IV causes, DV measures — the IV is what you change
Independent vs Dependent Variables
IV manipulated by researcher; DV is the outcome measured
IV = Independent Variable = what you control/change. DV = Dependent Variable = what you measure (it depends on the IV). 'Does caffeine (IV) affect reaction time (DV)?'
IV
Independent Variable — manipulated by researcher
DV
Dependent Variable — measured outcome
Correlation vs Causation
Correlation ≠ Causation: ice cream and drowning both rise in summer
Correlation vs Causation
Two things happening together doesn't mean one causes the other
Hot weather causes both ice cream sales and drowning rates to rise — neither causes the other. To establish causation: need an experiment with random assignment.
Statistical Significance
p < 0.05: less than 5% chance the result occurred by chance
Statistical Significance
The p-value tells you if a result is likely to be random
p < 0.05 = less than 5% probability the result is due to chance. Does NOT mean the effect is large — just unlikely to be random. Effect size tells you the magnitude.
Research Threats
RAVEN: Random assignment, Attrition, Validity, Experimenter bias, Null hypothesis
Research Threats
Five threats to the validity of a study
Random assignment eliminates pre-existing differences. Attrition (dropout) can bias results. Internal validity: did IV cause DV? Experimenter bias: researcher inadvertently influences results. Null hypothesis: default 'no effect' claim.
R
Random assignment
A
Attrition — dropout
V
Validity — internal and external
E
Experimenter bias
N
Null hypothesis
Reliability and Validity
Reliability vs Validity: consistency vs accuracy. Can be reliable but not valid.
Reliability and Validity
Two essential qualities of any good measurement tool
Reliability: gives same result each time (consistent). Validity: measures what it claims to measure (accurate). A scale that always reads 5 lbs too heavy is reliable but not valid. Both are required for a good study.
The structure of a true experiment — the only way to establish causation
Random assignment: participants randomly placed in experimental or control group — controls for pre-existing differences. Control group: doesn't receive treatment — provides baseline. Experimental group: receives the IV manipulation. Double-blind: neither participants nor researchers know who's in which group — prevents bias.
Effect Size
Effect size: Cohen's d. Small=0.2, Medium=0.5, Large=0.8. More meaningful than p-value alone.
Effect Size
How big is the effect — the practical significance question
Statistical significance (p-value) only tells you the result probably isn't random — not how important it is. A huge study can find a tiny, meaningless effect at p<0.001. Effect size measures the magnitude. Cohen's d = (mean₁ - mean₂)/pooled SD. Always report effect size alongside p-value.
Sampling Methods
Sampling methods: random (every member has equal chance), stratified (proportional subgroups), convenience (whoever is available)
Sampling Methods
How researchers select participants — affects generalizability
Simple random: each person equally likely to be selected — best for generalizability. Stratified: divide population into subgroups (strata), randomly sample from each — ensures representation. Cluster: randomly select groups then sample within. Convenience: whoever is available — biased, poor generalizability (WEIRD problem: Western, Educated, Industrialized, Rich, Democratic).
Research Methods Overview
Naturalistic observation: observe in natural setting, no manipulation. Strength: ecological validity.
Research Methods Overview
The main research designs and their key trade-offs
Naturalistic observation: high ecological validity, no cause-effect. Case study: rich detail, poor generalizability. Survey: large samples quickly, self-report bias. Correlational: shows relationships, no causation. Experimental: only method establishing causation, may lack ecological validity. Choose method based on research question.
Operational Definitions
Operational definition: precisely how a variable is measured. 'Intelligence' measured as 'IQ score on Wechsler.'
Operational Definitions
Turning abstract concepts into measurable variables
Operational definition: specifies the exact procedures used to measure or manipulate a variable. 'Stress' is abstract — measured as cortisol level, heart rate, or score on perceived stress scale. Good operational definitions: reliable (consistent), valid (measures what it claims), practical. Allows replication.
Longitudinal vs Cross-Sectional
Longitudinal study: same people over time. Cross-sectional: different age groups at one time. Each has limitations.
Longitudinal vs Cross-Sectional
Two ways to study development — each with different flaws
Longitudinal: follow same people over years/decades. Strength: sees actual change. Weaknesses: dropout (attrition), time-consuming, expensive, cohort effects. Cross-sectional: compare different age groups at same time. Strength: quick, no attrition. Weakness: cohort effects (different generations, not just age).
Demand characteristics: participants guess the study's purpose and change behavior accordingly. Experimenter bias: researcher unconsciously treats groups differently or interprets results based on expectations. Single-blind eliminates demand characteristics. Double-blind eliminates both. Placebo effect: inert treatment produces real changes because of expectations.