Abstract:

Missing data are not uncommon in longitudinal studies. Different techniques for handling missing data affect accuracy of the results and validity of statistical inference. Firstly, we will elaborate on missingness mechanism and how to judge them. Then we make a summary of missing data techniques that mainly used in longitudinal study, and how to choose an appropriate missing data technique as well as software for analysis. Secondly, based on a literature review of psychology research in China, among 92 studies, we found that 59 contain a certain degree of missing data. Among these, 39 studies reported using deletion method. The validity of missing data techniques needs further study, and the reporting of missing data in published research also needs to be better established.

Key words: longitudinal study, missing data, missingness mechanism, missing data technique