Using Structural Equation Modeling to Reproduce and Extend ANOVA-Based Generalizability Theory Analyses for Psychological Assessments

Generalizability theory provides a comprehensive framework for determining how multiple sources of measurement error affect scores from psychological assessments and using that information to improve those assessments. Although generalizability theory designs have traditionally been analyzed using analyses of variance (ANOVA) procedures, the same analyses can be replicated and extended using structural equation models. We collected multi-occasion data from inventories measuring numerous dimensions of personality, self-concept, and socially desirable responding to compare variance components, generalizability coefficients, dependability coefficients, and proportions of universe score and measurement error variance using structural equation modeling versus ANOVA techniques. We further applied structural equation modeling techniques to continuous latent response variable metrics and derived Monte Carlo-based confidence intervals for those indices on both observed score and continuous latent response variable metrics. Results for observed scores estimated using structural equation modeling and ANOVA procedures seldom varied. Differences in reliability between raw score and continuous latent response variable metrics were much greater for scales with dichotomous responses, thereby highlighting the value of doing analyses on both metrics to evaluate gains that might be achieved by increasing response options. We provide detailed guidelines for applying the demonstrated techniques using structural equation modeling and ANOVA-based statistical software

Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks

We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the Big Five Inventory (BFI-2) revealed that the lavaan SEM package in R produced results virtually identical to those obtained from the mGENOVA package, which historically has served as the gold standard for conducting multivariate GT analyses. We further extended lavaan analyses beyond what mGENOVA allows to produce Monte Carlo based confidence intervals for key GT parameters and correct score consistency and correlational indices for effects of scale coarseness characteristic of binary and ordinal data. Our comprehensive online Supplemental Material includes code for performing all illustrated analyses using lavaan and mGENOVA.</p

Extending Applications of Generalizability Theory-Based Bifactor Model Designs

In recent years, researchers have described how to analyze generalizability theory (GT) based univariate, multivariate, and bifactor designs using structural equation models. However, within GT studies of bifactor models, variance components have been limited to those reflecting relative differences in scores for norm-referencing purposes, with only limited guidance provided for estimating key indices when making changes to measurement procedures. In this article, we demonstrate how to derive variance components for multi-facet GT-based bifactor model designs that represent both relative and absolute differences in scores for norm- or criterion-referencing purposes using scores from selected scales within the recently expanded form of the Big Five Inventory (BFI-2). We further develop and apply prophecy formulas for determining how changes in numbers of items, numbers of occasions, and universes of generalization affect a wide variety of indices instrumental in determining the best ways to change measurement procedures for specific purposes. These indices include coefficients representing score generalizability and dependability; scale viability and added value; and proportions of observed score variance attributable to general factor effects, group factor effects, and individual sources of measurement error. To enable readers to apply these techniques, we provide detailed formulas, code in R, and sample data for conducting all demonstrated analyses within this article

Factors to consider during anesthesia in patients undergoing preemptive kidney transplantation: a propensity-score matched analysis

Abstract Background International guidelines have recommended preemptive kidney transplantation (KT) as the preferred approach, advocating for transplantation before the initiation of dialysis. This approach is advantageous for graft and patient survival by avoiding dialysis-related complications. However, recipients of preemptive KT may undergo anesthesia without the opportunity to optimize volume status or correct metabolic disturbances associated with end-stage renal disease. In these regard, we aimed to investigate the anesthetic events that occur more frequently during preemptive KT compared to nonpreemptive KT. Methods This is a single-center retrospective study. Of the 672 patients who underwent Living donor KT (LDKT), 388 of 519 who underwent nonpreemptive KT were matched with 153 of 153 who underwent preemptive KT using propensity score based on preoperative covariates. The primary outcome was intraoperative hypotension defined as area under the threshold (AUT), with a threshold set at a mean arterial blood pressure below 70 mmHg. The secondary outcomes were intraoperative metabolic acidosis estimated by base excess and serum bicarbonate, electrolyte imbalance, the use of inotropes or vasopressors, intraoperative transfusion, immediate graft function evaluated by the nadir creatinine, and re-operation due to bleeding. Results After propensity score matching, we analyzed 388 and 153 patients in non-preemptive and preemptive groups. The multivariable analysis revealed the AUT of the preemptive group to be significantly greater than that of the nonpreemptive group (mean ± standard deviation, 29.7 ± 61.5 and 14.5 ± 37.7, respectively, P = 0.007). Metabolic acidosis was more severe in the preemptive group compared to the nonpreemptive group. The differences in the nadir creatinine value and times to nadir creatinine were statistically significant, but clinically insignificant. Conclusion Intraoperative hypotension and metabolic acidosis occurred more frequently in the preemptive group during LDKT. These findings highlight the need for anesthesiologists to be prepared and vigilant in managing these events during surgery

Macrocyclic Immunoproteasome Inhibitors as a Potential Therapy for Alzheimer&apos;s Disease

Previously, we reported that immunoproteasome (iP)-targeting linear peptide epoxyketones improve cognitive function in mouse models of Alzheimer&apos;s disease (AD) in a manner independent of amyloid beta. However, these compounds&apos; clinical prospect for AD is limited due to potential issues, such as poor brain penetration and metabolic instability. Here, we report the development of iP-selective macrocyclic peptide epoxyketones prepared by a ring-closing metathesis reaction between two terminal alkenes attached at the P2 and P3/P4 positions of linear counterparts. We show that a lead macrocyclic compound DB-60 (20) effectively inhibits the catalytic activity of iP in ABCB1-overexpressing cells (IC50: 105 nM) and has metabolic stability superior to its linear counterpart. DB-60 (20) also lowered the serum levels of IL-1 alpha and ameliorated cognitive deficits in Tg2576 mice. The results collectively suggest that macrocyclic peptide epoxyketones have improved CNS drug properties than their linear counterparts and offer promising potential as an AD drug candidate.N

A top-crossover-to-bottom addressed segmented annular array using piezoelectric micromachined ultrasonic transducers

We design and fabricate segmented annular arrays (SAAs) using piezoelectric micromachined ultrasonic transducers (pMUTs) to demonstrate the feasibility of acoustic focusing of ultrasound. The fabricated SAAs have 25 concentric top-electrode signal lines and eight bottom-electrodes for grounding to enable electronic steering of selectively grouped ultrasonic transducers from 2393 pMUT elements. Each element in the array is connected by top-crossover-to-bottom metal bridges, which reduce the parasitic capacitance. Circular-shaped pMUT elements, 120 μm in diameter, are fabricated using 1 μm-thick sol-gel lead zirconate titanate on a silicon wafer. To utilize the high-density pMUT array, a deep reactive ion etching process is used for anisotropic silicon etching to realize the transducer membranes. The resonant frequency and effective coupling coefficient of the elements, measured with an impedance analyzer, yields 1.517 MHz and 1.29%, respectively, in air. The SAAs using pMUTs are packaged on a printed circuit board and coated with parylene C for acoustic intensity measurements in water. The ultrasound generated by each segmented array is focused on a selected point in space. When a 5 Vpp, 1.5 MHz square wave is applied, the maximum spatial peak temporal average intensity () is found to be 79 mW cm-2 5 mm from the SAAs&apos; surface without beamforming. The beam widths (-3 dB) of ultrasonic radiation patterns in the elevation and azimuth directions are recorded as 3 and 3.4 mm, respectively. The results successfully show the feasibility of focusing ultrasound on a small area with SAAs using pMUTs. © 2015 IOP Publishing Ltd.1