The Comparison of Model Selection Criteria When Selecting Among Competing Hierarchical Linear Models

Little is known about the use and accuracy of model selection criteria when selecting among a set of competing multilevel models. The practices of applied researchers and the performance of five model selection criteria are examined when selecting the correct multilevel model using simulation techniques

Determining Predictor Importance In Multiple Regression Under Varied Correlational And Distributional Conditions

This study examines the performance of eight methods of predictor importance under varied correlational and distributional conditions. The proportion of times a method correctly identified the dominant predictor was recorded. Results indicated that the new methods of importance proposed by Budescu (1993) and Johnson (2000) outperformed commonly used importance methods

Fully Latent Principal Stratification With Measurement Models

There is wide agreement on the importance of implementation data from randomized effectiveness studies in behavioral science; however, there are few methods available to incorporate these data into causal models, especially when they are multivariate or longitudinal, and interest is in low-dimensional summaries. We introduce a framework for studying how treatment effects vary between subjects who implement an intervention differently, combining principal stratification with latent variable measurement models; since principal strata are latent in both treatment arms, we call it "fully-latent principal stratification" or FLPS. We describe FLPS models including item-response-theory measurement, show that they are feasible in a simulation study, and illustrate them in an analysis of hint usage from a randomized study of computerized mathematics tutors.Comment: In Submissio

GbdR Regulates Pseudomonas aeruginosa plcH and pchP Transcription in Response to Choline Catabolites

Pseudomonas aeruginosa hemolytic phospholipase C, PlcH, can degrade phosphatidylcholine (PC) and sphingomyelin in eukaryotic cell membranes and extracellular PC in lung surfactant. Numerous studies implicate PlcH in P. aeruginosa virulence. The phosphorylcholine released by PlcH activity on phospholipids is hydrolyzed by a periplasmic phosphorylcholine phosphatase, PchP. Both plcH gene expression and PchP enzyme activity are positively regulated by phosphorylcholine degradation products, including glycine betaine. Here we report that the induction of plcH and pchP transcription by glycine betaine is mediated by GbdR, an AraC family transcription factor. Mutants that lack gbdR are unable to induce plcH and pchP in media containing glycine betaine or choline and in phosphatidylcholine-rich environments, such as lung surfactant or mouse lung lavage fluid. In T broth containing choline, the gbdR mutant exhibited a 95% reduction in PlcH activity. In electrophoretic mobility shift assays, a GbdR-maltose binding protein fusion bound specifically to both the plcH and pchP promoters. Promoter mapping, alignment of GbdR-regulated promoter sequences, and analysis of targeted promoter mutants that lack GbdR-dependent induction of transcription were used to identify a region necessary for GbdR-dependent transcriptional activation. GbdR also plays a significant role in plcH and pchP regulation within the mouse lung. Our studies suggest that GbdR is the primary regulator of plcH and pchP expression in PC-rich environments, such as the lung, and that pchP and other genes involved in phosphorylcholine catabolism are necessary to stimulate the GbdR-mediated positive feedback induction of plcH

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment

The Impact of Violating Factor Scaling Method Assumptions On Latent Mean Difference Testing in Structured Means Models

Type I error rates and power of the likelihood ratio test and bias of the standardized effect size measure associated with the latent mean difference in structured means modeling are examined when violating the assumptions underlying the two available factor scaling methods under various conditions. Implications and recommendations are discussed

The Impact of Item Parceling on Structural Parameter Invariance in Multi-group Structural Equation Modeling

Supplementary materials of the articl

Multivariate Models for Normal and Binary Responses in Intervention Studies

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses—a set of normal and binary outcomes—are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in intervention studies and analysis models that can simultaneously include such outcomes are available, we found very limited use of these models in intervention research. To encourage greater use of multivariate analysis for mixed outcomes, this article highlights the benefits and describes important features of models that can incorporate a mix of normal and binary outcomes. Models for intervention research are then fit using Mplus and results interpreted using data from an evaluation of the Early Head Start program, a randomized trial designed to improve child outcomes for an at-risk population. The models illustrated estimate treatment effects for mixed responses in standard and multilevel experimental designs

Flow With an Intelligent Tutor: A Latent Variable Modeling Approach to Tracking Flow During Artificial Tutoring

Increasing use of intelligent tutoring system (ITS) in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach to tracking flow during computer-interactive intelligent tutoring. Flow is a mental state a student achieves when immersed in deep learning. A student who flows in learning shows high engagement with learning activities and tends to achieve greater academic growth. When examined in ITS, flow state can evince student's interaction with the artificial tutor and modalities of optimal and suboptimal learning. The purpose of this study is to track progression of latent flow during tutoring. We apply latent variable models that allow discrete characterization of flow state and suggest practical model-fitting strategies that accommodate assumptions, estimation constraints and characteristics of ITS implementation data. Three models are considered to demonstrate the application: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. Using log data from Cognitive Tutor Algebra I, we show that the models provide practical information about learning flow. The models differed in handling the stochasticity and progression of problems but were generally consistent in inferring the flow trajectory and interaction modalities. The information drawn from the models was unique enough to warrant separate attention and well complemented with each other. Based on our experiential analyses, we discuss strengths and limitations of each model and illuminate areas that need future development