Psych 290 Exam 1 (WiP)

Ch1 | Personality — lecture definition

Characteristic patterns of thoughts, feelings, and behaviors across time and situations.   • Example: someone consistently tends to be organized, calm, and reserved.

Ch1 | Personality — textbook definition adds what?

Underlying psychological mechanisms, hidden or not, behind characteristic thoughts, emotions, and behaviors.   • Example: not just avoiding parties, but the anxiety/expectations helping produce that pattern.

Ch1 | Psychological triad

Thoughts, emotions/feelings, and behaviors.   • Example: thinks “they’ll judge me,” feels anxious, avoids the party.

Ch1 | Trait approach

Studies individual differences: how people differ on relatively stable traits.   • Example: compare people high vs. low in conscientiousness.

Ch1 | Biological approach

Explains personality through brain, physiology, genes, hormones, and evolution.   • Example: ask whether dopamine/brain systems relate to extraversion.

Ch1 | Psychoanalytic approach

Focuses on unconscious motives/conflicts and how they shape behavior.   • Example: interpret recurring behavior as reflecting an unconscious conflict.

Ch1 | Phenomenological/humanistic approach

Focuses on conscious experience, subjective meaning, free will, self-concept, and growth.   • Example: ask how someone personally interprets a major life event.

Ch1 | Learning/cognitive approach

Focuses on learning, reinforcement, social learning, thoughts, expectations, goals, and interpretations.   • Example: outgoing behavior increases because social interaction is rewarded.

Ch1 | Are personality approaches competitors or complements?

Mostly complements: they explain different pieces of personality and can work together.

Competitors would mean rival explanations where only one can be right.  

• Example: shyness can involve biology + learning + thoughts.

Ch1 | Why no One Big Theory of personality?

A theory strong in one domain will likely miss others; personality is too broad for one lens to explain everything.

Ch1 | Construct

Abstract psychological concept inferred from indicators.   • Examples: impulsivity, self-esteem, intelligence.

Ch1 | Construct vs. measure

Construct = abstract trait; measure = tool/indicator used to assess it.

Ch1 | Impulsivity

Tendency to act quickly without enough planning or consideration of long-term consequences.   • Example: making a risky purchase immediately without thinking it through.

Ch1 | Delay of gratification

Resist a smaller immediate reward to obtain a larger/better delayed reward.   • Example: waiting for two marshmallows instead of eating one now.

Ch1 | Impulsivity vs. delay of gratification

Related but not identical: impulsivity is acting quickly; delay is resisting an immediate reward for a later one.

Ch1 | Funder's First Law

Great strengths are usually great weaknesses, and often vice versa.   • Example: extreme confidence may help leadership but also become arrogance.

Ch1 | Phineas Gage — main lesson

Frontal-lobe damage was followed by impulsivity and poor self-regulation; biology can shape personality.

Ch1 | Positive illusions — main lesson

Inflated self-beliefs can feel helpful short term but become maladaptive when reality repeatedly contradicts them.

Ch2 | BLIS framework

B = Behavioral; L = Life outcomes; I = Informant; S = Self-report.

Ch2 | B-data

Direct observations/records of what a person actually does in real life or a task.   • Example: count how often someone speaks in a group.

Ch2 | B-data example

Number of times someone speaks in a group or how long a child waits for a reward.

Ch2 | B-data strengths

Gets at actual behavior; can be objective/precise; can sample real or structured contexts.

Ch2 | B-data weaknesses

Costly/time-consuming; behavior is context-dependent; meaning of one behavior can be ambiguous.

Ch2 | L-data

Objective/verifiable life outcomes or records.   • Examples: GPA, arrests, employment, medical records.

Ch2 | L-data strengths

Concrete, consequential, often externally verifiable.

Ch2 | L-data main weakness

Multidetermination: the same life outcome can have many causes besides personality.

Ch2 | I-data

Reports about the target from someone who knows or observes them.   • Example: roommate rates the target’s cleanliness.

Ch2 | I-data example

A roommate rates someone's cleanliness or conscientiousness.

Ch2 | I-data strengths

Outside perspective; can integrate behavior across time/settings; useful for observable traits.

Ch2 | I-data weaknesses

Bias, limited access to private experience, relationship effects, and informants may disagree.

Ch2 | S-data

The person reports their own traits, thoughts, feelings, intentions, or behavior.   • Example: completing a Big Five questionnaire.

Ch2 | S-data strengths

Direct access to private experience; cheap, fast, scalable; person knows own history.

Ch2 | S-data weaknesses

Bias, self-deception, memory errors, careless responding, limited self-knowledge.

Ch2 | Funder's Second Law

There are no perfect indicators of personality; all data are clues and clues are ambiguous.   • Example: lateness could reflect low conscientiousness, traffic, or an emergency.

Ch2 | Funder's Third Law

Something beats nothing, two times out of three; imperfect data can still be useful.   • Example: one imperfect observation can still add information when combined with other data.

Ch2 | Why combine BLIS sources?

Different sources have different errors; convergence across sources increases confidence.

Ch2 | Projective test

Uses an ambiguous stimulus/open-ended response to infer underlying personality.   • Example: Rorschach inkblot or TAT picture.

Ch2 | Projective hypothesis

People project their own motives, needs, conflicts, or expectations into ambiguous material.   • Example: two people create very different stories from the same unclear picture.

Ch2 | Rorschach

Projective test using ambiguous inkblots that the person interprets.   • Example: saying what an inkblot looks like and interpreting response patterns.

Ch2 | TAT

Thematic Apperception Test: tell stories about ambiguous social pictures.   • Example: describe what led to the scene and what happens next.

Ch2 | Ambiguous situations test

Interpret unclear social events; lecture used it to examine implicit racial bias in children.   • Example: decide why a peer acted negatively when the intent is unclear.

Ch2 | Ambiguous situations study — main finding

Interpretations varied with race/context; fourth graders showed stronger racial bias than first graders.

Ch2 | Projective test possible advantage

May reveal implicit/private material people cannot or will not report directly.

Ch2 | Projective test major weaknesses

Often subjective scoring, weak reliability/validity, examiner effects, and high time/cost.

Ch2 | Projective vs. objective tests

Projective = ambiguous/open-ended

Objective = standardized items and scoring.

Ch2 | Does 'objective test' mean perfectly accurate?

No. 'Objective' mainly refers to standardized scoring, not guaranteed truth or validity.

Ch2 | Rational/theoretical test construction

Write items because theory/common sense says they directly represent the construct.   • Example: extraversion item: “I enjoy being the center of attention.”

Ch2 | Rational method strength/weakness

Strength: interpretable/theory-driven.

Weakness: obvious items can be faked; theory/common sense can be wrong.

Ch2 | Face validity

How much an item/test appears on its face to measure what it claims.   • Example: “I enjoy parties” obviously looks like an extraversion item.

Ch2 | Factor-analytic test construction

Use correlations among many items to identify clusters measuring common underlying factors.   • Example: talkative, sociable, energetic items cluster into an extraversion factor.

Ch2 | Factor analysis

Statistical method that groups correlated variables/items into underlying factors.   • Example: many trait adjectives reduce into a smaller set such as the Big Five.

Ch2 | Factor-analytic strength/weakness

Strength: empirical data reduction.

Weakness: factors depend on item pool/sample and still require interpretation.

Ch2 | Empirical/criterion-keying construction

Keep items because they statistically distinguish groups or predict outcomes, even if they look unrelated.   • Example: retain an odd-looking item because it predicts membership in a criterion group.

Ch2 | Empirical method strength/weakness

Strength: prediction without face validity.

Weakness: can be hard to interpret and sample-specific.

Ch2 | Marshmallow task — BLIS type

B-data: researchers directly observe waiting behavior.

Ch2 | Happiness/EAR study — methodological lesson

Triangulating self-report, informant, and behavioral data can strengthen personality conclusions.

Ch3 | Measurement error

Score variation caused by influences other than the stable thing being measured.   • Example: fatigue lowers today’s score even though the trait did not change.

Ch3 | Measurement error effect on reliability

More measurement error = lower reliability.

Ch3 | Reliability

Consistency/repeatability of measurement when the underlying trait has not changed.   • Example: similar scores when taking the same personality test twice.

Ch3 | Test-retest reliability

Does the same measure give similar scores across occasions?   • Example: Big Five score today vs. two weeks later.

Ch3 | Internal consistency

Do items intended to measure the same construct correlate/hang together?   • Example: several extraversion items all tend to agree.

Ch3 | Inter-rater reliability

Do different observers/raters give similar judgments?   • Example: two coders rate the same behavior similarly.

Ch3 | What lowers reliability?

Low precision, temporary participant state, experimenter variation, environmental noise, too few observations, inconsistent procedures.

Ch3 | What increases reliability?

Precision, standardized procedures, enough items/observations, meaningful measures, and aggregation.

Ch3 | Aggregation

Combine/average multiple items, raters, occasions, or observations so random errors cancel out.   • Example: average 10 observations instead of relying on one.

Ch3 | Validity

Degree to which a measure actually measures what it claims and supports the intended interpretation.   • Example: an extraversion test should actually capture extraversion.

Ch3 | Reliability vs. validity

Reliability = consistent; validity = correct/appropriate measurement.

Ch3 | Can a measure be reliable but invalid?

Yes. A measure can consistently give the wrong answer.

Ch3 | Can a highly unreliable measure be strongly valid?

Generally no; too much random error prevents accurate measurement.

Ch3 | Construct validity

Overall evidence that a measure truly captures the intended psychological construct.   • Example: a sociability scale behaves as theory says sociability should across multiple tests.

Ch3 | Convergent validity

A measure agrees/correlates with other credible measures of the same or closely related construct.   • Example: self- and informant-rated extraversion agree.

Ch3 | Predictive/criterion validity

A measure predicts relevant behavior or outcomes.   • Example: conscientiousness predicts later punctuality/job performance.

Ch3 | Discriminant validity

A measure is not simply measuring a different construct; weak relation to things it should be distinct from.   • Example: an extraversion scale should not just measure intelligence.

Ch3 | Face validity vs. actual validity

Face validity is appearance only; a test can look right without truly measuring the construct.

Ch3 | Case method

Intensive study of one person/event/case.   • Example: detailed study of a patient whose brain injury changed personality.

Ch3 | Case method pros/cons

Pros: rich detail, useful for rare cases, generates ideas. Cons: weak generalizability and causal inference.

Ch3 | Correlational study

Measure naturally occurring variables and test whether they are associated.   • Example: correlate anxiety scores with exam performance.

Ch3 | Correlational study pros/cons

Pros: natural variables, prediction, ethically useful. Cons: directionality and third-variable problems; no causation.

Ch3 | Third-variable problem

An unmeasured variable may cause both X and Y, creating their correlation.   • Example: income could relate to both stress and health.

Ch3 | Directionality problem

A correlation cannot tell whether X causes Y, Y causes X, or both.   • Example: stress may reduce sleep, poor sleep may increase stress, or both.

Ch3 | Experiment

Manipulate an IV, ideally randomly assign participants, then measure a DV.   • Example: assign high- vs. low-stress condition and measure test performance.

Ch3 | Experiment pros/cons

Pro: strongest causal inference. Cons: artificiality, ethical/practical limits, possible confounds.

Ch3 | Independent variable (IV)

Variable the researcher manipulates/assigns as the presumed cause.   • Example: stress condition.

Ch3 | Dependent variable (DV)

Measured outcome presumed to depend on the IV.   • Example: number of test questions answered correctly.

Ch3 | Correlation coefficient range

r ranges from -1.00 to +1.00.

Ch3 | Correlation sign

Positive = variables move together; negative = one rises as the other falls.

Ch3 | Correlation strength

Use absolute value: closer to 1 = stronger; closer to 0 = weaker.

Ch3 | Which is stronger: r = -.70 or r = +.30?

r = -.70; sign gives direction, absolute value gives strength.

Ch3 | Does correlation establish causation?

No.

Ch3 | WEIRD

Western, Educated, Industrialized, Rich, Democratic.

Ch3 | Why are WEIRD samples a problem?

They may not represent humanity broadly, limiting generalizability.

Ch3 | How to address WEIRD-sample limits

Use diverse/community/international samples and replicate across cultures/populations.

Ch3 | Statistical significance

Result meets the chosen threshold for being unlikely under the null model, often p < .05.

Ch3 | p-value

Probability of data this extreme or more extreme assuming the null hypothesis is true.

Ch3 | What does p = .03 NOT mean?

It does not mean a 97% chance the hypothesis is true or only a 3% chance the result is wrong.

Ch3 | Effect size

Magnitude/strength of an effect or relationship; tells how big the finding is.

Ch3 | Statistical significance vs. effect size

Significance asks whether evidence beats a threshold; effect size asks how large the effect is.

Ch3 | Type I error

False positive: conclude an effect exists when it does not.   • Example: claim a treatment works when the apparent effect is chance.

Ch3 | Type II error

False negative: fail to detect a real effect.   • Example: conclude no treatment effect even though one truly exists.

Ch3 | p-hacking

Try/select many analyses or data decisions until a statistically significant result appears.   • Example: test many outcomes and report only the one with p < .05.