Chapter 2 Flashcards
testability
- Researchers should be able to put scientific theories to empirical tests
- It is particularly important that theories are falsifiable
deductive line of reasoning
going from theory -> hypothesis -> prediction
inductive line of reasoning
going from prediction -> hypothesis -> theory
abduction
- starting from an observation, then guessing what happened (ie. looking at symptoms and figuring out likeliest explanation for them)
- Usually start out with incomplete observations
- Problem because sometimes people have biases that mess up their abductive reasoning -> Ex. focusing on the worst possible potential result (ie. Assuming you have lung cancer instead of stress), or focusing on the potential result that’s the most appealing to you
theory
- overarching framework meant to organize and explain phenomena and data
- Generates hypotheses that test boundaries of the theory
hypothesis
- A tentative statement about variables -> derived from an existing theory
- ex. “People who play violent video games are more aggressive than those who don’t”
data can…
- support hypotheses
- Be (in)consistent with hypotheses
data cannot…
- Confirm hypotheses
- (Dis)prove hypotheses
what makes theories good?
- Supported by data
- Falsifiable
- Parsimonious
falsifiability
- Ability to show a theory to be wrong
- There can exist data that are inconsistent with a theory
- Freud’s ideas were not falsifiable (ex. Oedipus complex -> subconscious, so can’t be proven/disproven)
- Can be considered as a matter of degree
correlational/non-experimental theory
— is related to —
experimental theory
— causes —
what do good hypotheses do?
- Make predictions that expose itself to falsification
- Make specific predictions
what do bad hypotheses do?
- make predictions that are difficult to falsify
- Make general predictions (= weak support)
how does parsimony relate to falsifiability?
- More parsimonious -> more falsifiable
- Fewer conceptual relationships (assumptions) = less data required to counter a theory
- Fewer adjustments to established relationships in science = less data required to test adjustments
- We are evaluating the whole theory
conceptual variable
broad, general, abstract ideas (ex. aggression)
essentialism
- These is an unchanging, underlying essence of an entity that can be defined
- Sense of fixedness
- Ex. “A dog is a dog because it has a dog-ness to it”; there’s nothing you can do to a dog that would make it no longer a dog
- Focus is on the meaning of words
- we don’t use this!!
operationalism
- using operational definitions
- Observable indicator of each variable, used for the purposes of this particular study
- Enable reliable measurement or manipulation of each variable in your hypothesis
- These operational definitions might not be right, but are a place to start
- Ex. How would you operationally define aggression?
literature review
when research is summarized using narrative techniques and without following the typical 6-section format
prediction
- specific prediction about the outcome and operational definitions of particular experiment
- ex. “People who play mortal combat 3 will score higher on the cook-medley hostility scale than people who play my little pony”
5 sources of ideas
- Common assumptions
- Observation of the world around us
- Practical problems
- Theories
- Past research
common assumptions
- Questioning common sense/folk wisdom beliefs (ex. “Do opposites really attract?”)
- Valuable because these assumptions aren’t always correct; the real world is often more complicated than folk wisdom would have us believe
observation of the world around us
- Observing personal or social events (ex. Tipping behaviour in restaurants)
- By viewing the world inquisitively, sometimes we’ll discover things by accident or luck (ex. Pavlov’s discovery of classical conditioning)
practical problems
Experiencing a practical problem can trigger a research project idea (ex. Designing graphic warning labels on cigarette boxes to discourage smoking)
