PSYCH 217 - Quiz 1
Terms in this set (36)
Features that define science
1. Examination of falsifiable ideas
2. Systematic empiricism
3. Production of public knowledge
Examination of falsifiable ideas
- scientists only concern themselves with questions that can be answered, at least in principle, by collecting data
- testable or falsifiable ideas
- Empiricism: Understanding the world through observation
- repeated observation under carefully designed, controlled conditions, in order to eliminate alternative explanations
Production of public knowledge
- Peer-review process: Other scientists evaluate your research to see if it should be published.
- Enough detail must be given about the experimental procedcures and the data analysis to allow the scientific community to review and replicate your results.
- Science makes mistakes; all scientific knowledge is provisional. However, science should be self-corrective in the long run.
Pseudoscience is any methodology , belief,or practice that claims to be scientific or is made to appear scientific, but in reality does not adhere to the scientific method.
- generates untestable hypotheses
- supporting data were not collected systematically, relies on anecdotes, not reviewed by the scientific community
- claims are never revised
Goals of Science
- Describe behavior
- Predict behavior
- Determine causes of behavior
- Explain the causal mechanism
Major types of research designs
- no random assignment or control
- ex. gender differences, ethics
- can't randomly assign gender and split into different groups
A study that readily allows its findings to generalise to the population at large has high external validity
- To the degree that we are successful in eliminating confounding variables within the study itself is referred to as internal validity.
- Basic Research is curiosity-driven research, its main goal is to advance scientific knowledge
- how the visual system works in humans
- how humans respond to cognitive inconsistencies
- advance knowledge and fill holes
- Applied Research has immediate implications for "real life"
- seek solution to a problem in the real world
A theory is a system of logical ideas that explain a particular phenomenon and its causal relationship to other phenomena
A good theory has
- must be falsifiable to be scientific
- should explain existing data and predict future data
- is parsimonious - the principle of Occam'z Razor
- stingy - one with fewest assumption should be selected
- simpler better, because testable
- explains the causal mechanism
- leads to many hypotheses
- The demarcation problem — how to distinguish between science and pseudoscience/non-science
- Popper proposed to use falsifiability as the demarcation criterion
- It's not a perfect criterion
- An approach to doing science suggested by Popper is that scientists should actively try to disconfirm their theories rather than seek their confirmation
- Theories that survive rigorous attempts at falsification are not proved or established; they are corroborated (for the time being)
The demarcation problem
how to distinguish between science and pseudoscience/non-science
tendency of people to take note of evidence confirming their existing beliefs, and ignore evidence contradicting their beliefs
- Tendency to perceive two unrelated events as related (occurring together with greater frequency than chance)
- Ex. African americans and drug abuse
- the tendency to accept certain information as true, such as character assessments or horoscopes, even when the information is so vague as to be worthless.
- Ex. horoscopes
- A definition of the variable in terms of the specific operations or techniques the researcher uses to measure or manipulate it
- Types of measures:
- Self (or other) report
Operational definitions flaws
- Imperfect by design
- Linked to reality, some observable behavior or event
- Many are possible for the same construct
- Allow scientific claims to be replicated by other scientists
- A measure of the extent to which two variables are linearly related to each other.
- Correlation Coefficient, r, indicates the direction and magnitude of the linear relationship between two variables. Ranges from -1 to 1.
- Correlation does NOT imply causation!
Establishing causality requires establishing
- That there are no extraneous (3d) variables
- any variables you are not intending on studying
third variable thats not apparent
- Groups of participants are formed via random assignment
- The independent variable (IV) is manipulated: Different study groups are exposed to different levels of IV (treatments or conditions)
- The groups are treated exactly the same except for the level of IV each group is exposed to
- Dependent variable (DV) is measured in each group
Well-designed experiments allow for causal inferences
- If an experiment finds that different groups differ on the DV, correlation has been established
- Reverse directionality is not possible: random assignment ensures that groups are approximately equal on the DV at the start of the experiment; administration of IV comes first, then DV changes and is measured
- Extraneous variables have been eliminated: random assignment ensures groups are approximately equal on all extraneous variables at the start of the experiment
- Caveat: Sample size per group must be "large enough"
- Old advice: N=10-20; New advice: N=50 or more
Control Groups control for...
- Placebo effect
- Spontaneous remission
- unexpected improvement or cure from a disease that usually progresses worst
- Hawthorne effect
- the alteration of behavior by the subjects of a study due to their awareness of being observed.
- (and other confounds)
- Spontaneous remission
unexpected improvement or cure from a disease that usually progresses worst
the alteration of behavior by the subjects of a study due to their awareness of being observed.
Types of control groups
- No treatment
- Placebo treatment
- Best currently available treatment
- Qualitatively different groups
- Between-subject designs: assign different people to different conditions
- Also called independent groups designs
- Pros: cuts survey time down, unbiased response cause they see one ad
- Cons: twice the size of respondents, won't be a direct comparison
- assign the same people to different conditions
- Also called repeated measures designs
- ex. get same group of ppl to taste two different drinks
- pros: exact comparison from same ppl, fewer ppl needed for results
- cons: higher dropout rate, biased reaction
Confounds in experiments
- Confound—in an experimental design, any variable that has the potential to affect the DV, that is not the IV itself.
- In between-subjects designs, confounding can be due to experimental settings or experimental manipulation
- In within-subjects designs, must worry about confounding due to participants changing or being aware of multiple conditions, as well
A type of confounding where the order of conditions in a within-subjects design affects the DV
being in one condition affect show the participant perceives the next condition (the previous condition is not fully "erased")
Repeated testing effects
Practice, fatigue, and memory effects
Reducing order effects
- Counterbalance conditions when possible
- Wait longer between testing conditions
- Use parallel (similar) forms rather than exact same measure
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