مطالعه شبیه سازی با استفاده از برنامه های CFAو EFA مبتنی بر تاثیر داده های از دست رفته در ابعاد آزمون

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%.

The different missing imputation methods do not have a significant impact on the test dimensionality when the percentage of missingness is low. However, when the percentage of missingness is high (>30%), it is suggested that the Serial Mean and Trend missing imputation methods be used, because both methods offer a better model fit than the other methods. We also conclude that the missingness has relatively little impact on the validity based on the internal structure, unless the missingness is large. There are some limitations of this study. We assume that the missingness mechanism is MCAR, and include only continuously observed items in this study. We would like to further consider MAR or NMAR missingness mechanisms and dichotomous observed items to investigate the effect of missing imputation methods on the test structure.