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How much should we pay to remove the interdependence of biased information sources? This question is relevant in both statistics and political economy. When there are many information sources or variables, their dependence may be unknown, which creates multivariate ambiguity. One approach to answer our leading question involves use of decoupling inequalities from probability theory. We present a new inequality, designed to cope with this question, which holds for any type of dependence across information sources. We apply our method to a simple formalization of a political echo chamber. For a given set of marginal information, this bound is the sup over all possible joint distributions connecting the marginals. Our method highlights a price to pay for facing summed dependent (multivariate) data, similar to the probability premium required for univariate data. We show that a conservative decisionmaker will pay approximately 50% more than if the data were independent, in order to freely neglect the correlations.

Abstract. The erosion of a beach depends on various storm characteristics. Ideally, the risk associated with a storm would be described by a single multivariate return period that is also representative of the erosion risk, i.e. a 100 yr multivariate storm return period would cause a 100 yr erosion return period. Unfortunately, a specific probability level may be associated with numerous combinations of storm characteristics. These combinations, despite having the same multivariate probability, may cause very different erosion outcomes. This paper explores this ambiguity problem in the context of copula based multivariate return periods and using a case study at Durban on the east coast of South Africa. Simulations were used to correlate multivariate return periods of historical events to return periods of estimated storm induced erosion volumes. In addition, the relationship of the most-likely design event (Salvadori et al., 2011) to coastal erosion was investigated. It was found that the multivariate return periods for wave height and duration had the highest correlation to erosion return periods. The most-likely design event was found to be an inadequate design method in its current form. We explore the inclusion of conditions based on the physical realizability of wave events and the use of multivariate linear regression to relate storm parameters to erosion computed from a process based model. Establishing a link between storm statistics and erosion consequences can resolve the ambiguity between multivariate storm return periods and associated erosion return periods.

The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution - alternating least squares (MCR-ALS), which aims at reproducing raw (mixed) data by a bilinear model of chemically meaningful profiles. In many cases, the mixed raw data analyzed are not sparse by nature, but their decomposition profiles can be, as it is the case in some instrumental responses, such as mass spectra, or in concentration profiles linked to scattered distribution maps of powdered samples in hyperspectral images. To induce sparseness in the constrained profiles, one-dimensional and/or two-dimensional numerical arrays can be fitted using a basis of Gaussian functions with a penalty on the coefficients. In this work, a least squares regression framework with L0-norm penalty is applied. This L0-norm penalty constrains the number of non-null coefficients in the fit of the array constrained without having an a priori on the number and their positions. It has been shown that the sparseness constraint induces the suppression of values linked to uninformative channels and noise in MS spectra and improves the location of scattered compounds in distribution maps, resulting in a better interpretability of the constrained profiles. An additional benefit of the sparseness constraint is a lower ambiguity in the bilinear model, since the major presence of null coefficients in the constrained profiles also helps to limit the solutions for the profiles in the counterpart matrix of the MCR bilinear model. © 2017 Elsevier B.V. ; The authors thank Paul Eilers for his involvement in the development of the algorithms and appreciate the fruitful discussions on the topic. C. R. thanks the Agence National de la Recherche ( ANR-15-CE09-0020-01 Blink). C.B. and A.J have received funding from the European Research Council under the European Union's Seventh Framework Program ( FP/2007-2013 )/ ERC Grant Agreement n. 320737 (CHEMAGEB project). S.P. acknowledges the support of the Spanish government through project CTQ2015-66254-C2-2-P . ; Peer reviewed

Prior research documents widespread deficiencies in the quality and completeness of official criminal records in the United States. In an era when the social reach of criminal records has expanded to an unprecedented degree, these deficiencies carry serious consequences for criminal record subjects. The present study develops the concept of punitive ambiguity to characterize the burdens of incomplete criminal records and examines how they vary at the state level, providing evidence that punitive ambiguity is racially patterned. Using data from the biennial Surveys of State Criminal History Information Systems, multivariate analyses find that states where African Americans make up larger shares of felony record populations report rap sheet dispositions at significantly lower levels, pairing low criminal record data quality with extensive legally-mandated background screening. The results carry implications for understanding the racialized burdens of a criminal record, as well as broader processes in the development of the American penal state that combine harsh formal punishments with chronic administrative neglect.

We investigate performance measurement information (PMI) and role stress in local government using a Multivariate Analysis of Variance. We find that role ambiguity for public service managers who receive both non-financial and financial PMI is lower than for those who receive either non-financial information only, or financial information only. Performance is significantly higher when role ambiguity is low. Our results indicate that role ambiguity fully mediates the effects of PMI on performance, but there is no evidence that role conflict mediates those effects. Role ambiguity and role conflict do not mediate the association between job rotation and performance. ; peer-reviewed

This micro-level study combines multivariate and qualitative analyses to highlight the fragmented nature of debt in southern Indian rural households. It finds that debt is socially regulated in the sense that social interactions shape the cost, use and access to debt. Caste, social class and location affect how individuals borrow varying amounts from distinct money providers, for varied purposes and at differing costs. Debt thus is not purely an economic but first and foremost a social transaction which inscribes debtors and creditors into local systems of hierarchies. Furthermore, we find that debt is an illustration and catalyst of broader socio-economic and political trends, namely a lack of social protection, persistent under-employment and rising consumerism. In terms of policy implications, the study highlights the ambiguities and illusions inherent to 'financial inclusion' policies aiming to eradicate informal debt. Adapted from the source document.

AbstractMuch of the research on correctional staff over the past two decades has focused on how the job environment and demographic variables influence job stress and job satisfaction. The issue of job involvement has largely been ignored. As correctional employees are such an important part of corrections, understanding how job stressors affect job involvement is essential. An analysis of survey data from a private correctional facility in the US Midwest examined the effects of the relationship between the job stressors of role conflict, role ambiguity, role overload, and perceived dangerous of the job with job involvement. Multivariate analysis with ordinary least squares (OLS) regression indicated that role conflict and dangerousness of the job had significant effects on job involvement, but role ambiguity and role overload did not. It is hoped that this study will spark further research in the area.

AbstractThe emergence of various Corporate Social Responsibility (CSR) disclosure laws around the globe, in the past decade, indicates the significance of CSR for a country as well as for an organization. Organization belief CSR as a strategic tool for enhancing value, still evidence on how CSR disclosure affects financial performance has been inconclusive. To resolve the ambiguity presence in the literature, this study carried out a meta‐analytic investigation based on 168 effect sizes from 73 empirical studies. This study employs a multivariate framework to explore the potential moderators that leads to conflicting results among the focal relationship of the study. The study discovers that if CSR disclosure strategy and measurement technique are adequately addressed with the help of econometric tools, then the true effect of CSR can be observed. Moreover, implications are drawn for academicians and practitioners interested in exploring the relationship between CSR disclosure and financial performance.

AbstractPrevious research has reported inconsistent findings concerning the relationship between strategic planning and organizational performance and pointed out that a reason for this ambiguity is the fact that most research has traditionally focused on the direct effects of strategic planning on performance, taking insufficient consideration of the influence of several contextual variables on this relationship. Following this observation, this study adopts the perspective of contingency theory and develops six hypotheses predicting that strategic planning is positively related to organizational performance only when the two contextual variables of organizational structure and environmental uncertainty are aligned individually and jointly with the strategic planning process. Data were collected on senior executives and managers involved in formulating strategies in the United Arab Emirates (UAE) enterprises and analyzed by using multiple regression analysis involving moderated moderation. Results supported all the six hypotheses, confirming the central assumption of the present study that, at least in the UAE setting, a holistic or multivariate fit approach to contingency theory can shed light on the planning–performance relationship. The theoretical and managerial implications of the study findings are finally discussed.

Regional innovation is a complex economic category and in the era of knowledge-based economy it determines regional development. The ambiguity of its measurement, which is the consequence of problems with the quantification of the test area as well as the difficulties arising from the lack or limited availability of certain empirical data, is a methodological challenge that makes scientific and public services of statistics play a huge role in the diagnosis of the problem. This issue has been developed and perfected for decades and recently research in this area has gained more importance. What proves to be extremely useful here are quantitative methods, in particular the methods of multivariate statistical analysis (SAW). In this article, by using the capabilities of public information databases of the CSO (GUS), a set of regional innovation indicators in Poland has been proposed – the set methodically convergent with EU guidelines. The region is associated with each of the sixteen existing voivodships. By using selected SAW methods changes in this area in recent years in Poland has been analyzed, including the assessment of the similarity of results over time.

CONTEXT: Ankle injury is one of the most common conditions in athletics and military activities. Strength asymmetry (SA) and imbalance may represent a risk factor for injury, but past investigations have produced ambiguous conclusions. Perhaps one explanation for this ambiguity is the fact that these authors used univariate models to predict injury. OBJECTIVE: To evaluate the predictive utility of SA and imbalance calculations for ankle injury in univariate and multivariate prediction models. DESIGN: Prospective cohort study. SETTING: Laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of 140 male US Air Force Special Forces. MAIN OUTCOME MEASURE(S): Baseline testing consisted of body composition, isometric strength, and aerobic and anaerobic capacity. A clinician conducted medical chart reviews 365 days posttesting to document the incidence of ankle injury. Strength asymmetries were calculated based on the equations most prevalent in the literature along with known physiological predictors of injury in the military: age, height, weight, body composition, and aerobic capacity. Simple logistic regression was conducted using each predictor, and backward stepwise logistic regression was conducted with each equation method and the physiological predictors entered initially into the model. RESULTS: Strength asymmetry or imbalance or both, as a univariate predictor, was not able to predict ankle injury 365 days posttesting. Body mass (P = .01) and body mass index (P = .01) significantly predicted ankle injury. Strength asymmetry or imbalance or both significantly predicted ankle injury when considered with body mass (P = .002–.008). CONCLUSIONS: As a univariate predictor, SA did not predict ankle injury. However, SA contributed significantly to predicting ankle injury in a multivariate model using body mass. Interpreting SA and imbalance in the presence of other physiological variables can help elucidate the risk of ankle injury.

Individual innovativeness is particularly indispensable among health professionals. The healthcare environment is complex and its knowledge workers must continually adapt to change and be comfortable with ambiguity. The objective of this study was to determine the relative importance of individual, job-specific, and organizational factors on innovative output of health professionals. Employed Canadian Registered Dietitians (n = 237) completed an online survey incorporating relevant validated tools, including the 10-item Big Five Inventory and the Alberta Context Tool. Factors were classified by level and introduced in blocks to a multivariate linear regression model, with the outcome of self-reported innovative output. Factors included in the model explained 44% of variation in self-reported innovative output. Although all blocks contributed significantly to the model, minimal variation was explained by factors at the job-specific (4%) and organizational levels (4%). Factors at the individual level most predictive of innovative output were role innovation, the personality trait of conscientiousness and voluntary membership in a professional association. To encourage employee innovativeness, health administrators, and managers of health professionals should consider how best to incorporate screens for individual-level indicators of innovative output (eg, personality tests) in their institutional hiring and selection processes.

The present study looks into the organizational culture of a Brazilian company, concentrating on its main Brazilian branches as well as on its European, Latin American, Central American, North American and Asian branches, making a total sample of 36 cities and 1742 respondents. Results indicate the influence of national culture on organizational culture, as the dimensions found clearly reflect the ambiguity and double-edged ethic characteristic of Brazilian culture. This study also shows the importance of hierarchy, and of relational networks, which stresses the relevance of the cultural element in organizational structure and functioning. In brief, understanding the double-edged ethic that governs Brazilian culture helps us understand apparently different, ambiguous or even contradictory behaviors reflected in the organizational culture practices of a Brazilian company with international operations. Moreover, there is little empirical research that directly deals with what combination of factors makes individuals agree or disagree over their cultural viewpoints. Consequently, we consider that this study attempts to deal with that issue as the cultural clusters were obtained using a multivariate approach, using demographic variables and the identified organizational dimensions. Thus results suggest the organizational context may increase or reduce the probability of nationality affecting the cultural agreement of group members.

Purpose According to the National Institute for Occupational Safety and Health (NIOSH) survey, 60% of employed women cited stress as their most serious problem at work. Working outside the home and balancing a family create conflicts between work and family obligations which become a likely cause of stress for women. While stress may be a problem among working women in general, it seems highly probable that women who function in leadership positions may experience additional stressors. Consequently, this study focused on investigating the stressors of women who currently function in leadership positions and the coping strategies they use to combat stress within corporate America, education, and government agencies. Method This study used a mixed-method approach. A survey questionnaire was administered to 67 women at least 20 years old who functioned in leadership positions within corporate America, education, and government. The questionnaire was the Occupational Stress Inventory Revised (OSI-R), which has structured questions on a Likert-type rating scale. The OSI-R contains three categories: (a) Occupational Roles Questionnaires, (b) Personal Strain, and (c) Personal Resources. Data were analyzed through the use of descriptive and inferential statistics such as Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), and Univariate F Tests. All decisions on the statistical significance of findings used a criterion alpha level of .05. The qualitative portion was fulfilled by conducting a face-to-face semi-structured interview with 6 women leaders—2 each were from education, corporate America, and government. Results This research revealed that women functioning in leadership positions within corporate America, education, and governmental agencies experienced maladaptive levels of vocational personal strain and occupational stressors, which were role ambiguity, role boundary, and role overload. A statistically significant relationship was found between stress and length of service. As the length of time with their respective organizations increased, less occupational stress and personal strain were experienced by the women. Although prayer and spirituality were not identified as a coping resource on the OSI-R, it appeared throughout the interviews as a means of coping. Conclusions Women who hold leadership positions within corporate America, education, and governmental agencies experienced occupational stress in role ambiguity, role boundary, role overload, and vocational strain. Leaders in these areas must support women on issues that generate stress in the work environment.

International audience ; Background: With more than 160 000 confirmed COVID-19 cases and about 30 000 deceased people at the end of June 2020, France was one of the countries most affected by the coronavirus crisis worldwide. We aim to assess the efficiency of global lockdown policy in limiting spatial contamination through an in-depth reanalysis of spatial statistics in France during the first lockdown and immediate post-lockdown phases. Methods: To reach that goal, we use an integrated approach at the crossroads of geography, spatial epidemiology, and public health science. To eliminate any ambiguity relevant to the scope of the study, attention focused at first on data quality assessment. The data used originate from official databases (Santé Publique France) and the analysis is performed at a departmental level. We then developed spatial autocorrelation analysis, thematic mapping, hot spot analysis, and multivariate clustering. Results: We observe the extreme heterogeneity of local situations and demonstrate that clustering and intensity are decorrelated indicators. Thematic mapping allows us to identify five "ghost" clusters, whereas hot spot analysis detects two positive and two negative clusters. Our re-evaluation also highlights that spatial dissemination follows a twofold logic, zonal contiguity and linear development, thus determining a "metastatic" propagation pattern. Conclusions: One of the most problematic issues about COVID-19 management by the authorities is the limited capacity to identify hot spots. Clustering of epidemic events is often biased because of inappropriate data quality assessment and algorithms eliminating statistical-spatial outliers. Enhanced detection techniques allow for a better identification of hot and cold spots, which may lead to more effective political decisions during epidemic outbreaks.