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Studies of emotional-cognitive sequences are the growing body of research area in educational context. These studies focus on how emotions change during the learning-teaching process due to their dynamic nature. In affect transition studies, the change of emotion, depending on the event and time, is usually analyzed by using (a) lag sequential analysis (LSA), (b) L metric, (c) L* metric, and (d) Yule's Q metric. Yet, various methodological criticisms exist in the literature while utilizing these sequential analysis methods. In this study, it is aimed to comparatively examine lag analysis, L metric, L* metric, and Yule’s Q in terms of proportion of invalid values, maximum transition metrics, minimum transition metrics, and analysis results. For this reason, the emotional states of the fifteen prospective teachers were collected and their emotions were labeled every 0.5 seconds with EEG (Electroencephalogram), GSR (Galvanic Skin Response), and Microsoft Kinect in a teacher training simulator (SimInClass). The dataset contained 17570 emotions, and the data were analyzed by utilizing lag analysis, L, L* and Yule's Q. The results showed that LSA had yielded the most proportion of invalid results. In addition, it was observed that the number of invalid values increased as the segment length became shorter in all analysis methods. When the maximum and minimum transition metric values were examined, it was found that as the sequence length increased in L and L* analyses, the value of the metrics approached 1, which is the largest value they can reach. However, it was noted that the lag analysis maximum-minimum transition metrics fluctuate …