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Saukani, Nasir, and Noor Azina Ismail. "Identifying the Components of Social Capital by Categorical Principal Component Analysis (CATPCA)." Social Indicators Research 141, no. 2 (January 31, 2018): 631–55. http://dx.doi.org/10.1007/s11205-018-1842-2.
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Del Casale, Antonio, Stefano Ferracuti, Alessio Mosca, Leda Marina Pomes, Federica Fiaschè, Luca Bonanni, Marina Borro, Giovanna Gentile, Paolo Martelletti, and Maurizio Simmaco. "Multiple Chemical Sensitivity Syndrome: A Principal Component Analysis of Symptoms." International Journal of Environmental Research and Public Health 17, no. 18 (September 9, 2020): 6551. http://dx.doi.org/10.3390/ijerph17186551.
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Multiple Chemical Sensitivity (MCS) is a chronic and/or recurrent condition with somatic, cognitive, and affective symptoms following a contact with chemical agents whose concentrations do not correlate with toxicity in the general population. Its prevalence is not well defined; it mainly affects women between 40 and 50 years, without variations in ethnicity, education and economic status. We aimed to assess the core symptoms of this illness in a sample of Italian patients. Two physicians investigated different symptoms with a checklist compilation in 129 patients with MCS (117 women). We conducted a categorical Principal Component Analysis (CATPCA) with Varimax rotation on the checklist dataset. A typical triad was documented: hyperosmia, asthenia, and dyspnoea were the most common symptoms. Patients also frequently showed cough and headache. The CATPCA showed seven main factors: 1, neurocognitive symptoms; 2, physical (objective) symptoms; 3, gastrointestinal symptoms; 4, dermatological symptoms; 5, anxiety-depressive symptoms; 6, respiratory symptoms; 7, hyperosmia and asthenia. Patients showed higher mean prevalence of factors 7 (89.9%), 6 (71.7%), and 1 (62.13%). In conclusion, MCS patients frequently manifest hyperosmia, asthenia, and dyspnoea, which are often concomitant with other respiratory and neurocognitive symptoms. Considering the clinical association that is often made with anxiety, more studies are necessary on the psychosomatic aspects of this syndrome. Further analytical epidemiological studies are needed to support the formulation of aetiological hypotheses of MCS.
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Jiménez, Rafael, Elena Gervilla, Albert Sesé, Juan José Montaño, Berta Cajal, and Alfonso Palmer. "Dimensionality Reduction in Data Mining Using Artificial Neural Networks." Methodology 5, no. 1 (January 2009): 26–34. http://dx.doi.org/10.1027/1614-2241.5.1.26.
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The use of classic dimension reduction techniques can be considered customary practice within the context of data mining (DM). Nevertheless, although artificial neural networks (ANNs) are one of the most important DM techniques, specific ANN architectures for dimensionality reduction, such as the principal components analysis ANN (PCA-ANN) and the linear auto-associative ANN (LA-ANN), are used on far fewer occasions. In this study, categorical principal component analysis (CATPCA) and the two ANN procedures are studied and compared searching for uniqueness in an applied context relative to personality variables and drug consumption. A sample of 7,030 adolescents completed a personality test made up of 20 dichotomous items with a hypothesized four-factor latent model. Results point out that both ANN factor solutions converge to those obtained using CATPCA. Nevertheless, possible drawbacks of the ANN techniques lie in their relatively complex application, as well as in the need to use visual graphic analysis as a support for interpreting the factorized solutions.
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Navas González, Francisco, Jordi Jordana Vidal, Gabriela Pizarro Inostroza, Ander Arando Arbulu, and Juan Delgado Bermejo. "Can Donkey Behavior and Cognition Be Used to Trace Back, Explain, or Forecast Moon Cycle and Weather Events?" Animals 8, no. 11 (November 19, 2018): 215. http://dx.doi.org/10.3390/ani8110215.
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Donkeys have been reported to be highly sensitive to environmental changes. Their 8900–8400-year-old evolution process made them interact with diverse environmental situations that were very distant from their harsh origins. These changing situations not only affect donkeys’ short-term behavior but may also determine their long-term cognitive skills from birth. Thus, animal behavior becomes a useful tool to obtain past, present or predict information from the environmental situation of a particular area. We performed an operant conditioning test on 300 donkeys to assess their response type, mood, response intensity, and learning capabilities, while we simultaneously registered 14 categorical environmental factors. We quantified the effect power of such environmental factors on donkey behavior and cognition. We used principal component analysis (CATPCA) to reduce the number of factors affecting each behavioral variable and built categorical regression (CATREG) equations to model for the effects of potential factor combinations. Effect power ranged from 7.9% for the birth season on learning (p < 0.05) to 38.8% for birth moon phase on mood (p < 0.001). CATPCA suggests the percentage of variance explained by a four-dimension-model (comprising the dimensions of response type, mood, response intensity and learning capabilities), is 75.9%. CATREG suggests environmental predictors explain 28.8% of the variability of response type, 37.0% of mood, and 37.5% of response intensity, and learning capabilities.
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Kohler, Friedbert, Roger Renton, Hugh G. Dickson, John Estell, and Carol E. Connolly. "Subacute casemix classification for stroke rehabilitation in Australia. How well does AN-SNAP v2 explain variance in outcomes?" Australian Health Review 35, no. 1 (2011): 1. http://dx.doi.org/10.1071/ah09806.
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Objective. We sought the best predictors for length of stay, discharge destination and functional improvement for inpatients undergoing rehabilitation following a stroke and compared these predictors against AN-SNAP v2. Method. The Oxfordshire classification subgroup, sociodemographic data and functional data were collected for patients admitted between 1997 and 2007, with a diagnosis of recent stroke. The data were factor analysed using Principal Components Analysis for categorical data (CATPCA). Categorical regression analyses was performed to determine the best predictors of length of stay, discharge destination, and functional improvement. Results. A total of 1154 patients were included in the study. Principal components analysis indicated that the data were effectively unidimensional, with length of stay being the most important component. Regression analysis demonstrated that the best predictor was the admission motor FIM score, explaining 38.9% of variance for length of stay, 37.4%.of variance for functional improvement and 16% of variance for discharge destination. Conclusion. The best explanatory variable in our inpatient rehabilitation service is the admission motor FIM. AN- SNAP v2 classification is a less effective explanatory variable. This needs to be taken into account when using AN-SNAP v2 classification for clinical or funding purposes. What is known about the topic? AN-SNAP v2, a major classification tool for inpatient rehabilitation units has been described and used in a small number of published studies. The ability to predict variance by AN-SNAP v2 has not been previously described. What does this paper add? This paper indicates that AN-SNAP v2 is not a good predictor of outcomes in patients in medical rehabilitation units, challenging its utility as a classification tool. What are the implications for practitioners? Practitioners will have a broader understanding of the strengths and weaknesses of the AN-SNAP v2 classification.
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VOZALIS, MANOLIS G., ANGELOS I. MARKOS, and KONSTANTINOS G. MARGARITIS. "AN OPTIMAL SCALING FRAMEWORK FOR COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS." International Journal on Artificial Intelligence Tools 21, no. 06 (December 2012): 1250033. http://dx.doi.org/10.1142/s0218213012500339.
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Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations. Some of the most efficient approaches to CF are based on latent factor models and nearest neighbor methods, and have received considerable attention in recent literature. Latent factor models can tackle some fundamental challenges of CF, such as data sparsity and scalability. In this work, we present an optimal scaling framework to address these problems using Categorical Principal Component Analysis (CatPCA) for the low-rank approximation of the user-item ratings matrix, followed by a neighborhood formation step. CatPCA is a versatile technique that utilizes an optimal scaling process where original data are transformed so that their overall variance is maximized. We considered both smooth and non-smooth transformations for the observed variables (items), such as numeric, (spline) ordinal, (spline) nominal and multiple nominal. The method was extended to handle missing data and incorporate differential weighting for items. Experiments were executed on three data sets of different sparsity and size, MovieLens 100k, 1M and Jester, aiming to evaluate the aforementioned options in terms of accuracy. A combined approach with a multiple nominal transformation and a "passive" missing data strategy clearly outperformed the other tested options for all three data sets. The results are comparable with those reported for single methods in the CF literature.
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Pizarro Inostroza, María Gabriela, Vincenzo Landi, Francisco Javier Navas González, Jose Manuel León Jurado, Juan Vicente Delgado Bermejo, Javier Fernández Álvarez, and María del Amparo Martínez Martínez. "Integrating Casein Complex SNPs Additive, Dominance and Epistatic Effects on Genetic Parameters and Breeding Values Estimation for Murciano-Granadina Goat Milk Yield and Components." Genes 11, no. 3 (March 14, 2020): 309. http://dx.doi.org/10.3390/genes11030309.
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Assessing dominance and additive effects of casein complex single-nucleotide polymorphisms (SNPs) (αS1, αS2, β, and κ casein), and their epistatic relationships may maximize our knowledge on the genetic regulation of profitable traits. Contextually, new genomic selection perspectives may translate this higher efficiency into higher accuracies for milk yield and components’ genetic parameters and breeding values. A total of 2594 lactation records were collected from 159 Murciano-Granadina goats (2005–2018), genotyped for 48 casein loci-located SNPs. Bonferroni-corrected nonparametric tests, categorical principal component analysis (CATPCA), and nonlinear canonical correlations were performed to quantify additive, dominance, and interSNP epistatic effects and evaluate the outcomes of their inclusion in quantitative and qualitative milk production traits’ genetic models (yield, protein, fat, solids, and lactose contents and somatic cells count). Milk yield, lactose, and somatic cell count heritabilities increased considerably when the model including genetic effects was considered (0.46, 0.30, 0.43, respectively). Components standard prediction errors decreased, and accuracies and reliabilities increased when genetic effects were considered. Conclusively, including genetic effects and relationships among these heritable biomarkers may improve model efficiency, genetic parameters, and breeding values for milk yield and composition, optimizing selection practices profitability for components whose technological application may be especially relevant for the cheese-making dairy sector.
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Ciucurel, Constantin, and Elena Ioana Iconaru. "An Epidemiological Study on the Prevalence of the Clinical Features of SARS-CoV-2 Infection in Romanian People." International Journal of Environmental Research and Public Health 17, no. 14 (July 14, 2020): 5082. http://dx.doi.org/10.3390/ijerph17145082.
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The aim of this study was to investigate the prevalence of the clinical features of the SARS-CoV-2 infection in Romanian population through a novel online survey. The survey included categorical socio-demographic and health-related variables. A total of 1830 participants were selected for statistical data processing (a response rate of 90.9%). We determined reasonable reliability of the survey section for clinical features of SARS-CoV-2 infection (Cronbach’s Alpha 0.671). Two meaningful dimensions were identified through CATPCA (Categorical Principal Component Analysis) for the survey’s items. We separated two significant clusters of items, each measuring a distinct factor: the sociodemographic characteristics linked to social distancing and the relevant clinical features of SARS-CoV-2 infection. Next, a two-step cluster analysis helped to classify the sample group taking into consideration the similarity of subjects. The clustering revealed a three-cluster solution, with significant differences between clusters and allowed the cluster detection of a group of individuals, possibly more affected by the infection with the SARS-CoV-2 virus. Through binomial logistic regression analysis, we identified a statistically significant prediction model for the presumptive diagnostic of some relevant clinical features of SARS-CoV-2 infection. Our study validated a cost-effective model for rapid assessment of the health status of subjects, adapted to the context of SARS-CoV-2 pandemic.
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Dzurec, Laura, Aryn Karpinski, Monica Kennison, Randy Rair, and Shawn Fitzgerald. "Assessing Family-Like Dynamics in the Workplace as Possible Precursors of Workplace Bullying: Psychometric Analysis of a Modified Instrument." Journal of Nursing Measurement 27, no. 2 (August 1, 2019): 297–312. http://dx.doi.org/10.1891/1061-3749.27.2.297.
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Background and PurposeFamily-like dynamics in workplaces may serve as antecedents to workplace bullying. This study addressed the psychometric properties of an instrument modified to assess family-like dynamics in the workplace.DesignThe investigators used categorical principal components analysis (CATPCA) to investigate the psychometric properties of an instrument modified to measure coworker perceptions of family-like dynamics in the workplace.MethodsPrimarily White (95%) study participants (N = 273) completed a brief, demographic form and the modified Family Relationships Index (FRI) of the Family Assessment Scale (FES) (Moos & Moos, 1981, 1986). Demographic data were analyzed using descriptive statistics and perceptions of family-like dynamics in the workplace were analyzed using CATPCA.ResultsThe modified FRI served as a reasonable model for capturing coworker perceptions of family-like dynamics in the workplace.ConclusionsFurther research is indicated to determine the overall utility of the modified FRI and to ascertain whether family-like dynamics actually are precursors to workplace bullying victimization.
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Hamidi, M. Luthfi, and Andrew C. Worthington. "Islamic banking sustainability: theory and evidence using a novel quadruple bottom line framework." International Journal of Bank Marketing 39, no. 5 (March 4, 2021): 751–67. http://dx.doi.org/10.1108/ijbm-06-2020-0345.
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PurposeThe study aims to extend the conventional triple bottom line (TBL) framework (prosperity, people and planet) to the quadruple bottom line (QBL) by newly adding a “prophet” dimension for Islamic banks seeking compliance with Islamic law in their pursuit of sustainability.Design/methodology/approachEmploy Chapra's corollaries of maqasid al-shari'ah (the goals of Islamic law) to develop constructs for a survey of 504 Islamic bank stakeholders from five Indonesian provinces to gather primary data to quantitatively verify the dimensions and items in the proposed QBL framework. Categorical principal component analysis (CATPCA) then identifies the sustainability of ten Islamic banks from ten countries as a trial application of the resulting QBL index.FindingsUsing the dimensions and items identified using CATPCA, the authors develop a QBL index to assess the sustainability of the ten Islamic banks. The findings suggest that half of the banks are sufficiently sustainable, with three being proactive (doing more than is required) and two being accommodative (doing all that is required). The remaining five banks are unsustainable, with two banks being defensive (doing the least that is required) and three being reactive (doing less than is required). Most of the banks perform relatively poorly according to the “planet” (38%) and “people” (41%) dimensions and perform better on the “prosperity” (53%) and “prophet” (63%) dimensions. Nonetheless, there is ample room for improvement across all dimensions of sustainability.Research limitations/implicationsThe generalizability of the findings is limited by the small-scale single-country survey used in the CATPCA part of the analysis. Only ten Islamic banks were included in the QBL scoring and ranking exercisesPractical implicationsIslamic banks can improve their sustainability by increasing green financing and reaching out to rural areas and disadvantaged populations. In countries with Islamic banking systems, regulators can support this through training, guidance and incentives.Originality/valuePioneering exploration of TBL from maqasid al-shari'ah perspective. First, we develop a QBL index to assess the sustainability of Islamic banks in line with actual stakeholder expectations.
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Chung, Younshik, and Tai-Jin Song. "Safety Analysis of Motorcycle Crashes in Seoul Metropolitan Area, South Korea: An Application of Nonlinear Optimal Scaling Methods." International Journal of Environmental Research and Public Health 15, no. 12 (November 30, 2018): 2702. http://dx.doi.org/10.3390/ijerph15122702.
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This study identifies the critical factors that affect motorcycle crash severity based on Korean motorcycle crash data in 2009. Motorcyclists, the environment, roadways, other vehicles involved in the crashes, and traffic flow characteristics were used as variables for identifying critical factors. Multivariable statistical methods were used to analyze the data, including categorical principal components analysis (CatPCA) and nonlinear canonical correlation analysis (NLCCA). The results indicate that the following factors are the most critical in increasing motorcycle crash severity: age (motorcyclists in their teens and over fifty years old), motorcycle speed over 30 km/h, speed over 50 km/h for other vehicles involved in the crash, crashes with heavy vehicles such as buses and trucks, crashes on roadways less than six meters wide, crashes at curved sections, crashes at basic roadway segments without any speed control facilities, and head-on crashes. These findings are expected to serve as a valuable reference for formulating remedial policy measures to decrease the severity of motorcycle crashes on roadways in the Seoul metropolitan area of South Korea.
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Krefis, Anne Caroline, Jana Fischereit, Peter Hoffmann, Hans Pinnschmidt, Christina Sorbe, Matthias Augustin, and Jobst Augustin. "Temporal analysis of determinants for respiratory emergency department visits in a large German hospital." BMJ Open Respiratory Research 5, no. 1 (November 2018): e000338. http://dx.doi.org/10.1136/bmjresp-2018-000338.
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IntroductionAssociations between air pollutants, meteorological conditions and respiratory diseases have been extensively shown. The aim of this study was to investigate associations between daily meteorological data, data on air pollution and emergency department (ED) visits depending on the day of the week, season and year (study period from 2013 to 2015).MethodsHighly correlated environmental data entered a categorical principal components analysis (CATPCA). We analysed cross-correlation functions between the time series of the respective daily environmental factors and daily ED visits. Time lags with peak correlations of environmental variables obtained by the CATPCA on ED visits together with day of the week, year, running day (linear, quadratic and cubic), season and interaction terms entered the univariate analysis of variance (UNIANOVA) model.ResultsThe analyses demonstrated main effects on ED visits for the day of the week with highest admission rates on Mondays (B=10.69; ƞ2=0.333; p<0.001). A significant time trend could be observed showing increasing numbers of ED visits per each year (p<0.001). The variable ‘running day’ (linear, quadratic and cubic) indicated a significant non-linear effect over time. The variable season showed significant results with winter, spring and summer recording fewer ED visits compared with the reference season autumn. Environmental variables showed no direct associations with respiratory ED visits.DiscussionED visits were significantly associated with temporal variables. Our data did not show direct associations between environmental variables and ED visits.In times of rapid urbanisation, increases in respiratory diseases, temperature and air pollution, our analyses can help focus future studies and enhance strategies to reduce increasing numbers of respiratory diseases and ED visits. Because the potential costs of medical care in hospitals can be high compared with physicians, public health recommendations for reducing the increasing ED visits should be promoted and evaluated.
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Velasco-Martínez, Leticia-Concepción, Juan-Jesús Martín-Jaime, Ligia-Isabel Estrada-Vidal, and Juan-Carlos Tójar-Hurtado. "Environmental Education to Change the Consumption Model and Curb Climate Change." Sustainability 12, no. 18 (September 11, 2020): 7475. http://dx.doi.org/10.3390/su12187475.
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Environmental education plays a fundamental role in the fight against climate change and the transformation towards a more sustainable and environmentally friendly socio-economic model. This study shows how to evaluate the effectiveness of a program for compulsory education students in Spain. The subject of the program focused on the effects of climate change in relation to our consumption model and the generation of waste. A mixed research methodology is proposed that combines a quantitative (10 items on the Likert scale, n = 714) and qualitative approach (category construction and analysis on open-ended questions). A study of the reliability and validity of the measure was carried out through a categorical principal component analysis (CATPCA). The multivariate analysis of variance (MANOVA) correlates the gender and educational level of the students to the learning acquired in the program. For example, the results show how students are convinced that adopting minimal pro-environmental habits (turning off lights and unplugging electronics, choosing public transport to get around, or using solar and wind power to produce electricity) can help mitigate climate change. The conclusions show the difficulties and challenges of education for responsible consumption, emphasizing the development of environmental education programs for reducing the effects of climate change.
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Gil-Lebrero, Sergio, Francisco Javier Navas González, Victoria Gámiz López, Francisco Javier Quiles Latorre, and José Manuel Flores Serrano. "Regulation of Microclimatic Conditions inside Native Beehives and Its Relationship with Climate in Southern Spain." Sustainability 12, no. 16 (August 10, 2020): 6431. http://dx.doi.org/10.3390/su12166431.
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In this study, the Wbee Sensor System was used to record data from 10 Iberian beehives for two years in southern Spain. These data were used to identify potential conditioning climatic factors of the internal regulatory behavior of the hive and its weight. Categorical principal components analysis (CATPCA) was used to determine the minimum number of those factors able to capture the maximum percentage of variability in the data recorded. Then, categorical regression (CATREG) was used to select the factors that were linearly related to hive internal humidity, temperature and weight to issue predictive regression equations in Iberian bees. Average relative humidity values of 51.7% ± 10.4 and 54.2% ± 11.7 were reported for humidity in the brood nest and in the food area, while average temperatures were 34.3 °C ± 1.5 in the brood nest and 29.9 °C ± 5.8 in the food area. Average beehive weight was 38.2 kg ± 13.6. Some of our data, especially those related to humidity, contrast with previously published results for other studies about bees from Central and northern Europe. Conclusively, certain combinations of climatic factors may condition within hive humidity, temperature and hive weight. Southern Iberian honeybees’ brood nest humidity regulatory capacity could be lower than brood nest thermoregulatory capacity, maintaining values close to 34 °C, even in dry conditions.
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Krug, Isabel, Janet Treasure, Marija Anderluh, Laura Bellodi, Elena Cellini, David Collier, Milena di Bernardo, et al. "Associations of individual and family eating patterns during childhood and early adolescence: a multicentre European study of associated eating disorder factors." British Journal of Nutrition 101, no. 6 (August 28, 2008): 909–18. http://dx.doi.org/10.1017/s0007114508047752.
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The objective of this study was to examine whether there is an association between individual and family eating patterns during childhood and early adolescence and the likelihood of developing a subsequent eating disorder (ED). A total of 1664 participants took part in the study. The ED cases (n 879) were referred for assessment and treatment to specialized ED units in five different European countries and were compared to a control group of healthy individuals (n 785). Participants completed the Early Eating Environmental Subscale of the Cross-Cultural (Environmental) Questionnaire, a retrospective measure, which has been developed as part of a European multicentre trial in order to detect dimensions associated with ED in different countries. In the control group, also the General Health Questionnaire-28 (GHQ-28), the semi-structured clinical interview (SCID-I) and the Eating Attitudes Test (EAT-26) were used. Five individually Categorical Principal Components Analysis (CatPCA) procedures were adjusted, one for each theoretically expected factor. Logistic regression analyses indicated that the domains with the strongest effects from the CatPCA scores in the total sample were: food used as individualization, and control and rules about food. On the other hand, healthy eating was negatively related to a subsequent ED. When differences between countries were assessed, results indicated that the pattern of associated ED factors did vary between countries. There was very little difference in early eating behaviour on the subtypes of ED. These findings suggest that the fragmentation of meals within the family and an excessive importance given to food by the individual and the family are linked to the later development of an ED.
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Koerber, Georgia R., Peter A. Anderson, and Jack V. Seekamp. "Morphology, physiology and AFLP markers validate that green box is a hybrid of Eucalyptus largiflorens and E. gracilis (Myrtaceae)." Australian Systematic Botany 26, no. 2 (2013): 156. http://dx.doi.org/10.1071/sb12034.
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Prolonged drought and salinity on the Chowilla floodplain of the Murray River have caused deterioration of E. largiflorens F.Muell. A putative hybrid with E. gracilis F.Muell, green box, withstands the saline conditions. We aimed to substantiate that green box is a hybrid and to test for agreement between morphological and physiological characters with amplified fragment length polymorphisms (AFLP). Mature stands were measured for leaf, trunk, floral, cotyledon, carbon and nitrogen isotope discrimination, specific leaf area (SLA) and AFLP. Green box was placed between E. largiflorens and E. gracilis according to categorical principal components analysis (CATPCA) of 21 morphological and physiological characters and character states. The hybrid index of 11 AFLP markers that were 78% species specific separated E. gracilis and E. largiflorens, and the majority of green box plants displayed indices ranging from 0.42 to 0.53, reflecting mostly additive inheritance. Calculation of the hybrid index with all 232 AFLP markers, using maximum likelihood, similarly placed green box between E. gracilis and E. largiflorens. Our morphological, physiological and AFLP-marker observations substantiated that green box is a hybrid between E. largiflorens and E. gracilis.
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Caparrós Ruiz, Antonio. "Social capital, labour market status and wages: some evidence from Spain." International Journal of Social Economics 47, no. 4 (April 6, 2020): 539–60. http://dx.doi.org/10.1108/ijse-04-2019-0253.
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PurposeThis article analyses the social capital's influence on the Spanish labour market. In particular, this study examines to what extent the social capital increases the likelihood of being employed, taking into account different labour market status, and diverse dimensions of the social capital. Focusing on wage earners, it is also analysed whether network structures in Spain influence on the wage earnings.Design/methodology/approachThe methodology applied to analyse the labour market status is a multinomial logit model. For the analysis of wages, it is specified a wage model with sample selection bias. In both cases, social capital indicators are included as regressors.FindingsThe results show that social participation exerts a positive influence on the probability of being self-employed, and lowers the likelihood of being unemployed. Moreover, it is verified that the interaction with family members or close friends influence positively on wages.Research limitations/implicationsFurther research should emphasise how employers assess the workers' competences associated with the social capital.Practical implicationsThe findings provide knowledge to policymakers useful to increase the role of social participation in the labour market.Social implicationsThe importance of social network as an instrument for the job search must be enhanced.Originality/valueThis article overcomes some drawbacks associated with the analysis of social capital from an aggregate perspective. Furthermore, social capital indicators are obtained using the Categorical Principal Components Analysis (CATPCA), which is unprecedented in the economic literature.
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Ijabadeniyi, Abosede, Jeevarathnam Parthasarathy Govender, and Dayaneethie Veerasamy. "Cultural Diversity and its Influence on the Attitudes of Africans and Indians toward Marketing Communication: A South African Perspective." Journal of Economics and Behavioral Studies 8, no. 6(J) (January 24, 2017): 28–39. http://dx.doi.org/10.22610/jebs.v8i6(j).1481.
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Abstract: Culture has been reported to be one of the major factors influencing attitudes toward marketing communication. However, identification across prevailing cultural dimensions could have unique implications for attitudes toward marketing communication. This paper examines how African and Indian cultural values may or may not influence attitudes toward marketing communication. It explores how Africans converge with or diverge from Indians with regards to culturally sensitive attitudes toward marketing communication, based on a Marketing Communication-Specific Cultural Values (MCSCV) model adapted from the individualism-collectivism constructs. Attitudes toward marketing were measured based on the advertising scale of the Index of Consumer Sentiment toward Marketing (ICSM) practices. Data generated for this study were based on responses provided by 283 and 92 African and Indian shoppers at the main shopping malls in the most predominant African and Indian townships in Durban, South Africa viz. Umlazi and Chatsworth, respectively. Analysis of Variance (ANOVA) and Categorical Principal Component Analysis (CATPCA) were conducted on the dataset. Findings revealed that both races displayed more individualistic than collectivistic tendencies toward marketing communication, but Africans exhibited more collectivistic tendencies than their Indian counterparts. In addition, respondents’ individualistic tendencies have a significant influence on attitudes toward marketing communication which showed that consumers’ indigenous cultural disposition play a moderating role on attitudes toward marketing communication. This study builds on the marketing literature by validating the implications of cultural diversity for marketing communication. The study emphasizes how the interplay between target markets’ underlying cultural dispositions and cultural values held toward marketing communication, influence the consistency or inconsistency in consumers’ attitudes toward marketing communication.
Keywords: Culture, Individualism, Collectivism, Consumer behaviour, Advertising
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Lentjushenkova, Oksana, Vita Zarina, and Jelena Titko. "Disclosure of intellectual capital in financial reports: case of Latvia." Oeconomia Copernicana 10, no. 2 (June 30, 2019): 341–57. http://dx.doi.org/10.24136/oc.2019.017.
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Research background: Intellectual capital and its elements, such as reputation, customer relationships, staff competence, are an essential part of a company’s value. However, the issues regarding its recording in company’s accounting books have not been solved. Proper disclosure of an intellectual capital in financial re-ports will increase the transparency of company-related information, thus improving the quality of reporting.
Purpose of the article: The paper aims to investigate the opportunities of intellectual capital disclosure in company’s financial reports from the viewpoint of accounting experts.
Methods: Financial and accounting managers, board members of accounting services, companies and auditors were surveyed, using the authors’ developed questionnaire. The statements regarding the awareness of the intellectual capital and its disclosure-related questions, as well as a respondent profile section were offered to respondents for evaluation. Data was processed in SPSS, applying the method of frequency analysis and categorical Principal Component Analysis (CATPCA).
Findings & Value added: The research results indicate the problem of inconsistency between understanding of intellectual capital and its elements in management theory and accounting practice. The existing accounting standards and regulations do not allow for making a full disclosure of all companies’ assets. Thus, a reliable information about company’s value is not available for shareholders, executives and other stakeholders. The authors suggest using a non-financial reporting practice to reflect the real situation in all companies, irrespective to their status within the meaning of the European Directive on non-financial information disclosure. Current research results will be used for future research and elaboration of recommendations to companies for better disclosure of their assets. Besides, there is a potential for future studies regarding non-financial reporting practice and disclosure of intellectual capital in neighboring countries.
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Hui, Ng Xin, Mas’ud Hariadi, and Hardany Primarizky. "A Retrospective Study of Canine Pyometra in Segar Veterinary Hospital, Kuala Lumpur, Malaysia Year 2012-2016." KnE Life Sciences 3, no. 6 (December 3, 2017): 153. http://dx.doi.org/10.18502/kls.v3i6.1124.
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A retrospective study was used to analyse canine pyometra cases in Segar Veterinary Hospital, Kuala Lumpur, Malaysia from May 2012 to May 2016 and to investigate the relationship between pyometra and breed and age of dogs. The study was done through secondary collection of data from ambulatoirs of pyometra cases which were diagnosed based on anamnesis, examination of clinical signs and ultrasonography and/or radiography. The data collected includes breed categorised into small, medium, and large breeds, whereas the age are categorised into puppies, adulthood and geriatric. The data was then analysed with tree classification analysis and CATPCA (Principal Components Analysis for Categorical Data) analysis using SPSS program. A total of 80 cases of pyometra were recovered from female dog patients over the study period. Small breed dogs at 72.5% (n=58) and geriatric dogs at 62.5% (n=50) had the highest percentage of pyometra. The breeds Mongreal, German Shepard Dog, Mini Schnauzer, Silky Terrier, Toy Poodle, Beagle, Chow Chow, Golden Retriever, Rottweiler, Cocker Spaniel, White Terrier, Siberian Husky, and Pekingese aged older than 5.5 years had 100% from 37 cases of open-cervix pyometra. Geriatric and small breed dogs are inclined to have open-cervix pyometra. However adult and medium or large breed dogs have a higher possibility to have closed-cervix pyometra. These results serve to highlight the importance of public awareness regarding canine pyometra and further researches are needed to find out the effects of hormone therapy, frequency of births, and the bacteria present in uterus with pyometra. Keywords: Canine pyometra, Open-cervix Pyometra, Closed-cervix Pyometra, Age and Breed
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Nery, Newillames Gonçalves, Lidia Moraes Ribeiro Jordão, and Maria do Carmo Matias Freire. "School environment and oral health promotion: the National Survey of School Health (PeNSE)." Revista de Saúde Pública 53 (October 22, 2019): 93. http://dx.doi.org/10.11606/s1518-8787.2019053001376.
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OBJECTIVE: To evaluate the potential support of schools for oral health promotion and associated factors in Brazilian capitals. METHODS: Data from 1,339 public and private schools of the 27 Brazilian capitals were obtained from the National Survey of School Health (PeNSE) 2015. Data from the capitals were obtained from the United Nations Development Program and the Department of Informatics of the Brazilian Unified Health System (Datasus). The indicator “ambiente escolar promotor de saúde bucal” (AEPSB – oral health promoting school environment) was designed from 21 variables of the school environment with possible influence on students’ oral health employing the categorical principal components analysis (CATPCA). Associations between the AEPSB and characteristics of schools, capitals and regions were tested (bivariate analysis). RESULTS: Ten variables comprised CAPTCA, after excluding those with low correlation or high multicollinearity. The analysis resulted in a model with three dimensions: D1. Within-school aspects (sales of food with added sugar in the canteen and health promotion actions), D2. Aspects of the area around the school (sales of food with added sugar in alternative points) and D3. prohibitive policies at school (prohibition of alcohol and tobacco consumption). The sum of the scores of the dimensions generated the AEPSB indicator, dichotomized by the median. From the total of schools studied, 51.2% (95%CI 48.5–53.8) presented a more favorable environment for oral health (higher AEPSB). In the capitals, this percentage ranged from 36.6% (95%CI 23.4–52.2) in Rio Branco to 80.4% (95%CI 67.2–89.1) in Florianópolis. Among the Brazilian regions, it ranged from 45.5% (95%CI 40.0–51.2) in the North to 67.6% (95%CI 59.4–74.9) in the South. Higher percentages of schools with higher AEPSB were found in public schools [58.1% (95%CI 54.9–61.2)] and in capitals and regions with higher Human Development Index [61.0% (95%IC 55.8–66.0) and 57.4% (95%CI 53.2–61.4), respectively] and lower Gini index [55.7% (95%CI 51.2–60.0) and 52.8 (95%CI 49.8–55.8), respectively]. CONCLUSIONS: The potential to support oral health promotion in schools in Brazilian capitals, assessed by the AEPSB indicator, was associated with contextual factors of schools, capitals and Brazilian regions.
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Parellada, Xavier, Soumia Fahd, Xavier Santos, José Brito, Gustavo Llorente, and Juan Pleguezuelos. "Morphological variability of the Lataste's viper (Vipera latastei) and the Atlas dwarf viper (Vipera monticola): patterns of biogeographical distribution and taxonomy." Amphibia-Reptilia 27, no. 2 (2006): 219–40. http://dx.doi.org/10.1163/156853806777239940.
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AbstractThe Lataste's viper Vipera latastei is a medium-sized viper distributed throughout almost the entire Iberian Peninsula and north-west of Africa. Former morphological studies noted the existence of two subspecies, V. l. gaditana and V. l. latastei, as well as a full species, V. monticola, in the High Atlas, corresponding to the prior overall range described for V. latastei. However, some results remained unclear in these former studies, e.g. the specific status of the Medium Atlas populations, the intra-subspecific differences in V. l. gaditana and, the true status of some isolated populations of the northern range. For this reason, 45 morphological characters were analysed in 672 preserved specimens covering the entire range. Categorical Principal Components Analysis (CATPCA) and Discriminant Function Analysis (DFA) were used to assess geographic variability, treating specimens individually or assigning them a priori to groups, respectively. Geographic groups were established according to the origin of specimens in isolated areas of mountain chains. As the percentage of correct assignment was low in DFA, initial groups were combined to maximize the percentage. The results from the multivariate analysis suggest morphological differentiation between populations. Some variables accounted for geographic variability: e.g. rows of dorsal scales at mid-body are taxonomically stable and clearly separate the African populations; and number of ventral scales showed a clinal variation from 126 to 143 ventrals in extreme populations. The three African groups manifested clear morphological differences, and especially specimens from the High Atlas (V. monticola) and Alger. On the contrary, a large number of initial Iberian groups were merged because of the low scores in the correct classification. The final groups showed a vast central area with low morphological differentiation as well as isolated populations in the NW, NE and SW Iberian Peninsula. This conclusion matches well with allopatric speciation processes during the Quaternary ice ages, which contributed to the contraction/expansion of range, isolation events, and peripheral population refugia. Morphological differentiation in external characters of V. latastei exhibited similar results with respect to V. aspis and V. ammodytes, the vipers occupying other southern European peninsulas. Molecular markers will contribute to elucidate the relationships between V. latastei populations and the history of colonisation across the Strait of Gibraltar.
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Lu, Meng, Hye-Seung Lee, David Hadley, Jianhua Z. Huang, and Xiaoning Qian. "Supervised categorical principal component analysis for genome-wide association analyses." BMC Genomics 15, Suppl 1 (2014): S10. http://dx.doi.org/10.1186/1471-2164-15-s1-s10.
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Vavougios, George D., George Natsios, Chaido Pastaka, Sotirios G. Zarogiannis, and Konstantinos I. Gourgoulianis. "Phenotypes of comorbidity in OSAS patients: combining categorical principal component analysis with cluster analysis." Journal of Sleep Research 25, no. 1 (September 14, 2015): 31–38. http://dx.doi.org/10.1111/jsr.12344.
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McClard, Cynthia K., Rui Wang, Victoria Windham, Jose Munoz, Samuel Gomez, Sagit Fried, Namrata Saroj, Carl Regillo, Charles Clifton Wykoff, and Adriana M. Strutt. "Questionnaire to Assess Life Impact of Treatment by Intravitreal Injections (QUALITII): Development of a patient-reported measure to assess treatment burden of repeat intravitreal injections." BMJ Open Ophthalmology 6, no. 1 (April 2021): e000669. http://dx.doi.org/10.1136/bmjophth-2020-000669.
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ObjectiveTo understand patient burden of treatment of repeated intravitreal injections (IVI) in the management of exudative retinal diseases.Methods and analysisParticipants were sampled from a large urban retina specialty practice in Houston, Texas, USA, based on history of ongoing receipt of IVI. The 50-item Questionnaire to Assess Life Impact of Treatment by Intravitreal Injections questionnaire was developed to evaluate the patient experience including discomfort, anxiety, inconvenience and satisfaction. Categorial principal components analysis (CATPCA) was performed to assess construct validity and internal consistency. A subset of these items was used to establish a measure of total treatment burden, referred to as the IVI Treatment Burden Score (TBS).Results142 patients participated in this study. CATPCA analysis revealed five dimensions of patient burden: disruption of normal routine or capacity, anxiety, frequency of visits, chronicity of disease and perceived treatment value or satisfaction. Together, these dimensions accounted for 67% of variance explained. Cronbach’s alpha was 0.97. The most frequently cited cause of discomfort was the feeling after anaesthetic wore off. The most common source of anxiety was fear of injection and associated discomfort or pain. Regarding inconvenience, patients reported temporary postinjection debilitation, requiring an average of 8 hours for recovery per treatment. The most frequently identified sources of satisfaction were confidence in the provider or treatment and interactions with staff.ConclusionsUnderstanding and quantifying the patient burden associated with repeated IVI for exudative retinal diseases can reveal opportunities to improve delivery methods. The TBS could serve to inform strategies to maximise treatment adherence and optimise patient experiences.
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Ferrari, Pier Alda, Paola Annoni, Alessandro Barbiero, and Giancarlo Manzi. "An imputation method for categorical variables with application to nonlinear principal component analysis." Computational Statistics & Data Analysis 55, no. 7 (July 2011): 2410–20. http://dx.doi.org/10.1016/j.csda.2011.02.007.
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Kroonenberg, P. M., B. D. Harch, K. E. Basford, and A. Cruickshank. "Combined Analysis of Categorical and Numerical Descriptors of Australian Groundnut Accessions Using Nonlinear Principal Component Analysis." Journal of Agricultural, Biological, and Environmental Statistics 2, no. 3 (September 1997): 294. http://dx.doi.org/10.2307/1400447.
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Vilela, Alice, Bebiana Monteiro, and Elisete Correia. "Sensory profile of Port wines: categorical principal component analysis, an approach for sensory data treatment." Ciência e Técnica Vitivinícola 30, no. 1 (2015): 1–8. http://dx.doi.org/10.1051/ctv/20153001001.
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Honda, Katsuhiro, Yoshihito Nakamura, and Hidetomo Ichihashi. "Simultaneous Application of Fuzzy Clustering and Quantification with Incomplete Categorical Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 397–402. http://dx.doi.org/10.20965/jaciii.2004.p0397.
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This paper proposes the simultaneous application of homogeneity analysis and fuzzy clustering with incomplete data. Taking into account the similarity between the loss function for homogeneity analysis and the least squares criterion for principal component analysis, we define the new objective function in a formulation similar to linear fuzzy clustering with missing values. Numerical experiments demonstrate the feasibility of the proposed method.
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Cash, Ceilidh Barlow, Jessa Letargo, Steffen P. Graether, and Shoshanah R. Jacobs. "An Analysis of the Perceptions and Resources of Large University Classes." CBE—Life Sciences Education 16, no. 2 (June 2017): ar33. http://dx.doi.org/10.1187/cbe.16-01-0004.
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Large class learning is a reality that is not exclusive to the first-year experience at midsized, comprehensive universities; upper-year courses have similarly high enrollment, with many class sizes greater than 200 students. Research into the efficacy and deficiencies of large undergraduate classes has been ongoing for more than 100 years, with most research associating large classes with weak student engagement, decreased depth of learning, and ineffective interactions. This study used a multidimensional research approach to survey student and instructor perceptions of large biology classes and to characterize the courses offered by a department according to resources and course structure using a categorical principal components analysis. Both student and instructor survey results indicated that a large class begins around 240 students. Large classes were identified as impersonal and classified using extrinsic qualifiers; however, students did identify techniques that made the classes feel smaller. In addition to the qualitative survey, we also attempted to quantify courses by collecting data from course outlines and analyzed the data using categorical principal component analysis. The analysis maps institutional change in resource allocation and teaching structure from 2010 through 2014 and validates the use of categorical principal components analysis in educational research. We examine what perceptions and factors are involved in a large class that is perceived to feel small. Our analysis suggests that it is not the addition of resources or difference in the lecturing method, but it is the instructor that determines whether a large class can feel small.
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Huynh, Kim P., David T. Jacho-Chávez, Robert J. Petrunia, and Marcel Voia. "Functional Principal Component Analysis of Density Families With Categorical and Continuous Data on Canadian Entrant Manufacturing Firms." Journal of the American Statistical Association 106, no. 495 (September 2011): 858–78. http://dx.doi.org/10.1198/jasa.2011.ap10111.
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Ingram, Ruth U., Ajay D. Halai, Gorana Pobric, Seyed Sajjadi, Karalyn Patterson, and Matthew A. Lambon Ralph. "Graded, multidimensional intra- and intergroup variations in primary progressive aphasia and post-stroke aphasia." Brain 143, no. 10 (September 17, 2020): 3121–35. http://dx.doi.org/10.1093/brain/awaa245.
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Abstract Language impairments caused by stroke (post-stroke aphasia, PSA) and neurodegeneration (primary progressive aphasia, PPA) have overlapping symptomatology, nomenclature and are classically divided into categorical subtypes. Surprisingly, PPA and PSA have rarely been directly compared in detail. Rather, previous studies have compared certain subtypes (e.g. semantic variants) or have focused on a specific cognitive/linguistic task (e.g. reading). This study assessed a large range of linguistic and cognitive tasks across the full spectra of PSA and PPA. We applied varimax-rotated principal component analysis to explore the underlying structure of the variance in the assessment scores. Similar phonological, semantic and fluency-related components were found for PSA and PPA. A combined principal component analysis across the two aetiologies revealed graded intra- and intergroup variations on all four extracted components. Classification analysis was used to test, formally, whether there were any categorical boundaries for any subtypes of PPA or PSA. Semantic dementia formed a true diagnostic category (i.e. within group homogeneity and distinct between-group differences), whereas there was considerable overlap and graded variations within and between other subtypes of PPA and PSA. These results suggest that (i) a multidimensional rather than categorical classification system may be a better conceptualization of aphasia from both causes; and (ii) despite the very different types of pathology, these broad classes of aphasia have considerable features in common.
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Mendes, Glauco H. S., and Gilberto Miller Devós Ganga. "Predicting Success in Product Development: The Application of Principal Component Analysis to Categorical Data and Binomial Logistic Regression." Journal of technology management & innovation 8, no. 3 (2013): 15–16. http://dx.doi.org/10.4067/s0718-27242013000400008.
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Turkmen, Asuman S., Yuan Yuan, and Nedret Billor. "Evaluation of methods for adjusting population stratification in genome‐wide association studies: Standard versus categorical principal component analysis." Annals of Human Genetics 83, no. 6 (July 19, 2019): 454–64. http://dx.doi.org/10.1111/ahg.12339.
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Antino, Mirko, Jesús M. Alvarado, Rodrigo A. Asún, and Paul Bliese. "Rethinking the Exploration of Dichotomous Data: Mokken Scale Analysis Versus Factorial Analysis." Sociological Methods & Research 49, no. 4 (April 29, 2018): 839–67. http://dx.doi.org/10.1177/0049124118769090.
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The need to determine the correct dimensionality of theoretical constructs and generate valid measurement instruments when underlying items are categorical has generated a significant volume of research in the social sciences. This article presents two studies contrasting different categorical exploratory techniques. The first study compares Mokken scale analysis (MSA) and two-factor-based exploratory techniques for noncontinuous variables: item factor analysis and Normal Ogive Harmonic Analysis Robust Method (NOHARM). Comparisons are conducted across techniques and in reference to the common principal component analysis model using simulated data under conditions of two-dimensionality with different degrees of correlation ( r = .0 to .6). The second study shows the theoretical and practical results of using MSA and NOHARM (the factorial technique which functioned best in the first study) on two nonsimulated data sets. The nonsimulated data are particularly interesting because MSA was used to solve a theoretical debate. Based on the results from both studies, we show that the ability of NOHARM to detect dimensionality and scalability is similar to MSA when the data comprise two uncorrelated latent dimensions; however, NOHARM is preferable when data are drawn from instruments containing latent dimensions weakly or moderately correlated. This article discusses the theoretical and practical implications of these findings.
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Hsieh, Fushing, Elizabeth P. Chou, and Ting-Li Chen. "Mimicking Complexity of Structured Data Matrix’s Information Content: Categorical Exploratory Data Analysis." Entropy 23, no. 5 (May 11, 2021): 594. http://dx.doi.org/10.3390/e23050594.
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We develop Categorical Exploratory Data Analysis (CEDA) with mimicking to explore and exhibit the complexity of information content that is contained within any data matrix: categorical, discrete, or continuous. Such complexity is shown through visible and explainable serial multiscale structural dependency with heterogeneity. CEDA is developed upon all features’ categorical nature via histogram and it is guided by all features’ associative patterns (order-2 dependence) in a mutual conditional entropy matrix. Higher-order structural dependency of k(≥3) features is exhibited through block patterns within heatmaps that are constructed by permuting contingency-kD-lattices of counts. By growing k, the resultant heatmap series contains global and large scales of structural dependency that constitute the data matrix’s information content. When involving continuous features, the principal component analysis (PCA) extracts fine-scale information content from each block in the final heatmap. Our mimicking protocol coherently simulates this heatmap series by preserving global-to-fine scales structural dependency. Upon every step of mimicking process, each accepted simulated heatmap is subject to constraints with respect to all of the reliable observed categorical patterns. For reliability and robustness in sciences, CEDA with mimicking enhances data visualization by revealing deterministic and stochastic structures within each scale-specific structural dependency. For inferences in Machine Learning (ML) and Statistics, it clarifies, upon which scales, which covariate feature-groups have major-vs.-minor predictive powers on response features. For the social justice of Artificial Intelligence (AI) products, it checks whether a data matrix incompletely prescribes the targeted system.
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Parreira, Verônica F., Renata N. Kirkwood, Megan Towns, Isabel Aganon, Lauren Barrett, Catherine Darling, Michelle Lee, Kylie Hill, Roger S. Goldstein, and Dina Brooks. "Is There an Association between Symptoms of Anxiety and Depression and Quality of Life in Patients with Chronic Obstructive Pulmonary Disease?" Canadian Respiratory Journal 22, no. 1 (2015): 37–41. http://dx.doi.org/10.1155/2015/478528.
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BACKGROUND: In addition to symptoms, such as dyspnea and fatigue, patients with chronic obstructive pulmonary disease (COPD) also experience mood disturbances.OBJECTIVE: To explore the relationships between health-related quality of life measures collected from patients with stable COPD and a commonly used measure of depression and anxiety.METHODS: The present analysis was a retrospective study of patients with COPD enrolled in a pulmonary rehabilitation program. Hospital Anxiety and Depression Scale (HADS), Chronic Respiratory Disease Questionnaire (CRQ), Medical Research Council dyspnea scale and 6 min walk test data were collected. Statistical analyses were performed using Spearman’s correlations, and categorical regression and categorical principal component analysis were interpreted using the biplot methodology.RESULTS: HADS anxiety scores retrieved from 80 patients were grouped as ‘no anxiety’ (n=43 [54%]), ‘probable anxiety’ (n=21 [26%]) and ‘presence of anxiety’ (n=16 [20%]). HADS depression scores were similarly grouped. There was a moderate relationship between the anxiety subscale of the HADS and both the emotional function (r=−0.519; P<0.01) and mastery (r=−0.553; P<0.01) domains of the CRQ. Categorical regression showed that the CRQ-mastery domain explained 40% of the total variation in anxiety. A principal component analysis biplot showed that the highest distance between the groups was along the mastery domain, which separated patients without feelings of anxiety from those with anxiety. However, none of the CRQ domains were able to discriminate the three depression groups.CONCLUSIONS: The CRQ-mastery domain may identify symptoms of anxiety in patients with COPD; however, the relationship is not strong enough to use the CRQ-mastery domain as a surrogate measure. None of the CRQ domains were able to discriminate the three depression groups (no depression, probable and presence); therefore, specific, validated tools to identify symptoms of depression should be used.
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Gallego-Álvarez, Isabel, María Belén Lozano, and Miguel Rodríguez-Rosa. "Analysis of Social Sustainability Information in a Global Context According to the New Global Reporting Initiative 400 Social Standards." Sustainability 11, no. 24 (December 10, 2019): 7073. http://dx.doi.org/10.3390/su11247073.
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Interest is increasing in what information companies disclose regarding the social aspects of their operations. This research therefore develops an index to analyze the social disclosure of companies from various countries and geographical regions including Latin America, Europe, Africa, Asia, and the United States. Using categorical principal component analysis and partial triadic analysis, we build a numerical value for a specific social individual index by firm. Then, we analyze the extent to which this disclosure follows the Global Reporting Initiative 400 social standards, which became effective on 1 July 2018. In addition to considering geographical aspects, we also analyze social disclosure based on industry, which facilitates firms’ decision-making and policy formation in social disclosure.
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Ogunsakin, Ropo E., Sibusiso Moyo, Oludayo, O. Olugbara, and Connie Israel. "Relating Student Engagement Indicators to Academic Performance Using Multiple Correspondence Analysis." Cybernetics and Information Technologies 21, no. 1 (March 1, 2021): 87–102. http://dx.doi.org/10.2478/cait-2021-0007.
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Abstract Student engagement is an essential device for deepening learning, achieving learning outcomes, developing competencies, and improving academic performance in education settings. It is widely receiving increased attention among various scholars and higher education leaders. However, there are increasing concerns about the academic performance of students in higher education settings. The application of statistical data analytics for mining student engagement datasets is a candidate strategy for discovering essential indicators associated with academic performance. However, widely used data analytic methods like principal component analysis are ineffective when most of the indicators captured are categorical, making them inappropriate for establishing the weighty academic performance indicators. This study’s objective was to investigate the application of multiple correspondence analysis to establish weighty student engagement indicators of academic performance. This study’s findings have indicated that higher-order learning and student-staff interaction are weighty indicators that relate student engagement to academic performance.
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Wibowo, Ananto, and M. Rismawan Ridha. "Comparison of Logistic Regression Model and MARS Using Multicollinearity Data Simulation." JTAM | Jurnal Teori dan Aplikasi Matematika 4, no. 1 (April 24, 2020): 39. http://dx.doi.org/10.31764/jtam.v4i1.1801.
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There are several statistical methods used to model the effect of predictor variables on categorical response variables, namely logistic regression and Multivariate Adaptive Regression Splines (MARS). However, neither MARS nor logistic regression allows multicollinearity on any predictor variables. This study applies the use of both methods to the simulation data with principal component analysis as an improvement in multicollinearity to find out which regression has better performance. The result of the analysis shows that MARS is very powerful in modeling research simulation data. Besides, based on the criteria of the number of significant major components, accuracy, sensitivity, and specificity values, MARS has more appropriate performance than logistic regression.
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Scheevel, J. R., and K. Payrazyan. "Principal Component Analysis Applied to 3D Seismic Data for Reservoir Property Estimation." SPE Reservoir Evaluation & Engineering 4, no. 01 (February 1, 2001): 64–72. http://dx.doi.org/10.2118/69739-pa.
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Summary We apply a common statistical tool, Principal Component Analysis (PCA) to the problem of direct property estimation from three-dimensional (3D) seismic-amplitude data. We use PCA in a novel way to successfully make detailed effective porosity predictions in channelized sand and shale. The novelty of this use revolves around the sampling method, which consists of a small vertical sampling window applied by sliding along each vertical trace in a cube of seismic-amplitude data. The window captures multiple, vertically adjacent amplitude samples, which are then treated as vectors for purposes of the PCA analysis. All vectors from all sample window locations within the seismic-data volume form the set of input vectors for the PCA algorithm. Final output from the PCA algorithm can be a cube of assigned classes, whose clustering is based on the values of the most significant principal components (PC's). The clusters are used as a categorical variable when predicting reservoir properties away from well control. The novelty in this approach is that PCA analysis is used to analyze covariance relationships between all vector elements (neighboring amplitude values) by using the statistical mass of the large number of vectors sampled in the seismic data set. Our approach results in a powerful signal-analysis method that is statistical in nature. We believe it offers data-driven objectivity and a potential for property extraction not easily achieved in model-driven fourier-based time-series methods of analysis (digital signal processing). We evaluate the effectiveness of our method by applying a cross-validation technique, alternately withholding each of the three wells drilled in the area and computing predicted effective porosity (PHIE) estimates at the withheld location by using the remaining two wells as hard data. This process is repeated three times, each time excluding only one of the wells as a blind control case. In each of the three blind control wells, our method predicts accurate estimates of sand/shale distribution in the well and effective porosity-thickness product values. The method properly predicts a low sand-to-shale ratio at the blind well location, even when the remaining two hard data wells contain only high sand-to-shale ratios. Good predictive results from this study area make us optimistic that the method is valuable for general reservoir property prediction from 3D seismic data, especially in areas of rapid lateral variation of the reservoir. We feel that this method of predicting properties from the 3D seismic is preferable to traditional, solely variogram-based geostatistical estimation methods. Such methods have difficulty capturing the detailed lithology distribution when limited by the hard data control's sampling bias. This problem is especially acute in areas where rapid lateral geological variation is the rule. Our method effectively overcomes this limitation because it provides a deterministic soft template for reservoir-property distributions. Introduction Reservoir Prediction from Seismic. The use of the reflection seismic-attribute data for the prediction of detailed reservoir properties began at least as early as 1969.1 Use of seismic attributes for reservoir prediction has accelerated in recent years, especially with the advent of widely available high-quality 3D seismic data. In practice, a seismic attribute is any property derived from the seismic reflection (amplitude) signal during or after final processing. Any attributes may be compared with a primary reservoir property or lithology in an attempt to devise a method of attribute-guided prediction of the primary property away from well control. The prediction method can vary from something as simple as a linear multiplier (single attribute) to multi-attribute analysis with canonical correlation techniques,2 geostatistical methods,3 or fully nonlinear, fuzzy methods.4 The pace of growth in prediction methodologies using seismic attributes seems to be outpaced only by the proliferation in the number and types of seismic attributes reported in the literature.5 As more researchers find predictive success with one or more new attributes, the list of viable reservoir-predictive attributes continues to grow. Chen and Sidney6 have cataloged more than 60 common seismic attributes along with a description of their apparent significance and use. Despite the rich history of seismic attribute in reservoir prediction, the practice remains difficult and uncertain. The bulk of this uncertainty arises from the unclear nature of the physics connecting many demonstrably useful attributes to a corresponding reservoir property. Because of the complex and varied physical processes responsible for various attributes, the unambiguous use of attributes for direct reservoir prediction will likely remain a challenge for years to come. In addition to the questions about the physical origin of some attributes, there is the possibility of encountering statistical pitfalls while using multiple attributes for empirical reservoir-property prediction. For example, it has been demonstrated that as the number of attributes used in an evaluation increases, the potential arises that one or more attributes will produce a false correlation with well data.7 Also, many attributes are derived with similar signal-processing methods and can, in some cases, be considered largely redundant with respect to their seismic-signal description. Lendzionowski et al.8 maintain that the maximum number of independent attributes required to fully describe a trace segment is a quantity 2BT, where B=bandwidth (Hz) and T=trace-segment length (sec). If this is supportable, it suggests that most of the more common attributes are at least partially redundant. The danger of such redundancy is that it falsely enhances statistical correlation with the well property. Doing so may suggest that many seemingly independent seismic attributes display similar well-property trends. Finally, the use of a particular approach with attributes involves some subjectivity and prior experience on the part of the practitioner to be successful and reproducible. This is a source of potential error that cannot be quantified but also, in most cases, cannot be avoided. The most successful workers in the field of reservoir prediction from seismic, not coincidentally, are also the most experienced in the field.
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Wu, Jr-Wei, Yen-Feng Wang, Jong-Ling Fuh, Jiing-Feng Lirng, Shih-Pin Chen, Shu-Shya Hseu, and Shuu-Jiun Wang. "Correlations among brain and spinal MRI findings in spontaneous intracranial hypotension." Cephalalgia 38, no. 14 (October 9, 2018): 1998–2005. http://dx.doi.org/10.1177/0333102418804161.
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Objectives Several brain and spinal magnetic resonance imaging signs have been described in spontaneous intracranial hypotension. Their correlations are not fully studied. This study aimed to explore potential mechanisms underlying cerebral neuroimaging findings and to examine associations among spinal and brain magnetic resonance imaging signs. Methods We conducted a retrospective review of magnetic resonance myelography and brain magnetic resonance imaging records of patients with spontaneous intracranial hypotension. Categorical principal component analysis was employed to cluster brain neuroimaging findings. Spearman correlation was employed to analyze associations among different brain neuroimaging findings and between brain and spinal neuroimaging findings. Results In patients with spontaneous intracranial hypotension (n = 148), categorical principal component analysis of brain neuroimaging signs revealed two clusters: Cerebral venous dilation and brain descent. Among all brain magnetic resonance imaging signs examined, only midbrain-pons angle associated with anterior epidural cerebrospinal fluid collection length (surrogate spinal cerebrospinal fluid leak severity) (n = 148, Spearman’s ρ = −0.38, p < .001). Subgroup analyses showed that the association between midbrain-pons angle (within brain descent cluster) and spinal cerebrospinal fluid leak severity was presented in patients with convex margins of the transverse sinuses (n = 122, Spearman’s ρ = −0.43, p < .001), but not in patients without convex margins (n = 26, Spearman’s ρ = −0.19, p = .348). The association between severity of transverse sinus distension and spinal cerebrospinal fluid leak severity was only presented in patients without convex margins (n = 26, Spearman’s ρ = 0.52, p = .006). Conclusion This study indicates that there are two factors behind the brain neuroimaging findings in spontaneous intracranial hypotension: Cerebral venous dilation and brain descent. Certain brain neuroimaging signs correlate with spinal cerebrospinal fluid leakage severity, depending on different circumstances.
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Rajesh, Shipra, Suresh Jain, and Prateek Sharma. "Inherent vulnerability assessment of rural households based on socio-economic indicators using categorical principal component analysis: A case study of Kimsar region, Uttarakhand." Ecological Indicators 85 (February 2018): 93–104. http://dx.doi.org/10.1016/j.ecolind.2017.10.014.
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Kyrdoda, Yuliia, George Baltas, and A. Malek Hammami. "Analyzing Consumer Impulse Purchasing Behaviour Using Observational Data." International Journal of Food and Beverage Manufacturing and Business Models 3, no. 2 (July 2018): 16–28. http://dx.doi.org/10.4018/ijfbmbm.2018070102.
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This article identifies consumers' impulse purchasing behavior in supermarkets. The study includes an interpretation of the impulse decision relationship with the final purchase and an analysis of the distribution of impulse purchasers' demographic characteristics (age and shoppers' company). SPSS was used to analyze the observed data at a national retail supermarket chain. The logistic regression model was developed in order to identify the explanatory power of the variables. Categorical principal component analysis was employed to analyze the distribution of the variables. Empirical findings indicated that “impulsive decision” has a stronger intensity on “purchase” than “gender” does. Impulsive customers are split into three age groups and two company categories. These results could be used to design marketing strategies in order to increase sales. However, a few limitations occurred during the study such as: observation timing, unicity of location and observers' subjectivity.
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Kastenmüller, Gabi, Werner Römisch-Margl, Brigitte Wägele, Elisabeth Altmaier, and Karsten Suhre. "metaP-Server: A Web-Based Metabolomics Data Analysis Tool." Journal of Biomedicine and Biotechnology 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/839862.
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Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes. We developedmetaP-serverto facilitate data interpretation.metaP-serverprovides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross-references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided.metaP-serveris freely accessible athttp://metabolomics.helmholtz-muenchen.de/metap2/.
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Sonnberger, Marco, and Michael Ruddat. "Disclosing Citizens’ Perceptual Patterns of the Transition to Renewable Energy in Germany." Nature and Culture 13, no. 2 (June 1, 2018): 253–80. http://dx.doi.org/10.3167/nc.2018.130204.
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The article aims to explore citizens’ perceptual patterns underlying the public’s view of the German energy transition. By reducing the complexity of the public’s views to its main dimensions, the article contributes to a deeper understanding of citizens’ reactions to transition projects such as the German energy transition. This research is based on a German-wide representative survey that included items covering different aspects concerning the acceptance of energy technologies (trust in key actors, fairness, perceived risks and benefits, etc.). In order to explore citizens’ perceptual patterns of the German energy transition, we drew on the method of categorical principal component analysis. On the basis of our results, we hypothesize that risk-benefit/acceptance and trust/fairness are two main latent dimensions underlying citizens’ perception of the energy transition.
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VAN OS, J., C. GILVARRY, R. BALE, E. VAN HORN, T. TATTAN, I. WHITE, and R. MURRAY ON BEHALF OF THE UK700 GROUP. "A comparison of the utility of dimensional and categorical representations of psychosis." Psychological Medicine 29, no. 3 (May 1999): 595–606. http://dx.doi.org/10.1017/s0033291798008162.
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Background. The usefulness of any diagnostic scheme is directly related to its ability to provide clinically useful information on need for care. In this study, the clinical usefulness of dimensional and categorical representations of psychotic psychopathology were compared.Method. A total of 706 patients aged 16–65 years with chronic psychosis were recruited. Psychopathology was measured with the Comprehensive Psychopathological Rating Scale (CPRS). Lifetime RDC, DSM-III-R, and ICD-10 diagnoses and ratings of lifetime psychopathology were made using OPCRIT. Other clinical measures included: (i) need for care; (ii) quality of life; (iii) social disability; (iv) satisfaction with services; (v) abnormal movements; (vi) brief neuropsychological screen; and (vii) over the last 2 years – illness course, symptom severity, employment, medication use, self-harm, time in hospital and living independently.Results. Principal component factor analysis of the 65 CPRS items on cross-sectional psychopathology yielded four dimensions of positive, negative, depressive and manic symptoms. Regression models comparing the relative contributions of dimensional and categorical representations of psychopathology with clinical measures consistently indicated strong and significant effects of psychopathological dimensions over and above any effect of their categorical counterparts, whereas the reverse did not hold. The effect of psychopathological dimensions was mostly cumulative: high ratings on more than one dimension increased the contribution to the clinical measures in a dose-response fashion. Similar results were obtained with psychopathological dimensions derived from lifetime psychopathology ratings using the OCCPI.Conclusions. A dimensional approach towards classification of psychotic illness offers important clinical advantages.
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Mohanty, Ashima Sindhu, Krishna Chandra Patra, and Priyadarsan Parida. "Toddler ASD Classification Using Machine Learning Techniques." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 07 (July 2, 2021): 156. http://dx.doi.org/10.3991/ijoe.v17i07.23497.
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At present era, Autism Spectrum Disorder (ASD) has become one of the severe neurologically developed disorders throughout the world and early recognition can substantially get rid of this problem. The proposed work is based on the analysis of unbalanced ASD toddler dataset from UCI data repository. The work in this paper is performed in three stages. In first stage, the original data is preprocessed through converting the categorical attributes to numeric values by the process of frequency encoding followed by standardization of numeric attributes. In the second stage, the dimension of input is reduced using Principal component analysis (PCA). At the end, the classification of ASD Toddler data is performed through different machine learning classification models in two stages viz. through training parameter ε and through k-fold cross validation (k=10). The experimentation yields very high classification performance in comparison with other state-of-art approaches.
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Browne, M., G. F. Ortmann, and S. L. Hendriks. "Household food security monitoring and evaluation using a resilience indicator: an application of categorical principal component analysis and simple sum of assets in five African countries." Agrekon 53, no. 2 (April 3, 2014): 25–46. http://dx.doi.org/10.1080/03031853.2014.915477.
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Claveria, Oscar, Enric Monte, and Salvador Torra. "Time Series Features and Machine Learning Forecasts." Tourism Analysis 25, no. 4 (December 7, 2020): 463–72. http://dx.doi.org/10.3727/108354220x16002732379690.
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In this study we combine the results of two independent analyses to position Spanish regions according to both the characteristics of the time series of international tourist arrivals and the accuracy of predictions of arrivals at the regional level. We apply a seasonal trend decomposition procedure based on nonparametric regression to isolate the different components of the series and calculate the main time series features. Predictions are generated with several machine learning models in a recursive multistep-ahead forecasting experiment. Finally, we summarize all the information from the two previous experiments using categorical principal component analysis. By overlapping the distribution of the regions and the component loadings of each variable along both dimensions, we observe that entropy and dispersion show an inverse relation with forecast accuracy, but the interactions between the rest of the features and accuracy are heavily dependent on the forecast horizon. On this evidence, we conclude that in order to increase forecast accuracy of tourist arrivals at the regional level, model selection should be region specific and based on the forecast horizon.