Foundations of Psychology

Types of research data

• qualitative

• quantitative

Research data- qualitative

non-numerical (ex. structured interviews)

research data- quantitative

numerical data (ex. Likert scale)

types of bias

• hindsight bias

• overconfidence

• confirmation bias

• social desirability bias

• false consensus effect

hindsight bias

“i knew it all along” phenomenon

overconfidence

we think we know more than we do

confirmation bias

seek out information that supports one idea

social desirability bias

answer in a way that will be viewed favorably by others

false consensus effect

overestimate how much others share our beliefs

Aspects of research

• theory

• hypothesis

• falsifiable

• operational definition

• replication

operational definition

procedures used to define and simplify research variables

replication

repeating a study finding generalizing to other participants (see how specific it is using study)

Institutional review board (IRB) ethics

• no coercion

• safety

• confidentiality

• informed consent

• debrief

stanley milgram’s shock experiment (how is it unethical/what is it)

conflict between obedience to authority and conscience, no debrief, psychological harm

philip zimbardo- stanford prison experiment (how is it unethical/what is it)

psychological effects of perceived power, safety

what are the three types of research methods?

• non-experimental

• correlational

• experimental

case study

in depth study of an individual- no cuase/effect

Likert scale

scale used to measure opinions, attitudes

structured interviews

fixed set of closed questions are used

naturalistic observation

observing in natural environment

meta-analysis

result of multiple studies combined and analyzed

longitudinal

measures individual over extended period of time

cross-sectional

measures individuals of various ages at one point in time

non-experimental purpose

observe/describe behaviors without manipulating variables, cannot determine cause/effect

correlation

measure/observe relation between variables

correlational coefficient

indicates strength and direction between two variables

• numbers range from -1 to 1, closer to one = stronger

• positive= variables move in the same direction, negative= variables move in opposite directions

correlation and causation

indicates possibility of cause and effect, correlation does not prove causation

third/confounding variable

affects both variables; makes them seem casually related when they are not

scatterplot

• slope suggests direction

• scatter= strength

correlational research pros and cons

• pro- describe relationship between two variables

• con- can’t establish cause and effect

experimental research

manipulates factors - ind. variables - to observe effect on dep. variables

ind. variable

• the “if”

• manipulates

• cause

• what you’re studying

dep. variable

• the “then”

• measure effect- behavior or mental process

red bull experiment (IV, DV, confounding variable)

• iv- red bull

• dv- hyperactivity

• confounding- breakfast before school

experimental vs control group

• experimental- exposed to treatment, IV

• control- serves as comparison

population

group you want to study

random sample

every subject in the population has equal chance of being chosen

random assignment

assigning participants to groups by chance

convenience sampling

data from easily accessible/available group

sampling bias

does not accurately represent the population being studied

generalized results

how representative your sample is of the target population

double-blind procedure

both researcher and participant are ignorant

single-blind procedure

subjects dont know

placebo

inert substance that triggers effect of the active agent

experimental research pros and cons

pros

• cause and effect

• replicate

• manipulate

cons

• artificial environment

• confounding variables

• bias

mean

average

mode

most frequently occurring score

median

middle score when in order

standard deviation

measurement of how much scores vary around the mean

normal distribution

percentile rank

compare a specific raw score to the other scores

bimodal distribution

score with two peaks or modes

variance

how far a data set is spread out

variation (in relation to standard deviation)

larger variation= larger standard variation

range

variation between highest and lowest scores

skew

direction of the tail

positive skew

skewed right, tail on the right side

negative skew

skewed left, tail on left side

statistical significance

probability of result occurring by chance

effect size

size of relationship between two variables