ap psych u1

Biopsychosocial approach

combines biological, psychological, and social-cultural viewpoints into one complete picture of thought and behavior

Biological approach (Neuroscience)

also called biopsychology, studies how the body and brain enable emotions, memories, and sensory experiences (asks how blood chemistry affects your mood)

Behavior-genetics approach

studies how genes inherited from our parents shape our traits and behavior

Psychoanalytic approach (psychodynamic)

Freud's view, still controversial, holds that unconscious desires, repression, childhood experiences, and sexual and aggressive drives push behavior

Evolutionary approach

uses Darwin's natural selection to explain traits as adaptations that aided survival (extroversion as a trait that kept ancestors alive)

Evolutionary view of social interaction

people seek company because interacting with others helped ancestors survive

Fear of the dark (evolutionary view)

an innate fear carried through generations because it aided survival

Cognitive approach

cognition is thinking, studies how we interpret, process, and remember events since interpretation shapes behavior, with typical topics like knowledge acquisition, planning, and problem solving

Social-cultural approach

studies how culture shapes behavior, like whether talkativeness is valued or whether the individual or the group matters more

Behavioral approach

behaviorists (Skinner and Watson) explain behavior through conditioning, experience trains us to act certain ways

Humanistic approach

developed during the 1950s and 1960s, stresses personal growth, reaching full potential, and building a positive self-concept

Perspectives from past to present

structuralism, then psychoanalysis, then behaviorism, then humanistic psychology

Counseling psychologist

helps people with milder struggles that don't need medical care

Clinical psychologist

assesses and treats more serious issues like depression or anxiety attacks, does not prescribe medication

Psychiatrist

a medical doctor who treats severe disorders (major depression, bipolar disorder, schizophrenia) and can prescribe medication

Hindsight bias

after learning an outcome, feeling "I knew it all along," dangerous because it closes you off to new ideas and evidence

Overconfidence

assuming you already know an outcome (not checking directions because you're sure you know the way), which leads to poor judgment

Perceiving order in random events

reading meaningful patterns into random data when no pattern really exists

Critical thinking

a habit of asking good questions and analyzing our own motives, assumptions, and ideas

Skeptic

someone inclined to doubt and question things, skeptical testing reveals which theories match the facts

Scientific attitude

being curious, skeptical, and humble, including openness to error in our own judgments

Informed consent

participants volunteer, are told what will happen and any risks before agreeing, can withdraw anytime, and don't get every detail revealed up front

Do no harm

APA rule that participants can't be hurt physically, emotionally, or psychologically

Confidentiality

participant information stays private unless they sign written permission to release it

Debriefing

after the study the researcher explains the purpose, procedures, and results to participants, required whenever deception was used

Deception

withholding or misstating details during a study, considered ethical only when necessary for accurate results and followed by debriefing

IRB (Institutional Review Board)

committee whose approval researchers must get before running a study, confirming it's ethical

APA

American Psychological Association, publishes the ethics code covering research with both human participants and animal subjects

IACUC

Institutional Animal Care and Use Committee, required at every US institution receiving federal funding for animal research, ensures animals are treated humanely

Gambler's fallacy

believing past rolls change future odds, a coin that landed heads six times still has a 50% chance of heads next flip

Theory

a system of ideas explaining something, tested and generally accepted, but impossible to fully prove

Hypothesis

an untested, testable prediction, usually describing the relationship between two variables

Descriptive research methods

single-variable methods that describe a subject or behavior, where the researcher observes or asks questions and manipulates nothing

Case study

an in-depth look at one individual to find truths that apply to everyone, weakened because so few people are studied

Case study data collection

interviewing family, friends, and teachers of patients who share one diagnosis to find common themes

Naturalistic observation

watching subjects in their natural environment without interacting at all, which reduces artificiality

Artificiality

people not acting as they normally do, common in laboratory studies

Survey

questionnaires measuring self-reported attitudes and behaviors, reaches many diverse subjects but in less depth

Wording effect

the phrasing of survey questions strongly impacts the results

Social desirability bias

people rate themselves more positively than reality (harder-working, healthier eating, more sleep than is true)

Correlational study

measures the relationship between two variables to see if they "go together," usually uses a large population and predicts without explaining cause

Positive correlation

both variables rise and fall together

Positive correlation example

higher college entrance test scores pairing with higher GPAs

Negative correlation

as one variable rises, the other falls (an inverse relationship)

Correlation coefficient

ranges from -1 to +1 measuring relationship strength, +1 strongest positive, -1 strongest negative, 0 means none

Illusory correlation

perceiving a pattern that isn't there, correlation coefficients help restrain us from seeing these

Correlation does not mean causation

correlated variables don't cause each other, correlational studies never establish cause and effect

Correlation-causation error example (encyclopedias and grades)

concluding encyclopedia ownership improves grades straight from survey correlation

Correlation-causation error example (family size)

concluding family size causes achievement attitudes straight from survey ratings

Population

everyone who has a chance to be in a study

Sampling

the process of determining who from the population will participate

Representative sample

a sample that accurately mirrors the whole population being studied

Random sampling

every population member has an equal chance of selection, lets you generalize results to the whole population

Stratified sampling

splitting the population into subgroups by characteristics like gender or race so each appears in the sample in proportion to the population

Stratified sampling example

dividing a school's students by race, then drawing from each group to match population proportions

Convenience sampling

whoever is easiest to recruit gets used, so the sample won't represent the population

Directionality problem

in correlation you can't tell which variable influences the other (does A cause B or does B cause A?)

Third variable problem

an unmeasured variable may be causing changes in both correlated variables

Third variable example

hot weather raising both ice cream sales and snakebites at once

N

the sample size in a study, 20 participants would be N=20

Validity

research measures what it actually set out to measure

Reliability

research can be replicated, different researchers repeating it get the same results

Experimental method

manipulates a variable to test its impact, letting researchers isolate cause and effect, but expensive and time consuming

Odor-and-gambling hypothesis

claims cause, so it takes an experiment manipulating odor exposure and measuring gambling behavior

Laboratory experiment

run in a highly controlled lab setting, advantage is control

Field experiment

conducted out in the real world, more realistic than a lab

Experiment example (lighting and reading)

Dr. Lee manipulates lighting while participants read, then measures comprehension

Experiment example (TV violence study)

boys and girls randomly assigned to watch violent or nonviolent TV, with aggression observed afterward

Independent variable

the variable that gets varied or changed to test its effect (the stress in a stress/heart-rate study)

Independent variable example (TV violence study)

type of television program viewed

Dependent variable

what gets measured in the experiment, depends on the independent variable (the heart rate)

Dependent variable example (TV violence study)

incidence of aggressive behavior

Dependent variable example (hunger and aggression)

aggressive behavior measured after hunger levels are manipulated

Experimental group

the group that receives the treatment

Control group

the group that doesn't receive the treatment, takes a fake pill in drug tests

Placebo

the fake pill given to the control group

Placebo effect

control-group members feel drug effects just because they believe they took real medicine

Random assignment

randomly placing participants into experimental or control groups to even out preexisting differences and limit confounds (not the same as random sampling!)

Operational definition

precise definitions of study variables, often assigning numerical values, essential for replication

Hawthorne effect

people act differently just because they know they're being studied

Demand characteristics

cues participants pick up about a study's purpose that change their behavior

Single-blind procedure

participants don't know whether they're in the experimental or control group, reduces demand characteristics

Experimenter bias

a researcher who knows who received the real treatment perceives the results they expect to see

Double-blind procedure

neither participants nor research assistants know who's in which group, eliminates both experimenter bias and demand characteristics

Double-blind procedure example

half the group gets a real pill and half gets a sugar pill, neither participants nor assistants knowing who got which

Confounding variable

any outside variable besides the IV that could affect the DV and confuse the relationship between them

Confounding variable example (section comparison)

Professor Ahad's sections tested at different times of day, so time of day differs across groups

Participant-relevant confounding variables

differences carried in by participants themselves, like age, gender, or education

Situation-relevant confounding variables

testing conditions must stay constant across all participants: time of day, weather, setting, who else is in the room

Statistical literacy

reading data accurately and knowing how statistics get twisted to deceive the public

Descriptive statistics

quantify results and illustrate trends but don't allow conclusions beyond the data

Measures of central tendency

the mean, median, and mode

Mean

the average of a set of data and the measure most distorted by outliers

Mean example

for 12, 3, 3, 4, 8 the sum is 30 and the mean is 6

Median

the middle score in a distribution

Mode

the most frequently occurring score

Mode example

for 3, 4, 5, 5, 7, 7, 7, 8, 8, 10 the mode is 7

Mode example 2

for 2, 11, 2, 3, 3, 4, 2, 5 the mode is 2

Frequency distribution

an orderly arrangement of scores showing the frequency of each

Histogram

a bar graph built from a frequency distribution