The effect of misssing data handling methods on goodness of fit indices in confirmatory factor analysis

The primary objective of this study was to examine the effect of missing data on goodness of fit statistics in confirmatory factor analysis (CFA). For this aim, four missing data handling methods; listwise deletion, full information maximum likelihood, regression imputation and expectation maximization (EM) imputation were examined in terms of sample size and proportion of missing data. It is evident from the results that when the proportions of missingness %1 or less, listwise deletion can be preferred. For more proportions of missingness, full information maximum likelihood (FIML) imputation method shows visible performance and gives closest fit indices to original fit indices. For this reason, FIML imputation method can be preferred in CFA.   Key words:  Missing data, goodness of fit, confirmatory factor analysis, incomplate data, and missing value.