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

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc

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

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

Host genotype structures the microbiome of a globally dispersed marine phytoplankton

Phytoplankton support complex bacterial microbiomes that rely on phytoplankton-derived extracellular compounds and perform functions necessary for algal growth. Recent work has revealed sophisticated interactions and exchanges of molecules between specific phytoplankton-bacteria pairs, but the role of host genotype in regulating those interactions is unknown. Here, we show how phytoplankton microbiomes are shaped by intraspecific genetic variation in the host using global environmental isolates of the model phytoplankton host Thalassiosira rotula and a laboratory common garden experiment. A set of 81 environmental T. rotula genotypes from three ocean basins and eight genetically distinct populations did not reveal a core microbiome. While no single bacterial phylotype was shared across all genotypes, we found strong genotypic influence of T. rotula, with microbiomes associating more strongly with host genetic population than with environmental factors. The microbiome association with host genetic population persisted across different ocean basins, suggesting that microbiomes may be associated with host populations for decades. To isolate the impact of host genotype on microbiomes, a common garden experiment using eight genotypes from three distinct host populations again found that host genotype influenced microbial community composition, suggesting that a process we describe as genotypic filtering, analogous to environmental filtering, shapes phytoplankton microbiomes. In both the environmental and laboratory studies, microbiome variation between genotypes suggests that other factors influenced microbiome composition but did not swamp the dominant signal of host genetic background. The long-term association of microbiomes with specific host genotypes reveals a possible mechanism explaining the evolution and maintenance of complex phytoplankton-bacteria chemical exchanges