Assembly and Disassembly of Supramolecular Hypervalent Iodine Macrocycles via Anion Coordination.

This study explores the dynamic self-assembly and disassembly of hypervalent iodine-based macrocycles (HIMs) guided by secondary bonding interactions. The reversible disassembly and reassembly of HIMs are facilitated through anion binding via the addition of tetrabutylammonium (TBA) salts or removal of the anion by the addition of silver nitrate. The association constants for HIM monomers with TBA(Cl) and TBA(Br) are calculated and show a correlation with the strength of the iodine-anion bond. A unique tetracoordinate hypervalent iodine-based compound was identified as the disassembled monomer. Last, the study reveals the dynamic bonding nature of these macrocycles in solution, allowing for rearrangement and participation in dynamic bonding chemistry.

Interrogating the Mechanistic Features of Ni(I)-Mediated Aryl Iodide Oxidative Addition Using Electroanalytical and Statistical Modeling Techniques.

While the oxidative addition of Ni(I) to aryl iodides has been commonly proposed in catalytic methods, an in-depth mechanistic understanding of this fundamental process is still lacking. Herein, we describe a detailed mechanistic study of the oxidative addition process using electroanalytical and statistical modeling techniques. Electroanalytical techniques allowed rapid measurement of the oxidative addition rates for a diverse set of aryl iodide substrates and four classes of catalytically relevant complexes (Ni(MeBPy), Ni(MePhen), Ni(Terpy), and Ni(BPP)). With >200 experimental rate measurements, we were able to identify essential electronic and steric factors impacting the rate of oxidative addition through multivariate linear regression models. This has led to a classification of oxidative addition mechanisms, either through a three-center concerted or halogen-atom abstraction pathway based on the ligand type. A global heat map of predicted oxidative addition rates was created and shown applicable to a better understanding of the reaction outcome in a case study of a Ni-catalyzed coupling reaction.

Investigating Oxidative Addition Mechanisms of Allylic Electrophiles with Low-Valent Ni/Co Catalysts Using Electroanalytical and Data Science Techniques.

The catalysis by a π-allyl-Co/Ni complex has drawn significant attention recently due to its distinct reactivity in reductive Co/Ni-catalyzed allylation reactions. Despite significant success in reaction development, the critical oxidative addition mechanism to form the π-allyl-Co/Ni complex remains unclear. Herein, we present a study to investigate this process with four catalysis-relevant complexes: Co(MeBPy)Br2, Co(MePhen)Br2, Ni(MeBPy)Br2, and Ni(MePhen)Br2. Enabled by an electroanalytical platform, Co(I)/Ni(I) species were found responsible for the oxidative addition of allyl acetate. Kinetic features of different substrates were characterized through linear free-energy relationship (Hammett-type) studies, statistical modeling, and a DFT computational study. In this process, a coordination-ionization-type transition state was proposed, sharing a similar feature with Pd(0)-mediated oxidative addition in Tsuji-Trost reactions. Computational and ligand structural analysis studies support this mechanism, which should provide key information for next-generation catalyst development.

Using Repeated Ratings to Improve Measurement Precision in Incomplete Rating Designs.

When selecting a design for rater-mediated assessments, one important consideration is the number of raters who rate each examinee. In balancing costs and rater-coverage, rating designs are often implemented wherein only a portion of the examinees are rated by each judge, resulting in large amounts of missing data. One drawback to these sparse rating designs is the reduced precision of examinee ability estimates they provide. When increasing the number of raters per examinee is not feasible, another option may be to increase the number of ratings provided by each rater per examinee. This study applies a Rasch model to explore the effect of increasing the number of rating occasions used by raters to judge examinee proficiency. We used a simulation study to approximate a sparse but connected rater network with a sequentially increasing number of repeated ratings per examinee. The generated data were used to explore the influence of repeated ratings on the precision of rater, examinee, and task parameter estimates as measured by parameter standard errors, the correlation of sparse parameter estimates to true estimates, and the root mean square error of parameter estimates. Results suggest that increasing the number of rating occasions significantly improves the precision of examinee and rater parameter estimates. Results also suggest that parameter recovery levels of rater and task estimates are quite robust to reductions in the number of repeated ratings, although examinee parameter estimates are more sensitive to them. Implications for research and practice in the context of rater-mediated assessment designs are discussed.

Does Sparseness Matter? Examining the Use of Generalizability Theory and Many-Facet Rasch Measurement in Sparse Rating Designs.

Sparse rating designs, where each examinee's performance is scored by a small proportion of raters, are prevalent in practical performance assessments. However, relatively little research has focused on the degree to which different analytic techniques alert researchers to rater effects in such designs. We used a simulation study to compare the information provided by two popular approaches: Generalizability theory (G theory) and Many-Facet Rasch (MFR) measurement. In previous comparisons, researchers used complete data that were not simulated-thus limiting their ability to manipulate characteristics such as rater effects, and to understand the impact of incomplete data on the results. Both approaches provided information about rating quality in sparse designs, but the MFR approach highlighted rater effects related to centrality and bias more readily than G theory.

Comparing Person-Fit and Traditional Indices Across Careless Response Patterns in Surveys.

Methods to identify carelessness in survey research can be valuable tools in reducing bias during survey development, validation, and use. Because carelessness may take multiple forms, researchers typically use multiple indices when identifying carelessness. In the current study, we extend the literature on careless response identification by examining the usefulness of three item-response theory-based person-fit indices for both random and overconsistent careless response identification: infit MSE outfit MSE, and the polytomous lz statistic. We compared these statistics with traditional careless response indices using both empirical data and simulated data. The empirical data included 2,049 high school student surveys of teaching effectiveness from the Network for Educator Effectiveness. In the simulated data, we manipulated type of carelessness (random response or overconsistency) and percent of carelessness present (0%, 5%, 10%, 20%). Results suggest that infit and outfit MSE and the lz statistic may provide complementary information to traditional indices such as LongString, Mahalanobis Distance, Validity Items, and Completion Time. Receiver operating characteristic curves suggested that the person-fit indices showed good sensitivity and specificity for classifying both over-consistent and under-consistent careless patterns, thus functioning in a bidirectional manner. Carelessness classifications based on low fit values correlated with carelessness classifications from LongString and completion time, and classifications based on high fit values correlated with classifications from Mahalanobis Distance. We consider implications for research and practice.

The Stabilizing Influences of Linking Set Size and Model-Data Fit in Sparse Rater-Mediated Assessment Networks.

Previous research includes frequent admonitions regarding the importance of establishing connectivity in data collection designs prior to the application of Rasch models. However, details regarding the influence of characteristics of the linking sets used to establish connections among facets, such as locations on the latent variable, model-data fit, and sample size, have not been thoroughly explored. These considerations are particularly important in assessment systems that involve large proportions of missing data (i.e., sparse designs) and are associated with high-stakes decisions, such as teacher evaluations based on teaching observations. The purpose of this study is to explore the influence of characteristics of linking sets in sparsely connected rating designs on examinee, rater, and task estimates. A simulation design whose characteristics were intended to reflect practical large-scale assessment networks with sparse connections were used to consider the influence of locations on the latent variable, model-data fit, and sample size within linking sets on the stability and model-data fit of estimates. Results suggested that parameter estimates for examinee and task facets are quite robust to modifications in the size, model-data fit, and latent-variable location of the link. Parameter estimates for the rater, while still quite robust, are more sensitive to reductions in link size. The implications are discussed as they relate to research, theory, and practice.

The attenuating effect of role overload on relationships linking self-efficacy and goal level to work performance.

The reported research examines the moderating effects of role overload on the antecedents and consequences of self-efficacy and personal goal level in a longitudinal study conducted in an industrial selling context. The results indicate that role overload moderates the antecedent effect of perceived organizational resources on self-efficacy beliefs. They also show that role overload moderates the direct effects of both self-efficacy and goal level on performance, such that these relationships are positive when role overload is low but not significant when role overload is high. Further, the results reveal a pattern of moderated mediation, in which goal level mediates the indirect effect of self-efficacy on performance when role overload is low but not when it is high. Implications for theory and managerial practice are discussed.