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Week 12 mixed methods

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Mixed methods

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Week 12 mixed methods

  1. 1. MIXED METHODS 9715 – Doctoral Seminar Notes by M. Larsen Nov. 28, 2014 ‘The Third Methodological Movement’ ‘The Third Path’ ‘A research paradigm whose time has come.
  2. 2. WHY MIXED METHODS DESIGN? To provide a more complete picture of the phenomenon under study To increase data validity Enable researcher to develop analysis and build on original data MM research – “can help to clarify the formulation of the research problem and the most appropriate ways in which problems or aspects of problems may be theorised and studied….With multiple methods the researcher has to confront the tensions between different theoretical perspectives while at the same time considering the relationship between the data sets produced by the different methods” (Brannen, 1992, pp. 32-33)
  3. 3. CRITICS OF MIXED METHODS ‘Incompatibility thesis’ - qualitative and quantitative research paradigms cannot and should not be mixed (purists – paradigm wars) Critics of MM - different research paradigms embody incompatible assumptions about the nature of the world and what is important to know
  4. 4. CHALLENGING THE DIVIDE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH Hammersley (1992) “Deconstructing the qualitative-quantitative divide” - the distinction between these methodological paradigms is limited and dangerous Presents 7 issues concerning the quant/qual distinction and challenges each of them.
  5. 5. 1) Qualitative vs quantitative data Assumptions: Qual research - words Quant – numbers But… this distinction is problematic - large proportion of research reports combine both - ethnographers use words like “Regularly”, Frequently, Often, Sometimes, Generally, Typically If this is about precision, then precision doesn’t necessarily mean the use of numbers
  6. 6. 1) Qualitative vs quantitative data cont’d. “We are not faced, then, with a stark choice between words and numbers, or even between precise or imprecise data. Furthermore, our decisions about what level of precision is appropriate in relation to any particular claim should depend on the nature of what we are trying to describe, on the likely accuracy of our descriptions, on our purposes, and on the resources available to us; not on ideological commitments to one methodological paradigm or another.” (p.43)
  7. 7. 2) The investigation of natural vs artificial settings Assumptions: Quant – artificial setting – experimental Qual – natural But…this distinction is spurious What happens in classroom is not necessarily more natural than what goes on in a psych lab “To treat classrooms…as natural and experiments as artificial is to forget that social research is itself part of the social world.” (p. 44)
  8. 8. 2) The investigation of natural vs artificial settings Reactivity – individuals alter their performance or behavior due to the awareness that they are being observed - both quant and qual research can lead to reactivity(Hawthorne effect) “The terms ‘natural’ and ‘artificial’ have misleading connotations. And while the issue of ecological validity is important, it is not the only important methodological issue. Nor does research in ‘natural’ settings guarantee ecological validity, any more than research in ‘artificial’ settings automatically debars us from it.” (p. 45)
  9. 9. 3) A focus on meanings rather than behavior Assumptions: Qual research - interpretive (meaning) Quant research – positivist (behaviours) But… rare that qual research simply documents point of view of participants Researcher is involved in interpretation of data Much quant research concerned with attitudes not just behavior
  10. 10. 3) A focus on meanings rather than behavior “As regards differences in the approach that attitude researchers and ethnographers employ in identifying attitudes/perspectives, the contrast is between the use of attitude scales and more unstructured approaches…Here again, we do not have a clear-cut distinction between two contrasting approaches.” (p. 46)
  11. 11. 4) Adoption or rejection of natural science as a model Assumptions: Qual – reject natural science as model Quant –natural science – exemplary “Not even the most extreme positivist would argue that the methods of physics should be applied lock, stock and barrel to the study of the social world. And there are few supporters of qualitative research who would insist that there is no aspect of natural science method that is relevant to social research. What is involved here is a matter of degree.” (p. 47)
  12. 12. 5) An inductive vs deductive approach Assumption: Quant = deductive or hypothetico-deductive Qual = inductive But… “Quant research does not always test hypotheses: its goal is often descriptive.” (Brannen, 1992, p. 8) Some quant research is concerned with theory generation Some qual research is deductive Lots of qual research is simply descriptive
  13. 13. 5) An inductive vs a deductive approach All research involves induction and deduction to some degree– impossible that researchers not be influenced by prior knowledge “What is true is that one can distinguish between studies that are primarily exploratory, being concerned with generating theoretical ideas, and those which are more concerned with testing hypotheses. But these types of research are not alternatives; we need both. Nor need the former be quantitative and the latter qualitative in other senses of those terms.” (p. 48)
  14. 14. 6) The identification of cultural patterns as against seeking scientific laws Assumption: Quant –committed to discovery of scientific laws Qual – committed to identifying cultural patterns Yet… much quant is concerned with description Early qual researchers justified their practice by claiming that it produced scientific laws and even today they claim their goal is theory generation “Thus the distinction between identifying patterns and pursuing laws seems to provide little clear basis for the division between quant and qualitative methods.” (p. 50)
  15. 15. 7) Idealism vs realism Assumption: Quant – realist epistemology Qual- idealist “More important than the empirical question of whether it is true that quant researchers are realists and qual researchers idealists, though, is the philosophical issue of whether there is any necessary connection between qual method and a particular epistemological position….history suggest that there is little reason to believe that there is such a connection. And we must remember that there are many more than 2 epistemological positions available.” (p. 51)
  16. 16. METHODOLOGY OF COMBINING APPROACHES – CONSIDERATIONS 1) Timing - What will the timing of qual and quant methods be? What will the order be? Will it be concurrent or consecutive? 2) Weighting Dimension -What will be the relative importance, weight or priority, given to qualitative & quantitative methods? Equal Emphasis on Qualitative or Quantitative
  17. 17. METHODOLOGY OF COMBINING APPROACHES – CONSIDERATIONS cont’d. 3) Mixing Dimension - How will qual and quant methods be mixed? How will the 2 data sets be mixed? Merged Embedded within one another Connected in another way Complementary Integration
  18. 18. Fundamental Principle of Mixed Methods Research Combine the methods in a way that achieves complementary strengths and non-overlapping weaknesses (Johnson & Onwuegbuzie, 2004) 1) Timing 2) Weighting 3) Mixing Dimension
  19. 19. MIXED METHODS DESIGNS 1) Triangulation/ Multiple Methods Within-method – the same method being used on different occasions Between-method – using different methods in relation to the same topic Purpose of triangulation – to obtain complementary quant and qual data on the same topic
  20. 20. 2) Embedded Design Different research questions require different types of data to answer them Complementary – qual and quant data complement one another 1 or 2 phase
  21. 21. 3) Explanatory Design 1st phase – quantitative (e.g. surveys) 2nd phase – qualitative (e.g. classroom observation) Qualitative data needed to explain results 1st phase quant – may be used to guide the selection of 2nd phase in-depth qual study
  22. 22. 4) Exploratory Design 1st phase – qualitative 2nd phase – quantitative Need to develop a measurement instrument 1st phase - get a deeper understanding of the issue/phenomenon 2nd phase - survey to measure its distribution and prevalence.
  23. 23. PRACTICAL ISSUES 1) Politics of research – evidence-based research - AERA Scientifically Based Research 2) Costs 3) Researchers: skills, careers, disciplines 4) Social Organization of the Research Team

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