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Journal articles on the topic 'Ordinal data analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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1

Plackett, R. L., and A. Agresti. "Analysis of Ordinal Categorical Data." Biometrics 41, no. 3 (September 1985): 811. http://dx.doi.org/10.2307/2531302.

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2

Clogg, Clifford C., and Alan Agresti. "Analysis of Ordinal Categorical Data." Contemporary Sociology 14, no. 3 (May 1985): 374. http://dx.doi.org/10.2307/2071355.

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3

Lenz, Hans-Joachim. "Ordinal and symbolic data analysis." Computational Statistics & Data Analysis 26, no. 1 (November 1997): 108–9. http://dx.doi.org/10.1016/s0167-9473(97)82106-8.

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4

McCullagh, Peter. "Analysis of Ordinal Categorical Data." Technometrics 27, no. 3 (August 1985): 317–18. http://dx.doi.org/10.1080/00401706.1985.10488059.

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5

Whittaker, J., and Alan Agresti. "Analysis of Ordinal Categorical Data." Journal of the Royal Statistical Society. Series A (General) 148, no. 2 (1985): 163. http://dx.doi.org/10.2307/2981949.

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6

Tsutakawa, Robert K., and Alan Agresti. "Analysis of Ordinal Categorical Data." Journal of the American Statistical Association 80, no. 391 (September 1985): 778. http://dx.doi.org/10.2307/2288509.

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7

Sermeus, Walter, and Luc Delesie. "Ridit Analysis on Ordinal Data." Western Journal of Nursing Research 18, no. 3 (June 1996): 351–59. http://dx.doi.org/10.1177/019394599601800309.

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8

Doignon, Jean-Paul, and Marc Pirlot. "Ordinal and symbolic data analysis." Discrete Applied Mathematics 147, no. 1 (April 2005): 1–2. http://dx.doi.org/10.1016/j.dam.2004.11.006.

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9

Burke, John M., Charles P. Solomon, and Charles B. Seelig. "Ordinal and interval data analysis." Journal of General Internal Medicine 7, no. 5 (September 1992): 567. http://dx.doi.org/10.1007/bf02599468.

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10

Fernández, Daniel, Irene Epifanio, and Louise Fastier McMillan. "Archetypal analysis for ordinal data." Information Sciences 579 (November 2021): 281–92. http://dx.doi.org/10.1016/j.ins.2021.07.095.

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11

Weiß, Christian H. "Distance-Based Analysis of Ordinal Data and Ordinal Time Series." Journal of the American Statistical Association 115, no. 531 (June 19, 2019): 1189–200. http://dx.doi.org/10.1080/01621459.2019.1604370.

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12

Herden, Gerhard, and Andreas Pallack. "Consistency in ordinal data analysis I." Mathematical Social Sciences 43, no. 1 (January 2002): 79–113. http://dx.doi.org/10.1016/s0165-4896(01)00081-6.

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13

Agresti, Alan. "Analysis of Ordinal Paired Comparison Data." Applied Statistics 41, no. 2 (1992): 287. http://dx.doi.org/10.2307/2347562.

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14

Izadikhah, Mohammad, Razieh Roostaee, and Ali Emrouznejad. "Fuzzy Data Envelopment Analysis with Ordinal and Interval Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 03 (May 27, 2021): 385–410. http://dx.doi.org/10.1142/s0218488521500173.

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In this paper, we reformulate the conventional DEA models as an imprecise DEA problem and propose a novel method for evaluating the DMUs when the inputs and outputs are fuzzy and/or ordinal or vary in intervals. For this purpose, we convert all data into interval data. In order to convert each fuzzy number into interval data, we use the nearest weighted interval approximation of fuzzy numbers by applying the weighting function, and we convert each ordinal data into interval one. In this manner, we could convert all data into interval data. The presented models determine the interval efficiencies for DMUs. To rank DMUs based on their associated interval efficiencies, we first apply the Ω-index that is developed for ranking of interval numbers. Then, by introducing an ideal DMU, we rank efficient DMUs to present a complete ranking. Finally, we use one example to illustrate the process and one real application in health care to show the usefulness of the proposed approach. For this evaluation, we consider interval, ordinal, and fuzzy data alongside the precise data to evaluate 38 hospitals selected by OIG. The results reveal the capabilities of the presented method to deal with the imprecise data.
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15

Poon, Wai-Yin, and Hai-Bin Wang. "Analysis of ordinal categorical data with misclassification." British Journal of Mathematical and Statistical Psychology 63, no. 1 (February 2010): 17–42. http://dx.doi.org/10.1348/000711008x401314.

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16

Chan, Wai, and Peter M. Bentler. "Covariance structure analysis of ordinal ipsative data." Psychometrika 63, no. 4 (December 1998): 369–99. http://dx.doi.org/10.1007/bf02294861.

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17

de Mast, Jeroen, and Wessel van Wieringen. "Measurement System Analysis for Bounded Ordinal Data." Quality and Reliability Engineering International 20, no. 5 (July 29, 2004): 383–95. http://dx.doi.org/10.1002/qre.653.

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18

Cook, Wade D., Moshe Kress, and Lawrence M. Seiford. "On the Use of Ordinal Data in Data Envelopment Analysis." Journal of the Operational Research Society 44, no. 2 (February 1993): 133. http://dx.doi.org/10.2307/2584361.

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19

Cook, Wade D., Moshe Kress, and Lawrence M. Seiford. "On the Use of Ordinal Data in Data Envelopment Analysis." Journal of the Operational Research Society 44, no. 2 (February 1993): 133–40. http://dx.doi.org/10.1057/jors.1993.25.

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20

Liu, Xing, and Hari Koirala. "Ordinal Regression Analysis: Using Generalized Ordinal Logistic Regression Models to Estimate Educational Data." Journal of Modern Applied Statistical Methods 11, no. 1 (May 1, 2012): 242–54. http://dx.doi.org/10.22237/jmasm/1335846000.

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21

DiStefano, Christine, Heather L. McDaniel, Liyun Zhang, Dexin Shi, and Zhehan Jiang. "Fitting Large Factor Analysis Models With Ordinal Data." Educational and Psychological Measurement 79, no. 3 (December 29, 2018): 417–36. http://dx.doi.org/10.1177/0013164418818242.

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A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and misspecification affect parameter estimates, standard errors of parameter estimates, and selected fit indices. As the number of items increased, the number of admissible solutions and accuracy of parameter estimates improved, even when models were misspecified. Also, standard errors of parameter estimates were closer to empirical standard deviation values as the number of items increased. When evaluating goodness-of-fit for ordinal CFA with many observed indicators, researchers should be cautious in interpreting the root mean square error of approximation, as this value appeared overly optimistic under misspecified conditions.
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22

Feng, R., J. F. Leckman, and H. Zhang. "Linkage analysis of ordinal traits for pedigree data." Proceedings of the National Academy of Sciences 101, no. 48 (November 17, 2004): 16739–44. http://dx.doi.org/10.1073/pnas.0404623101.

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23

Gregoire, T. G., and B. L. Driver. "Analysis of ordinal data to detect population differences." Psychological Bulletin 101, no. 1 (1987): 159–65. http://dx.doi.org/10.1037/0033-2909.101.1.159.

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24

Verkuilen, Jay. "A Review of Analysis of Ordinal Categorical Data." Journal of Educational and Behavioral Statistics 36, no. 2 (April 2011): 277–78. http://dx.doi.org/10.3102/1076998610393970.

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25

Brown, Anna, and Alberto Maydeu-Olivares. "Ordinal Factor Analysis of Graded-Preference Questionnaire Data." Structural Equation Modeling: A Multidisciplinary Journal 25, no. 4 (November 22, 2017): 516–29. http://dx.doi.org/10.1080/10705511.2017.1392247.

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26

Willems, S. J. W., M. Fiocco, and J. J. Meulman. "Optimal scaling for survival analysis with ordinal data." Computational Statistics & Data Analysis 115 (November 2017): 155–71. http://dx.doi.org/10.1016/j.csda.2017.05.008.

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27

Kress, Moshe. "Applying Data Envelopment Analysis Principle in Ordinal Multi Criteria Decision Analysis." Foundations of Computing and Decision Sciences 46, no. 2 (June 1, 2021): 147–57. http://dx.doi.org/10.2478/fcds-2021-0010.

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Abstract We consider a multicriteria decision analysis (MCDA) problem where importance of criteria, and evaluations of alternatives with respect to the criteria, are expressed on a qualitative ordinal scale. Using the extreme-point principle of Data Envelopment Analysis (DEA), we develop a two-parameter method for obtaining overall ratings of the alternatives when preferences and evaluations are made on an ordinal scale. We assume no parametric setup other than the two parameters that reflect minimum intensities of discriminating among rank positions: one parameter for the alternatives’ ranking and one for the criteria ranking. These parameters are bounded by the ordinal input data, and they imply a universal tie among the alternatives when both parameters are selected to be zero. We describe the model, discuss its theoretical underpinning, and demonstrate its application.
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28

Lee, Chun-Ting, Guangjian Zhang, and Michael C. Edwards. "Ordinary Least Squares Estimation of Parameters in Exploratory Factor Analysis With Ordinal Data." Multivariate Behavioral Research 47, no. 2 (March 30, 2012): 314–39. http://dx.doi.org/10.1080/00273171.2012.658340.

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29

Shah, D. A., and L. V. Madden. "Nonparametric Analysis of Ordinal Data in Designed Factorial Experiments." Phytopathology® 94, no. 1 (January 2004): 33–43. http://dx.doi.org/10.1094/phyto.2004.94.1.33.

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Plant disease severity often is assessed using an ordinal rating scale rather than a continuous scale of measurement. Although such data usually should be analyzed with nonparametric methods, and not with the typical parametric techniques (such as analysis of variance), limitations in the statistical methodology available had meant that experimental designs generally could not be more complicated than a one-way layout. Very recent advancements in the theoretical formulation of hypotheses and associated test statistics within a nonparametric framework, together with development of software for implementing the methods, have made it possible for plant pathologists to analyze properly ordinal data from more complicated designs using nonparametric techniques. In this paper, we illustrate the nonparametric analysis of ordinal data obtained from two-way factorial designs, including a repeated measures design, and show how to quantify the effects of experimental factors on ratings through estimated relative marginal effects.
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30

Churches, Tim. "Exploratory data analysis using set operations and ordinal mapping." Computer Methods and Programs in Biomedicine 71, no. 1 (May 2003): 11–23. http://dx.doi.org/10.1016/s0169-2607(02)00057-3.

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31

Perin, J., J. S. Preisser, C. Phillips, and B. Qaqish. "Regression analysis of correlated ordinal data using orthogonalized residuals." Biometrics 70, no. 4 (August 18, 2014): 902–9. http://dx.doi.org/10.1111/biom.12210.

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32

Hamada, M. S., and K. J. Ryan. "The Analysis of Misclassified Ordinal Data from Designed Experiments." Quality and Reliability Engineering International 32, no. 1 (October 28, 2014): 223–29. http://dx.doi.org/10.1002/qre.1743.

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33

Jin, Shaobo, Maengseok Noh, and Youngjo Lee. "H-Likelihood Approach to Factor Analysis for Ordinal Data." Structural Equation Modeling: A Multidisciplinary Journal 25, no. 4 (December 5, 2017): 530–40. http://dx.doi.org/10.1080/10705511.2017.1403287.

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34

Graham, Patrick, and Rodney Jackson. "The analysis of ordinal agreement data: beyond weighted kappa." Journal of Clinical Epidemiology 46, no. 9 (September 1993): 1055–62. http://dx.doi.org/10.1016/0895-4356(93)90173-x.

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35

Whitehead, Anne, Rumana Z. Omar, Julian P. T. Higgins, Elly Savaluny, Rebecca M. Turner, and Simon G. Thompson. "Meta-analysis of ordinal outcomes using individual patient data." Statistics in Medicine 20, no. 15 (2001): 2243–60. http://dx.doi.org/10.1002/sim.919.

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36

Lefkovitch, L. P. "Analysis of rating scale data." Canadian Journal of Plant Science 71, no. 2 (April 1, 1991): 571–73. http://dx.doi.org/10.4141/cjps91-085.

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Rating scale data need not be manipulated to satisfy the assumptions of the analysis of variance, but formed into an incidence table classified by the ratings and other factors, and compacted over factors to focus on those which are important. References are given to methods for formal statistical analysis of these tables. Key words: Subjective rating, ordinal logistic
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37

Selosse, Margot, Julien Jacques, and Christophe Biernacki. "ordinalClust: An R Package to Analyze Ordinal Data." R Journal 12, no. 2 (2020): 173. http://dx.doi.org/10.32614/rj-2021-011.

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38

XU, Peng, Lu QI, Jian XIONG, and Haosheng YE. "A Regression Analysis Model of Ordinal Variable to Psychological Data." Acta Psychologica Sinica 47, no. 12 (2015): 1520. http://dx.doi.org/10.3724/sp.j.1041.2015.01520.

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39

Herden, Gerhard, and Andreas Pallack. "Adequateness and interpretability of objective functions in ordinal data analysis." Journal of Multivariate Analysis 94, no. 1 (May 2005): 19–69. http://dx.doi.org/10.1016/j.jmva.2004.06.001.

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40

Donaldson, Gary W. "Ridit scores for analysis and interpretation of ordinal pain data." European Journal of Pain 2, no. 3 (September 1998): 221–27. http://dx.doi.org/10.1016/s1090-3801(98)90018-0.

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41

Liu, Ivy. "Analysis of Ordinal Categorical Data, 2nd edn by Alan Agresti." Australian & New Zealand Journal of Statistics 53, no. 1 (March 2011): 124–26. http://dx.doi.org/10.1111/j.1467-842x.2011.00601.x.

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42

Jakobsson, Ulf. "Statistical presentation and analysis of ordinal data in nursing research." Scandinavian Journal of Caring Sciences 18, no. 4 (December 2004): 437–40. http://dx.doi.org/10.1111/j.1471-6712.2004.00305.x.

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43

Molenberghs, G. "The analysis of longitudinal ordinal data with nonrandom drop-out." Biometrika 84, no. 1 (March 1, 1997): 33–44. http://dx.doi.org/10.1093/biomet/84.1.33.

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44

Verkuilen, Jay. "A Review of Analysis of Ordinal Categorical Data, Second Edition." Journal of Educational and Behavioral Statistics 35, no. 5 (October 2010): 603–4. http://dx.doi.org/10.3102/1076998610385405.

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45

Berridge, Damon M., and John Whitehead. "Analysis of failure time data with ordinal categories of response." Statistics in Medicine 10, no. 11 (November 1991): 1703–10. http://dx.doi.org/10.1002/sim.4780101108.

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46

THOMSON, DAVID L., ROBERT W. FURNESS, and PAT MONAGHAN. "The analysis of ordinal response data in the behavioural sciences." Animal Behaviour 56, no. 4 (October 1998): 1041–43. http://dx.doi.org/10.1006/anbe.1998.0895.

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47

Kosorok, Michael R., and Wei-Hsiung Chao. "The Analysis of Longitudinal Ordinal Response Data in Continuous Time." Journal of the American Statistical Association 91, no. 434 (June 1996): 807–17. http://dx.doi.org/10.1080/01621459.1996.10476948.

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48

Best, B. J., and J. C. W. Rayner. "Nonparametric analysis of ordinal categorical response data with factorial structure." Journal of the Royal Statistical Society: Series C (Applied Statistics) 47, no. 3 (January 6, 2002): 439–46. http://dx.doi.org/10.1111/1467-9876.00120.

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49

Katsikatsou, Myrsini, Irini Moustaki, Fan Yang-Wallentin, and Karl G. Jöreskog. "Pairwise likelihood estimation for factor analysis models with ordinal data." Computational Statistics & Data Analysis 56, no. 12 (December 2012): 4243–58. http://dx.doi.org/10.1016/j.csda.2012.04.010.

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50

von Eye, Alexander, and Jochen Brandtstädter. "Application of Prediction Analysis to Cross Classifications of Ordinal Data." Biometrical Journal 30, no. 6 (January 19, 2007): 651–65. http://dx.doi.org/10.1002/bimj.4710300604.

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