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Applied Research Paradigms

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Part of the Contributions to Economics book series (CE)


This chapter discusses the research paradigms underpinning this study––i.e. dialectical pluralism (DP) (mixed methods research) and a complex thinking perspective. The chapter also explains the researcher’s scientific and personal paradigm biases and details some strategies utilised for objective data treatment.


  • Dialectical Pluralism (DP) (mixed methods research)
  • Complex thinking perspective
  • Personal and scientific biases
  • Strategies for objective data treatment

The paradigms and biases underlying this study reflect its dialectical pluralism (DP) (mixed methods research) and a complex thinking perspective.

13.1 Research Paradigms

13.1.1 Research Paradigm 1 (Dialectical Pluralism)

Dialectical Pluralism (DP) is a broad paradigm with manifold benefits for developing the meta-paradigm required in this research. The DP is an approach to research and policy that considers multiple paradigms carefully, thoroughly, and respectfully (Johnson, 2017). The basic idea behind the meta-paradigmatic perspective of this study is.

to empathetically and thoughtfully work with more than one paradigm (or perspective or theory) to produce a new, more complex ‘whole.’ The whole might be mostly divergent, mostly convergent, or, most often, a combination of divergent and convergent perspectives, practices, values, and assumptions (J. C. Greene as cited in Johnson, 2017, p. 159).

This mixed methods study applies DP as its primary paradigm. Hence, it investigates the possible influence of religion on prosperity from multiple paradigmatic, theoretical, and methodological perspectives.

Chapter 5 reveals that the main weakness of mainstream theories attempting to explain prosperity is that they tend to reject approaches from other disciplines. However, each of the explanatory theories employed in this study to understand prosperity imbalances between the investigated countries may contain a grain of truth (Moran et al., 2007, p. 3). Therefore, gaining a comprehensive and systemic understanding of the situation is vital and requires the explanatory theories of prosperity to be synthesised in a general model.

The traditional approach of mutual rejection of separate disciplines demonstrates the need for a transdisciplinary paradigm to the causes of the historical prosperity imbalance in Europe and the Americas. A model encompassing different theories in a holistic and transdisciplinary manner is necessary in order to understand the whole picture. Likewise, this also allows the inclusion of more variables that potentially influence prosperity. In fact, “the most productive conceptual frameworks are often those that integrate different approaches, lines of investigation, or theories that no one had previously connected” (Maxwell, 2013, p. 35).

Consequently, DP is an ideal paradigm for the aims and concerns of this study. This approach not only respects “different perspectives, but it is also designed to strategically enable adherents to carefully consider differences and possible ‘syntheses’ of their knowledge, abilities, and values”. This kind of synthesis is dynamic (Johnson, 2017, p. 161).

13.1.2 Research Paradigm 2 (Complex Thinking)

This study adopts the paradigm of complexity, since this opposes, yet contains mechanicist and systemic paradigms (Morin, 2001). Historically, the complex thinking paradigm emerged as an evolutive result of systemic thinking, which arose from mechanicism (Zuñiga & Tarride, 2010). These three approaches are reviewed separately below in light of this study.

Mechanicist Paradigm

One of the principles characterising the mechanicist paradigm is the concept of linear causality. This principle is a standard paradigm, which also guides traditional positivist science, including economics. Many prosperity theories (especially within the neoclassical economic approach) fall into this traditional paradigm (Chap. 5).

Systemic Paradigm

The systemic paradigm rejects the dominant reductionism of the mechanisist paradigm. It focuses on the interrelations between a system’s constituents and those between that system and its environment. It favours a holistic view and thus maintains the principle of generality that also characterises the mechanicist paradigm. However, it also recognises the systems’ particularities (Zuñiga & Tarride, 2010, pp. 1115–1116).

Theories revised in this study such as environment/geography (Sachs, 2003; Diamond, 1997) or institutions (Acemoglu & Robinson, 2012) (Chap. 4) have incorporated elements of the systemic paradigm. By including historical components and following unconventional approaches, these theories lay the foundations for systemic explanations of the origins of prosperity. However, by denying other disciplines’ approaches, they remain “trapped” in the mechanicist paradigm, thus accepting monocausal, linear explanations.

Complexity Paradigm

The complexity paradigm overcomes the adoption of either the mechanicist or the systemic approach in favour of a dialogic, complementary perspective (Zuñiga & Tarride, 2010). Accordingly, this study applies a linear, mechanicist approach (e.g. regressions) combined with systemic models (see QCA and CDA). Yet, the various theories (Chap. 5) are revised and the results analysed from a dialogic perspective. Likewise, complex logic leads the synthesis, analysis, and conclusions which are drawn. Therefore, this research rejects neither the mechanicist nor the systemic paradigm. On the contrary, it brings these paradigms into dialogue under the “complexity paradigm”.

Below I briefly describe the researcher’s scientific and personal paradigm biases.

13.2 Paradigm Biases

13.2.1 Scientific

In order of importance, according to the Philosophy of Social Sciences Inventory (PSSI) scoring test, the scientific paradigms guiding this study are: Mixed research methods: 6.0; Idealism: 5.3; Quantitative research methods: 4.5; Nomothetic methods: 4.5; Rationalism: 4.0; Empiricism: 4.0; Fallibilism: 3.6; Ethical realism: 3.5; Qualitative research methods: 3.5; Humanism: 3.5; Ontological relativism: 3.5. Biases

Several types of bias can threaten the validity of any meta-study (e.g. language, location, time, outcome, funding) (Hussain et al., 2019). Publication bias can be a salient issue that occurs when the researcher is favourably oriented towards studies with statistically significant results, while rejecting those that are contrary to expectations. Publication bias could also lead to an overestimation of effects in meta-analysis. Likewise, confirmatory bias “is the tendency to emphasize and believe experiences which support one’s views and to ignore or discredit those which do not” (Mahoney, 1977, p. 161). To counter these threats and limitations, this study applied some of the strategies recommended by Brown et al. (2017) and Hussain et al. (2019):

  • A systematic review of empirical studies in journals of multiple electronic databases (Hussain et al., 2019).

  • Triangulation for crosschecking information through multiple sources and procedures (see Sect.

  • Peer review processes as a source through which quality checks can be improved (Brown et al., 2017). This research was submitted to peer reviewers and critical friends (see Appendix 5).

  • Publication in Open Access Journals. Online, “open access journals improve the readability and reduce publication bias” (Hussain et al., 2019, p. 59; Brown et al., 2017). Some sections of this study were published in an open access peer-reviewed journal (Published: 1 June 2019; Garcia Portilla, 2019).

  • Sharing the data and making it publically available would reduce the probability of publication bias (Brown et al., 2017; Hussain et al., 2019). Some sections and appendices of this study have been publically available on the website of the open access peer-reviewed journal since June 2019 (Garcia Portilla, 2019).

  • Multi-lingual analysis (Spanish, English, French, and German) of the literature and primary data (i.e. interviews) instead of ignoring studies that are not published in the researcher’s mother tongue (Egger et al., 1997; Hussain et al., 2019).

13.2.2 Personal

Maxwell’s textbook “Qualitative Research Design” states:

Traditionally, what you bring to the research from your own background and identity has been treated as “bias,” something whose influence needs to be eliminated from the design, rather than a valuable component of it. This has been true to some extent even in qualitative research, despite the fact that qualitative researchers have long recognized that in this field, the researcher is the instrument of the research. In opposition to the traditional view, …[s]eparating your research from other aspects of your life cuts you off from a major source of insights, hypotheses, and validity checks (Maxwell, 2013, pp. 37–38).

Therefore, the explicit incorporation of the researcher’s personal identity and experience has gained extensive theoretical and philosophical support (e.g. Berg & Smith, 1988; Denzin & Lincoln, 2000; Jansen & Peshkin, 1992 as cited in Maxwell, 2013, p. 38). In fact,

…the most admirable scholars within the scholarly community … do not split their work from their lives. They seem to take both too seriously to allow such dissociation, and they want to use each for the enrichment of the other (Wright Mills as cited in Maxwell, 2013, p. 38).

Studies identifying their author’s personal positions are praised, among others, as follows: “Seen as virtuous, subjectivity is something to capitalize on rather than to exorcise” (Glesne & Peshkin as cited in Maxwell, 2013, p. 38). Or “mine your experience, there is potential gold there!” (Strauss as cited in Maxwell, 2013, p. 38). Maxwell (2013) therefore proposes a particular technique for incorporating the researcher’s subjectivity and experience in a study: “researcher identity memo”. This acknowledges the researcher’s personal perspective and stance. Taking Maxwell’s cue, I have included a positionality memo in Appendix 5 to ensure that my perspective does not unduly influence the analysis and coding, in particular in the qualitative part. Among others, Appendix 5 includes the strategies for objective data treatment, and amplifies the following personal information about the researcher: Personal Experiences from Each Belief System

Roman Catholicism

  • I grew up in a Roman Catholic family in Colombia (a strongly Roman Catholic country);

  • I was educated for 12 years in Roman Catholic schools and received Catholic religious instruction;

  • I studied for five years at a Pontifical Jesuit university.

Atheism (after being a firm Catholic believer)

  • I spent ten years living as a self-confessed atheist who embraced atheistic values;

  • I socialised in and applied the paradigm of scientific materialism (positivism).

Dissenting Protestantism (after being a convinced atheist)

  • I spent 12 years living as converted, free Christian and I have studied the Bible on my own;

  • I have lived and studied in the United Kingdom and Switzerland (two historically Protestant countries) (Inglehart & Baker, 2000). Strategies for Objective Data Treatment

The following strategies were employed to ensure objective data treatment: (1) Two independent quantitative researchers were engaged to select the variables using solely quantitative criteria (e.g. cross-validation) in regressions and QCA. (2) Two independent qualitative researchers were engaged to impartially validate the qualitative coding (a total of four independent researchers) (see Appendix 5). (3) The selected variables (chosen from a pool of more than 70 indicators) were associated with qualitative codes to prioritise them from a mixed methods perspective. (4) The quantitative and qualitative (QCA) databases and the twelve codes applied comparatively to the four case studies’ qualitative data are available for inspection (see Supplementary Materials). However, the names and original interviews conducted as part of this study will not be made publicly available to protect the interviewees’ identities.


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García Portilla, J. (2022). Applied Research Paradigms. In: “Ye Shall Know Them by Their Fruits”. Contributions to Economics. Springer, Cham.

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