A Moderated Nonlinear Factor Model for the Development of Commensurate Measures in Integrative Data Analysis

Integrative data analysis (IDA) is a methodological framework that allows for the fitting of models to data that have been pooled across two or more independent sources. IDA offers many potential advantages including increased statistical power, greater subject heterogeneity, higher observed frequencies of low base-rate behaviors, and longer developmental periods of study. However, a core challenge is the estimation of valid and reliable psychometric scores that are based on potentially different items with different response options drawn from different studies. In Bauer and Hussong (2009) we proposed a method for obtaining scores within an IDA called moderated nonlinear factor analysis (MNLFA). Here we move significantly beyond this work in the development of a general framework for estimating MNLFA models and obtaining scale scores across a variety of settings. We propose a five step procedure and demonstrate this approach using data drawn from n=1972 individuals ranging in age from 11 to 34 years pooled across three independent studies to examine the factor structure of 17 binary items assessing depressive symptomatology. We offer substantive conclusions about the factor structure of depression, use this structure to compute individual-specific scale scores, and make recommendations for the use of these methods in practice

A trifactor model for integrating ratings across multiple informants

Psychologists often obtain ratings for target individuals from multiple informants such as parents or peers. In this article we propose a trifactor model for multiple informant data that separates target-level variability from informant-level variability and item-level variability. By leveraging item-level data, the trifactor model allows for examination of a single trait rated on a single target. In contrast to many psychometric models developed for multitrait-multimethod data, the trifactor model is predominantly a measurement model. It is used to evaluate item quality in scale development, test hypotheses about sources of target variability (e.g., sources of trait differences) versus informant variability (e.g., sources of rater bias), and generate integrative scores that are purged of the subjective biases of single informants

Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood

Rationale: Substance use peaks during the developmental period known as emerging adulthood (ages 18–25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here, we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. Objectives: We test whether lower efficiency of evidence accumulation (EEA), a computationally characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults. Methods and results: In an fMRI substudy within a sociobehavioral longitudinal study (n = 106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18–21 are both prospectively related to greater substance use during ages 22–26, even after adjusting for ot

Regularized Finite Mixture Models for Probability Trajectories

finite mixture models, growth mixture models, latent class analysis, functional data analysis, penalized likelihood, longitudinal data analysis, trajectory analysis,

Search for intermediate mass black hole binaries in the first observing run of Advanced LIGO

International audienceDuring their first observational run, the two Advanced LIGO detectors attained an unprecedented sensitivity, resulting in the first direct detections of gravitational-wave signals produced by stellar-mass binary black hole systems. This paper reports on an all-sky search for gravitational waves (GWs) from merging intermediate mass black hole binaries (IMBHBs). The combined results from two independent search techniques were used in this study: the first employs a matched-filter algorithm that uses a bank of filters covering the GW signal parameter space, while the second is a generic search for GW transients (bursts). No GWs from IMBHBs were detected; therefore, we constrain the rate of several classes of IMBHB mergers. The most stringent limit is obtained for black holes of individual mass 100  M⊙, with spins aligned with the binary orbital angular momentum. For such systems, the merger rate is constrained to be less than 0.93  Gpc−3 yr−1 in comoving units at the 90% confidence level, an improvement of nearly 2 orders of magnitude over previous upper limits