file-which should include the data together with some of the missing data completed.

Exercise

Please download a data set here.