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Hypothesis
tentative explanation must be Falsifiable able to be supported or rejected.
Operational Definition
Clear, precise, quantifiable definition or your variables allows replication and collection of reliable data.
Qualitative Data:
Descriptive data (eye color)
Quantitative Data
Numerical data ideal and necessary for statistics.
Population:
everyone the research could apply to
Sample:
the people (or person specifically chosen for your study
Correlation:
identify relationship between two variables
adv: useful when experiments are unethical
dis: correlation does not equal causation
Directionality Problem:
which direction does the correlation go? depression can cause low self esteem, low self esteem causes depression, or a 3rd variable 3
3rd variable:
different variable responsible for relationship (ice cream and shark attacks) Possitive correlation:
Positive Correlation
Variable increases and decreases together.
Negative correlation
as one variable increases the other decreases
Experiments
Purposefully manipulate variables to determine cause and effect.
adv: only type that establishes cuase and effect
dis: can be unethical too artifical
Independent Variable
purposefully altered by researcher to look for effect
Experimental Group
Receive the treatment (part of the 4) can have multiple experiment groups.
Control Group:
Placebo, baseline (part of the 4) can only have 1.
Dependent Variable:
measured variable (is DEPENDENT on the INDEPENDENT variable)
Placebo Effect:
any observed effect on a behavior. that is caused by the placebo shows effectiveness of experiment treatment. Usually fixed with blinded studies
you believe it will work then it will work.
Double Blindness:
Experiment where neither the participant or the experiment or are aware of which condition people are assigned to (drug studies)
Singe Blindness:
only participant is blind used if experimenter can’t be blind (gender, age etc)
Confound
error flaw in study that is accidentally introduced (can be called a confounding variable)
Random Assignment
Assigns participants to either control or experimental group at random- increase chance of equal representation among groups )spreads the lefties across both groups) allows you to say cause and effect.
Naturalistic observation
observe people in their natural settings. Adv: real world validity
dis: no cause and effect
Case study
studies one person usually in great detail
adv: collects a lot of info
dis: no cause and effect
Meta Analysis
Combines multiple studies to increase sample size and examine effect sizes.
Descriptive Stats:
show shape of the data
Mean
Average: use in normal distibution
a measure of central tendency that represents the typical value in a dataset.
Median:
Middle number use in skewed distribution
Mode:
occurs most often
Bimodal:
has two modes: usually indicates good bad sources
Skews
created by outlers
Negative skews
mean is to the left (negative side) mode is to the right
Positive skew
mean is to the right
Range
Distance between smallest and biggest number
Standard Deviation
average amount the scores are spread from the mean
(bigger number = more spread)
Inferential Statistics
establishes significance (meningfulness)
Statisrical Significance
results not due to chance experiment manipulation caused the difference in means
Effect size
data has practical sigificance - bigger = better
confidentiality
names kept secret
informed consent
must agress to be part of study
informed assent:
Minors And their parents must agree
Debreifing
must be told the true purpose of the study (done after for deception)
Deception
when you dont tell the whole truth based on the experiment
must be warrented
Harm
There must be NO harm mentally and physically
short term is okay but not long term
surveys
usually turned into correlation. subject to self report bias
self report bias
errors when collect surve data
social desirability
people lie to look good
wording effct
the way questions are worded can impact your answers
random sample
method for choosing partciapants for your study