本研究目的在以蒙地卡羅法，瞭解加入輔助變項對結構方程模型全訊息最大概似法之參數估計與卡方值之影響。結果發現，加入輔助變項後，全訊息最大概似法參數估計更佳，僅當輔助變項與遺漏原因無關時，可能產生較大之估計偏誤。研究者應判斷輔助變項與遺漏原因之關係，以決定是否於分析時納入。

The aim of this study is to explore the effect of adding auxiliary variables on parameter estimation and chi-squares statistic of full-information maximum likelihood method (FIML) in structural equation models. Results showed that FIML with auxiliary variables performed better in parameter estimation except when the auxiliary variables were uncorrelated with the causes of missingness. Researchers should consider the relationship between auxiliary variables and the cause of missingness before adding auxiliary variables in missing data treatment.