Ramseymckay0976

Investigation of the effects of a solvent on the photophysical and redox properties of the photoredox catalyst (PC), N,N-di(2-naphthyl)-5,10-dihydrophenazine (PC 1), revealed the opportunity to use tetrahydrofuran (THF) to modulate the reactivity of PC 1 toward achieving a controlled organocatalyzed atom transfer radial polymerization (O-ATRP) of acrylates. Compared with dimethylacetamide (DMAc), in tetrahydrofuran (THF), PC 1 exhibits a higher quantum yield of intersystem crossing (ΦISC = 0.02 in DMAc, 0.30 in THF), a longer singlet excited-state lifetime (τ Singlet = 3.81 ns in DMAc, 21.5 ns in THF), and a longer triplet excited-state lifetime (τ Triplet = 4.3 μs in DMAc, 15.2 μs in THF). Destabilization of 1 •+, the proposed polymerization deactivator, in THF leads to an increase in the oxidation potential of this species by 120 mV (E 1/2 0 = 0.22 V vs SCE in DMAc, 0.34 V vs SCE in THF). The O-ATRP of n-butyl acrylate (n-BA) catalyzed by PC 1 proceeds in a more controlled fashion in THF than in DMAc, producing P(n-BA) with low dispersity, Đ (Đ less then 1.2). Model reactions and spectroscopic experiments revealed that two initiator-derived alkyl radicals add to the core of PC 1 to form an alkyl-substituted photocatalyst (2) during the polymerization. PC 2 accesses a polar CT excited state that is ~40 meV higher in energy than PC 1 and forms a slightly more oxidizing radical cation (E 1/2 0 = 0.22 V for 1 •+ and 0.25 V for 2 •+ in DMAc). A new O-ATRP procedure was developed wherein PC 1 is converted to 2 in situ. The application of this method enabled the O-ATRP of a number of acrylates to proceed with moderate to good control (Đ = 1.15-1.45 and I* = 83-127%).Organocatalyzed photoredox radical ring-opening polymerization (rROP) of vinylcyclopropanes (VCPs) is employed for the synthesis of polymers with controlled molecular weight (MW), dispersity, and composition. Herein, we report the study on the rROP of a variety of VCP monomers bearing diverse functional groups (such as amide, alkene, ketal, urea, hemiaminal ether, and so on) under organocatalyzed conditions with varying light sources and temperature. Notably, VCP monomers bearing natural product functionality or their derivatives can be polymerized in a controlled manner to produce poly(VCPs) with predictable MW, low dispersity, tunable composition, high thermal stability, and tailored glass transition temperature (T g), ranging 39 to 107 °C. Saracatinib Lastly, successful "grafting through" synthesis of molecular brush copolymers containing 1.0 or 5.0 kDa polydimethylsiloxane (PDMS) side chains from readily accessible EtVCP-PDMS macromonomers further demonstrates the robustness of this organocatalyzed photoredox rROP.The population discrepancy between unstandardized and standardized reliability of homogeneous multicomponent measuring instruments is examined. Within a latent variable modeling framework, it is shown that the standardized reliability coefficient for unidimensional scales can be markedly higher than the corresponding unstandardized reliability coefficient, or alternatively substantially lower than the latter. Based on these findings, it is recommended that scholars avoid estimating, reporting, interpreting, or using standardized scale reliability coefficients in empirical research, unless they have strong reasons to consider standardizing the original components of utilized scales.Cohen's kappa coefficient was originally proposed for two raters only, and it later extended to an arbitrarily large number of raters to become what is known as Fleiss' generalized kappa. Fleiss' generalized kappa and its large-sample variance are still widely used by researchers and were implemented in several software packages, including, among others, SPSS and the R package "rel." The purpose of this article is to show that the large-sample variance of Fleiss' generalized kappa is systematically being misused, is invalid as a precision measure for kappa, and cannot be used for constructing confidence intervals. A general-purpose variance expression is proposed, which can be used in any statistical inference procedure. A Monte-Carlo experiment is presented, showing the validity of the new variance estimation procedure.Contamination of responses due to extreme and midpoint response style can confound the interpretation of scores, threatening the validity of inferences made from survey responses. This study incorporated person-level covariates in the multidimensional item response tree model to explain heterogeneity in response style. We include an empirical example and two simulation studies to support the use and interpretation of the model parameter recovery using Markov chain Monte Carlo (MCMC) estimation and performance of the model under conditions with and without response styles present. Item intercepts mean bias and root mean square error were small at all sample sizes. Item discrimination mean bias and root mean square error were also small but tended to be smaller when covariates were unrelated to, or had a weak relationship with, the latent traits. Item and regression parameters are estimated with sufficient accuracy when sample sizes are greater than approximately 1,000 and MCMC estimation with the Gibbs sampler is used. The empirical example uses the National Longitudinal Study of Adolescent to Adult Health's sexual knowledge scale. Meaningful predictors associated with high levels of extreme response latent trait included being non-White, being male, and having high levels of parental support and relationships. Meaningful predictors associated with high levels of the midpoint response latent trait included having low levels of parental support and relationships. Item-level covariates indicate the response style pseudo-items were less easy to endorse for self-oriented items, whereas the trait of interest pseudo-items were easier to endorse for self-oriented items.Although collecting data from multiple informants is highly recommended, methods to model the congruence and incongruence between informants are limited. Bauer and colleagues suggested the trifactor model that decomposes the variances into common factor, informant perspective factors, and item-specific factors. This study extends their work to the trifactor mixture model that combines the trifactor model and the mixture model. This combined approach allows researchers to investigate the common and unique perspectives of multiple informants on targets using latent factors and simultaneously take into account potential heterogeneity of targets using latent classes. We demonstrate this model using student self-rated and teacher-rated academic behaviors (N = 24,094). Model specification and testing procedures are explicated in detail. Methodological and practical issues in conducting the trifactor mixture analysis are discussed.This study investigated the extent to which class-specific parameter estimates are biased by the within-class normality assumption in nonnormal growth mixture modeling (GMM). Monte Carlo simulations for nonnormal GMM were conducted to analyze and compare two strategies for obtaining unbiased parameter estimates relaxing the within-class normality assumption and using data transformation on repeated measures. Based on unconditional GMM with two latent trajectories, data were generated under different sample sizes (300, 800, and 1500), skewness (0.7, 1.2, and 1.6) and kurtosis (2 and 4) of outcomes, numbers of time points (4 and 8), and class proportions (0.50.5 and 0.250.75). Of the four distributions, it was found that skew-t GMM had the highest accuracy in terms of parameter estimation. In GMM based on data transformations, the adjusted logarithmic method was more effective in obtaining unbiased parameter estimates than the use of van der Waerden quantile normal scores. Even though adjusted logarithmic transformation in nonnormal GMM reduced computation time, skew-t GMM produced much more accurate estimation and was more robust over a range of simulation conditions. This study is significant in that it considers different levels of kurtosis and class proportions, which has not been investigated in depth in previous studies. The present study is also meaningful in that investigated the applicability of data transformation to nonnormal GMM.Simulation studies involving mixture models inevitably aggregate parameter estimates and other output across numerous replications. A primary issue that arises in these methodological investigations is label switching. The current study compares several label switching corrections that are commonly used when dealing with mixture models. A growth mixture model is used in this simulation study, and the design crosses three manipulated variables-number of latent classes, latent class probabilities, and class separation, yielding a total of 18 conditions. Within each of these conditions, the accuracy of a priori identifiability constraints, a priori training of the algorithm, and four post hoc algorithms developed by Tueller et al.; Cho; Stephens; and Rodriguez and Walker are tested to determine their classification accuracy. Findings reveal that, of all a priori methods, training of the algorithm leads to the most accurate classification under all conditions. In a case where an a priori algorithm is not selected, Rodriguez and Walker's algorithm is an excellent choice if interested specifically in aggregating class output without consideration as to whether the classes are accurately ordered. Using any of the post hoc algorithms tested yields improvement over baseline accuracy and is most effective under two-class models when class separation is high. This study found that if the class constraint algorithm was used a priori, it should be combined with a post hoc algorithm for accurate classification.An essential question when computing test-retest and alternate forms reliability coefficients is how many days there should be between tests. This article uses data from reading and math computerized adaptive tests to explore how the number of days between tests impacts alternate forms reliability coefficients. Results suggest that the highest alternate forms reliability coefficients were obtained when the second test was administered at least 2 to 3 weeks after the first test. Even though reliability coefficients after this amount of time were often similar, results suggested a potential tradeoff in waiting longer to retest as student ability tended to grow with time. These findings indicate that if keeping student ability similar is a concern that the best time to retest is shortly after 3 weeks have passed since the first test. Additional analyses suggested that alternate forms reliability coefficients were lower when tests were shorter and that narrowing the first test ability distribution of examinees also impacted estimates. Results did not appear to be largely impacted by differences in first test average ability, student demographics, or whether the student took the test under standard or extended time. It is suggested that for math and reading tests, like the ones analyzed in this article, the optimal retest interval would be shortly after 3 weeks have passed since the first test.