Generalization in quantitative and qualitative research: Myths and strategies Denise F. Polit a,b,*, Cheryl Tatano Beck c a Humanalysis, Inc., 75 Clinton Street, Saratoga Springs, NY 12866, United States b Research Centre for Clinical and Community Practice Innovation, Griffith University School of Nursing, Gold Coast, Australia c University of Connecticut School of Nursing, Storrs, CT, United States What is already known about the topic? The topic of generalization is less often discussed by qualitative than by quantitative researchers, who con- sider the ability to generalize a key quality criterion. Many leaders in qualitative research have begun to note the importance of addressing generalization, to ensure that insights from qualitative inquiry are recognized as important sources of evidence for practice. What this paper adds Generalization can be clarified by recognizing that there are three different models of generalization, each of which has relevance to nursing research and evidence- based practice: the classic statistical generalization model, analytic generalization, and the case-to-case transfer model (transferability). Both quantitative and qualitative researchers uphold certain myths about adherence to the three models of generalization, and these myths hinder the likelihood that real opportunities for generalization will be pursued. Many strategies can be adopted by both qualitative and quantitative nurse researchers to enrich the readiness of their studies for reasonable extrapolation. Generalization is an act of reasoning that involves drawing broad conclusions from particular instances���that is, making an inference about the unobserved based on the observed. In nursing and other applied health research, generalizations are critical to the interest of applying the findings to people, situations, and times other than those in International Journal of Nursing Studies 47 (2010) 1451���1458 A R T I C L E I N F O Article history: Received 15 April 2010 Received in revised form 31 May 2010 Accepted 3 June 2010 Keywords: Evidence-based nursing Generalization Methods Qualitative research A B S T R A C T 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 environmentwhereevidenceforimprovingpracticeisheldinhigh esteem,generalizationin 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). Sugges- tions 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. �� 2010 Elsevier Ltd. All rights reserved. * Corresponding author at: Humanalysis, Inc., 75 Clinton Street, Saratoga Springs, NY 12866, United States. Tel.: +1 518 587 3994 fax: +1 518 583 7907. E-mail address: dpolit@rocketmail.com (D.F. Polit). Contents lists available at ScienceDirect International Journal of Nursing Studies journal homepage: www.elsevier.com/ijns 0020-7489/$ ��� see front matter �� 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijnurstu.2010.06.004

a study. Without generalization, there would be no evidence-based practice: research evidence can be used only if it has some relevance to settings and people outside of the contexts studied. Although many articles and books have discussed the issue of generalizability, few have considered strategies for addressing it in nursing research. The purpose of this paper is to discuss three different models of generalization, to identify ������myths������ about the degree to which these models are adhered to in qualitative and quantitative research, and to offer suggestions for enhancing the capacity for generalization in nursing research. 1. 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 quali- tative 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, regard- less of the language used to describe it������ (p. 881). 2. Models of generalization Firestone (1993) developed a typology depicting three models of generalizability that provides a useful frame- work 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 re- search. The third model is case-to-case translation, which is more often called transferability. The latter two models have been described as mechanisms for dealing with the apparent paradox of qualitative research���its focus on the particular and its simultaneous interest in the general and abstract (Schwandt, 1997). 2.1. Statistical generalization In the familiar model of generalization���what Lincoln and Guba (1985) referred to as nomothetic generaliza- tion���quantitative researchers begin by identifying the population to which they wish to generalize their results. The population is the totality of elements or people that have common, defined characteristics, and about whom the study results are relevant. Researchers proceed to select participants from that population, with the goal of selecting a sample that is representative of the population. The best strategy for achieving a represen- tative sample is to use probability (random) methods of sampling, which give every member of the population an equal chance to be included in the study with a determinable probability of selection. Standard tests of statistical inference are based on the assumption that random sampling from the target population has occurred (Polit, 2010). Like most models, this generalizability model is an ideal���a goal to be achieved, rather than an accurate depiction of what transpires in real-world research. Yet the myth that this model is adhered to in quantitative scientific inquiry in the human sciences perseveres. One flaw stems from the starting point: most quantitative researchers begin with only a vague notion of a target population. They are more likely to have an explicit accessible population, that is, a group to which they have access and from which participants are sampled. Even accessible populations, which are linked to hypothetical target populations in a diffuse and often unarticulated way, frequently are ill-defined in research reports. In many cases, the population may be identified based on sample characteristics and relevant eligibility criteria���that is, the real starting point is often the sample, not the population. Random sampling is the vehicle through which the statistical model of generalization can be enacted. Even a casual perusal of journal articles in nursing and health care is sufficient to conclude that the vast majority of studies D.F. Polit, C.T. Beck / International Journal of Nursing Studies 47 (2010) 1451���1458 1452