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.

Evaluating dynamics in affect structure with latent Markov factor analysis.

In intensive longitudinal research, researchers typically consider the structure of affect to be stable across individuals and contexts. Based on an assumed theoretical structure (e.g., one bipolar or two separate positive and negative affect constructs), researchers create affect scores from items (e.g., sum or factor scores) and use them to examine the dynamics therein. However, researchers usually ignore that the affect structure itself is dynamic and varies across individuals and contexts. Understanding these dynamics provides valuable insights into individuals' affective experiences. This study uses latent Markov factor analysis (LMFA) to study what affect structures underlie individuals' responses, how individuals transition between structures, and whether their individual transition patterns differ. Moreover, we explore whether the intensity of negative events and the personality trait neuroticism relate to momentary transitions and individual differences in transition patterns, respectively. Applying LMFA to experience sampling data (N = 153; age: mean = 22; SD = 7.1; range = 17-66), we identified two affect structures-one with three and one with four dimensions. The main difference was the presence of negative emotionality, and the affect dimensions became more inversely related when the affect structure included negative emotionality. Moreover, we identified three latent subgroups that differed in their transition patterns. Higher negative event intensity increased the probability of adopting an affect structure with negative emotionality. However, neuroticism was unrelated to subgroup-membership. Summarized, we propose a way to incorporate contextual and individual differences in affect structure, contributing to advancing the theoretical basis of affect dynamics research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach.

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.

How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa.

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD.

Latent Markov Latent Trait Analysis for Exploring Measurement Model Changes in Intensive Longitudinal Data.

Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)-indicating how items relate to constructs-to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their item interpretation or because the response style of (some) subjects changes over time. The recently proposed "latent Markov factor analysis" (LMFA) evaluates (violations of) measurement invariance by classifying observations into latent "states" according to the MM underlying these observations such that MMs differ between states but are invariant within one state. However, LMFA is limited to normally distributed continuous data and estimates may be inaccurate when applying the method to ordinal data (e.g., from Likert items) with skewed responses or few response categories. To enable researchers and health professionals with ordinal data to evaluate measurement invariance, we present "latent Markov latent trait analysis" (LMLTA), which builds upon LMFA but treats responses as ordinal. Our application shows differences in MMs of adolescents' affective well-being in different social contexts, highlighting the importance of studying measurement invariance for drawing accurate inferences for psychological science and practice and for further understanding dynamics of psychological constructs.