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Journal articles on the topic 'Scoring methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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1

Tugwell, Peter, and J. André Knottnerus. "Scoring of methods indices." Journal of Clinical Epidemiology 101 (September 2018): vi—viii. http://dx.doi.org/10.1016/j.jclinepi.2018.08.001.

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2

Slikker, Marco, Peter Borm, and René van den Brink. "Internal slackening scoring methods." Theory and Decision 72, no. 4 (2011): 445–62. http://dx.doi.org/10.1007/s11238-011-9281-4.

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3

CETIN, Bayram, Nese GULER, and Rabia SARICA. "Using Generalizability Theory to Examine Different Concept Map Scoring Methods." Eurasian Journal of Educational Research 16, no. 66 (2016): 1–30. http://dx.doi.org/10.14689/ejer.2016.66.12.

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4

Zurada, Jozef. "Rule Induction Methods For Credit Scoring." Review of Business Information Systems (RBIS) 11, no. 2 (2007): 11–22. http://dx.doi.org/10.19030/rbis.v11i2.4421.

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5

Baraliakos, Xenofon, and Juergen Braun. "Imaging Scoring Methods in Axial Spondyloarthritis." Rheumatic Disease Clinics of North America 42, no. 4 (2016): 663–78. http://dx.doi.org/10.1016/j.rdc.2016.07.006.

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6

Copas, J. B. "The Shrinkage of Point Scoring Methods." Applied Statistics 42, no. 2 (1993): 315. http://dx.doi.org/10.2307/2986235.

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7

Faulkner, K., and J. Law. "Methods of Scoring Mammographic Phantom Films." Radiation Protection Dosimetry 49, no. 1-3 (1993): 183–85. http://dx.doi.org/10.1093/rpd/49.1-3.183.

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8

Chan, Alicia K. J., Joanne M. Langley, and John C. LeBlanc. "Validation of croup scoring methods 694." Pediatric Research 41 (April 1997): 118. http://dx.doi.org/10.1203/00006450-199704001-00714.

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9

Noseworthy, John H. "Clinical scoring methods for multiple sclerosis." Annals of Neurology 36, S1 (1994): S80—S85. http://dx.doi.org/10.1002/ana.410360718.

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10

Faulkner, K., and J. Law. "Methods of Scoring Mammographic Phantom Films." Radiation Protection Dosimetry 49, no. 1-3 (1993): 183–85. http://dx.doi.org/10.1093/oxfordjournals.rpd.a081932.

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11

Glickman, Seth W., William Boulding, Jason M. T. Roos, Richard Staelin, Eric D. Peterson, and Kevin A. Schulman. "Alternative Pay-for-Performance Scoring Methods." Medical Care 47, no. 10 (2009): 1062–68. http://dx.doi.org/10.1097/mlr.0b013e3181a7e54c.

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12

Xi, Xiaoming, Derrick Higgins, Klaus Zechner, and David Williamson. "A comparison of two scoring methods for an automated speech scoring system." Language Testing 29, no. 3 (2012): 371–94. http://dx.doi.org/10.1177/0265532211425673.

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13

van der Burg, Marjoke, Anne Bouwen, Johan Stessens, et al. "Scoring clock tests for dementia screening: a comparison of two scoring methods." International Journal of Geriatric Psychiatry 19, no. 7 (2004): 685–89. http://dx.doi.org/10.1002/gps.1143.

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14

Singh, Rajbir, Sinapreet Kaur, and Dheeraj Pal Kaur. "Phylogenetic Tree Generation using Different Scoring Methods." International Journal of Computer Applications 100, no. 14 (2014): 38–45. http://dx.doi.org/10.5120/17597-8404.

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15

Becker, Charles E. "Comparison of two methods for scoring examinations." Journal of Chemical Education 68, no. 4 (1991): 309. http://dx.doi.org/10.1021/ed068p309.

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16

Yang, Yingxu. "Adaptive credit scoring with kernel learning methods." European Journal of Operational Research 183, no. 3 (2007): 1521–36. http://dx.doi.org/10.1016/j.ejor.2006.10.066.

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17

Wang, Ya Ning, and Lan Nan Xiao. "Research on Automatic Scoring Methods for Programs Based on Program Understanding." Applied Mechanics and Materials 513-517 (February 2014): 2054–58. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2054.

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This paper discusses three general automatic scoring algorithms including software quality metric algorithm, dynamic testing algorithm and static analysis algorithm based on program understanding, analyzes and designs their rationality, practicality and maneuverability. Therefore, a new automatic scoring strategy which combines dynamic testing and pattern matching based on program understanding is proposed. This method focuses on the accurate expressions on program algorithm and semantic points in student program, enhances the performance of the automatic scoring system and matches the idea of manual scoring better.
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18

Zhang, Weixing, Zhangzhi Li, Muhammad Safwan Akram, et al. "Gastric Cancer Screening Methods: A Comparative Study of Two Scoring Methods." Cancer Management and Research Volume 13 (July 2021): 5785–91. http://dx.doi.org/10.2147/cmar.s308395.

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19

Eggen, Theo J. H. M., and Tecla T. M. Lampe. "Comparison of the reliability of scoring methods of multiple-response items, matching items, and sequencing items." CADMO, no. 2 (January 2012): 85–104. http://dx.doi.org/10.3280/cad2011-002008.

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Multiple-response items, sequencing items, and matching items are three innovative item types often included in systems for computer-based assessment that offer the benefit of polytomous scoring and the possibility to measure partial knowledge. In the present study, different scoring methods of these three item types were compared. Based on the assumption that different response patterns to these item types represent different knowledge levels, these knowledge levels are described. Features of different scoring methods were studied to select the scoring methods included in this study. Subsequently, a probability distribution of scoring results for each knowledge level was derived and computed. Based on classical test theory, a measure for the reliability of the different scoring methods on the level of a single item was derived. To compare the results of the scoring methods selected, reliabilities were computed for several distributions of knowledge levels in a population. For a multiple-response item, when an examinee must select all the right options, the dichotomous scoring method resulted in higher reliabilities than scoring the response patterns polytomously. For matching items and for multiple-response items, when an examinee is asked to select fewer options than the total number of right options given, polytomous scoring methods gave higher reliabilities than the dichotomous scoring method. Simple polytomous scoring by counting the selected right options or relations is recommended instead of more complex polytomous scoring methods, for instance, using a correction for wrong answers or a so-called "floor". The results of scoring sequencing items were not as conclusive as for the other two item types explored.
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20

Volkova, E. S., V. B. Gisin, and V. I. Solov'ev. "Methods of fuzzy set theory in credit scoring." Finance and Credit 23, no. 35 (2017): 2088–106. http://dx.doi.org/10.24891/fc.23.35.2088.

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21

HOLDGRAFER, GARY. "COMPARISON OF TWO METHODS FOR SCORING SYNTACTIC COMPLEXITY." Perceptual and Motor Skills 81, no. 2 (1995): 498. http://dx.doi.org/10.2466/pms.1995.81.2.498.

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22

Teles, Germanno, Joel J. P. C. Rodrigues, Kashif Saleem, and Sergei A. Kozlov. "Classification Methods Applied to Credit Scoring With Collateral." IEEE Systems Journal 14, no. 3 (2020): 4557–66. http://dx.doi.org/10.1109/jsyst.2019.2937552.

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23

van der Heijde, D. "Psoriatic arthritis imaging: a review of scoring methods." Annals of the Rheumatic Diseases 64, suppl_2 (2005): ii61—ii64. http://dx.doi.org/10.1136/ard.2004.030809.

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24

Holdgrafer, Gary. "Comparison of two Methods for Scoring Syntactic Complexity." Perceptual and Motor Skills 81, no. 2 (1995): 498. http://dx.doi.org/10.1177/003151259508100227.

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Summary scores for Developmental Sentence Scoring and the Index of Productive Syntax were obtained from the language samples of 29 preterm children at preschool age. A moderate correlation obtained between these two measures of syntactic complexity. Only Index of Productive Syntax scores distinguished the language abilities of 19 neurologically normal from 10 suspect children.
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25

Frary, Robert B. "Partial-Credit Scoring Methods for Multiple-Choice Tests." Applied Measurement in Education 2, no. 1 (1989): 79–96. http://dx.doi.org/10.1207/s15324818ame0201_5.

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26

Hooman, Alireza, Govindan Marthandan, Wan Fadzilah Wan Yusoff, Mohana Omid, and Sasan Karamizadeh. "Statistical and data mining methods in credit scoring." Journal of Developing Areas 50, no. 5 (2016): 371–81. http://dx.doi.org/10.1353/jda.2016.0057.

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27

Paramonov, Pavel, and Nadezhda Sutula. "Simplified scoring methods for HMM-based speech recognition." Soft Computing 20, no. 9 (2015): 3455–60. http://dx.doi.org/10.1007/s00500-015-1831-1.

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28

Kaye, Jeremy J. "Radiographic Methods of Assessment (Scoring) of Rheumatic Disease." Rheumatic Disease Clinics of North America 17, no. 3 (1991): 457–70. http://dx.doi.org/10.1016/s0889-857x(21)00102-2.

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29

Tarigan, Suria Darma. "Methods for Delineating Degraded Land at Citarum Watershed, West Java, Indonesia." Journal of Tropical Soils 17, no. 3 (2013): 267–74. http://dx.doi.org/10.5400/jts.2012.v17i3.267-274.

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Accurate information on the extent and spatial location of degraded lands is very important to plan their rehabilitation. So far, various institutions issue different estimation on the extent of degraded land in Indonesia led to big confusion for rehabilitation planning. Ministry of Forestry estimates around 30.2 million ha of degraded land both inside and outside forestry area throughout Indonesia based on data released in 2007. Ministry of Forestry implementes the so called scoring method in delineating degraded land. Criteria used in the scoring methods are: land cover, slope steepness, erosion, and management. Scoring method applies different weight to each of those criteria. This study aimed to analyze accuracy of scoring method and to compare it to propose alternative methods in delineating degraded land such as: a) Inconsistency of land use, and b) Combination of Inconsistency of land use and scoring method. The accuracy of these methods were obtained by comparing to the field observation. The slope map was derived from SRTM 30 m, soil map was obtained from Soil Research Institute and land cover/land use from Ministry for Environment. Using GIS analysis, those maps were used to compose land capability classification (LCC) and inconsistency of land use. The study showed that scoring method had 66% accuracy in delineating degraded land. When scoring method was combined with Inconsistency method the accuracy increased about 7%.Keywords: Degraded land; inconsistency of land use; land capability class; scoring method[How to Cite: Tarigan SD. 2012. Methods for Delineating Degraded Land at Citarum Watershed, West Java, Indonesia. J Trop Soils, 17 (3): 267-274. doi: 10.5400/jts.2012.17.3.267][Permalink/DOI: www.dx.doi.org/10.5400/jts.2012.17.3.267]
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30

Schnakenberg, Keith E., and Elizabeth Maggie Penn. "Scoring from Contests." Political Analysis 22, no. 1 (2014): 86–114. http://dx.doi.org/10.1093/pan/mpt018.

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This article presents a new model for scoring alternatives from “contest” outcomes. The model is a generalization of the method of paired comparison to accommodate comparisons between arbitrarily sized sets of alternatives in which outcomes are any division of a fixed prize. Our approach is also applicable to contests between varying quantities of alternatives. We prove that under a reasonable condition on the comparability of alternatives, there exists a unique collection of scores that produces accurate estimates of the overall performance of each alternative and satisfies a well-known axiom regarding choice probabilities. We apply the method to several problems in which varying choice sets and continuous outcomes may create problems for standard scoring methods. These problems include measuring centrality in network data and the scoring of political candidates via a “feeling thermometer.” In the latter case, we also use the method to uncover and solve a potential difficulty with common methods of rescaling thermometer data to account for issues of interpersonal comparability.
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31

Xiang, Ling, Andrea Hsiu Ling Low, Ying Ying Leung, et al. "Improving sensitivity of the connective tissue disease screening questionnaire: A comparative study of various scoring methods." Lupus 30, no. 1 (2020): 35–44. http://dx.doi.org/10.1177/0961203320966378.

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Objectives Early detection of autoimmune rheumatic diseases is crucial given their high morbidity and mortality and short window of opportunity to improve patient outcomes. Self-administered screening questionnaires such as the connective tissue disease screening questionnaire (CSQ) have been shown to promote early detection of autoimmune rheumatic diseases. However, optimal scoring of screening questionnaires may differ with prevalence of clinical features and changes in classification criteria. We compared the performance of 3 scoring methods for the CSQ for early detection of autoimmune rheumatic diseases in a multi-ethnic Asian population. Methods Patients who were newly referred for evaluation of possible autoimmune rheumatic diseases were invited to answer the cross-culturally adapted CSQ. Detection of autoimmune rheumatic diseases using 1) the original CSQ scoring, 2) a modified CSQ scoring and 3) a scoring based on current classification criteria, were compared to classification of autoimmune rheumatic diseases by classification criteria. Results Of 819 participants, 85 were classified as having autoimmune rheumatic diseases screened for by the adapted CSQ. The original CSQ scoring yielded relatively lower sensitivities in detecting both any and individual autoimmune rheumatic diseases (67% and 20-57%, respectively) compared to the modified CSQ scoring (81% and 60-73%, respectively) and the scoring based on current classification criteria (89% and 50-88%, respectively). Conclusion The adapted CSQ with the classification criteria-based scoring achieved relatively high sensitivities in detecting autoimmune rheumatic diseases, suggesting this could be employed as the first step in population screening.
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32

KUBERKA, ZBIGNIEW, and ARKADIUSZ DORS. "Scoring methods for respiratory tract lesions in pigs at slaughter." Medycyna Weterynaryjna 74, no. 1 (2018): 6020–2018. http://dx.doi.org/10.21521/mw.6020.

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The complexity of respiratory diseases in pigs, which results from simultaneous infections with many microorganisms and environmental conditions in the pig facilities, leads to diagnostic difficulties. In order to assess the health status of the herd, monitoring is commonly used. It provides useful information, for example, about the seasonality of diseases, as well as the variety and dynamics of macroscopic lesions in the lungs depending on the preventive program used. One way to monitor respiratory diseases in pigs is to score lesions in the respiratory system. The scoring of lesions should be an integral part of diagnostic investigations. The aim of the paper is to describe and evaluate selected systems for the scoring of macroscopic lesions in the lungs during post-mortem examination. Some of these systems assess the severity of atrophic changes in the nasal cavity caused by infectious atrophic rhinitis in pigs. Another type of respiratory tract assessment is the evaluation (scoring) of lesions resulting from infection by Mycoplasma hyopneumoniae in the course of mycoplasmal pneumonia of swine. A number of scoring systems and their modifications have been described in the literature for this type of lesions. Various methods have also been devised for the scoring of macroscopic lesions in the pig lungs described as pleurisy. Regardless of the method used, these examinations are useful diagnostically and complement other types of diagnostic examinations. They are important primarily in the course of subacute and chronic forms of disease or infections associated with atypical clinical signs. .
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33

Wilson, A. P. R., N. Helder, S. K. Theminimulle, and G. M. Scott. "Comparison of wound scoring methods for use in audit." Journal of Hospital Infection 39, no. 2 (1998): 119–26. http://dx.doi.org/10.1016/s0195-6701(98)90325-5.

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34

Hanzlíková, I., J. Athens, and K. Hébert-Losier. "Clinical implications of Landing Error Scoring System calculation methods." Physical Therapy in Sport 44 (July 2020): 61–66. http://dx.doi.org/10.1016/j.ptsp.2020.04.035.

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35

Webster, G. D., J. A. Shea, J. J. Norcini, L. J. Grosso, and D. B. Swanson. "Strategies in Comparison of Methods for Scoring Patient Managementproblems." Evaluation & the Health Professions 11, no. 2 (1988): 231–48. http://dx.doi.org/10.1177/016327878801100206.

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36

Mak, Man-Wai, and Sun-Yuan Kung. "Fusion of feature selection methods for pairwise scoring SVM." Neurocomputing 71, no. 16-18 (2008): 3104–13. http://dx.doi.org/10.1016/j.neucom.2008.04.024.

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37

Hawker, Robert J., Ann McKillop, and Stephen Jacobs. "Postanesthesia Scoring Methods: An Integrative Review of the Literature." Journal of PeriAnesthesia Nursing 32, no. 6 (2017): 557–72. http://dx.doi.org/10.1016/j.jopan.2016.10.007.

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38

Boudebesse, Carole, Marion Leboyer, Amy Begley, et al. "Comparison of Five Actigraphy Scoring Methods with Bipolar Disorder." Behavioral Sleep Medicine 11, no. 4 (2013): 275–82. http://dx.doi.org/10.1080/15402002.2012.685997.

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39

Marzano, Robert J. "A Comparison of Selected Methods of Scoring Classroom Assessments." Applied Measurement in Education 15, no. 3 (2002): 249–68. http://dx.doi.org/10.1207/s15324818ame1503_2.

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40

Cho, Soyun, Seong-Hyun Kim, Kyu-Kwang Whang, and Jeong-Hee Hahm. "A Comparison of Two Scoring Methods in Atopic Dermatitis." Annals of Dermatology 12, no. 2 (2000): 102. http://dx.doi.org/10.5021/ad.2000.12.2.102.

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41

Chen, Jing, James H. Fife, Isaac I. Bejar, and André A. Rupp. "Buildinge-rater® Scoring Models Using Machine Learning Methods." ETS Research Report Series 2016, no. 1 (2016): 1–12. http://dx.doi.org/10.1002/ets2.12094.

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42

Biagioni, Bradly J., Dafna D. Gladman, Richard J. Cook, et al. "Reliability of Radiographic Scoring Methods in Axial Psoriatic Arthritis." Arthritis Care & Research 66, no. 9 (2014): 1417–22. http://dx.doi.org/10.1002/acr.22308.

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43

Feldmann, Uwe, and Ingo Steudel. "Methods of ordinal classification applied to medical scoring systems." Statistics in Medicine 19, no. 4 (2000): 575–86. http://dx.doi.org/10.1002/(sici)1097-0258(20000229)19:4<575::aid-sim357>3.0.co;2-a.

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44

Hand, D. J., and W. E. Henley. "Statistical Classification Methods in Consumer Credit Scoring: a Review." Journal of the Royal Statistical Society: Series A (Statistics in Society) 160, no. 3 (1997): 523–41. http://dx.doi.org/10.1111/j.1467-985x.1997.00078.x.

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45

Johnson, William L. "Two Methods of Scoring Discrepancy Format Needs Assessment Instruments." Journal of Experimental Education 55, no. 1 (1986): 39–42. http://dx.doi.org/10.1080/00220973.1986.10806433.

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46

Namkung, Jessica M., Michael Hebert, Sarah R. Powell, Marisa Hoins, Nicole Bricko, and Meghann Torchia. "Comparing and Validating Four Methods for Scoring Mathematics Writing." Reading & Writing Quarterly 36, no. 2 (2019): 157–75. http://dx.doi.org/10.1080/10573569.2019.1700858.

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47

Salaffi, Fausto, Marina Carotti, Giacomo Beci, Marco Di Carlo, and Andrea Giovagnoni. "Radiographic scoring methods in rheumatoid arthritis and psoriatic arthritis." La radiologia medica 124, no. 11 (2019): 1071–86. http://dx.doi.org/10.1007/s11547-019-01001-3.

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48

Zgurovskii, M. Z., P. I. Bidyuk, and A. N. Terent’ev. "Methods of constructing Bayesian networks based on scoring functions." Cybernetics and Systems Analysis 44, no. 2 (2008): 219–24. http://dx.doi.org/10.1007/s10559-008-0021-x.

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49

Diss, Mostapha, and Ahmed Doghmi. "Multi-winner scoring election methods: Condorcet consistency and paradoxes." Public Choice 169, no. 1-2 (2016): 97–116. http://dx.doi.org/10.1007/s11127-016-0376-x.

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50

Marchant, Thierry. "The probability of ties with scoring methods: Some results." Social Choice and Welfare 18, no. 4 (2001): 709–35. http://dx.doi.org/10.1007/s003550000078.

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