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Plausible competing developmental models show similar or identical structural equation modeling model fit indices, despite making very different causal predictions. One way to help address this problem is incorporating outside information into selecting among models. This study attempted to select among developmental models of children's early mathematical skills by incorporating information about the extent to which models forecast the longitudinal pattern of causal impacts of early math interventions. We tested for the usefulness and validity of the approach by applying it to data from three randomized controlled trials of early math interventions with longitudinal follow-up assessments in the United States (Ns = 1,375, 591, 744; baseline age 4.3, 6.5, 4.4; 17%-69% Black). We found that, across data sets, (a) some models consistently outperformed other models at forecasting later experimental impacts, (b) traditional statistical fit indices were not strongly related to causal fit as indexed by models' accuracy at forecasting later experimental impacts, and (c) models showed consistent patterns of similarity and discrepancy between statistical fit and models' effectiveness at forecasting experimental impacts. We highlight the importance of triangulation and call for more comparisons of experimental and nonexperimental estimates for choosing among developmental models.

The transitional years of early adulthood, with key tasks of identity and intimacy development, engender both opportunities and risks for well-being. We propose that the conceptualization and measurement of early adults' well-being can be improved through (a) an integration of ideas from developmental and psychological science on well-being, (b) the use of short, daily momentary assessments of well-being, and (c) a developmentally-informed examination of the structure of well-being within (and not just across) time. We developed a daily assessment of well-being based on the PERMA model (Seligman, 2011) and used network analysis to gain understanding from this data. Using Ecological Momentary Assessments, we assessed the five PERMA elements in college students' daily life and their network properties. Consistent with the PERMA model, network analysis showed items clustered around theorized elements and formed a unitary network of well-being. Consistent with developmental theory, we found that having positive relationships and positive emotion were most central to early adults' daily well-being.

BackgroundMobile health (mHealth) methods often rely on active input from participants, for example, in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral indicators and deliver interventions in everyday life settings. For short-term studies or interventions, these techniques are deployed intensively, causing nontrivial participant burden. For cases where the goal is long-term maintenance, limited infrastructure exists to balance information needs with participant constraints. Yet, the increasing precision of passive sensors such as wearable physiology monitors, smartphone-based location history, and internet-of-things devices, in combination with statistical feature selection and adaptive interventions, have begun to make such things possible. ObjectiveIn this paper, we introduced Wear-IT, a smartphone app and cloud framework intended to begin addressing current limitations by allowing researchers to leverage commodity electronics and real-time decision making to optimize the amount of useful data collected while minimizing participant burden. MethodsThe Wear-IT framework uses real-time decision making to find more optimal tradeoffs between the utility of data collected and the burden placed on participants. Wear-IT integrates a variety of consumer-grade sensors and provides adaptive, personalized, and low-burden monitoring and intervention. Proof of concept examples are illustrated using artificial data. The results of qualitative interviews with users are provided. ResultsParticipants provided positive feedback about the ease of use of studies conducted using the Wear-IT framework. Users expressed positivity about their overall experience with the framework and its utility for balancing burden and excitement about future studies that real-time processing will enable. ConclusionsThe Wear-IT framework uses a combination of passive monitoring, real-time processing, and adaptive assessment and intervention to provide a balance between high-quality data collection and low participant burden. The framework presents an opportunity to deploy adaptive assessment and intervention designs that use real-time processing and provides a platform to study and overcome the challenges of long-term mHealth intervention.

A general method is introduced in which variables that are products of other variables in the context of a structural equation model (SEM) can be decomposed into the sources of variance due to the multiplicands. The result is a new category of SEM which we call a Products of Variables Model (PoV). Some useful and practical features of PoV models include the estimation of interactions between latent variables, latent variable moderators, manifest moderators with missing values, and manifest or latent squared terms. Expected means and covariances are analytically derived for a simple product of two variables and it is shown that the method reproduces previously published results for this special case. It is shown algebraically that using centered multiplicands results in an unidentified model, but if the multiplicands have non-zero means, the result is identified. The method has been implemented in OpenMx and Ωnyx and is applied in five extensive simulations. [ABSTRACT FROM AUTHOR]

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest. [ABSTRACT FROM AUTHOR]

Abstract Nonverbal visual cues accompany speech to supplement the meaning of spoken words, signify emotional state, indicate position in discourse, and provide back-channel feedback. This visual information includes head movements, [...]

The transitional years of early adulthood, with key tasks of identity and intimacy development, engender both opportunities and risks for well-being. We propose that the conceptualization and measurement of early adults' well-being can be improved through (a) an integration of ideas from developmental and psychological science on well-being, (b) the use of short, daily momentary assessments of well-being, and (c) a developmentally-informed examination of the structure of well-being within (and not just across) time. We developed a daily assessment of well-being based on the PERMA model (Seligman, 2011) and used network analysis to gain understanding from this data. Using Ecological Momentary Assessments, we assessed the five PERMA elements in college students' daily life and their network properties. Consistent with the PERMA model, network analysis showed items clustered around theorized elements and formed a unitary network of well-being. Consistent with developmental theory, we found that having positive relationships and positive emotion were most central to early adults' daily well-being. [ABSTRACT FROM AUTHOR]