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Misconceived Relationships Between Logical Positivism and Quantitative Research Chong Ho Yu, Ph.D., D

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Misconceived relationships between logical positivism and quantitative research Chong Ho Yu, Ph.D., D. Phil. Paper presented at the Annual Meeting of the 2001 American Educational Research Association, Seattle, WA Published in Research Method Forum, 2003 Included in the book Philosophical foundations of quantitative research methodology (2006, Laham, MD: University Press of America) Correspondence: Chong Ho Yu, Ph.D., D. Phil. Email: [email protected] Website: http://www.creative-wisdom.com/pub/pub.html Logical positivism and Quantitative Research 2 Abstract Although quantitative research methodology is widely applied by social scientists, there is a common misconception that quantitative research is based upon logical positivism. This misconception leads to misguided disputes between qualitative and quantitative researchers. This article points out that the polarities between the two are unnecessary, and the richness and continuity of “research traditions” is therefore proposed as a replacement for the incommensurability in the Kuhnian “paradigm.” Further, this article examines the relationship between quantitative research and eight major notions of logical positivism: (a) verification, (b) pro-observation, (c) anti-cause, (d) downplaying explanation, (e) anti- theoretical entities, (f) anti-metaphysics, (g) logical analysis and (h) frequentist probability. It is argued that the underlying philosophy of modern quantitative research does not subscribe to logical positivism. Associating an outdated philosophy with quantitative research may discourage social science researchers from applying this research approach. Researchers and students should be encouraged to keep an open mind to different methodologies because mixed methods have the potential to achieve the goals of convergent validity and completeness. Logical positivism and Quantitative Research 3 Misconceived relationships between logical positivism and quantitative research Introduction Feldman (1998) observed that while positivism has been universally rejected by philosophers of science over the past fifty years, current textbooks still either associate quantitative methods with positivist ones or cover quantitative methods within a positivist frame of reference. Despite the fact that newer epistemologies and methodologies, such as post-positivism, critical realism, and critical multiplism, have been discussed in numerous books and articles (Cook, 1985, 1991, 1993; Cook & Campbell, 1979; Cook & Shadish, 1994; Phillips, 1987, 1990a, 1990b, 1992, 2000; Phillips & Burbules, 2000), the debate regarding the paradigm of quantitative methods seems to be trapped in a time warp. The objectives of this paper are threefold. First, I argue that the dichotomy between the two approaches is misguided due to the popular notion of “paradigm” introduced by Kuhn, which tends to polarize methodological differences and thus leads to epistemological incommensurability. Instead, the problem would be reframed under the notion of “research tradition” advocated by Laudan. Second, historical and theoretical evidence is cited in attempt to break the philosophical ties between quantitative methodology and logical positivism. Logical positivism, which rejects theoretical constructs and causality and emphasizes reductionism, is too restrictive to apply to quantitative methodology, which supports the use of latent constructs, causal inferences, and the iterative process of undestanding the data and developing constructs. Last, I argue that when quantitative research departs from logical positivism and methodological differences/similarities are re-conceptualized in research tradition, it widens the door to triangulation with the goals of convergence and completeness. Misconceptions of quantitative research The misconceived relationships between positivism and quantitative research can still be found in recent textbooks. For example, Berg (2001) explicitly identified quantitative research as a positivistic approach: “Positivists utilize empirical methodologies borrowed from the natural sciences to investigate phenomena. Quantitative strategies serve this positive-science ideal by providing rigorous, reliable, and Logical positivism and Quantitative Research 4 verifiably large aggregates of data and the statistical testing of empirical hypotheses” (p.10). Merriam (1998) also related certain “positivist” characteristics to quantitative methods: In positivist form of research…knowledge gained through scientific and experimental research is objective and quantifiable…on the topic dropping out of high school…From a positivist perspective you might begin by hypothesizing that students drop out of high school because of low self-esteem. You could then design an intervention program to raise the self-esteem of students at risk. You set up an experiment controlling for as many variables as possible, and then measure the results. (p. 4) Further, in many texts comparing qualitative and quantitative research, the attributes of the latter are often misidentified. The following are some examples: particularistic (quantitative) vs. holistic (qualitative) emphasis, outcome-oriented (quantitative) vs. process-oriented (qualitative), fixed (quantitative) vs. emergent (qualitative) categories, static (quantitative) vs. fluid (qualitative) reality (Huysamen, 1997), mechanical (quantitative) vs. creative (qualitative), formulaic (quantitative) vs. interpretive (qualitative) (Howe, 1988), expansionist (qualitative) vs. reductionist (quantitative), and grounded (qualitative) vs. ungrounded (quantitative) (Reichardt & Cook, 1979). These comparisons are grounded in the misunderstood relationship between quantitative research and the positivist/logical positivist paradigms. For example, qualitative researchers use the grounded theory (Glaser & Strauss, 1967) to develop “categories” that could fit the data until the categories are saturated. The process is said to be an iterative abstraction grounded on the data. In a similar vein, quantitative researchers employ exploratory factor analysis to develop latent constructs until all dimensions emerge and all observed variables can be properly loaded into the abstracted dimensions. It is difficult to see why the former is said to be grounded while the latter is ungrounded. Probably, this misunderstanding is due to the association between the one-way reductionism endorsed by certain logical positivists and quantitative methods. Further, the perception that quantitative research assumes static reality is attributable to the myth that logical positivists are realists. The notion that quantitative researchers are confined by fixed categories results from the omission of the fact that quantitative researchers utilize open concepts and Logical positivism and Quantitative Research 5 customized instruments in different contexts. In a later section this article will fully discuss the core ideas of logical positivism and point out that the preceding perceived connection is mistaken. Research tradition vs. Paradigm One of the sources of the misunderstanding can be traced back to the oversimplification of quantitative research. Usually quantitative research is viewed as Fisherian hypothesis testing, and various statistical procedures are regarded as a unitary approach that can be summarized in a single paradigm. However, at the ontological, epistemological, and methodological levels, the Fisherian school, the Neyman/Pearson school, the Bayesian school, the resampling school, and the exploratory data analysis (EDA) school are fundamentally different, and to some extent are incompatible (Lehmann, 1993; Berger, 2001; Behrens, 1997; Behrens & Yu, 2003). Further, in the arena of measurement, the classical test theory and the item response theory are also very different in their premises and assumptions (Embretson & Reise, 2000). According to Kuhn (1962), following a paradigm, all members of a specific scientific community accept a set of commonly agreed exemplars. However, it is doubtful whether the Kuhnian paradigm theory could be applied to such a rich collection of epistemologies and methodologies in quantitative research. Laudan (1977) argued that the paradigm theory does not fit with the history of science. Indeed, it is not uncommon for a number of competing theories based upon incompatible exemplars to coexist. Thus, Laudan proposed the concept of “research tradition” in an attempt to replace “paradigm.” Laudan is not alone. In reaction to the viewing of quantitative research as a positivist approach, Clark (1998) embraced the view that quantitative research is shaped by more than one philosophy. In a similar vein, Cook (1985) doubted that all social and natural scientists have subscribed to all positivist assumptions, that positivism adequately describes scientific practice as it occurs, and that this practice has evolved only from the positivist framework. Rather, Cook asserted that “scientific practice has multiple origins that include trial-and-error behavior of practitioners, selective adaptations from prior philosophies and research” (p.23). Thus, it would be an oversimplification to treat quantitative methods as a single Logical positivism and Quantitative Research 6 paradigm. A more appropriate treatment of this issue is to classify different schools of quantitative methods into different research traditions. Further, in the Kuhnian framework, paradigms are competing worldviews that would inevitably lead to incommensurability. Paradigms are said to be so different that
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