Generalization in quantitative and qualitative research: Myths and strategies

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Abstract

Generalization, which is an act of reasoning that involves drawing broad inferences from particular observations, is widely-acknowledged as a quality standard in quantitative research, but is more controversial in qualitative research. The goal of most qualitative studies is not to generalize but rather to provide a rich, contextualized understanding of some aspect of human experience through the intensive study of particular cases. Yet, in an environment where evidence for improving practice is held in high esteem, generalization in relation to knowledge claims merits careful attention by both qualitative and quantitative researchers. Issues relating to generalization are, however, often ignored or misrepresented by both groups of researchers. Three models of generalization, as proposed in a seminal article by Firestone, are discussed in this paper: classic sample-to-population (statistical) generalization, analytic generalization, and case-to-case transfer (transferability). Suggestions for enhancing the capacity for generalization in terms of all three models are offered. The suggestions cover such issues as planned replication, sampling strategies, systematic reviews, reflexivity and higher-order conceptualization, thick description, mixed methods research, and the RE-AIM framework within pragmatic trials.

Introduction

In quantitative research, generalizability is considered a major criterion for evaluating the quality of a study (Kerlinger and Lee, 2000, Polit and Beck, 2008). Within the classic validity framework of Cook and Campbell (e.g., Shadish et al., 2002), external validity—the degree to which inferences from a study can be generalized—has been a valued standard for decades. Yet, generalizability is a thorny, complex, and illusive issue even in studies that are considered to yield high-quality evidence (Kerlinger and Lee, 2000, Shadish et al., 2002).

In qualitative studies, the issue of generalization is even more complicated, and more controversial. Qualitative researchers seldom worry explicitly about the issue of generalizability. The goal of most qualitative studies is to provide a rich, contextualized understanding of human experience through the intensive study of particular cases. Qualitative researchers do not all agree, however, about the importance or attainability of generalizability. Some challenge the possibility of generalizability in any type of research, be it qualitative or quantitative. In this view, generalization requires extrapolation that can never be fully justified because findings are always embedded within a context. According to this way of thinking, knowledge is idiographic, to be found in the particulars (Guba, 1978, Erlandson et al., 1993). On the other hand, some qualitative researchers believe that in-depth qualitative research is especially well suited for revealing higher-level concepts and theories that are not unique to a particular participant or setting (Glaser, 2002, Misco, 2007). In this view, the rich, highly detailed, and potentially insightful nature of qualitative findings make them especially suitable for extrapolation.

In the current evidence-based practice environment, the issue of the applicability of research findings beyond the particular people who took part in a study has gained importance for qualitative researchers. Groleau et al. (2009), in discussing generalizability in a recent article in Qualitative Health Research, argued that an important goal of qualitative studies is to shape the opinion of decision-makers whose actions affect people's health and well-being. Thorne (2008) echoed similar sentiments about the need to adopt a practical perspective: “…the moral mandate of a practice discipline requires usable general knowledge…(Qualitative) researchers in this field are obliged to consider their findings ‘as if’ they might indeed be applied in practice” (p. 227). Ayres et al. (2003) observed that, “Just as with statistical analysis, the end product of qualitative analysis is a generalization, regardless of the language used to describe it” (p. 881).

Section snippets

Models of generalization

Firestone (1993) developed a typology depicting three models of generalizability that provides a useful framework for considering generalizations in quantitative and qualitative studies. The first model is extrapolating from a sample to a population (statistical generalization), the classical model underpinning most quantitative studies. The second model is analytic generalization, a model that has relevance in both qualitative and quantitative research. The third model is case-to-case

Strategies to enhance generalized inferences

Although it would be impossible to catalogue and discuss the many strategies that could be used to enhance generalization in nursing studies, we offer a few suggestions. Some are appropriate for individual researchers, and others are more global strategies.

Discussion

Generalizability or applicability is an issue of great importance in all forms of health and social research, and this is particularly true in the current environment in which evidence is held in high esteem. Qualitative and quantitative studies have developed their own special ways of dealing with generalization, none of them with perfect success. Arguably though, there are fewer “myths” relating to the analytic generalization and transferability models than to the statistical model of

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