Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2025 | 55 | 1 | 39-55

Article title

Stevens’ measurement scales in marketing research – A continuation of discussion on whether researchers can ignore the Likert scale’s limitations as an ordinal scale

Content

Title variants

Languages of publication

Abstracts

EN
This article discusses the use of Stevens’ measurement scales in marketing research, contributing to a broader discussion, underway for over 70 years, as to whether researchers can ignore the Likert scale’s limitations as an ordinal scale. The central question explored is whether the use of various statistical methods and techniques in marketing research has gone too far, limiting researchers’ horizon of thought, leading erroneous conclusions to be drawn, and diverting attention from trying to explain the non-quantitative attitudes of consumers (who are people, not machines or AIs). Stevens’ measurement scales are still widely used in data analysis across social sciences, including marketing research. Although they were revolutionary, they had certain flaws which have fueled an ongoing debate about the acceptability or permissibility of using different tests and statistical techniques at different scales and levels of measurement. The Likert scale, one of the scales most frequently used to measure customer attitudes, was intended to overcome the limitations of simple scales, having the advantage of being multi-item. However, historically, two competing views have evolved independently of each other, in the related literature and in the practice of empirical research: one emphasizing the ordinal nature of Likert scales, the other interpreting them as having interval-scale properties. This debate has significant consequences for the permissible scope of statistical analysis of empirical data. The problem discussed here is likely to become even more complex with the development of artificial intelligence (AI), machine learning, data science and big data, as data scientists perform computational analysis but are not often involved in data collection or deciding about how data is represented.

Year

Volume

55

Issue

1

Pages

39-55

Physical description

Dates

published
2025

Contributors

  • Poznań University of Economics, Institute of Management, Poznań, Poland

References

  • Adams, E. W., Fagot, R. F., & Robinson, R. E. (1965). A theory of appropriate statistics. Psychometrika, 30(1), 99–127.
  • Białas, S. (1999). Metrologia techniczna z podstawami tolerowania wielkości geometrycznych dla mechaników [Technical metrology with fundamentals of geometric dimensioning for mechanics]. Oficyna Wydawnicza Politechniki Warszawskiej.
  • Blaikie, N. (2003). Analyzing quantitative data. SAGE Publications.
  • Bleichrodt, H., & Wakker, P. (2015). Regret theory: A bold alternative to the alternatives. The Economic Journal, 125(583), 493–532.
  • Bligh, J. (2004). Ring the changes: Some resolutions for the new year and beyond. Medical Education, 38(1), 2–4.
  • Bryman, A. (2005). Research methods and organization studies. Routledge.
  • Bryman, A. (1988). Quantity and quality in social research (1st ed.). Routledge. https://doi.org/10.4324/9780203410028
  • Burke, C. J. (1953). Additive scales and statistics. Psychological Review, 60(1), 73–75.
  • Carifio, J., & Perla, R. (2007). Ten common misunderstandings, misconceptions, persistent myths, and urban legends about Likert scales and Likert response formats and their antidotes. Journal of Social Sciences, 3(2), 106–116.
  • Churchill, G. A. (2002). Badania marketingowe. Podstawy metodologiczne [Marketing research: Methodological foundations]. Wydawnictwo Naukowe PWN.
  • Clegg, F. (1998). Simple statistics. Cambridge University Press.
  • Cohen, L., Manion, L., & Morrison, K. (2000). Research methods in education (5th ed.). Routledge Falmer. https://doi.org/10.4324/9780203224342
  • Devine, F. (2006). Metody jakościowe [Qualitative methods]. In D. Marsh & G. Stoker (Eds.), Teorie i metody w naukach politycznych [Theories and methods in political sciences] (pp. 197–200).
  • Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(2), 269–277.
  • Escher, I. (2010). Pomiar kierunku i siły marketingowej postawy pracownika – kompromis pomiędzy teorią a praktyką marketingową [Measuring the direction and strength of employee marketing attitudes – a compromise between theory and marketing practice]. Acta Universitatis Nicolai Copernici, Ekonomia, 41(397), 159–174.
  • Fornell, C., & Bookstein, F. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19(4), 440–452.
  • Francuz, P., & Mackiewicz, R. (2007). Liczby nie wiedzą skąd pochodzą. Przewodnik po metodologii i statystyce nie tylko dla psychologów [Numbers don’t know where they come from. A guide to methodology and statistics not only for psychologists]. Redakcja Wydawnictw Katolickiego Uniwersytetu Lubelskiego.
  • Frankfort-Nachmias, C., & Nachmias, D. (2001). Metody badawcze w naukach społecznych [Research methods in social sciences]. Zysk i S-ka.
  • Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972). Consequences of failure to meet assumptions underlying the fixed effects analyses of variance and covariance. Review of Educational Research, 42(3), 237–288.
  • Główny Urząd Statystyczny. (n.d.). Pojęcia stosowane w statystyce publicznej [Concepts used in public statistics]. Główny Urząd Statystyczny. Retrieved March 1, 2025, from https://stat.gov.pl/ metainformacje/slownik-pojec/pojecia-stosowane-w-statystyce-publicznej/2924,pojecie.html
  • Hansen, J. P. (2003). CAN’T MISS – Conquer any number task by making important statistics simple. Part 1. Types of variables, mean, median, variance, and standard deviation. Journal of Healthcare Quality, 25(4), 19–24.
  • Jamieson, S. (2005). Likert scales: How to (ab)use them. Medical Education, 38(12), 1217–1218. https://doi.org/10.1111/j.1365-2929.2004.02012.x
  • Jezior, J. (2013). Metodologiczne problemy zastosowania skali Likerta w badaniach postaw wobec bezrobocia [Methodological problems of using the Likert scale in research on attitudes towards unemployment]. Przegląd Socjologiczny, 62(1), 117–138.
  • Johnson, H. M. (1936). Pseudo-mathematics in the mental and social sciences. American Journal of Psychology, 48(3), 342–351.
  • Kaczmarek, M., & Tarka, P. (2013). Metoda gromadzenia danych a ekwiwalencja wyników pomiaru systemu wartości w 5- i 7-stopniowych skalach ratingowych Likerta [Data collection method and equivalence of value system measurement results in 5- and 7-point Likert rating scales]. Handel Wewnętrzny, 5(346), 42–56.
  • Kaczmarczyk, S. (2014). Badania marketingowe. Podstawy metodyczne [Marketing research: Methodological foundations]. PWE.
  • Kampen, J., & Swyngedouw, M. (2000). The ordinal controversy revisited. Quality and Quantity, 34(1), 87–102.
  • Kero, P., & Lee, D. (2016). Likert is pronounced ‘LICK-urt’ not ‘LIE-kurt’ and the data are ordinal not interval. Journal of Applied Measurement, 17(4), 502–509.
  • Knapp, T. R. (1990). Treating ordinal scales as interval scales: An attempt to resolve the controversy. Nursing Research, 39(2), 121–123.
  • Krajewski, W. (1977). Correspondence principle and growth of science. Reidel.
  • Kuzon, W. M., Urbanchek, M. G., & McCabe, S. (1996). The seven deadly sins of statistical analysis. Annals of Plastic Surgery, 37(3), 265–272.
  • Lieberson, S. (1964). Limitations in the application of non-parametric coefficients of correlation. American Sociological Review, 29(5), 744–746.
  • Lissowski, G., Haman, J., & Jasiński, M. (2008). Podstawy statystyki dla socjologów [Fundamentals of statistics for sociologists]. Wydawnictwo Naukowe Scholar.
  • Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 55.
  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18, 50–60. https://doi.org/10.1214/aoms/ 1177730491
  • Mayntz, R., Holm, K., & Hubner, P. (1985). Wprowadzenie do metod socjologii empirycznej [Introduction to methods of empirical sociology]. PWN.
  • Mitchell, J. (1986). Measurement scales and statistics: A clash of paradigms. Psychological Bulletin, 100(3), 398–407.
  • Myers, J. L., & Well, A. D. (2003). Research design and statistical analysis (2nd ed.). Lawrence Erlbaum Associates.
  • Nowak, S. (1973). Teorie postaw [Theories of attitudes]. Państwowe Wydawnictwo Naukowe.
  • Nowak, S. (1985). Metodologia badań społecznych [Methodology of social research]. Państwowe Wydawnictwo Naukowe.
  • Pett, M. A. (1997). Nonparametric statistics for health care research. SAGE Publications.
  • Pearson, K. (1909). On a new method of determining the correlation between a measured character A and a character B. Biometrika, 7, 96–105.
  • Regenwetter, M., & Dana, J. (2011). Transitivity of preferences. Psychological Review, 118(1), 42–56.
  • Sagan, A. (2003). Skale i indeksy jako narzędzia pomiaru w badaniach marketingowych [Scales and indices as measurement tools in marketing research]. Zeszyty Naukowe / Akademia Ekonomiczna w Krakowie, 640, 21–36.
  • Sagan, A. (2014). Wprowadzenie do modelowania zjawisk społecznych i przykłady zastosowań w Statistica [Introduction to modeling social phenomena and examples of applications in Statistica]. StatSoft Polska.
  • Santina, M., & Perez, J. (2003). Health professionals’ sex and attitudes of health science students to health claims. Medical Education, 37(6), 509–513.
  • Siegel, S. (1956). Nonparametric statistics for the behavioral sciences. McGraw-Hill.
  • Sobczyk, M. (2007). Statystyka [Statistics]. Wydawnictwo Naukowe PWN.
  • Steczkowski, J., & Zeliaś, A. (1981). Statystyczne metody analizy cech jakościowych [Statistical methods of qualitative trait analysis]. PWE.
  • Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680.
  • Stevens, S. S. (1951). Mathematics, measurement and psychophysics. In S. S. Stevens (Ed.), Handbook of experimental psychology (pp. 1–49). John Wiley & Sons.
  • Stevens, S. S. (1959). Measurement, psychophysics and utility. In C. W. Churchman & P. Ratoosh (Eds.), Measurement; definitions and theories (pp. 18–61). Wiley.
  • Szewczak, W. (2010). Jak zmierzyć demokrację? Teoretyczne i metodologiczne podstawy budowy skal demokracji politycznej w politologii porównawczej [How to measure democracy? Theoretical and methodological foundations for constructing democracy scales in comparative political science]. Przegląd Politologiczny, 4, 98–100.
  • Thomas, M. A. (2019). Mathematization, not measurement: A critique of Stevens’ scales of measurement. Journal of Methods and Measurement in the Social Sciences, 10(2), 76–94.
  • Thurstone, L. L., & Chave, E. J. (1930). Theory of attitude measurement. In L. L. Thurstone & E. J. Chave (Eds.), The measurement of attitude (pp. 1–21). University of Chicago Press.
  • Townsend, J. T., & Ashby, F. G. (1984). Measurement scales and statistics: The misconception misconceived. Psychological Review, 96(3), 394–401.
  • Walesiak, M. (1993). Statystyczna analiza wielowymiarowa w badaniach marketingowych [Multivariate statistical analysis in marketing research]. Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 654.
  • Walesiak, M. (1996). Metody analizy danych marketingowych [Methods of marketing data analysis]. Wydawnictwo Naukowe PWN.
  • Walesiak, M. (2014). Wzmacnianie skali pomiaru dla danych porządkowych w statystycznej analizie wielowymiarowej [Strengthening measurement scales for ordinal data in multivariate statistical analysis]. Prace Naukowe Uniwersytetu Ekonomicznego We Wrocławiu, 327, 60–68.
  • Wiktorowicz, J., Grzelak, M. M., & Grzeszkiewicz-Radulska, K. (2020). Analiza statystyczna z IBM SPSS Statistics [Statistical analysis with IBM SPSS Statistics]. Wydawnictwo Uniwersytetu Łódzkiego. https://doi.org/10.18778/8220-387-5
  • Wiśniewski, J. W. (1987). Teoria pomiaru a teoria błędów w badaniach statystycznych [Measurement theory and error theory in statistical research]. Wiadomości Statystyczne, 11, 18–20.
  • Zeller, R. A., & Carmines, E. G. (1980). Measurement in the social sciences: The link between theory and data. American Political Science Review, 76(4), 996–1008.

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
62759623

YADDA identifier

bwmeta1.element.ojs-doi-10_2478_minib-2025-0003
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.