The characteristics of comparative social research.
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The central goal of this study is to clarify to what degree former education and students' personal characteristics (the 'Big Five personality characteristics', personal orientations on learning and students' study approach) may predict study outcome (required credits and study continuance). Analysis of the data gathered through questionnaires of 1,471 Universities of Applied Sciences students make clear that former Education did not come forth as a powerful predictor for Credits or Study Continuance. Significant predictors are Conscientiousness and Ambivalence and Lack of Regulation. The higher the scores on Conscientiousness the more credits students are bound to obtain and the more likely they will continue their education. On the other hand students with high scores on Ambivalence and Lack of Regulation will most likely obtain fewer Credits or drop out more easily. The question arises what these results mean for the present knowledge economy which demands an increase of inhabitants with an advanced level of education. Finally, implications and recommendations for future research are suggested.
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Background: Early detection and remediation of language disorders are important in helping children to establish appropriate communicative and social behaviour and acquire additional information about the world through the use of language. In the Netherlands, children with (a suspicion of) language disorders are referred to speech and hearing centres for multidisciplinary assessment. Reliable data are needed on the nature of language disorders, as well as the age and source of referral, and the effects of cultural and socioeconomic profiles of the population served in order to plan speech and language therapy service provision. Aims: To provide a detailed description of caseload characteristics of children referred with a possible language disorder by generating more understanding of factors that might influence early identification. Methods & Procedures: A database of 11,450 children was analysed consisting of data on children, aged 2–7 years (70% boys, 30% girls), visiting Dutch speech and hearing centres. The factors analysed were age of referral, ratio of boys to girls, mono‐ and bilingualism, nature of the language delay, and language profile of the children. Outcomes & Results:Results revealed an age bias in the referral of children with language disorders. On average, boys were referred 5 months earlier than girls, and monolingual children were referred 3 months earlier than bilingual children. In addition, bilingual children seemed to have more complex problems at referral than monolingual children. They more often had both a disorder in both receptive and expressive language, and a language disorder with additional (developmental) problems. Conclusions & Implications: This study revealed a bias in age of referral of young children with language disorders. The results implicate the need for objective language screening instruments and the need to increase the awareness of staff in primary child healthcare of red flags in language development of girls and multilingual children aiming at earlier identification of language disorders in these children.
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The paper explores the effectiveness of automated clustering in personalized applications based on data characteristics. It evaluates three clustering algorithms with various cluster numbers and subsets of characteristics. The study compares the accuracy of models in different clusters against original results and examines the algorithmic approaches and characteristic selections for optimal clustering performance. The research concludes that the proposed method aids in selecting appropriate clustering strategies and relevant characteristics for datasets. These insights may also guide further research on coaching approaches within applications.
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In the rapidly evolving field of Machine Learning , selecting the most appropriate model for a given dataset is crucial. Understanding the characteristics of a dataset can significantly influence the outcomes of predictive modeling efforts, making the study of the properties of the dataset an essential component of data science. This study investigates the possibilities of using simulated human data for personalized applications, specifically for testing clustering approaches. In particular, the study focuses on the relationship between dataset characteristics and the selection of the optimal classification model for clusters of datasets. The results of this study provide critical insights for researchers and practitioners in machine learning, emphasizing the importance of dataset characteristics and variability in building and selecting robust models for diverse data conditions. The use of human simulation data provide valuable insights but requires further refinement to capture the full variability of real-world conditions.
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Speech-language pathologists generally agree that cluttering and stuttering represent two different fluency disorders. Differential diagnostics between cluttering and stuttering is difficult because these disorders have similar characteristics and often occur in conjunction with each other. This paper presents an analysis of the differential diagnostic characteristics of the two disorders, and a proposal for distinguishing between the two in clinical settings. The main goal of this two-part article is to set objective norms for differential diagnostic assessment of cluttering and stuttering symptoms, based on the three main characteristics of cluttering indicated/identified by St. Louis, Raphael, Myers & Bakker [St. Louis, K. O., Raphael, L. J., Myers, F. L., & Bakker, K. (2003). Cluttering updated. The ASHA leader. ASHA, 4–5, 20–22]: a fast and/or irregular articulatory rate together with errors in syllable, word or sentence structure and or a high frequency of normal disfluencies (not being stuttering). In the first half of the article objective measures are compared to the subjective clinical judgement made by fluency experts. In other words, which characteristics can be found in the speech profiles of persons who were diagnosed as people who clutter or stutter? In the second part of the article results on the Predictive Cluttering Inventory [Daly, D. A., & Cantrell, R. P. (2006). Cluttering characteristics identified as diagnostically significant by 60 fluency experts. Proceedings of second world congress on fluency disorders] are discussed in relationship to the subjective and objective measurements studied in the first half of the article.
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This article reports on a post hoc study using a randomised controlled trial with 31,842 students in the Netherlands and an instrument consisting of 21 paired problems. The trial showed a variability in the differences of students’ results in solving contextual mathematical problems with either a descriptive or a depictive representation of the problem situation. In this study the relation between this variability and two task characteristics is investigated: (1) complexity of the task representation; and (2) the content domain of the task. We found indications that differences in performance on descriptive and depictive representations of the problem situation are related to the content domain of the problems. One of the tentative conclusions is that for depicted problems in the domain of measurement and geometry the inferential step from representation of the problem situation to the mathematical problem to be solved is smaller than for word problems.
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Stormwaters, flowing into storm sewers, are known to significantly increase the annual pollutant loads entering urban receiving waters and this results in significant degradation of the receiving water quality. Knowledge of the characteristics of stormwater pollution enables urban planners to incorporate the most appropriate stormwater management strategies to mitigate the effects of stormwater pollution on downstream receiving waters. This requires detailed information on stormwater quality, such as pollutant types, sediment particle size distributions, and how soluble pollutants and heavy metals attach themselves to sediment particles. This study monitored stormwater pollution levels at over 150 locations throughout the Netherlands. The monitoring has been ongoing for nearly 15 years and a total of 7,652 individual events have been monitored to date. This makes the database the largest stormwater quality database in Europe. The study compared the results to those presented in contemporary international stormwater quality research literature. The study found that the pollution levels at many of the Dutch test sites did not meet the requirements of the European Water Framework Directive (WFD) and Dutch Water Quality Standards. Results of the study are presented and recommendations are made on how to improve water quality with the implementation of Sustainable Urban Drainage Systems (SUDS) devices.
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Stevenson & Landström (2002) state that Opportunity, Ability and Motives predict entrepreneurship in general. Connecting thirty previous studies we test if the market awareness, endurance, planning and preparation as entrepreneurial ability factors, staff as opportunity factor and the reason for transfer as motive predicts three short term performance (needed transfer time, satisfaction and emotional attachment after transfer). We tested our hypotheses on a representative sample of 130 Dutch business owners who succeeded in a business transferring in 2005 and 2006. Market awareness predicts a faster transfer. Surprisingly more planning and preparation is the best predictor for a long transfer time as does the absence of the selling business owner. More or less forced transfers (illness, declining performance) predict lower satisfaction were as endurance predicts a higher satisfaction. This is valuable information for buyers, business brokers, accountants and bankers. The operationalisation of transfer performance seems vital. All main predictors, even the control variables, show only effect on either the needed transfer time (effectiveness measure) or satisfaction (experience measure). This confirms earlier findings (Van Teeffelen, 2007b). Our common challenge in future is to compare internationally the succeeded, non-succeeded transfer and exits.
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Assessment of the seismic vulnerability of the building stock in the earthquake-prone Marmara region of Turkey is of growing importance since such information is needed for reliable estimation of the losses that possible future earthquakes are likely to induce. The outcome of such loss assessment exercises can be used in planning of urban/regional-scale earthquake protection strategies; this is a priority in Turkey, particularly following the destructive earthquakes of 1999. Considering the size of the building inventory, Istanbul and its surrounding area is a case for which it is not easy to determine the structural properties and characteristics of the building stock. In this paper, geometrical, functional and material properties of the building stock in the northern Marmara Region, particularly around Istanbul, have been investigated and evaluated for use in loss estimation models and other types of statistic- or probability-based studies. In order to do that, the existing reinforced concrete (RC) stock has been classified as 'compliant' or 'non-compliant' buildings, dual (frame-wall) or frame structures and emergent or embedded-beam systems. In addition to the statistical parameters such as mean values, standard deviations, etc., probability density functions and their goodness-of-fit have also been investigated for all types of parameters. Functionalities such as purpose of use and floor area properties have been defined. Concrete properties of existing and recently constructed buildings and also characteristics of 220 and 420 MPa types of steel have been documented. Finally, the financial effects of retrofitting operations and damage repair have been investigated. © 2007 Elsevier Ltd. All rights reserved.
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