eng TQMP The Quantitative Methods for Psychology 1913-4126 2014-09-01 10 2 143 152 10.20982/tqmp.10.2.p143 article Weaver, Bruce bweaver@lakeheadu.ca 1 Maxwell, Hillary hmaxwell@lakeheadu.ca 1 Lakehead University Missing data is a frequent problem for researchers conducting exploratory factor analysis (EFA) or reliability analysis. The SPSS FACTOR procedure allows users to select listwise deletion, pairwise deletion or mean substitution as a method for dealing with missing data. The shortcomings of these methods are well-known. Graham (2009) argues that a much better way to deal with missing data in this context is to use a matrix of expectation maximization (EM) covariances(or correlations) as input for the analysis. SPSS users who have the Missing Values Analysis add-on module can obtain vectors ofEM means and standard deviations plus EM correlation and covariance matrices via the MVA procedure. But unfortunately, MVA has no /MATRIX subcommand, and therefore cannot write the EM correlations directly to a matrix dataset of the type needed as input to the FACTOR and RELIABILITY procedures. We describe two macros that (in conjunction with an intervening MVA command) carry out the data management steps needed to create two matrix datasets, one containing EM correlations and the other EM covariances. Either of those matrix datasets can then be used asinput to the FACTOR procedure, and the EM correlations can also be used as input to RELIABILITY. We provide an example that illustrates the use of the two macros to generate the matrix datasets and how to use those datasets as input to the FACTOR and RELIABILITY procedures. We hope that this simple method for handling missing data will prove useful to both students andresearchers who are conducting EFA or reliability analysis. https://www.tqmp.org/RegularArticles/vol10-2/p143/p143.pdf Exploratory factor analysis reliability analysis missing data expectation maximization SPSS