Estimating the Reliability Coefficient of Tests in Presence of Missing Values

Background: The problem of missing values at the item level is common in studies using educational and psychological tests. The aim of the present work is to explore how the estimation of reliability is affected by missing values.

Method: Using real data, we simulated missing values in accordance with a "missing at random mechanism". Four factors were manipulated with the aim of checking their effect on the estimation of the reliability of the instrument: missing data mechanism, percentage of missing data in the database, sample size, and procedure employed for the treatment of missing values.

Results: The results show that the quality of estimations depends on the interaction of various factors. The general tendency is that the estimations are worse when the sample size is small and the percentage of missing values increases. Listwise is the worst procedure for treatment of the missing data in the simulated conditions.

Conclusions: It is concluded that with a small percentage of missing values one can obtain estimations that are acceptable from a practical point of view with all the procedures employed, except Listwise.