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Attrition bias is one of the major threats to multiwave studies, and it can bias the sample in two ways. First, attrition bias can affect the external validity of the study. If some groups of people drop out of the study more frequently than others, the subsequent longitudinal sample no longer resembles the original sample in the study. As a result, the remaining sample is not generalizable to the original population that was sampled. For example, a longitudinal sample examining the grieving process of women following the death of a spouse may fail to retain those participants who have become too distraught to fill out the questionnaire. The nonparticipation of this group may bias the findings of the study toward a minimization of depressive symptomatology as a component of the grieving process. In other words, the composition of the sample changes to the point that the results are no longer generalizable to the original population of widows.