Evaluating Paired Categorical Data when the Pairing is Lost

We encountered a problem in which a study's experimental design called for the use of paired data, but the pairing between subjects had been lost during the data collection procedure. Thus we were presented with a data set consisting of pre and post responses but with no way of determining the dependencies between our observed pre and post values. The aim of the study was to assess whether an intervention called Self-Revelatory Performance had an impact on participant's perceptions of Alzheimer's disease. The participant's responses were measured on an Affect grid before the intervention and on a separate grid after. To address the underlying question in light of the lost pairing we utilized a modified bootstrap approach to create a null hypothesized distribution for our test statistic, which was the distance between the two Affect Grids' Centers of Mass. Using this approach we were able to reject our null hypothesis and conclude that there was evidence the intervention influenced perceptions about the disease.