A variety of sequential analysis methods exist to quantify close temporal associations between events from direct observation data. In the present study, we compared the relative accuracy and interpretability of five sequential-analysis methods using simulated data. The methods included three existing approaches (event lag, concurrent interval, and time window) and two proposed modifications of the event lag approach (event lag with contiguous pauses and event lag with noncontiguous pauses) designed to address limitations of the existing approaches. We evaluated accuracy on the basis of the extent to which the mean contingency estimates produced by each method approximated a known mean (i.e., zero). We evaluated interpretability on the basis of the extent to which the contingency estimates produced by each method were independent from chance estimates of the two-event sequence. The results indicated that the event lag with contiguous pauses method produced the most accurate and interpretable estimates of contingency. This modified method prevents the total number of event types from influencing contingency estimates, thus solving a problem associated with the traditional event lag method.