Coming Out: Quantitative Design with Inductive Traits

sacnas strengthened by diversity sci march

The Problem

The “reproducibility” crisis suggests a growing number of research studies published across disciplines are unreliable due to the declining quality and integrity of research and publication practices. The data and recommendation outputs of these endeavours of increasing paucity feed our businesses. A problem indeed!  My blog today considers whether a rigid compartmentalisation of reasoning and methods adopted is partly to blame. 

“Deductive reasoning arrives at a specific conclusion based on generalizations. Inductive reasoning takes events and makes generalizations”  (anon).

Hard-wired dichotomies of method and reasoning

Thumbing through weighty Research Method texts, the probability of my finding for example, explicit links between quantitative methods and inductive reasoning is low. Quantitative research with its objective measurement and numerical based data analysis [1] naturally “begins with the general and ends with the specific” [2]; almost antithetical to induction [3]. A set of hypotheses marks the start for the quantitative researcher [4]. Methods are chosen and applied, to prove them right or wrong [5]; epistemologically hard-aligned to deductive ‘top-down’ data crunching [6].

Overly simplification

The polarisation of quantitative and qualitative methods has brought order to research proceedings for a long time. With their epistemological differences, many researchers agree on the fundamental antinomies and their practical implications [7].

A source of paradigm polarisation is in fact seeded in each side’s own sensibilities. Modern induction, subjected to years of attack on its very core [8], extends as far back to 18th century Hume [9].  The “new riddle of induction” [10] demonstrated that whenever a conclusion was based upon inductive reasoning, the same rules of inference based on different criteria of classification could draw an opposite conclusion [11].

Equally, Bacon (1620/1960) subjected deductive users to ‘spider’ taunts, that “they make a web of knowledge out of their own entrails” [12].

The interpretation and exegesis on the incommensurability of rival paradigms, as exemplified by Kuhn [13] maintains a dominating voice [14].

Coming Out: quantitative-induction affiliations

I believe though, an uprising has begun. More researchers are stepping forward to criticise the rigidity and compartmentalization that has starved oxygen from a more productive position of complementarity and integration [15].

Sparingly, albeit, in both qualitative and quantitative papers, there are a growing number of research papers supporting inductive reasoning studies [16; 17]. One of the more examples is in “Exploratory Data Analysis”  [18];  which is concerned with discovery, exploration, and empirically detecting phenomena in data.

table dub 2018 Table 1: Source: Dudovskiy, 2018

Other examples do exist. Glaser indirectly set the momentum going with Grounded Theory developing solely from secondary quantitative data [19]. Organisational science also harbours a propensity for ‘quantitative research’ to uncover atypical and unexpected patterns [3; 11]. Other “quantitative modes of discovery may involve meta-analysis, replication research, and evaluation studies” [3].  Alternative interpretations of probability also exist, such as the subjective and propensity interpretations [20]. Bayes’ rule; is a method which provides means to update beliefs based on access to new, relevant pieces of evidence;  thus combining inductive reasoning with probability [21]. Despite the incidences of these aforementioned techniques, and the rising popularity of mixed-methods [22; 23], full complementarity and integration of reasoning approaches are yet to be achieved.

Diversity Attitude

I do not call for a relaxed disposition towards robustness and rigour, particularly given the reproducibility crisis, but suggest that there can only be merit in mass education of the implications of both an inductive and deductive approach on a selection of primary data collection methods and research processes [18].

Even if disruptive to comfortable segmentations, quantitative researchers should be more confident to use inductive methodology if appropriate;  be assured that research need not come in confirmatory and deductive packages [24].

Whilst superficial induction runs the risk of superficial and incorrect conclusions [20]. Research outputs following emancipation from the quantitative/deductive-qualitative/inductive dichotomy surely is well worth the risk!

Implications for Industry 4.0

As educators, researchers and professionals, we should be urgently committed to questioning the detail behind how research is achieved. This issue is not the preserve of academic or research institutions,  to deliberate on how to overcome the erosion of confidence in “research”. The challenge belongs to us all, as it affects the present and future breakthroughs that will move our industries forward;  the innovations, discoveries and new strategies that will underpin the successful economies on which we all depend. 


  1. Labaree, R.V. (‎2009) ‘Organizing Your Social Sciences Research Paper: Purpose of Guide’ [online]. Available at: [Accessed 26/10/2018].
  2. Soiferman, K.L. (2010) ‘Compare and Contrast Inductive and Deductive Research Approaches’, University of Manitoba.
  3. Bamberger, P. & Ang, S. (2016) ‘The quantitative discovery: What is it and how to get it published’, Academy of Management Discoveries, 2(1) pp. 1-6. Available at: [Accessed 26/10/2018].
  4. What is Philosophy? (n.d.) Inductive and deductive reasoning [online]. Available at: [Accessed 26/10/2018].
  5. Snieder, R. & Larner, K. (2009) ‘The Art of Being a Scientist: A Guide for Graduate Students and their Mentors’, Cambridge University Press
  6. Creswell, J. W. & Plano Clark, V. L. (2011) ‘Designing and conducting mixed methods research’, 2nd ed., SAGE Publications: Los Angeles
  7. Brynman, A. (1982) ‘The Debate about Quantitative and Qualitative Research: A Question of Method or Epistemology?’, The British Journal of Sociology, 35(1), pp. 75-92.
  8. Hacking, I. (1975) ‘The emergence of probability: A philosophical study of early ideas about probability, induction and statistical inference’, Cambridge University Press: New York.
  9. Hume, D. (1739) ‘A Treatise of Human Nature’, Oxford: Oxford University Press.
  10. Goodman, N. (1954/1983) ‘Facts, fictions, and forecast’, Hackett: Indianapolis.
  11. Jebb, A .T. (2017) ‘Exploratory data analysis as a foundation of inductive research’, Human Resource Management Review, 27(2), pp. 265-277.
  12. Bacon, F. (1620/1960) ‘The new organon, and related writings’, Liberal Arts Press: New York.
  13. Kuhn, T.S. (1962) ‘The structure of scientific revolutions’, University of Chicago Press, Chicago.
  14. Naughton, J. (2012) ‘Thomas Kuhn: the man who changed the way the world looked at science’ [online]. Available at: [Accessed 24/10/2018].
  15. Sánchez-Algarra, P., & Anguera, M. (2013) ‘Qualitative/quantitative integration in the inductive observational study of interactive behaviour: Impact of recording and coding among predominating perspectives’, Quality & Quantity, 47(2), pp. 1237-125.
  16. Schwandt, T.A. (1997) ‘Qualitative inquiry: a dictionary of terms’, Sage: Thousand Oaks, CA.
  17. Thorne, S. (2000) ‘Data analysis in qualitative research’, Evidence- Based Nursing, 3(1) [online). Available at: [Accessed 24/10/2018].
  18. Dudovskiy, J. (2018) ‘Inductive Approach’ [online]. Available at: [Accessed 24/10/2018].
  19. Glaser, B. (1998) ‘Doing grounded theory’, Sociology Press: Mill Valley.
  20. Yu, C. H. (2005) ‘Abduction, Deduction, and Induction: Their implications to quantitative methods’, Ph.D. [online]. Available at: [Accessed 24/10/2018].
  21. Galavotti, M.C. (2017) ‘The Interpretation of Probability: Still an Open Issue?’ Philosophies 2017, 2(1), doi:10.3390/philosophies2030020.
  22. Onwuegbuzie, A.J. & Leech, N.L. (2005a) ‘Taking the “Q” out of research: teaching research methodology courses without the divide between quantitative and qualitative paradigms’, Qual. Quant. 39(1), pp. 267–29.
  23. Onwuegbuzie, A.J. & Leech, N.L. (2005b) ‘On becoming a pragmatist researcher: the importance of combining quantitative and qualitative research methodologies’, Int. J. Soc. Res. Methodology, 8(5), p. 375–381.
  24. Trochim, W.M. (2006) ‘The Research Methods Knowledge Base’, 2nd ed., Science and Education Publishing.


One Reply to “Coming Out: Quantitative Design with Inductive Traits”

  1. I agree with you – it is time for researchers to step out of their comfortable boxes and recognise the opportunities that a yet unexplored paradigm can offer. There are risks but there is also the opportunity to enrich research projects by adding substance that could not be added otherwise.


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