Estimating nonlinear effects of random slopes: A comparison of multilevel structural equation modeling with a two-step, a single-indicator, and a plausible values approach

Multilevel structural equation modeling (MSEM) is a statistical framework of major relevance for research concerned with people's intrapersonal dynamics. An application domain that is rapidly gaining relevance is the study of individual differences in the within-person association (WPA) of variables that fluctuate over time. For instance, an individual's social reactivity - their emotional response to social situations - can be represented as the association between repeated measurements of the individual's social interaction quantity and momentary well-being. MSEM allows researchers to investigate the associations between WPAs and person-level outcome variables (e.g., life satisfaction) by specifying the WPAs as random slopes in the structural equation on level 1 and using the latent representations of the slopes to predict outcomes on level 2. Here, we are concerned with the case in which a researcher is interested in nonlinear effects of WPAs on person-level outcomes - a U-shaped effect of a WPA, a moderation effect of two WPAs, or an effect of congruence between two WPAs - such that the corresponding MSEM includes latent interactions between random slopes. We evaluate the nonlinear MSEM approach for the three classes of nonlinear effects (U-shaped, moderation, congruence) and compare it with three simpler approaches: a simple two-step approach, a single-indicator approach, and a plausible values approach. We use a simulation study to compare the approaches on accuracy of parameter estimates and inference. We derive recommendations for practice and provide code templates and an illustrative example to help researchers implement the approaches.

Perspectives on resilience: Trait resilience, correlates of resilience in daily life, and longer-term change in affective distress

Resilience describes successful adaptation in the face of adversity, commonly inferred from trajectories of well-being following major life events. Alternatively, resilience was conceptualised as a psychological trait, facilitating adaptation through stable individual characteristics. Both perspectives may relate to individual differences in how stress is regulated in daily life. In the present study, we combined these perspectives on resilience. Our sample consisted of N = 132 middle-aged adults, who experienced major life events in between two waves of a longitudinal study. We implemented latent change regression models to predict change in affective distress. As predictors, we investigated trait resilience and correlates of resilience in daily life (stressor occurrence, stress reactivity, positive reappraisal, mindful attention, and acceptance), measured using experience sampling (T = 70 occasions). Unexpectedly, trait resilience was not associated with change in distress. In contrast, resilience correlates in daily life, most notably lower stress reactivity, were associated with more favourable change. Higher trait resilience related to higher average mindfulness, higher reappraisal, and lower negative affect. Overall, while trait resilience translated into everyday correlates of resilience, it was not predictive of changes in affective distress. Instead, precursors of changes in well-being may be found in correlates of resilience in daily life.

Intelligence IS Cognitive Flexibility: Why Multilevel Models of Within-Individual Processes Are Needed to Realise This

Despite substantial evidence for the link between an individual’s intelligence and successful life outcomes, questions about what defines intelligence have remained the focus of heated dispute. The most common approach to understanding intelligence has been to investigate what performance on tests of intellect is and is not associated with. This psychometric approach, based on correlations and factor analysis is deficient. In this review, we aim to substantiate why classic psychometrics which focus on between-person accounts will necessarily provide a limited account of intelligence until theoretical considerations of within-person accounts are incorporated. First, we consider the impact of entrenched psychometric presumptions that support the status quo and impede alternative views. Second, we review the importance of process-theories, which are critical for any serious attempt to build a within-person account of intelligence. Third, features of dynamic tasks are reviewed, and we outline how static tasks can be modified to target within-person processes. Finally, we explain how multilevel models are conceptually and psychometrically well-suited to building and testing within-individual notions of intelligence, which at its core, we argue is cognitive flexibility. We conclude by describing an application of these ideas in the context of microworlds as a case study.

Advancing the Study of Resilience to Daily Stressors

Historically, studies of childhood and adult resilience have typically focused on adaptation to chronic life adversities, such as poverty and maltreatment, or isolated and potentially traumatic events, such as bereavement and serious illness. Here, we present a complementary view and suggest that stressors experienced in daily life may also forecast individual health and well-being. We argue that daily process approaches that incorporate intensive sampling of individuals in natural settings can provide powerful insights into unfolding adaptational processes. In making this argument, we review studies that link intraindividual dynamics with diverse health-related phenomena. Findings from this research provide support for a multiple-levels-analysis perspective that embraces greater unity in pivotal resilience constructs invoked across childhood and adult literatures. Drawing on insights and principles derived from life-span theory, we conclude by outlining promising directions for future work and considering their broader implications for the field of resilience.

Affect dynamics and well-being: explanatory power of the model of intraindividual variability in affect (MIVA)

Affective experience is inherently dynamic and short-term changes in affect are supposed to offer important insights into well-being. Past years have shown a tremendous rise in investigations into the relation between affect dynamics and well-being. The indicators that have been introduced to capture unique dynamical aspects of affect, however, have been criticised for being purely statistical measures without theoretical foundation and were shown to have little added value for explaining well-being over and above mean levels of affect. To address these concerns, we applied our newly developed theory-based MIVA model to data on daily affective experience. The MIVA model allows estimating parameters for anchoring, reactivity, and regulation based on affective states in combination with daily events. Everyday affective experience was measured with a high temporal resolution, multiple indicators of well-being (e.g. life satisfaction, depression) were assessed, and the incremental value of the MIVA model parameters in predicting well-being was determined. The MIVA model parameters reflect essential processes that accounted for observed fluctuations in affective experience. Incremental validity for predicting well-being over and above mean levels of affect, however, was low. Together, our results suggest that research on affect dynamics needs to identify how affect dynamics can be assessed more validly.