Assessing and accounting for measurement in intensive longitudinal studies: current practices, considerations, and avenues for improvement.

PURPOSE: Intensive longitudinal studies, in which participants complete questionnaires multiple times a day over an extended period, are increasingly popular in the social sciences in general and quality-of-life research in particular. The intensive longitudinal methods allow for studying the dynamics of constructs (e.g., how much patient-reported outcomes vary across time). These methods promise higher ecological validity and lower recall bias than traditional methods that question participants only once, since the high frequency means that participants complete questionnaires in their everyday lives and do not have to retrospectively report about a large time interval. However, to ensure the validity of the results obtained from analyzing the intensive longitudinal data (ILD), greater awareness and understanding of appropriate measurement practices are needed. METHOD: We surveyed 42 researchers experienced with ILD regarding their measurement practices and reasons for suboptimal practices. RESULTS: Results showed that researchers typically do not use measures validated specifically for ILD. Participants assessing the psychometric properties and invariance of measures in their current studies was even less common, as was accounting for these properties when analyzing dynamics. This was mainly because participants did not have the necessary knowledge to conduct these assessments or were unaware of their importance for drawing valid inferences. Open science practices, in contrast, appear reasonably well ingrained in ILD studies. CONCLUSION: Measurement practices in ILD still need improvement in some key areas; we provide recommendations in order to create a solid foundation for measuring and analyzing psychological constructs.

Exploring Within-Person Variability in Qualitative Negative and Positive Emotional Granularity by Means of Latent Markov Factor Analysis.

Emotional granularity (EG) is an individual's ability to describe their emotional experiences in a nuanced and specific way. In this paper, we propose that researchers adopt latent Markov factor analysis (LMFA) to investigate within-person variability in qualitative EG (i.e., variability in distinct granularity patterns between specific emotions across time). LMFA clusters measurement occasions into latent states according to state-specific measurement models. We argue that state-specific measurement models of repeatedly assessed emotion items can provide information about qualitative EG at a given point in time. Applying LMFA to the area of EG for negative and positive emotions separately by using data from an experience sampling study with 11,662 measurement occasions across 139 participants, we found three latent EG states for the negative emotions and three for the positive emotions. Momentary stress significantly predicted transitions between the EG states for both the negative and positive emotions. We further identified two and three latent classes of individuals who differed in state trajectories for negative and positive emotions, respectively. Neuroticism and dispositional mood regulation predicted latent class membership for negative (but not for positive) emotions. We conclude that LMFA may enrich EG research by enabling more fine-grained insights into variability in qualitative EG patterns.