Statistical analysis with missing data
Statistical analysis with missing data
Modeling and control for nonlinear structural systems via a NN-based approach
Expert Systems with Applications: An International Journal
The stability of an oceanic structure with T-S fuzzy models
Mathematics and Computers in Simulation
Application of data mining to the spatial heterogeneity of foreclosed mortgages
Expert Systems with Applications: An International Journal
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
Modeling, control, and stability analysis for time-delay TLP systems using the fuzzy Lyapunov method
Neural Computing and Applications
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This study examines the impact of missing rates and data imputation methods on test dimensionality. We consider how missing rate levels (10%, 20%, 30%, and 50%) and the six missed data imputation methods (Listwise, Serial Mean, Linear Interpolation, Linear Trend, EM, and Regression) affect the structure of a test. A simulation study is conducted using the SPSS 15.0 EFA and CFA programs. The EFA results for the six methods are similar, and all results obtained two factors. The CFA results also fit the hypothesized two factor structure model for all six methods. However, we observed that the EM method fits the EFA results relatively well. When the percentage of missing data is less than 20%, the impact of the imputation methods on test dimensionality is not statistically significant. The Serial Mean and Linear Trend methods are suggested for use when the percentage of missing data is greater than 30%.