Specifying Ability Growth Models Using a Multidimensional Item Response Model for Repeated Measures Categorical Ordinal Item Response Data

When categorical ordinal item response data are collected over multiple timepoints from a repeated measures design, an item response theory (IRT) modeling approach whose unit of analysis is an item response is suitable. This study proposes a few longitudinal IRT models and illustrates how a popular compensatory multidimensional IRT model can be utilized to formulate such longitudinal IRT models, which permits an investigation of ability growth at both individual and population levels. The equivalence of an existing multidimensional IRT model and those longitudinal IRT models is also elaborated so that one can make use of an existing multidimensional IRT model to implement the longitudinal IRT models.     

Data Analytic Advances That Bridge the Scientist-Practitioner Gap

In this commentary, I advance the view that the scientist-practitioner gap is partly due to the research designs commonly used in psychotherapy research. Specifically, I believe that randomized controlled trials, which are important for establishing treatment efficacy and as leverage when making the case for the value of psychotherapy in relation to various stakeholders, are limited for further development of clinical theories. Instead, I find recent advances in cross-lagged panel modeling to be both clinically intuitive and stronger for causal inference than most other nonexperimental designs. In addition to discussing causal inference and clinical interpretation of cross-lagged panel models, I discuss the potential of improving mediation analysis, personalization of research, and studying issues of clinical timing. Finally, I briefly discuss some limitations of cross-lagged panel models. It is my belief that the use of these data analytic advances can make empirical research better live up to the innovations in Beck’s work.     

Compositional insights on the association between physical activity and sedentary behavior on momentary mood in daily life

ObjectiveGiven the increasing number of mental disorders, a growing body of studies now focuses on the relationship between physical activity (PA), sedentary behavior (SB), and mental health. However, momentary mechanisms and interrelatedness between PA, SB, and mood in daily life are highly understudied.MethodsTo investigate whether the composition of light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and SB influences mood, we conducted an Ambulatory Assessment study in the everyday life of 103 university students over five days. We measured PA and SB via accelerometers and assessed mood up to six times each day on smartphone diaries. We combined compositional data analyses (CoDA) with multilevel modeling to analyze within-person effects of the behavioral composition on mood.ResultsHigher ratio of the SB to LPA and MVPA composition within the 60 min prior to a diary rating negatively influenced valence (p = 0.006) and energetic arousal (p < 0.001). Furthermore, a higher ratio of LPA to SB and MVPA positively influenced energetic arousal (p < 0.001) and a higher ratio of MVPA to SB and LPA positively influenced valence (p = 0.004) and energetic arousal (p = 0.022). Simulation analyses revealed that replacing 20 min of SB with PA can influence mood rating up to 3.39 units [range from 0–100].ConclusionsFindings suggest that minutely replacements of SB with PA may lead to mood enhancements. Given the high prevalence of mental disorders, more studies are warranted to deepen the understanding of momentary compositional mechanisms between SB, PA, and mood. Applying CoDA to intensive longitudinal data can serve as a starting point to identify the optimal composition of SB, LPA, and MVPA for mood enhancements in everyday life.     

国内中介效应的方法学研究

中介效应可以分析自变量对因变量的影响过程和作用机制, 已成为分析多个变量之间关系的一种重要统计方法。最近20年, 中介效应成了研究方法的一个热点。从中介效应的检验方法、效应量、类别变量的中介效应检验、纵向数据的中介效应检验和模型拓展(包括多重中介、多层中介、有调节的中介和有中介的调节模型)五个方面系统总结了国内中介效应的方法学研究的发展历程。最后对中介效应的国外方法学研究进展和中介效应的未来研究方向做了讨论和拓展。     

Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony

Abstract

CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis’ (EFA) flexibility, and CFA/SEM’s rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse “real” data applications with accessible, heuristic explanations of best practice.     

Longitudinal associations between impulsivity and lie-telling in childhood and adolescence

Lie-telling and impulsivity levels peak during late childhood to early adolescence and have been suggested to be related. Heightened impulsivity may lead adolescents to lie in favor of short-term benefits without consideration for the potential consequences of deception. The present study assessed longitudinal relations between self-reported impulsivity and lie-telling frequency. Participants from a large-scale longitudinal study (N = 1148; M_age = 11.55, SD = 1.69, 9–15 years at Time 1) reported on their impulsivity (Barratt Impulsiveness Scale) and their frequency of lie-telling to parents, to teachers, to friends, and about cheating across two time points 1 year apart. Cross-lagged path analysis revealed greater impulsivity was associated with more frequent lie-telling to parents, friends, and teachers, and about cheating over time. Our findings demonstrate the role of impulsivity in the development of lie-telling behaviors.

Research Highlights

• Impulsivity predicts lying across time in multiple contexts (to parents, friends, teachers, and about cheating).
• Previous research has demonstrated the role of top-down influences on lie-telling, but the current study suggests that bottom-up processes are also influential.

     

A longitudinal investigation of the relationship between maternal mind‐mindedness and theory of mind

Data are presented from a longitudinal investigation examining the relationship between maternal mind‐mindedness (MM) in infancy and socio‐cognitive development in childhood. We revisited children (n = 18) who had taken part in a longitudinal study as infants. MM had been assessed at 10, 12, 16, and 20 months of age. We followed up these children at 5–6 years of age to test their higher order theory of mind (ToM) (using the strange stories task). The convergent validity, temporal stability, and predictive validity of the construct of MM were examined in a longitudinal data set. The five measures of MM were not significantly correlated. Mother's production of appropriate mind‐related comments (but no other measures) showed evidence of temporal stability throughout infancy. Thus, MM (as measured by appropriate mind‐related comments) was confirmed as a stable construct. Children's ToM at 5–6 years of age was significantly predicted by their mother's MM up to 4 years earlier, with MM accounting for 40% of the variance of the strange stories task scores. These findings identify a relationship between MM across a protracted period of infancy and socio‐cognitive development at 5–6 years of age.     

Estimation of Controlled Direct Effects in Longitudinal Mediation Analyses with Latent Variables in Randomized Studies

Abstract

In a randomized study with longitudinal data on a mediator and outcome, estimating the direct effect of treatment on the outcome at a particular time requires adjusting for confounding of the association between the outcome and all preceding instances of the mediator. When the confounders are themselves affected by treatment, standard regression adjustment is prone to severe bias. In contrast, G-estimation requires less stringent assumptions than path analysis using SEM to unbiasedly estimate the direct effect even in linear settings. In this article, we propose a G-estimation method to estimate the controlled direct effect of treatment on the outcome, by adapting existing G-estimation methods for time-varying treatments without mediators. The proposed method can accommodate continuous and noncontinuous mediators, and requires no models for the confounders. Unbiased estimation only requires correctly specifying a mean model for either the mediator or the outcome. The method is further extended to settings where the mediator or outcome, or both, are latent, and generalizes existing methods for single measurement occasions of the mediator and outcome to longitudinal data on the mediator and outcome. The methods are utilized to assess the effects of an intervention on physical activity that is possibly mediated by motivation to exercise in a randomized study.