Options for Blending Qualitative and Quantitative Research Methods Ian McDowell (Based on a seminar presentation in 1997)

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?

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

Changing philosophies of knowledge 17th & 18th centuries: order, logic and science, world seen through senses. Mechanical world. Realism and logical positivism. Laplace & description of determinism, th 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 20 th century - phenomenology; qualitative research

Two paradigms Biological variability poses a major challenge: should we focus on the general or the specific? We can often predict the general (“How many?”) but not the individual (“Which?”) ‘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.

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

Qualitative approach Interprets, explains; generates concepts Responds to Bacon’s challenge of induction: to begin from careful observation Seeks to be open, flexible Asks the “Why?” question Particularizes; internally valid The investigator is the instrument; art versus science Sampling becomes a crucial issue (in data collection and in analysis) “Somewhat magical approach to analysis”

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.

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

Applying these types of blend In different stages of research: 1.Conceptualizing the study 2.Collecting data 3.Analyzing data 4.Interpreting the data

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)

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

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.

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

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!