James mahoney

Solo disponible en BuenasTareas
  • Páginas : 51 (12742 palabras )
  • Descarga(s) : 0
  • Publicado : 6 de febrero de 2011
Leer documento completo
Vista previa del texto
A tale of two cultures: contrasting quantitative and qualitative research

James Mahoney Departments of Political Science and Sociology Northwestern University Evanston, IL 60208-1006 email: James-Mahoney@northwestern.edu and Gary Goertz Department of Political Science University of Arizona Tucson, Arizona 85721 email: ggoertz@u.arizona.edu

January 27, 2006

The quantitative andqualitative research traditions can be thought of as distinct cultures marked by different practices, beliefs, and norms. In this essay, we adopt this imagery toward the end of contrasting these research traditions across ten areas: (1) approaches to explanation, (2) conceptions of causation, (3) multivariate explanations, (4) equifinality, (5) scope and causal generalization, (6) case selection,(7) weighting observations, (8) substantively important cases, (9) lack of fit, and (10) concepts and measurement. We suggest that an appreciation of the alternative assumptions and goals of the traditions can help scholars avoid misunderstandings and contribute to more productive “cross-cultural” communication in political science.

Comparisons of the quantitative and qualitativeresearch traditions sometimes call to mind religious metaphors. In his commentary for this issue, for example, Beck likens the traditions to the worship of alternative gods. Schrodt (this issue), inspired by Brady’s (2004a, 53) prior casting of the controversy in terms of theology versus homiletics, is more explicit: “while this debate is not in any sense about religion, its dynamics are bestunderstood as though it were about religion. We’ve always known that, it just needed to be said.” We prefer to think of the two traditions as alternative cultures. Each has its own values, beliefs, and norms. Each is sometimes privately suspicious or skeptical of the other though usually more publicly polite. Communication across traditions tends to be difficult and marked by misunderstanding. Whenmembers of one tradition offer their insights to members of the other community, the advice is likely to be viewed (rightly or wrongly) as unhelpful and even belittling. As evidence, consider the reception of Ragin’s The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies (1987) and King, Keohane, and Verba’s Designing Social Inquiry: Scientific Inference in Qualitative Research(1994). Although Ragin’s book was intended to combine qualitative and quantitative methods, it was written from the perspective of a qualitative researcher, and it became a classic in the field of qualitative methodology. However, statistical methodologists largely ignored Ragin’s ideas, and when they did engage them, their tone was often quite dismissive (e.g., Lieberson 1991, 1994; Goldthorpe 1997).For its part, King, Keohane, and Verba’s famous work was explicitly about qualitative research, but it assumed that quantitative researchers have the best tools for making scientific inferences, and hence qualitative researchers should attempt to emulate these tools to the degree possible. Qualitative methodologists certainly did not ignore King, Keohane, and Verba’s work. Instead, they reacted byscrutinizing the book in great detail, pouring over each of its claims, and sharply criticizing many of its conclusions (e.g., see the essays in Brady and Collier 2004).


Table 1: Contrasting qualitative and quantitative research
Section 1 Criterion Approaches to explanation Conceptions of causation Multivariate explanations Equifinality Scope and generalization Case selection practicesWeighting observations Substantively important cases Lack of fit Concepts and measurement Qualitative explain individual cases; “causes-of-effects” approach necessary and sufficient causes; mathematical logic INUS causation; occasional individual effects core concept; few causal paths adopt a narrow scope to avoid causal heterogeneity oriented toward positive cases on dependent variable; no (0,0,0)...
tracking img