Transformation and classification of ordinal survey data

• Autorzy: Sadh Roopam , Kumar Rajeev

Identyfikatory

DOI

10.7494/csci.2023.24.2.4871

• Języki publikacji: EN

Abstrakty

EN

Currently, machine learning is being significantly used in almost all of the research domains; however, its applicability in survey research is still in its infancy. In this paper, we attempt to highlight the applicability of machine learning in survey research while working on two different aspects in parallel. First, we introduce a pattern-based transformation method for ordinal survey data. Our purpose for developing such a transformation method is two-fold: our transformation facilitates the easy interpretation of ordinal survey data and provides convenience while applying standard machine-learning approaches; and second, we demonstrate the application of various classification techniques over real and transformed ordinal survey data and interpret their results in terms of their suitability in survey research. Our experimental results suggest that machine learning coupled with a pattern-recognition paradigm has tremendous scope in survey research.

Słowa kluczowe

EN

machine learning; classification; transformation; ordinal data; survey research

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2023
• Tom: T. 24 (2)
• Strony: 205--224
• Opis fizyczny: Bibliogr. 48 poz., rys., tab.

Twórcy

autor

Sadh Roopam

• Jawaharlal Nehru University, School of Computer & Systems Sciences, New Delhi – 110067, India

autor

Kumar Rajeev

• Jawaharlal Nehru University, School of Computer & Systems Sciences, New Delhi – 110067, India

Bibliografia

• [1] Aggarwal C.C.: Data classification. In: Data Mining. The Textbook, pp. 285–344, Springer, Cham, 2015.
• [2] Behrend T.S., Sharek D.J., Meade A.W., Wiebe E.N.: The viability of crowdsourcing for survey research, Behavior Research Methods, vol. 43(3), pp. 800–813, 2011.
• [3] Belloni A., Chernozhukov V., Hansen C.: Inference on treatment effects after selection among high-dimensional controls, The Review of Economic Studies, vol. 81(2), pp. 608–650, 2014.
• [4] Bien J., Taylor J., Tibshirani R.: A lasso for hierarchical interactions,Annals ofStatistics, vol. 41(3), 1111, 2013.
• [5] Borkan J.M., Quirk M., Sullivan M.: Finding meaning after the fall: injury narratives from elderly hip fracture patients, Social Science & Medicine, vol. 33(8), pp. 947–957, 1991.
• [6] Brahma D.: Essays in the Application of Machine Learning in Development Economics, Western Michigan University, 2019.
• [7] Brahma D., Mukherjee D.: India’s Universal Immunization Program: a lesson from Machine Learning, Economics Bulletin, vol. 39(1), pp. 581–591, 2019.
• [8] Brahma D., Mukherjee D.: Infant malnutrition, clean-water access and government interventions in India: a machine learning approach towards causal inference, Applied Economics Letters, vol. 28(16), pp. 1426–1431, 2021.
• [9] Breiman L.: Statistical modeling: The two cultures (with comments and a rejoinder by the author), Statistical Science, vol. 16(3), pp. 199–231, 2001.
• [10] Buskirk T.D., Kirchner A., Eck A., Signorino C.S.: An introduction to machine learning methods for survey researchers, Survey Practice, vol. 11(1), pp. 1–10, 2018.
• [11] Bzdok D., Altman N., Krzywinski M.: Statistics versus machine learning, Nature Methods, vol. 15(4), pp. 233–234, 2018.
• [12] Carlson J.A., Parkin M.: Inflation expectations, Economica, vol. 42(166), pp. 123–138, 1975.
• [13] Church A.H., Waclawski J., Kraut A.I.: Designing and Using Organizational Surveys: A Seven-Step Process, Business and Management Series, Jossey-Bass, San Francisco, 2001.
• [14] DeMers D., Cottrell G.W.: Non-linear dimensionality reduction. In: Advances inneural information processing systems, pp. 580–587, Citeseer, 1993.
• [15] Gob R., McCollin C., Ramalhoto M.F.: Ordinal methodology in the analysis of Likert scales, Quality & Quantity, vol. 41(5), pp. 601–626, 2007.
• [16] Grice J.W.: From means and variances to persons and patterns, Frontiers in Psychology, vol. 6, 1007, 2015.
• [17] Grice J.W.: Observation oriented modeling: preparing students for research in the 21st century, Comprehensive Psychology, vol. 3, pp. 1–27, 2014.
• [18] Harvey L., Green D.: Defining quality, Assessment & Evaluation in Higher Education, vol. 18(1), pp. 9–34, 1993.
• [19] Harvey L., Williams J.: Fifteen Years of Quality in Higher Education, 2010.
• [20] Holmes G., Donkin A., Witten I.H.: Weka: A machine learning workbench. In: Proceedings of ANZIIS’94-Australian New Zealnd Intelligent Information Systems Conference, pp. 357–361, IEEE, 1994.
• [21] Japkowicz N., Shah M.:Evaluating learning algorithms: a classification perspec-tive, Cambridge University Press, 2011.
• [22] Johansson S., Johansson J.: Interactive dimensionality reduction through user-defined combinations of quality metrics, IEEE transactions on visualization andcomputer graphics, vol. 15(6), pp. 993–1000, 2009.
• [23] Jongbloed B., Enders J., Salerno C.: Higher education and its communities: Interconnections, interdependencies and a research agenda, The Future of Higher Education and the Future of Higher Education Research, vol. 56(3),pp. 303–324, 2008.
• [24] Kern C., Klausch T., Kreuter F.: Tree-based machine learning methods for survey research, Survey Research Methods, vol. 13(1), pp. 73–93, 2019.
• [25] Knobl A.: Price expectations and actual price behavior in Germany, Staff Papers, vol. 21(1), pp. 83–100, 1974.
• [26] Lee J.W., Jones P.S., Mineyama Y., Zhang X.E.: Cultural differences in responsesto a Likert scale, Research in Nursing&Health, vol. 25(4), pp. 295–306, 2002.
• [27] Leon de A.R., Chough K.C.: Analysis of Mixed Data: Methods & Applications, CRC Press, 2013.
• [28] Ley C., Martin R.K., Pareek A., Groll A., Seil R., Tischer T.: Machine learning and conventional statistics: making sense of the differences, 2022.
• [29] Manikandan S.: Data transformation, Journal of Pharmacology and Pharmacotherapeutics, vol. 1(2), 126, 2010.
• [30] Merbitz C., Morris J., Grip J.C.: Ordinal scales and foundations of misinference, Archives of Physical Medicine and Rehabilitation, vol. 70(4), pp. 308–312, 1989.
• [31] Mitchell R.K., Agle B.R., Wood D.J.: Toward a theory of stakeholder identification and salience: Defining the principle of who and what really counts, Academy of Management Review, vol. 22(4), pp. 853–886, 1997.
• [32] Morgan J.N., Sonquist J.A.: Problems in the analysis of survey data, and a proposal, Journal American Statistical Association, vol. 58(302), pp. 415–434, 1963.
• [33] Nardo M.: The quantification of qualitative survey data: a critical assessment, Journal of Economic Surveys, vol. 17(5), pp. 645–668, 2003.
• [34] Onwuegbuzie A.J., Combs J.P.: Data analysis in mixed research: A primer, International Journal of Education, vol. 3(1), 2011.
• [35] Pesaran M.H., Weale M.: Survey Expectations, Handbook of Economic Forecasting, vol. 1, pp. 715–776, 2006.
• [36] Sadh R., Kumar R.: Clustering of Quantitative Survey Data based on Marking Patterns, INFOCOMP Journal of Computer Science, vol. 19(2), pp. 109–119, 2020.
• [37] Sadh R., Kumar R.: Clustering of quantitative survey data: A subsystem of EDM framework. In: Computational Methods and Data Engineering, pp. 307–319, Springer, 2021.
• [38] Sadh R., Kumar R.: Directional Pattern based Clustering for Quantitative Survey Data: Method and Application, Survey Research Methods, vol. 15(2),pp. 169–185, 2021.
• [39] Sadh R., Kumar R.: Dimensional Inadequacy of Rankings: Exploring Substantial and Meta-quality Dimensions for Higher Educational Institutions, Academia, vol. 26, pp. 25–48, 2022.
• [40] Sandelowski M.: Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies, Research in Nursing & Health, vol. 23(3), pp. 246–255, 2000.
• [41] Stevens S.S.: On the Theory of Scales of Measurement, Science, vol. 103(2684), pp. 677–680, 1946.
• [42] Tashakkori A., Teddlie C., Teddlie C.B.: Mixed Methodology: Combining Qualitative and Quantitative Approaches, vol. 46, sage, 1998.
• [43] Tharwat A.: Classification assessment methods, Applied Computing and Informatics, 2020.
• [44] Theil H.: On the time shape of economic microvariables and the Munich business test, Revue de l’Institut International de Statistique, pp. 105–120, 1952.
• [45] Tibshirani R.: Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological), vol. 58(1), pp. 267–288, 1996.
• [46] Tourangeau R.: Cognitive aspects of survey measurement and mismeasurement, International Journal of Public Opinion Research, vol. 15(1), pp. 3–7, 2003.
• [47] Valsiner J., Molenaar P.C., Lyra M.C., Chaudhary N.: Dynamic Process Methodology in the Social and Developmental Sciences, Springer, 2009.
• [48] Zhang C.H., Zhang S.S.: Confidence intervals for low dimensional parameters inhigh dimensional linear models, Journal of the Royal Statistical Society: Series B(Statistical Methodology), vol. 76(1), pp. 217–242, 2014.