This editorial suggests moving beyond relying on the dominant logic of multiple regression analysis (MRA) toward thinking and using algorithms in advancing and testing theory in accounting, consumer ...research, finance, management, and marketing. The editorial includes an example of testing an MRA model for fit and predictive validity. The same data used for the MRA is used to conduct a fuzzy-set qualitative comparative analysis (fsQCA). The editorial reviews a number of insights by prominent scholars including Gerd Gigerenzer's treatise that “Scientists' tools are not neutral.” Tools impact thinking and theory crafting as well theory testing. The discussion may be helpful for early career scholars unfamiliar with David C. McClelland's brilliance in data analysis and in introducing business research scholars to fsQCA as an alternative tool for theory development and data analysis.
This essay describes tenets of complexity theory including the precept that within the same set of data X relates to Y positively, negatively, and not at all. A consequence to this first precept is ...that reporting how X relates positively to Y with and without additional terms in multiple regression models ignores important information available in a data set. Performing contrarian case analysis indicates that cases having low X with high Y and high X with low Y occur even when the relationship between X and Y is positive and the effect size of the relationship is large. Findings from contrarian case analysis support the necessity of modeling multiple realities using complex antecedent configurations. Complex antecedent configurations (i.e., 2 to 7 features per recipe) can show that high X is an indicator of high Y when high X combines with certain additional antecedent conditions (e.g., high A, high B, and low C)—and low X is an indicator of high Y as well when low X combines in other recipes (e.g., high A, low R, and high S), where A, B, C, R, and S are additional antecedent conditions. Thus, modeling multiple realities—configural analysis—is necessary, to learn the configurations of multiple indicators for high Y outcomes and the negation of high Y. For a number of X antecedent conditions, a high X may be necessary for high Y to occur but high X alone is almost never sufficient for a high Y outcome.
The objective is to identify the degree of explanation of goal difference for different teams in the 2021-2022 Bundesliga season. A total of 306 matches were collected, with 34 matches per team. ...Among the ball-related variables, there are 15 simple and 4 composite variables. The data organization was done by team and in the complete competition. Multiple Linear Regression with backward model was applied as a statistical treatment. The set of explanatory variables was selected by identifying the highest adjusted R-squared value and VIF less than 10. The association degrees indicate high values in all scenarios (<0.750), with all teams analyzed individually showing higher values compared to the complete competition. Among the variables identified as explanatory, goals/shots stands out in 95% of the analyses. Shots from inside the box and shots/pass occur in 63% of the analyses, while opponent's block/shot (53%) and goalkeeper's save (47%) are the highlights in defensive actions. It is concluded that the set of explanatory variables is specific to each club, indicating their uniqueness in the game demands faced by the teams. The need for a specific game analysis for each team is also emphasized in order to better characterize the game demands.
The purpose of this study was to come up with a method to screen ultrasonication parameters that mainly predict ultrasound extraction process efficiency, using multiple regression analysis and ...response surface method (RSM). The relevance of this study is the fact that combining RSM with multiple regression modeling gives a conclusive report on the actual effects of individual treatment parameters. Although all predictors time (A), frequency (B), power (C) and ratio (D) showed significance in RSM models, multiple regression models further indicated that only D significantly influenced the variability observed in total phenol content and antioxidant activity of buckwheat extracts.
Continuous growth in fluoroarene production has led to environmental pollution and health concerns owing to their persistence, which is attributed to the stable C–F bond in their structures. Herein, ...we investigated fluoroarene decomposition via hydrodefluorination using a rhodium-based catalyst, focusing on the effects of the chemical structure and functional group on the defluorination yield. Most compounds, except (pentafluoroethyl)benzene, exhibited full or partial reduction with pseudo-first-order rate constants in the range of 0.002–0.396 min−1 and defluorination yields of 0%–100%. Fluoroarenes with hydroxyl, methyl, and carboxylate groups were selected to elucidate how hydrocarbon and oxygen-containing functional groups influence the reaction rate and defluorination. Inhibition of the reaction rate and defluorination yield based on functional groups increased in the order of hydroxyl < methyl < carboxylate, which was identical to the order of the electron-withdrawing effect. Fluoroarenes with polyfluoro groups were also assessed; polyfluoro groups demonstrated a different influence on catalyst activity than non-fluorine functional groups because of fluorine atoms in the substituents undergoing defluorination. The reaction kinetics of (difluoromethyl)fluorobenzenes and their intermediates suggested that hydrogenation and defluorination occurred during degradation. Finally, the effects of the type and position of functional groups on the reaction rate and defluorination yield were investigated via multivariable linear regression analysis. Notably, the electron-withdrawing nature of functional groups appeared to have a greater impact on the defluorination yield of fluoroarenes than the calculated C–F bond dissociation energy.
Display omitted
•Reductive treatment of 20 species of fluoroarene were conducted using Rh/alumina catalyst.•Types and position of functional groups in fluoroarenes influenced degradation and defluorination.•Low bond dissociation energy and high electron-withdrawing functional group enhanced defluorination.•Position of non-fluoro functional group influenced the degradation rate but not defluorination yield.•A multilinear regression model was developed to predict degradation and defluorination of fluoroarenes.
Stable carbon isotope ratios (δ13C) of soil record information regarding C3 and C4 plants at the landscape scale that can be used to document vegetation distribution patterns. The Central Brazilian ...savanna (locally called the Cerrado) has a substantial potential to develop studies of patterns of dynamics and distribution of soil δ13C, due to its environmental diversity. The purpose of this work was to develop a spatial model of soil δ13C (soil δ13C isoscape) to the Cerrado, based on multiple linear regression analysis, and compare the results with the existing model to obtain greater detail of the soil δ13C distribution. The model used 219 soil samples (0–20 cm depth) and a set of climatic, pedological, topographic, and vegetation correlations. The soil δ13C isoscape model presented amplitude between −29‰ and −13‰, with the highest estimated values in the southern and the lowest values in the northern of the Cerrado. Results indicate that soil δ13C, by reflecting the relative contribution of C3 and C4 species to plant community productivity, served as a proxy indicator of the vegetation history at the landscape scale for the Central Brazilian savanna. Despite the large sampling effort, there are still regions with some gaps that the model could not estimate. However, the soil δ13C isoscape model filled most the existing gaps and provided greater detail of some unique local aspects of the Cerrado.
•Climatic, topographic, soil and vegetation aspects explain the soil δ13C isoscape.•Modeled soil δ13C values decrease from south to north.•Modeled soil δ13C provided greater detail for the local aspects of the Cerrado.
Introduction: Early pregnancy hyperglycemia threshold (FPG ≥92mg/dl (≥5.1mmol/l)) suggested by International Association of Diabetes and Pregnancy Study Groups (IADPSG) is debateable. There is no ...evidence of their glycemic status at 24-28 weeks in Indians.
Aim: To understand the 24-28 weeks glycemic status in women with early pregnancy hyperglycemia.
Methods: STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, designed to identify the role of HbA1c in early pregnancy on incident GDM, recruited 2703 pregnant women from 7 centres in south India and 566 (20.9%) women had early pregnancy hyperglycemia. Of these 477 women underwent 24-28 weeks screening (OGTT n=150, FPG n=327)
Results: Abnormal glucose values were present in 32.7% of women at 24-28 weeks (high FPG or GDM by OGTT). These women had higher weight, BMI, waist circumference, family history of diabetes, FPG, and HbA1c at booking compared to women who were normoglycaemic. In multiple regression analysis, early pregnancy FPG ≥95mg/dl (5.3mmol/l) was independently associated with abnormal glucose values at 24-28 weeks (aOR: 1.9; 95% CI: 1.3-3.0, p <0.001), adjusted for key covariates.
Conclusion: Majority of women who had early pregnancy hyperglycaemia became normoglycaemic at 24-28 weeks. HAPO study FPG threshold for the adverse outcomes with aOR: 2.0 was ≥95mg/dl. It may be prudent to classify Indian women with this threshold in early pregnancy as abnormal.
Disclosure
W.Hannah: None. M.Deepa: None. C.Shivashri: None. H.Saite: None. U.Ram: None. R.Anjana: None. Y.Ghebremichael-weldeselassie: None. P.Saravanan: Other Relationship; Novo Nordisk, Research Support; Novo Nordisk, Amgen Inc., Abbott. V.Mohan: None.
Funding
Medical Research Council, UK (MR/N006232/1)
When data contradict theory, data usually win. Yet, the conclusion of Van Iddekinge, Aguinis, Mackey, and DeOrtentiis (2018) that performance is an additive rather than multiplicative function of ...ability and motivation may not be valid, despite applying a meta-analytic lens to the issue. We argue that the conclusion was likely reached because of a common error in the interpretation of moderated multiple-regression results. A Monte Carlo study is presented to illustrate the issue, which is that moderated multiple regression is useful for detecting the presence of moderation but typically cannot be used to determine whether or to what degree the constructs have independent or nonjoint (i.e., additive) effects beyond the joint (i.e., multiplicative) effect. Moreover, we argue that the practice of interpreting the incremental contribution of the interaction term when added to the first-order terms as an effect size is inappropriate, unless the interaction is perfectly symmetrical (i.e., X-shaped), because of the partialing procedure that moderated multiple regression uses. We discuss the importance of correctly specifying models of performance as well as methods that might facilitate drawing valid conclusions about theories with hypothesized multiplicative functions. We conclude with a recommendation to fit the entire moderated multiple-regression model in a single rather than separate steps to avoid the interpretation error highlighted in this article.
Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. An important source of this uncertainty lies in the dependency of photosynthesis on the ...maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax). Understanding and making accurate prediction of C fluxes thus requires accurate characterization of these rates and their relationship with plant nutrient status over large geographic scales. Plant nutrient status is indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Correlations between Vcmax and Jmax and leaf nitrogen (N) are typically derived from local to global scales, while correlations with leaf phosphorus (P) and specific leaf area (SLA) have typically been derived at a local scale. Thus, there is no global‐scale relationship between Vcmax and Jmax and P or SLA limiting the ability of global‐scale carbon flux models do not account for P or SLA. We gathered published data from 24 studies to reveal global relationships of Vcmax and Jmax with leaf N, P, and SLA. Vcmax was strongly related to leaf N, and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N. Jmax was strongly related to Vcmax, and neither leaf N, P, or SLA had a substantial impact on the relationship. Although more data are needed to expand the applicability of the relationship, we show leaf P is a globally important determinant of photosynthetic rates. In a model of photosynthesis, we showed that at high leaf N (3 gm−2), increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally, we show that plants may employ a conservative strategy of Jmax to Vcmax coordination that restricts photoinhibition when carboxylation is limiting at the expense of maximizing photosynthetic rates when light is limiting.
Great uncertainty exists in the global exchange of carbon between the atmosphere and the terrestrial biosphere. To reduce this uncertainty we analysed data collected in the literature from across the globe on the maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) in relation to plant nutrient status indicated by the traits: leaf nitrogen (N), leaf phosphorus (P), and specific leaf area (SLA). Vcmax was strongly related to leaf N and increasing leaf P substantially increased the sensitivity of Vcmax to leaf N and in a model of photosynthesis we showed that at high leaf N (3 gm−2) increasing leaf P from 0.05 to 0.22 gm−2 nearly doubled assimilation rates. Finally we show that plants may employ a conservative strategy of Jmax to Vcmax co‐ordination that restricts photoinhibition when carboxylation is limiting at the expense of maximising photosynthetic rates when light is limiting.
Van Iddekinge et al. (2018)'s meta-analysis revealed that ability and motivation have mostly an additive rather than an interactive effect on performance. One of the methods they used to assess the ...ability × motivation interaction was moderated multiple regression (MMR). Vancouver et al. (2021) presented conceptual arguments that ability and motivation should interact to predict performance, as well as analytical and empirical arguments against the use of MMR to assess interaction effects. We describe problems with these arguments and show conceptually and empirically that MMR (and the ΔR and ΔR2 it yields) is an appropriate and effective method for assessing both the statistical significance and magnitude of interaction effects. Nevertheless, we also applied the alternative approach Vancouver et al. recommended to test for interactions to primary data sets (k = 69) from Van Iddekinge et al. These new results showed that the ability × motivation interaction was not significant in 90% of the analyses, which corroborated Van Iddekinge et al.'s original conclusion that the interaction rarely increments the prediction of performance beyond the additive effects of ability and motivation. In short, Van Iddekinge et al.'s conclusions remain unchanged and, given the conceptual and empirical problems we identified, we cannot endorse Vancouver et al.'s recommendation to change how researchers test interactions. We conclude by offering suggestions for how to assess and interpret interactions in future research.
PDF
