Modeling motivation and affect

Affective states like feeling happy, relaxed or fatigued fluctuate substantially within individuals due to influences such as diurnal rhythms, daily events, and self-regulatory behavior. In recent years, constructs that have originally been conceptualized from an individual differences perspective (e.g., work engagement or personality traits) are also being studied as states that vary within individuals from day to day or from moment to moment (e.g., the degree of being absorbed in one’s work or acting extraverted). The ways in which momentary affect, other experiences and behavior are interconnected over time, as well as their momentary predictors and consequences are far from being fully understood. To gain insight into the dynamics of psychological processes, more and more researchers collect intensive longitudinal data (ILD) in people’s everyday lives, which is greatly facilitated by recent technological advances (e.g., smartphones, sensors). ILD have a complex structure and require statistical modeling techniques that disentangle within- and between-person variance. Typically, hierarchical models (multilevel models) are used to analyze ILD. However, to answer specific research questions on within-persons dynamics (and between-persons differences there-in), a combination of different modeling frameworks (e.g., multilevel and latent-class analytic approaches) or the application of modeling approaches that have mainly been used in other areas of psychological research (e.g., in experimental psychology) might be fruitful.