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Sel�n, Jan. "Multidimensional descriptions of social indicators." Social Indicators Research 17, no. 4 (November 1985): 435–45. http://dx.doi.org/10.1007/bf00290324.
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Zhao, Rongying, and Xu Wang. "Research on impact evaluation of academic journals from multidimensional perspective." Library Hi Tech 38, no. 2 (July 24, 2019): 458–78. http://dx.doi.org/10.1108/lht-03-2019-0067.
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Purpose The purpose of this paper is to introduce altmetric indicators and combine with traditional citation indicators to comprehensively evaluate the impact of academic journals from the perspective of multidimensional and multi-indicator fusion. Design/methodology/approach The authors take international multidisciplinary journals as an example, combining 14 traditional citation indicators of academic journals and introducing 14 altmetric indicators to build a comprehensive evaluation model of the impact of academic journals (academic impact and societal impact). At the same time, the authors systematically construct a journal evaluation indicator system from three dimensions. Then, the indicators data of three dimensions are evaluated by normalized processing, correlation analysis, reliability and validity analysis, PCA and factor analysis. Findings Two-dimensional and three-dimensional analyses can exactly provide some useful information for academic journals’ location in the respective coordinate systems. There are strong positive correlations among the measured indicators in the three dimensions, and each indicator has a significant consistency between whole and internal. The correlation coefficient between FD1 and FD2 is 0.888 with a strong positive correlation. It shows that the traditional citation indicators provided by WoS and Scopus database are highly consistent, and they are comparable and alternative in evaluating the academic impact of journals. The correlation coefficients of FD1, FD2 with FD3 are 0.831 and 0.798. There are strong positive correlations among them, which indicate that the evaluation of journals’ societal impact based on altmetrics indicator can be considered as a potential supplement to academic impact evaluation based on citation and to reflect the multidimensional nature of journals impact in an immediate way. Originality/value Multidimensional and multi-indicator perspective evaluation can provide references for the selection of impact evaluation indicators and model optimization of academic journals, and also provide new ideas for improving the status of the impact evaluation of academic journals.
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Ameur, H. Ben, G. Chavent, F. Clément, and P. Weis. "Image segmentation with multidimensional refinement indicators." Inverse Problems in Science and Engineering 19, no. 5 (July 2011): 577–97. http://dx.doi.org/10.1080/17415977.2011.579609.
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Valdés, Javier. "Arbitrariness in Multidimensional Energy Security Indicators." Ecological Economics 145 (March 2018): 263–73. http://dx.doi.org/10.1016/j.ecolecon.2017.09.002.
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Ambel, Alemayehu Azeze, Harriet Kasidi Mugera, and Robert E. S. Bain. "Accounting for drinking water quality in measuring multidimensional poverty in Ethiopia." PLOS ONE 15, no. 12 (December 15, 2020): e0243921. http://dx.doi.org/10.1371/journal.pone.0243921.
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The Multidimensional Poverty Index is used increasingly to measure poverty in developing countries. The index is constructed using selected indicators that cover health, education, and living standards dimensions. The accuracy of this tool, however, depends on how each indicator is measured. This study explores the effect of accounting for water quality in multidimensional poverty measurement. Access to drinking water is traditionally measured by water source types. The study uses a more comprehensive measure, access to safely managed drinking water services, which are free from E. coli contamination, available when needed and accessible on premises in line with Sustainable Development Goal target 6.1. The study finds that the new measure increases national multidimensional headcount poverty by 5–13 percentage points, which would mean that 5–13 million more people are multidimensionally poor. It also increases the poverty level in urban areas to a greater extent than in rural areas. The finding is robust to changes in water contamination risk levels and Multidimensional Poverty Index aggregation approaches and weighting structures.
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Akimkina, E. E. "Structuring and visualization of indicators in multidimensional data cubes." Informacionno-technologicheskij vestnik, no. 4 (December 30, 2018): 79–87. http://dx.doi.org/10.21499/2409-1650-2018-4-79-87.
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The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.
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Cerdeiro, Diego, and Rachel Nam. "A Multidimensional Approach to Trade Policy Indicators." IMF Working Papers 18, no. 32 (2018): 1. http://dx.doi.org/10.5089/9781484342350.001.
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Nur, Widi Astuti, Firmansyah Firmansyah, and Widodo Wahyu. "A Multidimensional Approach of Child Poverty in Indonesia." E3S Web of Conferences 73 (2018): 10015. http://dx.doi.org/10.1051/e3sconf/20187310015.
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This research discuss about child poverty, which is commonly called deprivation. The purpose of this research is to count multidimensional child poverty index as child poverty measuring istrument in 34 provinces of Indonesia, by using composite idex count calculation. Dimensions and indicators used to compose Multidimensional Child Poverty Index in this research are adapting from dimensions and indicators that used in research by Mahadewi and Kodoatie1. Besides that, the determination of dimensions and indicator also adjust the availability of data from SUSENAS 2015.The calculation result of Multidimensional Child Poverty shows that 7 provinces pertain to number I priority (high), 9 provinces belong to number II priority (medium-high), 10 provinces include to number III priorities (medium-low), and 8 provinces are number IV priority (low).
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Leplat, L., R. S. Torres, D. Aspen, and A. Amundsen. "VIZPLAN: A VISUAL ANALYTICS PLATFORM FOR THE ASSESSMENT OF MULTIDIMENSIONAL INDICATORS OVER TIME." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W3-2022 (October 14, 2022): 127–34. http://dx.doi.org/10.5194/isprs-annals-x-4-w3-2022-127-2022.
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Abstract. In this paper, we introduce VizPlan, a new platform to support the assessment of multidimensional indicators over time. VizPlan includes a visualisation scheme based on a radial visual structure that allows the direct comparison of indicator values over time, a search tool to support the identification of entities whose indicators are similar to each other, and a clustering tool to group entities according to their indicator scores. VizPlan was designed and implemented to be flexible; it can be easily tailored to the visualization and analysis of any multidimensional temporal data. In this paper, the use of VizPlan is illustrated in the context of three case studies concerning the analysis of sustainability indicators to support urban planning: key performance indicators related to the sustainable development goals, walkability analysis, and bus service availability assessment. All case studies refer to real data related to Norwegian cities, especially Ålesund. VizPlan is available as an open source software at https://github.com/Rylern/VizPlan – As of May 2022.
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Shinkarenko, Volodymyr, Maksym Matskul, and Dean Linok. "Investment attractiveness modeling using multidimensional statistical analysis methods." SHS Web of Conferences 65 (2019): 04007. http://dx.doi.org/10.1051/shsconf/20196504007.
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The article examines the investment attractiveness of the main branches of the food industry of Ukraine as a latent variable. For the first time in this area, a combination of various methods of multivariate statistical analysis is used for research (cluster analysis and factor analysis – the principal component method). These methods made it possible to use a large number of various indicators of the activities of industries to characterize investment attractiveness. As a result, the set of the branches was divided into three groupsclusters: “leaders” are the most attractive sectors for investment, “middle peasants” are attractive branches for investment, and “outsiders” are the least attractive branches for investment. The generalizing factors (principal components), which influence the resulting factor - investment attractiveness, were found. The interrelation of the generalizing factors and initial indicators is established. As a result of the research, it was possible to make an objective assessment of the investment attractiveness (as a latent indicator) of the main branches of the food industry in Ukraine, using instead of a multitude of indicators only three latent factors.
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Sun, Chaoxing, Guohe Huang, and Yurui Fan. "Temporal and Spatial Characteristics of Multidimensional Extreme Precipitation Indicators: A Case Study in the Loess Plateau, China." Water 12, no. 4 (April 24, 2020): 1217. http://dx.doi.org/10.3390/w12041217.
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Extreme precipitation can seriously affect the ecological environment, agriculture, human safety, and property resilience. A full-scale and scientific assessment in extreme precipitation characteristics is necessary for water resources management and providing decision-making support to mitigate the potential losses brought by extreme precipitation. In the present study, a multidimensional risk assessment framework is developed to investigate the spatial–temporal changes in different extreme precipitation indicators. The Gaussian mixture model (GMM) is applied to fit the distribution for each indicator and carry out single index risk assessment. The joint probabilistic features of multiple extreme indicators can be explored through coupling the GMM distributions into copulas. In addition, the moving window approach and the Mann–Kendall test are integrated to examine non-stationary risks (evaluated by “AND”, “OR”, and Kendall return periods) of multidimensional indicators along with their changing trends and significance. The proposed assessment framework is applied to the Loess Plateau, China. Four extreme precipitation indicators are characterized: the amount (P95), the number of days (D95), the intensity (I95), and the proportion (R95) of extreme precipitation. The spatial–temporal changes of these indicators and their multidimensional combinations (including six two-dimensional and three three-dimensional combinations) are fully identified and quantitatively evaluated.
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Schlör, Holger, Wolfgang Fischer, and Jürgen-Friedrich Hake. "ISD: A New Methodological Approach for Measuring the Sustainability of the German Energy System." Economics Research International 2011 (July 10, 2011): 1–10. http://dx.doi.org/10.1155/2011/428326.
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The research community has developed three main concepts and indicator systems to measure sustainability: the capital concept, the ecological concept and the multidimensional concept. Whereas a lot of research has been dedicated to the pros and cons of the three/four-pillar sustainability concept, to the shaping of the pillars and their indicators, research on standardized methods to aggregate the indicators to one index is lacking. However, a useful model exists—the GDP—which summarizes the different economic activities of various social actors in one index. An overall sustainability index has the advantage that the sustainability of a system can be expressed in one index. This allows the sustainability status of a system to be better communicated both to the public and to politicians. Against this background, we developed the Index of Sustainable Development (ISD) to measure the sustainability of systems described by multidimensional sustainability concepts. We demonstrate that it is possible to aggregate sustainability indicators of the multidimensional sustainability concepts to one index. We have chosen exemplarily the German sustainability strategy and selected the energy indicators within it because of the importance of the energy sector and due to the good statistical database in this sector.
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Haunschild, Robin. "Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact." Journal of Scientometric Research 4, no. 1 (2015): 40. http://dx.doi.org/10.4103/2320-0057.156020.
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Anga, Jayvee Faust, Jonah Marie Enerlas, Kristine June Uy, and Marjurie Lourince Zanoria. "Multidimensional Poverty Indicators in light of ASEAN Integration." Recoletos Multidisciplinary Research Journal 3, no. 1 (March 2, 2017): 1–23. http://dx.doi.org/10.32871/rmrj1503.01.01.
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Jamal, Haroon. "Assessing Poverty with Non-Income Deprivation Indicators: Pakistan, 2008-09." Pakistan Development Review 50, no. 4II (December 1, 2011): 913–27. http://dx.doi.org/10.30541/v50i4iipp.913-927.
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The approach to measure poverty in terms of financial deprivation has been widely criticised in the literature of welfare and wellbeing. It is argued that to understand the complex phenomenon of poverty or to evaluate household or individual wellbeing, a multidimensional exercise is imperative. This research quantifies the level of multidimensional poverty in Pakistan using household data of Pakistan Social and Living Standard Measurement Survey. Multidimensional poverty in terms of the popular FGT (headcount, poverty gap, poverty severity) indices is estimated for the year 2009. Indicators of human poverty, poor housing and deprivation in household physical assets are included in estimating poverty in multi-dimensional context. For assessing the inter-temporal consistency in the methodology, poverty indices are also developed for the year 2005. JEL classification: I32, I31 Keywords: Poverty, Multidimensional, Categorical Principal Component Analysis, Poverty Indices, Pakistan
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Temme, Dirk, and Adamantios Diamantopoulos. "Higher-order models with reflective indicators." Journal of Modelling in Management 11, no. 1 (February 8, 2016): 180–88. http://dx.doi.org/10.1108/jm2-05-2014-0037.
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Purpose – Higher-order factor models have recently been dismissed as a ‘misleading’, ‘meaningless’, and ‘needless’ approach for modeling multidimensional constructs (Lee and Cadogan, 2013; L & C, 2013 hereafter). The purpose of this paper is to show that – in contrast to L & C’s (2013) verdict – higher-order factor models are still a legitimate operationalization option for multidimensional constructs. Design/methodology/approach – Basic conceptual and statistical premises of L & C’s (2013) arguments against higher-order factor models are scrutinized both conceptually and statistically as to their logic and validity. Findings – A thorough analysis of L & C’s (2013) arguments shows that they are fundamentally flawed both conceptually and statistically, rendering their conclusions invalid. Research limitations/implications – Researchers should not remove the well-established higher-order factor models from their methodological toolkit. Furthermore, empirical findings should not automatically be considered suspect simply because higher-factor models have been used to model multidimensional constructs. Originality/value – So far, L & C’s (2013) arguments against higher-order factor models have gone unchallenged in the literature. This rejoinder is a first, much needed attempt to protect applied researchers from getting the false impression that by using higher-factor models, they rely on a “misleading” or “meaningless” modeling approach.
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Gallardo, Mauricio. "Measuring Vulnerability to Multidimensional Poverty." Social Indicators Research 148, no. 1 (September 19, 2019): 67–103. http://dx.doi.org/10.1007/s11205-019-02192-y.
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Abstract A method to measure vulnerability to multidimensional poverty is proposed under a mean–risk behaviour approach. We extend the unidimensional downside mean–semideviation measurement of vulnerability to poverty towards the multidimensional space by incorporating this approach into Alkire and Foster’s multidimensional counting framework. The new approach is called the vulnerability to multidimensional poverty index (VMPI), alluding to the fact that it can be used to assess vulnerability to poverty measured by the multidimensional poverty index (MPI). The proposed family of vulnerability indicators can be estimated using cross-sectional data and can include both binary and metric welfare indicators. It is flexible enough to be applied for measuring vulnerability in a wide range of MPI designs, including the Global MPI. An empirical application of the VMPI and its related indicators is illustrated using the official MPI of Chile as the reference poverty measurement. The estimates are performed using the National Socioeconomic Characterisation Survey (CASEN) for the year 2017.
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Agustin, Diana, Aulia Adita Rahma, Frengky Sele, Raihan Fitrika Azzahra, Rhevita Lula Eksanti, Zahrotul Firdaus, and Rani Nooraeni. "Analysis of Multidimensional Poverty Indicators in Indonesia with Association Rules." Jurnal Ekonomi Pembangunan 18, no. 2 (December 21, 2020): 136. http://dx.doi.org/10.22219/jep.v18i2.14244.
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This study was conducted to find patterns of relationships between 14 multidimensional poverty indicators in Indonesia from 2015-2019. To provide a more specific description of the relationship pattern, association rules with the apriori algorithm is used as the analysis method. The preprocessing stage to transform data was carried out using fuzzy functions and data reduction with Multiple Correspondence Analysis (MCA) to support the association analysis process. The results obtained are 15 relationship patterns or rules between items from the multidimensional poverty indicator with a support value of 60%-80% and 100% confidence. This means that the relationship pattern is significantly formed from objects with a strong relationship between the items and can represent poverty records in the last five years. The relationship pattern consists of four combinations of things. Suppose there is a high category decrease in the percentage of poor people indicator, a low category decrease in the open unemployment indicator, a high category increase in the percentage of households indicator according to the source of lighting from electricity, and a low category increase in the percentage indicator of households according to the broadest wall, not bamboo / other. In that case, there is a reduction in multidimensional poverty in Indonesia.
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Pereira, Inês, Ualerson Iran Peixoto, Wendell Medeiros-Leal, Morgan Casal-Ribeiro, and Régis Santos. "Multidimensional Indicators to Assess the Sustainability of Demersal Small-Scale Fishery in the Azores." Sustainability 14, no. 24 (December 11, 2022): 16585. http://dx.doi.org/10.3390/su142416585.
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The Azorean demersal fishery sector is one of the most important in the archipelago. As a small-scale fishery, it plays an important role in the livelihood of the community, being a source of employment and income, and contributing to poverty alleviation. Because fisheries are a complex system, a multidisciplinary approach that includes socioeconomic indicators is required for a broader assessment of fishery sustainability. This study analyzes the Azorean bottom longline fishery using the Fishery Performance Indicators tool, regarding its ecology, economy, and community indicators. The findings indicated that the fishery is mostly sustainable, although there is still opportunity for improvement. Its ecological indicators had a good performance, mainly due to the effort and work of the scientific community that makes continuous studies to examine the state of its stocks. The economic indicators are in good condition as well, but some obstacles stopped the indicator from obtaining a better performance; mainly the landing volatility and the fishery’s main source of capital (subsidies), which can make the fishery less competitive. Finally, its community indicator had a very good performance, which reflects the fishery's socioeconomic and cultural relevance for the Azores.
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Bersisa, Mekonnen, and Almas Heshmati. "A Distributional Analysis of Uni-and Multidimensional Poverty and Inequalities in Ethiopia." Social Indicators Research 155, no. 3 (February 2, 2021): 805–35. http://dx.doi.org/10.1007/s11205-021-02606-w.
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AbstractThis study analyzes uni-and multidimensional poverty and inequalities in rural and small towns in Ethiopia. Unlike the unidimensional measure, the multidimensional measure of poverty shows all the channels through which poverty may manifest itself; it also shows the extent of deprivation. The analysis uses 6 dimensions with 14 indicators to construct a multidimensional index of poverty and inequalities using Ethiopian Households’ Socioeconomic Survey dataset. The study also uses multiple correspondence analyses for determining relative weights in computing a multidimensional index and conducts a stochastic dominance analysis of distribution of poverty for different population segments. The paper sheds light on the degree of inequalities in consumption expenditure and multidimensional deprivations. In addition, it also compares the degree of poverty using the conventional measure of poverty and the multidimensional approach. It also examines the determinants of household poverty status using both unidimensional and multidimensional measures using the logit model. The results show that the intensity, severity, and depth of poverty varies substantially across the 2 measures. The unidimensional measure of poverty shows that 36 percent of the households were poor as compared to 46 percent multidimensionally poor households. Moreover, demographic, regional, and household heads’ characteristics also affect households’ poverty status.
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Altintas, Tugba. "Grouping OECD Countries According to Better Life Index Indicators by Multidimensional Scaling Analysis." Cognizance Journal of Multidisciplinary Studies 2, no. 12 (December 30, 2022): 18–22. http://dx.doi.org/10.47760/cognizance.2022.v02i12.003.
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The aim of this study is to group OECD countries using the better life index indicators through multidimensional scaling analysis. The Better Life Index consists of thirty-three variables under the dimensions of material living conditions (income and wealth, work and earnings, housing conditions) and quality of life (health status, work-life balance, education and skills, social connections, civic engagement and governance, environmental quality, personal safety and life satisfaction). These thirty-three indicators constitute the variables of the multidimensional scaling analysis. As a result of the analysis, the positions of countries in two-dimensional space were obtained and it was observed that some countries formed distinct clusters. In this way, OECD countries are compared not on the basis of a selected indicator, but on the basis of an approach that encompasses all the indicators of the Better Life Index.
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Abdullah, Fahad Bin, Rizwan Iqbal, Sadique Ahmad, Mohammed A. El-Affendi, and Pardeep Kumar. "Optimization of Multidimensional Energy Security: An Index Based Assessment." Energies 15, no. 11 (May 26, 2022): 3929. http://dx.doi.org/10.3390/en15113929.
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This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were partially met, while conservation programs, such as the renewable policy (2006) and national climate change policy (2012), fell short. Indicators such as price, reserves, governance, corruption, and consumption contributed to PMESI across five dimensions. Thus, PMESI and indices guiding policymakers to focus on improving governance and exploiting local energy resources in order to provide affordable and sufficient energy in the long run.
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Jiang, Renyan, and Binbin Xiong. "A hybrid approach for modelling multidimensional degradation processes." IOP Conference Series: Materials Science and Engineering 1207, no. 1 (November 1, 2021): 012023. http://dx.doi.org/10.1088/1757-899x/1207/1/012023.
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Abstract Degradation processes are often multidimensional. Modeling such degradation processes needs to address two key issues: indicator fusion and degradation model selection; and they have been separately addressed in the literature. This paper proposes a hybrid approach to jointly address these two issues. The proposed approach first fuses multiple degradation indicators into a composite degradation indicator. This fusion step involves data normalization, aggregation model selection and determination of indicator weights. After the fusion step, the problem becomes one-dimensional, and the existing method to select the degradation model for a one-dimensional degradation process can be applied. The resulting model obtained from the proposed approach can be a two-phase model; and the model for the second phase has a closed-form expression. This considerably facilitates residual life prediction. A real-world example is included to illustrate the proposed approach and its appropriateness.
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Zhu, Shilian. "Multidimensional Relative Poverty of Farmers in Tibetan Areas – Based on a Data Analysis on Micro Surveys." Proceedings of Business and Economic Studies 4, no. 6 (December 31, 2021): 84–90. http://dx.doi.org/10.26689/pbes.v4i6.2866.
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In 2020, the issue of absolute poverty has been solved, and China is building a well-off society in an all-round way. The issue of relative poverty is an important content of poverty reduction. Based on a survey data from Danba County in October 2020, this paper uses the AF method to calculate the incidence of multidimensional poverty and the multidimensional poverty index. The results showed that 44.65% of the farmers have multidimensional deprivation of any three indicators of relative poverty, and 2.79% of the farmers have serious multidimensional deprivation; the incidence of one-dimensional poverty in terms of “educational level index of head of household,” “per capita non-transfer income of households in 2019,” and “per capita household income in 2019” is the highest; at the same time, the contribution rate of the three indicators to the multidimensional poverty index is also higher than other indicators. Therefore, several suggestions have been put forward to alleviate the multidimensional relative poverty in the region from the aspects of industry development and education.
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., Muhsimin, Nyoto Nyoto Santoso, and Hariyadi . "STATUS KEBERLANJUTAN PENGELOLAAN EKOSISTEM MANGROVE DI WILAYAH PESISIR DESA AKUNI KECAMATAN TINANGGEA KABUPATEN KONAWE SELATAN Sustainability Management Status of Mangrove Ecosystem in Coastal Area of Akuni Village District of Tinanggea, South Konawe." Journal of Tropical Silviculture 9, no. 1 (May 4, 2018): 44–52. http://dx.doi.org/10.29244/j-siltrop.9.1.44-52.
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This study aims to analyze the sustainability status of mangrove ecosystem management in coastal areas of Akuni Village by integrating all interests in multidimensional (ecological, economic, social, and institutional) and determining sensitive indicators in sustainability management of mangrove ecosystem. The method used in this study was Rapid Appraisal for Fisheries (RAPFISH) based on multidimensional scaling (MDS). The results showed that the multidimensional sustainability status of mangrove ecosystem management in coastal area of Akuni Village was less sustainable (46.67%); Ecological dimension of 73.29% (sustainable enough); Economic dimension of 53.77%(sustainable enough); Social dimension of 38.32% (less sustainable); Institutional dimension of 25.36% (less sustainable). Improvement efforts toward sensitive indicators on each dimension are necessary to minimizeenvironmental quality degradation especially for mangrove ecosystem due to anthropogenic activity. Those dimension indicators improvement expected to give some positive feedback for managers in order to improve the sustainabilitystatus of mangrove ecosystem management in a multidimensional manner.Key words: Akuni Village, Multidimensional scaling (MDS) mangrove ecosystems, Sustainability index and status
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Ji, Zuzhen, Hongxin Su, Yuchen Wang, Yi Cao, and Shuanghua Yang. "Assessing the Risk of Hazards with Multidimensional Consequences for Industrial Processes." Processes 10, no. 6 (June 8, 2022): 1145. http://dx.doi.org/10.3390/pr10061145.
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Risk assessment plays an important role in process safety. The result of the assessment is used to determine risk priorities and then develop preventions to reduce risks. A hazard may have multidimensional consequences, including loss of health and safety, asset loss, and environmental damage. Traditionally, these multidimensional consequences are often measured disjointedly. A comprehensive risk assessment would be conducted by many professionals from multiple areas. Each of these professionals uses different indicators to evaluate risks. The poor integration among risk indicators further confuses managers in the risk resilience and prevention development. In addition, this lacks a solid method for assessing the risk of hazards that with multidimensional consequences. The aim of the work is to develop a risk-measuring instrument using a newly proposed approach, the Risk Assessment for Hazards with Multidimensional Consequences (RAMC), which is developed based on the theory of quality of life (QOL), a theory from health management. RAMC uses the ‘diminished quality of life in organization safety’ (DQLOS) as a risk indicator to represent the level of risk exposure. The main results of the work show that the method of RAMC and the indicator ‘DQLOS’ are able to support practitioners to assess the risk of a hazard with multidimensional consequences and could be used to deliver reasonable risk control priorities. A case study associated with the coal-to-methanol gasification process is discussed for RAMC’s application and validation. The case study result also indicates that the DQLOS has the potential to assist the industry to design safe process systems and develop ongoing improvements in safety.
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Raza, Faiza, and Noman Arshed. "Macroeconomic determinants of multidimensional risk of Islamic banks across the world." Journal of Management Info 9, no. 3 (January 21, 2023): 384–401. http://dx.doi.org/10.31580/jmi.v9i3.2681.
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Banking system crises and bank failures can have painful impacts. Being an Islamic Bank is riskier than being a conventional bank. As IBIs risk management emphasizes risk-sharing, conventional banks transfer their risk to borrowers. In our study, we have taken 4 risk factors which are Rate of Return Risk, Operational Risk, Credit Risk and Liquidity Risk, as a proxy of all the risks faced by IBIs and developed a Composite Risk Index using these major risk indicators of Islamic banks. Further, this study has proposed indicators like Size1 for liquid assets and Size2 for fixed assets, as in Islamic banks, there is no concept of money markets. The data for the research was collected from the secondary sources of Islamic banks’ financial statements, the EIKON database and World development indicators. The data for 96 Islamic banks were analyzed. The data was analyzed through Python, R and Stata. This study is instrumental in using the unsupervised machine learning clustering algorithm known as KMeans to estimate the prevalent risk level of the bank by aggregating four-dimensional risk indicators to form a single indicator. Moreover, the study explored macroeconomic determinants i.e., Asymmetric Stock Market, Real Business Cycle (GDP), Regulatory Quality and Economic Globalization of the Composite Risk Index and developed a channel via which it affects the risk level of banks. The results showed that in a country, better regulatory quality, higher fixed assets and economic boom in the business cycle play an important role in reducing the risk level of Islamic banks. This study provides a quantified contribution of Islamic banking to social welfare, compared to focusing on the numbers of risk indicators only and comparing it with fixed boundaries developed for the case other indicators.
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Albert, Fruzsina, and Gábor Hajdu. "Association between poverty indicators and social relations." Journal of Poverty and Social Justice 28, no. 3 (October 1, 2020): 401–23. http://dx.doi.org/10.1332/175982720x15966472176159.
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We analyse the association between poverty indicators and social relations using nationally representative data from Hungary. We focus on four poverty indicators (the three standard indicators of Eurostat and perceived financial problems) and a rich set of social relationship indicators (18 variables). Material deprivation is the most strongly linked to the measures of social ties and social integration, whereas income poverty is associated the least strongly with them. Although income poverty is probably the most widely used poverty indicator, our results suggest that material deprivation and even subjective poverty reflect better the multidimensional nature of poverty and social exclusion.
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Nikitin, V. V., and D. V. Bobin. "Principal Component Analysis for Weighted Data in the Procedure of Multidimensional Statistical Forecasting." Statistics and Economics 18, no. 2 (May 12, 2021): 4–11. http://dx.doi.org/10.21686/2500-3925-2021-2-4-11.
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Purpose of the research. Let’s assume that the dynamics of the state of some object is being investigated. Its state is described by a system of specified indicators. Among them, some may be a linear combination of other indicators. The aim of any forecasting procedure is to solve two problems: first, to estimate the expected forecast value, and second, to estimate the confidence interval for possible other forecast values. The prediction procedure is multidimensional. Since the indicators describe the same object, in addition to explicit dependencies, there may be hidden dependencies among them. The principal component analysis effectively takes into account the variation of data in the system of the studied indicators. Therefore, it is desirable to use this method in the forecasting procedure. The results of forecasting would be more adequate if it were possible to implement different forecasting strategies. But this will require a modification of the traditional principal component analysis. Therefore, this is the main aim of this study. A related aim is to investigate the possibility of solving the second forecasting problem, which is more complex than the first one. Materials and research methods. When estimating the confidence interval, it is necessary to specify the procedure for estimating the expected forecast value. At the same time, it would be useful to use the methods of multidimensional time series. Usually, different time series models use the concept of time lag. Their number and weight significance in the model may be different. In this study, we propose a time series model based on the exponential smoothing method. The prediction procedure is multidimensional. It will rely on the rule of agreed upon data change. Therefore, the algorithm for predictive evaluation of a particular indicator is presented in a form that will be convenient for building and practical use of this rule in the future. The principal component analysis should take into account the weights of the indicator values. This is necessary for the implementation of various strategies for estimating the boundaries of the forecast values interval. The proposed standardization of weighted data promotes to the implementation of the main theorem of factor analysis. This ensures the construction of an orthonormal basis in the factor area. At the same time, it was not necessary to build an iterative algorithm, which is typical for such studies. Results. For the test data set, comparative calculations were performed using the traditional and weighted principal component analysis. It shows that the main characteristics of the component analysis are preserved. One of the indicators under consideration clearly depends on the others. Therefore, both methods show that the number of factors is less than the number of indicators. All indicators have a good relationship with the factors. In the traditional method, the dependent indicator is included in the first main component. In the modified method, this indicator is better related to the second component. Conclusion. It was shown that the elements of the factor matrix corresponding to the forecast time can be expressed as weighted averages of the previous factor values. This will allow us to estimate the limits of the confidence interval for each individual indicator, as well as for the complex indicator of the entire system. This takes into account both the consistency of data changes and the forecasting strategy.
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Yosifa, Adelia Frielady, Dita Aris Novianti, Nina Auliyah, Sugha FaizAl Maula, Dita Amelia, M. Fariz Fadillah, and Elly Ana. "CLASSIFICATION OF CITIES IN EUROPE BASED ON SMART CITY INDICATORS USING MULTIDIMENSIONAL SCALING ANALYSIS." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 02 (February 16, 2023): 3243–47. http://dx.doi.org/10.47191/ijmcr/v11i2.03.
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A decent life has become a hot issue for discussion as stated in the SDGs points, one of them is Smart City. In this research, the researchers used cities in Europe, a continent with countries that tend to have developed. By using multidimensional scaling analysis techniques, the authors present the mapping from an objective data-based point of view, aiming to analyze the closeness of characteristics between 70 cities spread across Europe, and provide ideas and valuable sources for policymakers. From the results of this analysis using R software, it was found that cities within the same country tend to have similar characteristics, which can take into consideration by the European Union to carry out equitable development for cities that do not have good enough smart city indicators.
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Varsei, Mohsen, Claudine Soosay, Behnam Fahimnia, and Joseph Sarkis. "Framing sustainability performance of supply chains with multidimensional indicators." Supply Chain Management: An International Journal 19, no. 3 (May 6, 2014): 242–57. http://dx.doi.org/10.1108/scm-12-2013-0436.
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Purpose – This paper aims to provide a framework which can assist focal companies in the development of sustainable supply chains. Sustainable development from an industrial perspective has extended beyond organisational boundaries to incorporate a supply chain approach. Design/methodology/approach – The literature related to sustainable supply chain management is reviewed by incorporating concepts from four organisational theories, including the resource-based, institutional, stakeholder and social network perspectives, to illustrate key drivers and enablers of sustainability initiatives in the supply chain. A conceptual multidimensional framework is then developed that can be used for the initial assessment of supply chain sustainability. Findings – Development and assessment of sustainability in supply chains are being increasingly incorporated as part of supply chain management today. This paper presents a multidimensional framework which can serve as a tool for research scholars and supply chain practitioners in identifying and assessing various economic, environmental and social performance indicators. Research limitations/implications – The framework and approach presented are conceptual, and require additional and broader validation. Additional theories, at differing levels, such as individual behaviour theory, should be utilised to further enhance and evaluate the framework. Developing and integrating analytical models for prescriptive and practical supply chain solutions can enhance the applicability of the framework. Practical implications – The framework adopts a multidimensional approach to assessing and designing sustainable supply chains, as it not only incorporates economic and environmental dimensions but also provides a practical approach to quantifying and embedding the social dimension into decision-making. The framework helps industry practitioners in initial exploration of trade-offs among economic, environmental and social performance of supply chains, which, in turn, could assist them in creating a business case for sustainability. Originality/value – The paper is one of few studies that incorporates some of the key aspects of all three dimensions of sustainability in a single overarching framework for supply chains and offers significant theoretical contribution and implications for sustainable supply chain management.
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Falcone, Maurizio, Giulio Paolucci, and Silvia Tozza. "Multidimensional smoothness indicators for first-order Hamilton-Jacobi equations." Journal of Computational Physics 409 (May 2020): 109360. http://dx.doi.org/10.1016/j.jcp.2020.109360.
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Phat, Huynh Dinh. "Indicators used in measuring poverty under the multidimentional approach: a case study in Quang Ngai province." Science & Technology Development Journal - Economics - Law and Management 4, no. 2 (June 21, 2020): First. http://dx.doi.org/10.32508/stdjelm.v4i2.624.
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Since the concept of multidimensional poverty was initiated in Sen’s works, coupled with the national database providing increasingly complete information on meeting the basic needs of the society, the measurement and evaluation of multidimensional poverty have become popular and attracted the attention of many researchers. The multidimensional poverty line can be based on indicators that are not related to incomes or expenditures. However, it includes other dimensions such as the lack of basic social services. Hence, measuring and implementing poverty reduction policies under the multidimensional approach should be comprehensive in all aspects and cover fields such as education, health, and living conditions (including housing, water supply, possession of sustainable assets, etc.). Since 2016, Quang Ngai province has changed from using the unidimensional to multidimensional poverty measurement method following Decision No. 59/2015/QD-TTg on the multidimensional approach poverty lines applicable for the period 2016-2020. From studies and consultation with experts and researchers in the field of poverty reduction, 11 indicators suitable for measuring multidimensional poverty in Quang Ngai province were proposed, including education level of adults; school attendance of children; access to health services; health insurance, social assistance; child mortality; nutrition; housing quality; fuel used for cooking; water supply sources; toilets; and the possession of sustainable assets.
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Lee, Soo, Okan Bulut, and Youngsuk Suh. "Multidimensional Extension of Multiple Indicators Multiple Causes Models to Detect DIF." Educational and Psychological Measurement 77, no. 4 (May 25, 2016): 545–69. http://dx.doi.org/10.1177/0013164416651116.
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A number of studies have found multiple indicators multiple causes (MIMIC) models to be an effective tool in detecting uniform differential item functioning (DIF) for individual items and item bundles. A recently developed MIMIC-interaction model is capable of detecting both uniform and nonuniform DIF in the unidimensional item response theory (IRT) framework. The goal of the current study is to extend the MIMIC-interaction model for detecting DIF in the context of multidimensional IRT modelling and examine the performance of the multidimensional MIMIC-interaction model under various simulation conditions with respect to Type I error and power rates. Simulation conditions include DIF pattern and magnitude, test length, correlation between latent traits, sample size, and latent mean differences between focal and reference groups. The results of this study indicate that power rates of the multidimensional MIMIC-interaction model under uniform DIF conditions were higher than those of nonuniform DIF conditions. When anchor item length and sample size increased, power for detecting DIF increased. Also, the equal latent mean condition tended to produce higher power rates than the different mean condition. Although the multidimensional MIMIC-interaction model was found to be a reasonably useful tool for identifying uniform DIF, the performance of the model in detecting nonuniform DIF appeared to be questionable.
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Biegun, Krzysztof, and Jacek Karwowski. "Macroeconomic imbalance procedure (MIP) scoreboard indicators and their predictive strength of “multidimensional crises”." Equilibrium 15, no. 1 (March 31, 2020): 11–28. http://dx.doi.org/10.24136/eq.2020.001.
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Research background: The evaluation of the predictive strength of MIP indicators in relation to crises is extremely important for the process of coordinating the economic policies of the EU countries. MIP is one of the pillars of the economic crisis prevention procedure. Predictive power of individual indicators has not been tested before their introduction.
Purpose of the article: Evaluation of the predictive strength of fourteen MIP indicators in relation to multidimensional crises in the EU countries.
Methods: We used ordered probit model to test the ability of MIP indicators to correctly predict episodes of “multidimensional crises” (as defined by the authors) in the period between 2008 and 2017 in all EU Member States.
Findings & Value added: We defined “multidimensional crises”, combining several negative phenomena into one limited dependent variable. This work is also novel in its application of probit regression to test the predictive strength of MIP indicators with an ordered probit model. We identified five MIP variables which were statistically significant in predicting “multidimensional crises” for all EU countries: net international investment position, nominal unit labour cost index, house price index, private sector credit flow and general government gross debt. Other variables turned out to be less important or not effective in crises prediction.
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Bakumenko, L. P., and I. A. Lipatova. "Mortgage Lending Market in Modern Russia: a Multidimensional Approach ." Vestnik NSUEM, no. 4 (January 2, 2022): 144–65. http://dx.doi.org/10.34020/2073-6495-2021-4-144-165.
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The market of one of the most basic participants of the market of housing and communal services – credit organizations is considered. A statistical analysis of mortgage housing lending in Russia was carried out, the classification of the regions of the Russian Federation according to two groups of indicators was carried out: according to the indicators of the housing and communal services market and indicators of the level of socio-economic development of regions by cluster analysis methods and it was shown that not all regions (36 regions) correspond to their levels of groups according to two groups of indicators. The reasons for the development of regions according to the state of development of the housing and communal services market are revealed.
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Trusova, Alla Yu, Alla I. Ilyina, and Evgeniya N. Osipova-Barysheva. "Methodology of application of methods of multidimensional and dynamic analysis when studying living standards of the population." Vestnik of Samara University. Economics and Management 13, no. 2 (July 20, 2022): 182–204. http://dx.doi.org/10.18287/2542-0461-2022-13-2-182-204.
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The article presents fundamental approaches to the study of periods of development of socio-economic indicators and their mutual influence. The forms of influence of indicators on each other are investigated. The dynamic analysis of the standard of living of the population and the factors of social and economic spheres is completed with the tools of econometric modeling and canonical analysis. Birth rate, mortality, employment, unemployment, investments in fixed capital, GRP per capita, the account of resource production, the standard of living of the population and fixed assets according to the data of the Samara Region for the period 20062019, registered in the annual collections of state statistics bodies, are considered as indicators. The predicted values of the standard of living by various methods are calculated, confidence intervals for the studied indicators are constructed. By means of adaptive forecasting using the Brown model, forecast values are calculated and confidence intervals are constructed. Using the tools of canonical analysis, integral indicators are calculated and grouping by time factor is carried out. The spatial grouping of the time factor depending on the standard of living of the population and canonical integral factors is presented. According to the results of the analysis of autoregressive models, it was found that in terms of employment, unemployment, fertility, mortality, investment in fixed assets, GRP per capita, resource production and fixed assets, the impact of the indicator of the previous year is statistically significant, and in terms of the standard of living of the population statistically insignificant. In the second-order autoregression, it was found that all statistical indicators have an impact on the studied indicator, except for indicators of employment and the standard of living of the population. Thus, the forms of models of multiple linear regression, paired linear regression and autoregressive models allow us to assess the numerical impact of all indicators on the studied indicators, as well as their impact on the Standard of living of the population. Visualization of multidimensional data contributes to an in-depth analysis of indicators when grouping, for example, by the time factor.
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Abdul Rahman, Mariah, Nor Samsiah Sani, Rusnita Hamdan, Zulaiha Ali Othman, and Azuraliza Abu Bakar. "A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group." PLOS ONE 16, no. 8 (August 2, 2021): e0255312. http://dx.doi.org/10.1371/journal.pone.0255312.
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The Multidimensional Poverty Index (MPI) is an income-based poverty index which measures multiple deprivations alongside other relevant factors to determine and classify poverty. The implementation of a reliable MPI is one of the significant efforts by the Malaysian government to improve measures in alleviating poverty, in line with the recent policy for Bottom 40 Percent (B40) group. However, using this measurement, only 0.86% of Malaysians are regarded as multidimensionally poor, and this measurement was claimed to be irrelevant for Malaysia as a country that has rapid economic development. Therefore, this study proposes a B40 clustering-based K-Means with cosine similarity architecture to identify the right indicators and dimensions that will provide data driven MPI measurement. In order to evaluate the approach, this study conducted extensive experiments on the Malaysian Census dataset. A series of data preprocessing steps were implemented, including data integration, attribute generation, data filtering, data cleaning, data transformation and attribute selection. The clustering model produced eight clusters of B40 group. The study included a comprehensive clustering analysis to meaningfully understand each of the clusters. The analysis discovered seven indicators of multidimensional poverty from three dimensions encompassing education, living standard and employment. Out of the seven indicators, this study proposed six indicators to be added to the current MPI to establish a more meaningful scenario of the current poverty trend in Malaysia. The outcomes from this study may help the government in properly identifying the B40 group who suffers from financial burden, which could have been currently misclassified.
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Mackevičius, Jonas, Daiva Tamulevičienė, and Romualdas Valkauskas. "Use of the Statistical Comparison Method in a Company's Financial Status Analysis." Buhalterinės apskaitos teorija ir praktika, no. 19 (July 1, 2019): 7. http://dx.doi.org/10.15388/batp.2019.7.
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General economic, mathematical and heuristic study methods were used when analyzing the financial status of companies. In practice, the comparison method is the most widely applied one of all the general economic research methods. When it is applied, the actual indicators (absolute and relative) are compared to the ones planned inside the company; the indicators of the company are compared with the average ones of the industry or the best ones of other companies; indicators of different periods are compared. However, until now, the comparison method has barely been applied in practice using the averages of certain indicators and especially the multidimensional ones. The aim of the article is to develop a methodology for applying the statistical comparison method based on the calculation and evaluation of the multidimensional average. For achievement of the aim were used research methods: analysis of scientific literature, information collection, aggregation, detailing and summarizing. The article analyzes the significance, advantages and disadvantages of the traditional comparison method. The methodology for the comparison method based on the calculation of the multidimensional average and their comparison is proposed in the article. A concrete example of how to apply the comparison method using the multidimensional average calculation method to assess the financial position of companies is provided.
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Рудяга, А. А., and Г. В. Павленко. "STATISTICAL ASSESSMENT OF REGIONAL RISK: A MULTIDIMENSIONAL APPROACH." Учет и статистика, no. 4(64) (January 28, 2022): 87–96. http://dx.doi.org/10.54220/1994-0874.2022.64.4.011.
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В статье предлагается методика статистической оценки регионального экономического риска, основанная на применении факторного анализа. Источником исходных данных служит статистический сборник «Регионы России». Оценка включает 46 показателей, агрегированных в 11 следующих блоков: бюджетная система региона; банковская система региона; финансы предприятий региона; страховая система региона; инфляция; демография региона; уровень жизни населения региона; безопасность жизни в регионе; экономическая система региона; предпринимательство региона; инновации в регионе. Методика включает два этапа. На первом проводится одномерная оценка риска по каждому показателю в отдельности с использованием коэффициентов вариации. На втором формируется набор интегральных показателей регионального риска с использованием факторного анализа. Полученная система показателей может служить инструментом поддержки принятия управленческих решений. Апробация методики проведена на данных за 2018 год. Особенностью методики является макроэкономический характер полученной оценки, характеризующей риск как диспропорции развития региональных экономик. Универсальность и унифицированность методики создают возможность для ее автоматизации и использования в системе поддержки принятия управленческих решений на региональном и федеральном уровнях. The paper proposes a statistical assessment methodology of regional economic risk method based on the factor analysis. The source of initial data is the statistical compendium "Regions of Russia". The assessment includes 46 indicators aggregated into 11 blocks: budget system of the region; banking system of the region; finances of enterprises of the region; insurance system of the region; inflation; demography of the region; standard of living of the population of the region; security of life in the region; economic system of the region; entrepreneurship of the region; innovations in the region. The method includes two stages. At the first stage, a one-dimensional risk assessment is performed for each indicator separately using coefficients of variation. At the second stage, a set of integral indicators of regional risk is formed using factor analysis. The resulting indicators system can serve as a tool for supporting management decision-making. The method was tested on data for 2018. A special feature of the methodology is the macroeconomic character of the resulting assessment, which characterizes the risk as the development of regional economies disproportion. The universality and uniformity of the methodology make it possible to automate it and use it in the system of support for management decision-making at the regional and federal levels.
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Ayanwale, Musa Adekunle, and Funmilayo Rebecca Abayomi. "MULTIDIMENSIONAL INSTRUMENT OF EXAMINERS’ ATTITUDE TOWARDS KNOWLEDGE OF MALPRACTICE INDICATORS." Journal of Applied Structural Equation Modeling 4, no. 1 (January 5, 2020): 17–25. http://dx.doi.org/10.47263/jasem.4(1)02.
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In this study, the authors constructed and validated instrument to measure examiners’ disposition towards knowledge of examination malpractice indicators of West African Examinations Council (WAEC). Consequently, scale development research type of single-subject design was employed. The sample for the two phases consisted of nine hundred and sixty (960) WAEC mathematics examiners from 9 marking centres, which were drawn randomly from Osun, Ogun and Ondo State, Nigeria. The instrument used for data collection was Examiners’ Attitude Towards Knowledge of Malpractice Indicators Scale (EAKMIS). Data were analyzed using Principal Component Analysis (PCA), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) and Ordinal Alpha. The results showed that the scale was reduced from twenty (20) to eleven (11) items across three (3) dimensions. Final compliance indices were: χ2 = 457.54, p = 0.01, RMSEA= 0.03, GFI = 0.96, AGFI = 0.96, TLI = 0.94, NFI = 0.90, SRMR= 0.01, CFI= 0.91 and IFI= 0.91. The ordinal alpha reliability index for the three (3) factors of EAKMIS was 0.79, while the reliability index for each of the subscale of the EAKMIS ranged from 0.85 to 0.93. It was recommended that there should be intensive training and re-training for all the examiners by the examining body where issues related to the detection of examination malpractice cases while marking would be discussed.
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Bacquelaine, Myriam. "The Measurement of Multidimensional Development: Comments on Commonly Accepted Indicators." International Journal of Social Economics 20, no. 11 (November 1993): 4–14. http://dx.doi.org/10.1108/03068299310046054.
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Mishra, Udaya S., and Vachaspati Shukla. "Welfare Comparisons with Multidimensional Well-Being Indicators: An Indian Illustration." Social Indicators Research 129, no. 2 (October 7, 2015): 505–25. http://dx.doi.org/10.1007/s11205-015-1117-0.
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Gnaldi, Michela, and Simone Del Sarto. "Variable Weighting via Multidimensional IRT Models in Composite Indicators Construction." Social Indicators Research 136, no. 3 (November 24, 2016): 1139–56. http://dx.doi.org/10.1007/s11205-016-1500-5.
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Li, Guie, Zhongliang Cai, Ji Liu, Xiaojian Liu, Shiliang Su, Xinran Huang, and Bozhao Li. "Multidimensional Poverty in Rural China: Indicators, Spatiotemporal Patterns and Applications." Social Indicators Research 144, no. 3 (January 30, 2019): 1099–134. http://dx.doi.org/10.1007/s11205-019-02072-5.
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Dehghan, Mohammad, Seyed Nematolla Mousavi, Ibrahim Zare, and Mohammad Bazrafshan. "Evaluating Poverty Indicators among Government-Supported Households: A Multidimensional Approach." Social Welfare 22, no. 86 (November 13, 2022): 313–51. http://dx.doi.org/10.32598/refahj.22.86.3745.1.
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Introduction: Poverty is an undesirable socio-economic phenomenon that endangers the political stability, social solidarity, and mental health of the different sections of society. The literature on poverty in recent years has not been limited to income poverty, but also to other aspects such as health, education, and living standards. By identifying patterns of poverty, instead of prejudging the suitable economic and social policies to combat poverty, we can explain the appropriate and effective policy on poverty at the community level in accordance with the nature of these patterns. Method: In the present study, with a multidimensional approach to poverty (including educational poverty, housing poverty, food poverty, health, and income poverty), the vulnerability of government-supported households in Iran were investigated. For this purpose, the expenditure information of government-supported households in 2017 was the basis of the study. Findings: The results showed that nearly 47% of the households surveyed are below the food poverty line. In terms of depth and quality of dominance of different dimensions of poverty among the studied households, housing poverty has the largest poverty gap, followed by education poverty with a poverty gap of nearly 40%. In summary, the poverty gap in income and health dimensions in this study is very low compared to the mentioned dimensions. In general, it was found that government-sponsored households, in addition to income poverty, also suffer from poor health, housing, education, and food poverty. Discussion: Based on the findings, it is suggested that policy makers adopt and formulate policy-support tools in which the various dimensions of poverty are considered in the form of a comprehensive system.
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miri, somayeh, and mohammad ganji. "Evaluating Poverty Indicators among Government-Supported Households: A Multidimensional Approach." Social Welfare 22, no. 87 (February 26, 2023): 285–319. http://dx.doi.org/10.32598/refahj.22.87.2046.4.
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Introduction The experiences of different countries as well as the results of numerous research studies show that health-promoting behaviors are necessary to maintain the health of citizens, and people with inadequate health literacy have poorer health status. Therefore, the main purpose of this study is to investigate the role of health literacy in coping with anxiety caused by Covid-19 pandemic among Tehran citizens. Method This descriptive-analytical study was conducted on 384 citizens over the age of 20 in Tehran. Sampling was conducted using Sample Power software and data were collected using two questionnaires of health literacy of Iranians from Montazeri et al. and anxiety caused by Corona from Alipour et al. (2009). Cronbach's alpha and composite reliability have been used to calculate validity, and face validity has been used to obtain the necessary validity. Findings Findings indicate that the mean scores of anxiety caused by Covid-19 pandemic were lower in men than those of women. Also, based on analytical results it can be said that the dimensions of health literacy (reading, assessment, comprehension, decision making, and behavior and access) are able to approximately account for 47% of anxiety caused by Covid-19. In general, anxiety caused by Covid-19 is high among citizens and due to the direct relationship between health literacy and control of Covid-19, the need for health-oriented education programs based on the areas of health-promoting behaviors and health literacy components is suggested.
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Kornukov, I. I., and A. Yu Domnikov. "Comparative Evaluation of the Performance of Commercial Banks in Russia Based on a Multidimensional Analysis of Financial Indicators." Journal of Applied Economic Research 22, no. 1 (2023): 142–64. http://dx.doi.org/10.15826/vestnik.2023.22.1.007.
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Banks occupy a central place in the modern global economy. Their stability is an indicator of the state of the country's economy as a whole, which has been repeatedly confirmed by the history of financial crises. The trend and intensity of global digitalization are radically changing the architecture of markets, which requires the development of new theories, methodologies, the search for new management technologies for immediate response to all kinds of challenges and for making informed decisions based on a comprehensive system assessment. The purpose of the study is to conduct a comparative probabilistic assessment of the performance of banks based on the generation of a random multidimensional value of financial indicators. The hypothesis of the study is that probabilistic multidimensional comparative evaluation models will eliminate subjectivism, increase the efficiency and reliability of the raw data base for generating management decisions. The authors have developed a new methodology for comparative evaluation of banks using multidimensional probabilistic analysis. The main problems of managerial decision-making in conditions of uncertainty are identified, taking into account the presence of an anthropogenic factor in the system, the stages of formation of a training sample of commercial banks are described, a list of statistically significant financial indicators is selected, a mathematical problem is formulated, a methodology and mathematical tools for analyzing multidimensional indicators are defined. Using a practical example for 2015-2020, a training sample of banks was formed, divided into two clusters, the coefficients of the equation of the separating hyperplane were determined, a multidimensional random variable was generated, the probability of banks being assigned to one of the clusters was calculated. The results of the calculations showed that only some banks managed to keep their place in the "positive" cluster and the units showed a positive increase in probability. The scientific and practical significance of the research lies in the increment of knowledge on the development of a methodology for multidimensional probabilistic assessment of the position of banks in the training sample. The basis of this methodology can be extended to related spheres of economic life of society, to form the basis of automation models for assessing the financial condition of the subject, finding solutions to optimization problems and developing management solutions.
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Casini, Margherita, Simone Bastianoni, Francesca Gagliardi, Massimo Gigliotti, Angelo Riccaboni, and Gianni Betti. "Sustainable Development Goals Indicators: A Methodological Proposal for a Multidimensional Fuzzy Index in the Mediterranean Area." Sustainability 11, no. 4 (February 24, 2019): 1198. http://dx.doi.org/10.3390/su11041198.
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This paper describes a methodology for the construction of a multidimensional index for sustainability assessment in the context of the Sustainable Development Goals (SDGs) of the UN 2030 Agenda. The methodology is designed to properly capture the multidimensional nature of sustainable development and the SDG framework, introducing an innovative Fuzzy Multidimensional Index to measure the performance of Mediterranean countries. The focus is on agro-food sustainability, in-line with the aims of the Partnership for Research and Innovation in the Mediterranean Area (PRIMA). Drawing on fuzzy set theory, a step-by-step procedure was developed: the underlying dimensions of a set of selected indicators for the SDGs are identified by exploratory factor analysis; an innovative weighting method is applied to aggregate the indicators and calculate the country scores for each dimension and the Fuzzy Multidimensional Index. The PRIMA program will be a first step towards the implementation of innovative solutions, by funding international cooperation projects between countries on both sides of the Mediterranean for a decade: the Fuzzy Multidimensional Index will be the primary source of data for evaluating such projects and policies implemented from them; the Index will therefore be able to close a gap in the availability of appropriate data.
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Zhang, Zhishuo, Yao Xiao, Zitian Fu, Kaiyang Zhong, and Huayong Niu. "A Study on Early Warnings of Financial Crisis of Chinese Listed Companies Based on DEA–SVM Model." Mathematics 10, no. 12 (June 20, 2022): 2142. http://dx.doi.org/10.3390/math10122142.
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In the era of big data, investor sentiment will have an impact on personal decision making and asset pricing in the securities market. This paper uses the Easteconomy stock forum and Sina stock forum as the carrier of investor sentiment to measure the positive sentiment index based on stockholders’ comments and to construct an evaluation index system for the public opinion dimension. In addition, the evaluation index system is constructed from four dimensions, which include operation, innovation, finance and financing, to evaluate the overall condition of listed companies from multiple perspectives. In this paper, the SBM model in the data envelopment analysis method is used to measure the efficiency values of each dimension of the multidimensional efficiency evaluation index system, and the efficiency values of each dimension are the multidimensional efficiency indicators. Subsequently, two sets of input feature indicators of the SVM model were established: one set contains traditional financial indicators and multidimensional efficiency indicators, and another set has only traditional financial indicators. The early warning accuracy of the two sets of input feature indicators was empirically analyzed based on the support vector machine early warning model. The results show that the early warning model incorporating multidimensional efficiency indicators has improved the accuracy compared with the early warning model based on traditional financial indicators. Then, the model was optimized by the particle swarm intelligent optimization algorithm, and the robustness of the results was tested. Moreover, six mainstream machine learning methods, including Logistic Regression, GBDT, CatBoost, AdaBoost, Random Forest and Bagging, were used to compare with the early warning effect of the DEA–SVM model, and the empirical results show that DEA–SVM has high early warning accuracy, which proves the superiority of the proposed model. The findings of this study have a positive effect on further preventing and controlling the financial crisis risk of Chinese-listed companies and promoting as well as facilitating the healthy growth of Chinese-listed companies.