Abstract:

So far, many researches have been conducted to investigate the impact of missing data on statistical analysis and various methods have been developed to deal with the problem. The methods based on removing observations with missing values from the dataset cause the sample size to drop dramatically and the statistical power of the analyzes to be decreased. Therefore, as an alternative solution, the estimation of missing values seized intensive attention of researchers. Among these methods, multiple imputation techniques are relatively more recent and provide better estimations. Considering the superiority of multiple imputation techniques, the aim of the current study is to investigate the effects of different multiple imptutation techniques on the model fit of confirmatory factor analysis. For this aim, datasets with the unidimensional structure were simulated to manipulate sample size, missing data mechanism, percentage of missing data, number of items and missing data imputation technique. The effect of multiple imputation techniqes was evaluated based on the difference of 𝜒² model fit statistics for complete datasets and imputed datasets. The results showed that, multiple impuation techniques provided better results than conventional regression based imputation. Those finding were discussed later and some recommendations were given for better testing applications.

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