The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models

A Monte Carlo simulation examined the performance of 4 missing data methods in structural equation models: full information maximum likelihood (FIML), listwise deletion, pairwise deletion, and similar response pattern imputation. The effects of 3 independent variables were examined (factor loading magnitude, sample size, and missing data rate) on 4 outcome measures: convergence failures, parameter estimate bias, parameter estimate efficiency, and model goodness of fit. Results indicated that FIML estimation was superior across all conditions of the design. Under ignorable missing data conditions (missing completely at random and missing at random), FIML estimates were unbiased and more efficient than the other methods. In addition, FIML yielded the lowest proportion of convergence failures and provided near-optimal Type 1 error rates across both simulations.

[1]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[2]  David E. Booth,et al.  Analysis of Incomplete Multivariate Data , 2000, Technometrics.

[3]  Jürgen Baumert,et al.  Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples. , 2000 .

[4]  Herbert W. Marsh,et al.  Pairwise Deletion for Missing Data in Structural Equation Models: Nonpositive Definite Matrices, Parameter Estimates, Goodness of Fit, and Adjusted Sample Sizes. , 1998 .

[5]  D P MacKinnon,et al.  Maximizing the Usefulness of Data Obtained with Planned Missing Value Patterns: An Application of Maximum Likelihood Procedures. , 1996, Multivariate behavioral research.

[6]  James L. Arbuckle,et al.  Full Information Estimation in the Presence of Incomplete Data , 1996 .

[7]  David Kaplan,et al.  The Impact of BIB Spiraling-Induced Missing Data Patterns on Goodness-of-Fit Tests in Factor Analysis , 1995 .

[8]  P. Roth.  MISSING DATA: A CONCEPTUAL REVIEW FOR APPLIED PSYCHOLOGISTS , 1994 .

[9]  Roger L. Brown.  Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods , 1994 .

[10]  R. Little.  Regression with Missing X's: A Review , 2011 .

[11]  Karl G. Jöreskog,et al.  Lisrel 8: User's Reference Guide , 1997 .

[12]  Bengt Muthén,et al.  On structural equation modeling with data that are not missing completely at random , 1987 .

[13]  P. Allison.  Estimation of Linear Models with Incomplete Data , 1987 .

[14]  Peter J. Cameron,et al.  Rank three permutation groups with rank three subconstituents , 1985, J. Comb. Theory, Ser. B.

[15]  A. Boomsma.  Nonconvergence, improper solutions, and starting values in lisrel maximum likelihood estimation , 1985 .

[16]  James C. Anderson,et al.  The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis , 1984 .

[17]  Carl T. Finkbeiner.  Estimation for the multiple factor model when data are missing , 1979 .

[18]  Jae-On Kim,et al.  The Treatment of Missing Data in Multivariate Analysis , 1977 .

[19]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[20]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[21]  R. R. Hocking,et al.  The analysis of incomplete data. , 1971 .

[22]  Y. Haitovsky.  Missing Data in Regression Analysis , 1968 .

[23]  T. W. Anderson.  Maximum Likelihood Estimates for a Multivariate Normal Distribution when Some Observations are Missing , 1957 .

[24]  S. S. Wilks.  Moments and Distributions of Estimates of Population Parameters from Fragmentary Samples , 1932 .

[25]  J. Baumert,et al.  Longitudinal and multi-group modeling with missing data , 2022 .