Dominance Analysis Research Articles

Quantifying the relative importance of influencing factors on NPP in Hengduan Mountains of the Tibetan Plateau from 2002 to 2021: A Dominance Analysis

Net primary productivity (NPP) stands as a crucial indicator of ecosystem health and carbon cycling, shaped by a complex interplay of climate change and anthropogenic disturbances. Understanding the relative contributions of these factors is essential for informing strategies aimed at achieving carbon neutrality and peaking emissions. In this study, we used the Carnegie-Ames-Stanford-Approach (CASA) model to assess NPP dynamics in the ecologically fragile area of the middle Hengduan Mountains on the southeastern Tibetan Plateau (TPHM). Dominance Analysis (DA) was employed to quantify and visualize the relative importance of climate change and anthropogenic disturbances on NPP. Our findings revealed significant annual fluctuations in NPP, with 61.8% of the area experiencing improvement in NPP, although the overall trend was not statistically significant. Needleleaf forests and mixed forests below medium elevation (< 4000 m) suffered negative impacts from human activity intensity, while vegetation at medium-high elevation (4000–5000 m) showed positive effects. The area dominated by anthropogenic disturbances influencing NPP accounted for 49.16% of the study area, followed by combined effects (36.86%) and climatic factors (13.98%). Human activity intensity, particularly in nature reserves, emerged as the predominant driver of NPP in plateau regions, while climatic factors primarily drove NPP decline in river valleys and a few alpine mountains. Moreover, combined effects contributed to NPP enhancement in most regions. This study underscores the critical role of human activity intensity in shaping NPP dynamics in high-elevation regions of the Tibetan Plateau, providing valuable insights for ecological conservation and carbon stock enhancement efforts in ecologically fragile areas.

Who emits more emission? the association between CO2 emissions and socio-economics characteristics of Indonesian household

Much research has been done on identifying socio-economic household links in developed countries. However, the study of household carbon emission (HCE) levels and related variables still needs to be examined, especially in developing countries. The study uses an ordinary least squares model to pinpoint the socio-economic elements that affect a household's carbon emission levels. SUSENAS (National Socio-economic Survey) data from March 2019 and 2021, covering 655,694 households, were used. This study used ordinary least squares (OLS) for the regression and dominance analyses (DA) to determine the most crucial factors affecting the HCE. The household characteristics, individuals, and residential conditions are used to measure socio-economic situations. The DA analysis shows that income and household size are the most crucial determinants of HCE. The OLS analysis reveals that the income variable exhibits a non-linear relationship with HCE as an inverted U-shape in the total HCE and most consumption categories. Wealthier households generate higher levels of household carbon emissions than poorer households. The variable of household size demonstrates a positive relationship with the HCE. The composition of household members also significantly affects household carbon emission levels, where the presence of working members and toddlers tends to increase household carbon emissions. The research also finds differences in consumption patterns between urban and rural households, resulting in varying levels of carbon emissions. The findings of this study can assist policymakers in formulating targeted policies to reduce household carbon emissions.

Influences of Different Factors on Gravity Wave Activity in the Lower Stratosphere of the Indian Region

The gravity wave (GW) potential energy (Ep) in the lower stratosphere (LS) of the altitude range between 20 and 30 km over the Indian region (60°E–100°E, 0°–30°N) is retrieved using the dry temperature profiles from the Constellation Observing System for Meteorology Ionosphere and Climate-2 (COSMIC-2) radio occultation (RO) mission from December 2019 to November 2021. Through correlation analysis and dominance analysis (DA) methods, the impacts of multiple influencing factors on the local LS GW activity are quantified and compared. The results demonstrate that in the central and northern part of Indian region, the three factors, including the convective activity (using outgoing long-wave radiation as the proxy) mainly caused by the Indian summer monsoon, the mean zonal wind speed between 15 and 17 km, the height range where the maximum tropical easterly jet (TEJ) wind speed appears, and the mean zonal wind speed between 20 and 30 km, have the greatest impacts on the LS GW activity. In the southern part of the Indian Peninsula and over the Indian Ocean, the mean zonal wind shear between 20 and 30 km plays a dominant role in the LS GW activity, which is due to the fact that the GW energy can be attenuated by large background wind shears. It can be concluded that the LS GW activity in the Indian region is mainly influenced by the Indian summer monsoon, the TEJ, and the wind activity in the LS, while over different local areas, differences exist in which factors are the dominant ones.

Digitalization of HRM for strategic human resource management orientation: Logistic regression and dominance analysis approach

Background: According to the contemporary HRM theory, strategic human resource management (SHRM) orientation, as a company's philosophy of aligning human resource management (HRM) policies and practices with an organizational strategy for maximizing organizational results, is considered the highest level of a company's HRM development. At the same time, one of the trends in HRM is the digitalization of HRM activities, following the global trend of adopting digital tools for increasing individual and organizational efficiency and effectiveness. The question that consequently arises is whether the digital component entered the SHRM perspective, in other words, whether SHRM orientation could exist without digitalization of HRM endeavours. Purpose: The paper strives to provide answers, both theoretically and empirically, to the following central and three supporting research questions: CRQ = Is digitalization of HRM contributing to the strategic HRM orientation of a company?, SRQ1 = Are digital HRM practices positively related to the SHRM orientation?, SRQ2 = Are some digital HRM practices more relevant for the SHRM orientation than others?, and SRQ3 = If the relationship between digital HRM practices and SHRM orientation does exist, which digital HRM practice is the most relevant one for SHRM orientation? Study design/methodology/approach: To provide empirically based answers to research questions, we utilized the CRANET 2021 dataset, covering 4495 organizations with more than 200 employees from 38 countries worldwide. We used nine indicators for revealing the existence of digital HRM (manager self-service, employee self-service, HRIS, algorithm-based HRM, HR analytics, telework, digital learning, social media recruitment, social media selection) and five indicators for determining the SHRM orientation (HR manager's membership in the management board or equivalent; involvement of HR manager in the development of business strategy; existence of written HRM strategy; HR to employee ratio, the level of HR department performance evaluation). SHRM orientation was determined using K-Means cluster analysis, and relationships between variables were analysed using correlation analysis, multiple logistic regression and dominance analysis (DA). Findings/conclusions: Correlation analysis revealed that all indicators of digitalization of HRM processes have a positive and a significant relationship with SHRM orientation, implying that digital HRM practices are relevant for SHRM orientation. The results of logistic regression indicated that the usage of manager self-service, algorithm-based HRM, HR analytics, telework, digital learning and social media selection increase the probability of organizations' SHRM orientation more than other explored indicators. The results of DA demonstrate that the most important digital HRM practices for the SHRM orientation is digital learning. Limitations/future research: As CRANET dataset is limited in its variables of digital HRM and indicators of SHRM orientation, further research is needed to confirm our findings.

Evaluating how facets of openness to experience predict music preference

Listening to music is a widespread activity. Openness to experience in particular has been found to be one of the dimensions of personality that most consistently predicts music preference. However, the singular facets of openness to experience have never been looked at in depth. This study tried to uncover the impact of the openness-to-experience facets of both the five-factor (FFM) and HEXACO models of personality on music preference. A total of 374 college students completed two openness-to-experience measures (FFM and HEXACO) and one music preference measure (ratings of music excerpts). In line with Rentfrow and colleagues, confirmatory factor analysis (CFA) suggested five dimensions of music preference. Dominance analysis (DA) was used to evaluate the relative importance of each facet in predicting music preference. It was observed that openness facets relate to music preference in ways that are not apparent when general openness to experience is used as a predictor of music preference.

Dominance Analysis for Latent Variable Models: A Comparison of Methods With Categorical Indicators and Misspecified Models.

Dominance analysis (DA) is a very useful tool for ordering independent variables in a regression model based on their relative importance in explaining variance in the dependent variable. This approach, which was originally described by Budescu, has recently been extended to use with structural equation models examining relationships among latent variables. Research demonstrated that this approach yields accurate results for latent variable models involving normally distributed indicator variables and correctly specified models. The purpose of the current simulation study was to compare the use of this DA approach to a method based on observed regression DA and DA when the latent variable model is estimated using two-stage least squares for latent variable models with categorical indicators and/or model misspecification. Results indicated that the DA approach for latent variable models can provide accurate ordering of the variables and correct hypothesis selection when indicators are categorical and models are misspecified. A discussion of implications from this study is provided.

Determination of Fish Diversity in the Northern Coasts of Cyprus (Eastern Mediterranean) by Visual Census Method

The present study aims to determine fish assemblages spreading between 0 and 40 m depth on the northern coasts of Cyprus. During the study, Underwater Observations (UO) have been conducted in 54 locations and photographed the fish species. The hourly imaging figures of the recorded species were determined by using the Time-Transect Method (TTM). Also, dominance Analysis (DA) and Incidence Frequency Analysis (IFA) of the identified species were performed. As a result, 72 different fish species belonging to 26 families were found to occur in the studied area. After the evaluation of identified species, 56 of them were determined as native species of the Mediterranean and 14 of them as Indo-Pacific origin. Additionally, we provide two new records (Dasyatis chrysonota and Gobius fallax) from Cyprus.

Biting the Bullet: Firearm Ownership in Persons with Dementia. A Registry-Based Observational Study.

Background:In Sweden, 2,296,000 firearms were legally owned by private persons in 2017 and there were 150,000 persons living with a dementia diagnosis. A proportion of these persons owning a firearm may pose safety concerns.Objective:The aim was to describe firearm ownership in persons with dementia in Sweden and examine which characteristics are explaining physicians’ decision to report a person to the police as unsuitable to possess a firearm.Methods:This was a registry-based observational study. 65,717 persons with dementia registered in the Swedish Dementia Registry were included in the study. Logistic regression was used to evaluate which of the persons’ characteristics were most important in predicting the likelihood of being reported as unsuitable to possess a firearm. Relative importance of predictors was quantified using standardized coefficients (SC) and dominance analysis (DA).Results:Out of 53,384 persons with dementia, 1,823 owned a firearm and 419 were reported to the police as unsuitable owners. Firearm owners were predominantly younger, males, living alone, and without assistance of homecare. The most important predictors of being reported to the police were: living with another person (SC = 0.23), frontotemporal dementia (SC = 0.18), antipsychotics prescription (SC = 0.18), being diagnosed in a memory/cognitive clinic (SC = –0.27), female gender (SC = 0.18), mild (SC = –0.25) and moderate (SC = –0.21) dementia, and hypnotics prescription (SC = 0.17).Conclusion:Firearm owners with dementia were mostly younger males who were still living more independent lives. The decision to remove a weapon was not solely based on a diagnosis of dementia but a combination of factors was considered.

Testing Demands and Resources as Determinants of Vitality among Different Employment Contract Groups. A Study in 30 European Countries.

The aim of this study was to investigate the relative importance of four job demands and five job resources for employee vitality, i.e., work engagement and exhaustion, in three different employment groups: permanent, temporary and temporary agency workers. We employed data from the sixth European Working Conditions Survey (EWCS) collected in 2015 comprising 28,042 employees from 30 European countries. We used linear regression analyses and dominance analysis (DA). The results showed minor mean differences in work engagement and exhaustion and that temporary agency workers had the highest job insecurity and lowest job control. The associations between job resources and job demands, and work engagement and exhaustion of the groups, did not differ considerably. DA showed that in all three employment groups, job feedback made the strongest contribution to work engagement and workload to exhaustion. In addition, among the temporary agency workers, supervisor support contributed to work engagement and job control (negatively) to exhaustion more than in the other groups. This study suggests that the key determinants of vitality at work may be similar, regardless of contract, and that to have sustainably performing vital workers, organizations should focus on enabling job feedback and preventing high workload in all employment groups.

Plant structure as a determinant of coriander (Coriandrum sativum L.) seed and straw yield

Abstract Coriander (Coriandrum sativum L., Apiaceae) is a Mediterranean aromatic plant that is mainly cultivated for its fruits. Understanding the association between various vegetative and reproductive structures as they pertain to yield can help addressing efforts for achieving the highest productivity levels, by improving both cropping techniques and breeding. Multiple Regression (MR) analysis is a suitable tool to understand this association; notwithstanding, due to the plant’s inherent “weedy” growth, the interpretation of MR results in coriander is often difficult. Therefore, in our analysis we also calculated additional indicators of variable contribution to the model and to one another. We analyzed data (n = 193) obtained from field experiments on coriander carried out in Sicily from 1999 to 2007. Nine plant variables were submitted to different MR approaches, including Path Analysis (PA), Commonality Analysis (CA) and Dominance Analysis (DA). All MR analyses attributed to the number of umbels per plant the highest importance in assessing both seed and straw yield. Additionally, the mass of one umbel proved to be a strong determinant of fruit yield per plant, whereas the umbel diameter was relevant for the assessment of plant straw production. This information will be useful for addressing coriander cropping technique and breeding.

The accuracy of dominance analysis as a metric to assess relative importance: The joint impact of sampling error variance and measurement unreliability.

Dominance analysis (DA) has been established as a useful tool for practitioners and researchers to identify the relative importance of predictors in a linear regression. This article examines the joint impact of two common and pervasive artifacts-sampling error variance and measurement unreliability-on the accuracy of DA. We present Monte Carlo simulations that detail the decrease in the accuracy of DA in the presence of these artifacts, highlighting the practical extent of the inferential mistakes that can be made. Then, we detail and provide a user-friendly program in R (R Core Team, 2017) for estimating the effects of sampling error variance and unreliability on DA. Finally, by way of a detailed example, we provide specific recommendations for how researchers and practitioners should more appropriately interpret and report results of DA. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Exploratory regression analysis of the geometric targeting accuracy of the Cyberknife system

Quality Assurance is an important part of a safe radiotherapy service. The End to End (E2E) test assesses the overall spatial targeting accuracy of the integrated CyberKnife system for multiple beams delivered isocentrically. The targeting accuracy is quantified based on the dose distribution recorded on film. The complexity of the entire process suggests that data-driven approaches can provide robust solutions for the detection and prediction of errors. Regression analysis is a statistical technique for determining the relationship between a single dependent (criterion) variable (in this analysis: total targeting error) and one or more independent (predictor) variables, which are specific error components from the Anterior-Posterior(AP), Left–Right(LR) or Superior–Inferior(SI) directions. Dominance analysis (DA) was used as a method to find whether one independent variable contributes more variance to the regression effect than another independent variable on average across all possible set of independent variables. We analysed the E2E results for different tracking modalities: Stationary modes (6d Skull, Fiducials, Xsight Spine) and Motion Tracking Modes (Synchrony with fiducials, and XsightLung tracking). In the stationary tracking modes the largest contribution to the total targeting error for the trigeminal, 1path_head and 1path_body path sets was from the LR, AP and AP error components, respectively. For the Motion tracking mode with 1path_body, the most dominant component of the total error was from the SI direction.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000–2015 using quantile and multiple line regression models

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000–2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Determining Predictor Weights in Military Selection: An Application of Dominance Analysis

This article illustrates the use of dominance analysis (DA) to identify the most appropriate set of weights for combining predictor data. We used meta-analytic information on cognitive ability, structured interviews, conscientiousness, and emotional stability to determine relative weights in predicting entry-level job performance and turnover. For job performance, the optimal predictor weights were 51% (cognitive ability), 38% (structured interview), 9% conscientiousness, and 2% (emotional stability). The weights differed considerably for turnover: 48% (structured interview), 27% (emotional stability), 13% (conscientiousness), and 11% (cognitive ability). Using a simulated data set, we showed how the rank-order of applicants can change based on DA and traditional regression analysis. The results suggest that DA has wide applicability for military selection.

Relative Importance Analysis With Multicategory Dependent Variables:

Determining independent variable relative importance is a highly useful practice in organizational science. Whereas techniques to determine independent variable importance are available for normally distributed and binary dependent variable models, such techniques have not been extended to multicategory dependent variables (MCDVs). The current work extends previous research on binary dependent variable relative importance analysis to provide a methodology for conducting relative importance analysis on MCDV models from a dominance analysis (DA) perspective. Moreover, the current work provides a set of comprehensive data analytic examples that demonstrate how and when to use MCDV models in a DA and the advantages general DA statistics offer in interpreting MCDV model results. Moreover, the current work outlines best practices for determining independent variable relative importance for MCDVs using replicable examples on data from the publicly available General Social Survey. The present work then contributes to the literature by using in-depth data analytic examples to outline best practices in conducting relative importance analysis for MCDV models and by highlighting unique information DA results provide about MCDV models.

Evaluating Predictors of Dispersion: A Comparison of Dominance Analysis and Bayesian Model Averaging

Conventional measures of predictor importance in linear models are applicable only when the assumption of homoscedasticity is satisfied. Moreover, they cannot be adapted to evaluating predictor importance in models of heteroscedasticity (i.e., dispersion), an issue that seems not to have been systematically addressed in the literature. We compare two suitable approaches, Dominance Analysis (DA) and Bayesian Model Averaging (BMA), for simultaneously evaluating predictor importance in models of location and dispersion. We apply them to the beta general linear model as a test-case, illustrating this with an example using real data. Simulations using several different model structures, sample sizes, and degrees of multicollinearity suggest that both DA and BMA largely agree on the relative importance of predictors of the mean, but differ when ranking predictors of dispersion. The main implication of these findings for researchers is that the choice between DA and BMA is most important when they wish to evaluate the importance of predictors of dispersion.

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in HLM (i.e., deviance, pseudo- R2, and proportional reduction in prediction error) were evaluated in terms of their appropriateness as measures of model adequacy for DA. Empirical examples were used to illustrate the procedures for comparing the relative importance of Level-1 predictors and Level-2 predictors in a person-in-group design. Finally, a simulation study was conducted to evaluate the performance of the proposed procedures and develop recommendations.

Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis

Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.