Research Design‘Positivist’ versus‘Interpretive’ methods

Methods of data collection• Positivist methods: theory drives datacollection and analysis techniques“Theory-driven” research; theory testing• Interpretive methods: observation (data)
drives analysis; new theory is created“Data-driven” research; theory buildingPositivist methods• Start with a theory• Gather data to support or reject (test) thetheory• Associated with deductive reasoning*• Usually use quantitative methods(measurement, coding, statistical tests), but
can also use qualitative methods*But may include induction or generalization

Example: Positivist method• Experimental study. Drug testing– Begins with a hypothesis about cause and effect.
“Drug A prevents disease X”– Select subjects randomly & assign randomly totreatment or control group to reduce chances ofaccidental or spurious correlation.
– Measure the presence or absence of disease.
– Compare statistical correlation of drug, disease.

Interpretive methods• Start with data or observation• Builds a theory which can account for the dataobserved• Primarily use inductive reasoning*• Often use qualitative methods (observation,open interviews, case studies), but can also
use quantitative methods

Example: Interpretive method• Semi-structured interviews– Starts with a general area of interest, but nospecific theory– Selection need not be random; subjects may beinterested in or connected to the topic being
researched.
– Interviewer asks some questions s/he wants
answered, but also allows subjects to speak freely.
– After data is collected, researcher builds aninterpretation to make sense of subjects’ ideas.

Theory building, theory testing• Data-driven observations can build new theory.
• Theory can be tested using new data.

Design a study• Imagine you are a Mei-Dai graduate student inagriculture.
– You want the best way to start maple tree seedlings.
– Your professor suggests that either soil orvermiculite might be the best growth medium.
– Formulate a research question.
– What kind of data do you need to investigate yourresearch question?
– What kind of analysis can you do with the data?

Design a study• Imagine you are a Mei-Dai graduate student ineducation.
– You want to know what type of teachers are mostpopular with junior high school students.

– Formulate a research question.
– What kind of data do you need to investigate yourresearch question?
– What kind of analysis can you do with the data?

Validity• Internal validity: Is the dependent variable
actually caused by the independent variables?
– Co-variation. A change in the “cause” variable
should show the same change in “effect”.
– Temporal precedence. The “cause” must happen
before the “effect”.
– Beware: Sometimes two variables seem to co-vary
but it is an accident (spurious correlation) or iscaused by a third variable (confounding variable).

Validity• External validity: Can the results begeneralized beyond the current research?
– Laboratory studies tend to have better internal
validity, because they have more control.
– BUT, field studies or surveys tend to have better
external validity, because results don’t rely onsuch control.
– Ideally, try to balance internal and externalvalidity.

Validity• Construct validity: Are you actually measuring
what you think you are measuring?
– Define your terms carefully.

• Statistical validity: If you use statisticalmethods, are they appropriate?
– Are the tests appropriate to the researchquestion? Is the sample size appropriate for thetest?

Threats to validity• Choose one of the research projects yourgroup designed.
• What type of threats to internal validity do
you need to watch for?
• What type of threats to external validity do
you need to watch for?

Positivist or Interpretive?
• Some methods are more common in certain
fields.
– Experiments in medical testing (positivist)– Ethnography in social anthropology (interpretive)• BUT that does not mean other methods
should be ruled out of the field completely.
– Case studies in clinical testing (interpretive)– Typology in linguistic anthropology (positivist)Positivist or Interpretive?
• Ideally, the choice of methods should reflect
the nature of what is being studied.
• In practice, researchers tend to choose themethods (and therefore the kind of questions)they are most comfortable with and best able
to use.

Positivist or Interpretive?
• Questions to ask yourself:– Does my research question suggest a testable
hypothesis or theory?
– Do I have the skills to turn observations into
convincing explanation or theory?
– What is the best way to approach my question?
What is the most practical way for me to approach it?

– Does my field have a strong preference for certain
methods or theories? (That doesn’t mean you mustchoose that approach, but you must justify your choice.)Positivist or Interpretive?
• Some scholars claim that interpretive research
is “not science” because it lacks theory.
• Some scholars claim that positivism is “badscience” because theory harms data collection.
• This is, in my opinion, the least interestingargument in academia.

Positivist or Interpretive?
• Both sides are correct.
– Good science needs good theory.
– Good science needs good data.

• Both sides are wrong.
– Theory does not come from nowhere. Good
theory must respond to real phenomena.
– Though bad data collection is possible (in any
research paradigm), it is not inevitable.

(Not necessarily) associated
Theory-driven, positivist

Data-driven, interpretive

Quantitative data

Qualitative data

Deductive reasoning

Inductive reasoning

(Not necessarily) associated
Theory-driven, positivist

Macfarlane & O’Reilly(2012) started with atheory, tested itagainst nurses’
opinions of a service
Qualitative data

Deductive reasoning

Medicine (Clinical)

(Not necessarily) associatedWassman & Dasen(1994) observed people
whose language has nonumbers; found they
have ways to count.

Data-driven, interpretive

Quantitative data

Deductive reasoning

Psychology; ethnology

(Not necessarily) associatedReimers & Johnson(2008) interviewed

Theory-driven, positivist

Quantitative datacompany workers, thencoded their responses totest their theory ofinformation evolution

Qualitative data

Inductive reasoning

Information Systems

(Not necessarily) associated• Can you combine methods, data, andreasoning in other ways?
Theory-driven, positivist

Data-driven, interpretive

Quantitative data

Qualitative data

Deductive reasoning

Inductive reasoning