Analysis of longitudinal data
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In the field of motivation science, we deal with many different types of longitudinal data (e.g., panel data, intensive longitudinal data, time-series data from behavioral experiments and neuroimaging). Despite the popularity, we still do not have a good idea to analyze such data given a specific research question. We aim to understand how applied researchers can appropriately choose the right methods to analyze longitudinal data.
Key words: Statistical simulation, mixed-effects modeling, structural equation modeling