Options for Blending Qualitative and Quantitative Research Methods

1. Options for BlendingQualitative and Quantitative Research Methods Ian McDowell (Based on a seminar presentation in 1997)

2. Overview • Epidemiologic research methods are gradually evolving in recognition of inadequacies in current methods • Two paradigms: positivist & quantitative vs. subjectivist or postmodern • There are strengths in each … • So how can we blend the two, in: • study design • data collection • analysis • communicating results?

3. Styles of Thought(how do we know that we know what we think we know?) Perennial dualisms throughout history of thought: • Yin and Yang • Greek Apollonian vs. Dionysiac • Male and female • Right brain and left • Deductive vs. inductive • Quantitative vs. qualitative • Reductionist vs. systems thinking

4. Changing philosophies of knowledge • 17th & 18th centuries: order, logic and science, world seen through senses. Mechanical world. Realism and logical positivism • 19th century - social revolution: can we analyze behaviour logically? Idealism: the human mind as source of knowledge; people, as well as logic, crucial in explaining reality. Nonetheless, still used mechanical metaphors • 20th century - phenomenology; qualitative research

5. Two paradigms • The challenge of biological variability – should we focus on the general or the specific? • ‘Nomothetic’ science seeks general truths, using deductive methods. Public health; epidemiology. • Yet the ultimate purpose of science is to explain specific instances: ‘idiographic’ studies. Clinical medicine; psychology; inductive methods.

6. Quantitative approach • Describes and imposes external structure on data (e.g., fixed questions in questionnaire) • Gives parsimonious summary of results: reductionist (for example, statistical analysis assigns shared variance to one variable, so reducing complexity) • Seeks to isolate systems from their environment and to generalize findings • Efficient, but incomplete view of interconnectedness of reality • Asks the “How?” question • Externally valid: generalizing rather than particularizing

7. Qualitative approach • Interprets, explains; generates concepts • Seeks to be open, flexible • The investigator is the instrument; art versus science • Sampling becomes a crucial issue (in data collection and in analysis) • “Somewhat magical approach to analysis” • Asks the “Why?” question • Particularizes; internally valid

8. Blending Qualitative and Quantitative • Metaphor of binocular vision • A combination seeks to array strengths of one against limitations of the other • Nature of the balance may depend on stage of the study: for example qualitative may predominate in a process evaluation, quantitative in an outcome evaluation study.

9. Five blends of qualitative & quantitative Hierarchical model: one method takes the lead • Qualitative leads, or • Quantitative leads Partnership model: equal but contrasting contributions • Sequential • Cyclical • Simultaneous application (triangulation)

10. Applying these types of blend In different stages of research: • Conceptualizing the study • Collecting data • Analyzing data • Interpreting the data

11. Stage 1: Conceptualizing the Study • Hierarchical model, quantitative leading, in “hard” science (a rise in cancer cases) • Hierarchical model, qualitative leading, in “soft” topics (public concern over rise in cancers) • Partnership model applicable in mixed studies or in broad programme of research that involves sequence of individual studies • Sequential partnership in formulating study: qualitative leads into quantitative (public concern leads to an evaluation of an intervention to address this)

12. Stage 2: Collecting the Data • Goal of blending approaches is to compensate for limitations in each approach • Hierarchical model illustrated by data supplementation (e.g., qualitative interviews with a few respondents offer interpretation of responses to a standardized questionnaire) • Partnership sequential model illustrated in qualitative work to develop questionnaires

13. Stage 3: Data analysis • Generally hierarchical; determined by design of study. Orientation of funding agencies often makes it hard to achieve a true balance (“disciplinary racism”) • Hierarchical, with quantitative leading, illustrated by analyses of outliers • Hierarchical, qualitative leading: case studies are followed by secondary analysis of quantitative data (e.g. surveys) to estimate representativeness of insights gained from the case study • Iterative analyses in partnership model, liable to be criticized from both camps.

14. 4: Interpreting & disseminating results • Hierarchical, quantitative leading: • Use case histories or quotations to illustrate quantitative results • Use qualitative results to comment on exceptions to the rule • Hierarchical, qualitative leading: use quantitative results to validate what people suspected all along

15. Future Directions • Funding agencies now recognize importance of qualitative research. It’s a start, but…. • The paradigms are sufficiently different that it’s very hard to blend them: attempts rapidly lead to criticism that you are perverting the tenets of each approach • Disciplinary purity seems remarkably important to academics – a fundamental part of personal identity – so conflicts will be common • A successful blend will be truly “transdisciplinary” • Now we need to figure out what that means!