Personality Psychology - Exam 1!

Personality

• An individual’s characteristic patterns of thought, emotion, and behavior, together with the psychological mechanisms behind those patterns.

  • Characteristic Patterns: Individual differences that are relatively stable across situations and over time

  • What personality is not:

    • Physical characteristics (Height, weight) 

    • Abilities, skills (Singing ability, basketball skills) 

    • Social/demographic attributes (Sex, race, religion) 

    • States (Hungry, tired)

Personality Psychology

• the scientific study of personality, gaining knowledge through systematic observation

  • Unique mission to explain whole persons

  • Goal: assemble an integrated view of whole, functioning individuals in their daily environments

    • Problem: this mission is impossible → you must limit what you look at

Basic Approach/Paradigm

• systematic self-imposed limitation to certain kinds of patterns, observations etc.

• different approaches are often portrayed as competitors but in actuality they complement each other because they address different sets of questions. 

• trait, biological, psychoanalytic, phenomenological, learning and cognitive processes

Funder’s First Law

• If its scope were narrowed, the field would be more manageable and research would become easier. But then the study of personality would lose much of what makes it distinctive, important, and interesting.

• Each basic approach has made a decision to ignore certain aspects of psychology that it is not good at explaining

• Personality psychology tends to emphasize how individuals are different from one another → some argue “pigeonholes” human beings.

• A theory that accounts for certain things extremely well will probably not explain everything else so well. And a theory that tries to explain almost everything—the OBT—would probably not provide the best explanation for any one thing.  

Psychological Triad

• Thought: what you think, how you think

• Emotion: mood, motivations

• Behavior: what you do, how you do it

• Most interesting in combination because they conflict

Psychological Mechanisms

• The biological, social, cognitive, motivational, and emotional processes that explain these patterns 

• Goal is not just to describe, but also to explain (why)!

Personality as Clues

• Funder’s Second Law: There are no perfect indicators of personality; there are only clues, and clues are always ambiguous. 

  • Psychologists can look at behavior, test scores, daily living or responses to procedure – all possible clues about personality. But should maintain healthy skepticism that some or all might be misleading.

  • Better strategy is to gather all the clues you can with resources you have → Funder’s Third Law, then, is this: Something beats nothing, two times out of three.

• Can’t directly observe personality characteristics and processes 

• Must be inferred based on various clues

S Data

• Self-judgments

  • Ask the person about themselves directly 

  • Principle behind S data is that the world’s best expert about your personality is probably you

  • Subjective measurement 

  • Gathered through: Questionnaires and Interviews

    • Questionnaires have face validity - intend to measure what they seem

S Data Advantages

• Based on lots of information

• Filtered through common sense

• Access to internal states (thoughts feelings, intentions)

• Easy to obtain

• Some are true by definition (self esteem)

• Causal force

  • Self-verification: people work hard to bring others to treat them in a way that confirms their self-conception

S Data Disadvantages

• Can be mistaken, biased, or deceptive

  • Some people have positive or negative bias about themselves

  • Person can keep some things private 

  • Person could lie/fake - but harder than it looks

  • Carelessness - people are no longer paying attention at end

• Aren’t always aware of own behavior

  • Fish-and-water effect: fail to notice something about yourself

• Too simple and easy

I Data

• Informant judgments

  • Ask knowledgeable informant(s) about the target 

    • Roommate, friends, family 

  • Judgements: derive from somebody observing somebody else in whatever context they happen to have encountered them and then rendering a general opinion → Subjective measurement, very human

  • Gathered through: Questionnaires and Interviews

I Data Advantages

• Based on lots of information (potentially)

  • Goes beyond just one source of information, can average rating

• Filtered through common sense

  • Consider two contexts: immediate situation and person’s other behaviors

• External Perspective/real-world basis

  • Derive from behaviors in daily social interactions, more relevant to aspects of personality that affect important life outcomes

• Definitional truth (ex: likeability, charming, attractiveness)

• Causal force

  • Expectancy effect/behavioral confirmation: people become what others expect them to be

I Data Disadvantages

• Limited behavioral information

  • Not with that person all of the time, person could have different compartments

• Can be mistaken, biased, or deceptive

  • Informants might forget about ordinary events but remember extreme ones, usually consistent behaviors are most informative about personality

  • Person does not like them, is in love, racist, sexist

• Can’t always access internal states/private information

• More difficult to obtain

B Data

• Behavioral observations 

  • Observe what the person does; how they react

  • Participants are found or put in testing situation

  • Objective measurement of behavior

Naturalistic Settings

• Behaviors in everyday life

• Examples

  • Daily diary of specific behaviors

  • Electronically-recorded (EAR

  • Online environment (e.g., Instagram)

• Problems: 

  • some behaviors/contexts are rare- not provided in normal situations

  • hard to interpret/understand why

  • lots of factors - what about the situation is driving behavior

  • subjective based on observer

Laboratory Settings

• Behaviors in the lab

  • Contrived (unusual) or more natural

• Must control elements to make sure it is due to them not the environment

• Examples

  • Delay of gratification paradigm

  • Emotional expressions

  • Physiological data (HR, BP - things person does)

  • Certain personality tests (e.g., TAT, Rorschach)

B Data Objectivity

• Is it really objective?

  • Intuitively, it seems to be

  • However, involves many subjective decisions

    • Which behaviors to code

    • What counts as a behavior

    • Whether to allow for interpretation

  • Observers often don’t agree

B Data Usefulness

• When is it most useful?

  • When participants can’t or won’t tell you

  • When comparing different groups and you are worried about response styles

    • Meaningful quantities

  • For convergent validity

    • Test accuracy of S or I data

B Data Advantages

• Are “objective” 

• Intrinsically important (care about, predict life outcomes)

• Can create particular situations in the lab

• Wide range of contexts - unique experiments

B Data Disadvantages

• Can be difficult to interpret - most important info is how B data associated with other kinds

• Difficult to obtain, expensive

• Possible lack of psychological relevance

L Data

• Verifiable, concrete, real-life outcomes

  • Reflects past behaviors

• Can be obtained from archival records

  • e.g., arrests, GPA, marriage, Facebook friends

• Easier to ask the person instead?

  • Yes, but people are motivated to lie/present someone in a certain way

• Behavioral Residue

  • Physical space - judgment of personality in living space

  • L data - person is not actually there in the room

L Data Advantages

• Are “objective” and verifiable 

  • Concrete and may be expressed in exact numbers

• Intrinsically important (care about, predict life outcomes)

L Data Disadvantages

• Can be difficult to interpret

• Difficult and expensive

• Multidetermination

  • many causes-difficult to establish direct connection, L data often psychologically caused only to a small degree

Mixed Data

• Not all data fit neatly into one category

• Many examples of hybrid data

  • Roommate’s report (I?) of how often you make your bed (B?)

  • Behavioroid: participants report what they think they would do (B/S)

Electronically Activated Recorder (EAR)

• Digital audio recorder with that records ambient sounds of people’s daily lives

• Wore for 4 days (Fri-Tue); recorded 30s every 12.5 min 

• Participants could not tell when the recorder was on or off

• Disadvantages: audio only and sample only intermittently during day

• Used to test Lays Perceptions of Accuracy

Lays Perceptions of Accuracy

• Setup:

  • gave questionnaire to students to rank their perceived knowledge about how well they know their behavior

  • we assume that we are the best judges of our behavior

  • self-knowledge was all in the high section, other was mostly in the low section

  • compared accuracy of predictions made by self vs. others

  • nominated 3 people that knew them well

    • rated how much the target engaged in each behavior

  • measured behavior with the EAR (electronically activated recorder)

• Results:

  • correlated self and informant rating with recorded behaviors

  • found that self and others are similarly accurate (~0.26 where 0.2 is threshold of significance)

  • both self and informant judgements provide useful and unique information

SOKA Model (Vazire)

• Self-Other Knowledge Asymmetry

  • can make predictions about when self/others will be more accurate

• Self knowledge is better for internal traits (e.g., irritability) - in body/head, not providing cues to others about

• Other knowledge is better for external traits (e.g., sociability) and for evaluative traits (e.g., intelligence)

  • Some informants better than others

• Informants are more accurate for extremely desirable or undesirable traits

Triangulation/Convergent Validation

• Many possible sources of personality data

  • NO PERFECT INDICATORS (Funder’s 2nd Law)

• The single “best” data source depends your research question and available resources

• It’s often best to draw on multiple sources (Duck Test)

  • Triangulation

  • But some data is almost always better than no data (Funder’s 3rd Law)

• Many things point to the same conclusion, which increases confidence in that conclusion → it is often best to draw on multiple sources

Reliability

• The extent to which measurements are stable, dependable, and can be replicated

  • Assuming your target is stable (trait vs. state)

• All measurements are somewhat imperfect

  • Observed score = “true” score + error

  • Measurement error: cumulative effect of extraneous influences, extraneous depends on what’s being measured - state vs. trait

Sources of Error

• State of the participant

  • sick, tired, upset

• Characteristics of the experimenter

  • attention, attire, race, gender

• Experimental environment

  • distractions, temperature

• Low precision

  • human error, tools - measure in wrong units

Ways of Boost Reliability

• Careful control of the setting/experiment

  • e.g., standardized protocol and clear script

• Measure something important rather than trivial - engages participants

• Aggregation (averaging)

  • The more error-filled your measurements are, the more of them you need

  • Truth will emerge near average

3 Types of Reliability

• test-retest

• internal consistency

• interjudge agreement

Test-retest

• Are results the same across time?

• Assesses the stability of a measurement

• Should get a similar result every time

  • e.g., if you took a personality test today, do you get the same score on that test next week?

Internal Consistency

• Are results the same across items?

  • Correlating every item on a scale with each other

  • alpha (α) reliability

  • similar results across multiple items about same trait

• Items on a personality test should “hang together” to measure the construct of interest

  • e.g., extraversion (is talkative, is outgoing, is sociable)

  • use reverse keyed/scored items

Interjudge Agreement

• Are results the same across observers?

• agreement or consensus among observers

• e.g., A set of observers or “judges” rates the attractiveness of a target

Validity

• Extent to which a measure captures what it should

  • e.g., shyness measure taps levels of shyness

• Evidence of validity?

  • Scores predict expected behaviors or life outcomes

• Can be reliable without being valid

  • Measuring something, but not the RIGHT thing

• 2 complications:

  • For a measure to be valid, it must be reliable

  • Seems to invoke a notion of ultimate truth

Constructs

Something that cannot be directly seen or touched, but which affects and helps to explain things that are visible. 

Attributes like intelligence or sociability, including personality

3 Types of Validity

• face validity

• content validity

• predictive validity

Face Validity

• Does the item/question look valid? (intuitively seems to measure construct)

• not face valid statements help avoid bias people can have about things they are trying to hide

• might be problematic because easy to sway results with face valid statements

Content Validity

• Does the assessment adequately sample the construct you hope to measure?

• High content validity (good coverage)

• Low content validity (bad coverage)

  • e.g., single item measures

Predictive Validity

• Does the assessment predict appropriate outcomes?

  • e.g., aggression self-report predicts actual fighting

Construct Validation

• Concerns the extent to which your test or measure accurately assesses what it's supposed to.

• The strategy of establishing the validity of a measure by comparing it with a wide range of other measures.

  • use convergent and discriminant validity

Convergent Validity

• Shows whether a test that is designed to assess a particular construct correlates with other tests that assess the same construct

Discriminant Validity

Measures whether constructs that theoretically should not be related to each other are, in fact, unrelated

Generalizability

• To what extent to results apply more broadly

  • Other people

  • Other places

  • Other times

• Representative sampling

Common Roadblocks for Generalizability

• Undergrads

• WEIRD - western, educated, industrialized, rich, democratic

  • Most research based on subset of population but ethnic diversity is wide

• Cohort effects - findings influenced by historical period

• Gender bias - used to be only male participants, now more women sign up

• Show vs. no shows - people in studies may not be similar to those who aren’t

Aggregation

• Average across multiple items/observations

• Random influences cancel out

  • e.g., some judges overestimate and some underestimate

Replicability Crisis

•  many cases of fraud in psychology led people to question whether data is replicable

• Replication: do the study again, indication of stability

Publication Bias

• the fact that studies with strong results are more likely to be published than studies with weak results—leading to a published literature that makes effects seem stronger than they really are  

Questionable Research Practices

• hacking around in one’s data until one finds the necessary degree of statistical significance, or p-level, that allows one’s findings to be published. 

Reproducibility Project

• Large scale effort to test whether findings replicate (top 100 studies in top 3 journals)

• Other labs tried to reproduce studies published in psychology journals

• Found that only 1/3 were replicated (39/100)

Open Science Movement

• Increase disclosure in methods, results, and hypothesis presentation

• Pre-register hypotheses and studies (before collect/analyze data, plan for testing)

• Share data (others can check and analyze)

• Be a responsible scientist regardless of outcome

Case Study

• In-depth examination of a particular person or event

• Usually someone/something interesting or unusual

• Standard approach for some (Freud, McAdams)

• Ex: Columbine Shootings

Case Study Advantages

• Can study rare events and behaviors, can be absolutely necessary

• Can study real-life behaviors and outcomes

• Does justice to the topic, describes whole phenomenon

• Can be a source of ideas

Case Study Disadvantages

• Can’t fully establish causal relationships.

• Findings may not generalize to other cases

Correlational Study

• Examines the associations between two or more variables, using a sample of participants

• Variables are measured but not manipulated

• Variables often referred to as predictors and outcomes

Correlational Study Advantages

• Can study variables that can’t be manipulated.

• Can study real-life behaviors and outcomes.

Correlational Study Disadvantages

• Can’t fully establish causal relationships

• Third variable problem

Experiment

• A researcher manipulates one or more independent variables (IV), then observes the effects on one or more dependent variables (DV).

• Each participant is randomly assigned to a level of \n the independent variable (i.e., study condition)

• Compare experimental condition(s) to control condition

Experiment Advantages

can establish causal relationships

Experiment Disadvantages

• Can’t manipulate some variables, some experiments are not possible

• Behavior in the lab may not generalize to real life

• Cannot be sure exactly what you have manipulated

Correlational vs. Experiment

• Both methods attempt to assess the relationship between two variables

• The only real difference between the two designs is that in the experimental method, the presumably causal variable is manipulated, whereas in the correlational method, the same variable is measured as it already exists. 

• An experiment can determine whether one variable can affect another, but not how often or how much it actually does, in real life. For that, correlational research is required.  

Predictor and Outcome Variables/IV and DV

• An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable.

Experimental Control

• Manipulate only variable of interest, all else held constant

• Impacts internal validity (ability to claim causality)

Random Selection

• Randomly sample from the population of interest

• Impacts generalizability (say something more broadly about target population)

• important for both correlational and experiments (no cherry picking)

• not as important for case study bc no sample

Random Assignment

• Equal chance of being in any condition

• Balances out preexisting differences between groups

• Impacts internal validity

Directionality Problem

• predictor vs. outcome could be both ways, which is which not known for sure, or it could run in both directions

Third-variable effect

Possibility that both of two correlated variables were caused by an unmeasured third variable, that either of them might have caused the other, or even that both of them cause each other.

Ecological Validity

• Experiments show if an effect can happen, not that it does happen

  • want it to be high - how close or relevant to daily life

  • if experiment is farther removed from daily life, ecological validity decreases

Personality Tests

• Any standardized procedure for assessing one or more personality characteristics.

  • Same instructions, items, and scoring procedure

• Most are omnibus inventories: measure a wide range of personality traits, while others measure just one trait, must be thousands of tests

• Most provide S-data–ask what you are like, others yield B data–ex: prefer shower to bath

• Good for: 

  • Psychological research

  • Clinical screening and diagnosis

  • Organizational use

  • used widely!

Objective Tests

• Objective tests present items that people respond to using a fixed set of options

  • Stimuli: relatively clear (adjectives, sentences)

  • Response format: fixed (check boxes, rating scales)

  • Response interpretation: straightforward; not necessarily obvious

  • Examples: Rating scales, true-false (ACL), TIPI (10 item)

• Typically have many questions → aggregation, more reliable

ACL

• advantages:

  • broad coverage

  • easy to administer

• disadvantages

  • self report (typical drawbacks)

  • check/no-check response

  • vocabulary

• to improve:

  • add scale

  • give to someone else (informant)

True/False Keyed Items

attention check, easily find people’s inaccuracies or discrepancies, screen out bad data

Commonality Scale

• consists of items that are answered in the same way by at least 95 percent of all people. 

  • Created to detect illiterates pretending they know how to read and individuals trying to sabotage the test. 

Projective Tests

• present ambiguous stimuli that people respond to

  • Open-ended responses

  • Responses should reflect their own personalities

  • Responses interpreted by a trained scorer

  • All projective tests provide B data - observed responses to stimuli

  • Examples: TAT, Rorschach

  • Based on projective hypothesis: answer cannot come from stimulus so thus must be a projection of needs, feelings, thoughts

  • Stimuli: ambiguous (inkblots, pictures)

  • Response interpretation: complex, uncertain; hidden aspects

Thematic Apperception Test (TAT)

• Thirty-one picture cards serve as stimuli for stories and descriptions about relationships or social situations

• responses scored for implicit motives –achievement, power, and intimacy

• most commonly used projective test

• scoring: identify and count prevalence of narrative themes —> need for affiliation, power, and achievement

Need for Affiliation

• positive feelings towards others (establish/maintain/ restore relationship); negative feelings about separation/disruption of relationship

Need for Power

• strong actions with inherent impact on others or society at large (influence, persuade, concern for prestige)

Need for Achievement

• reference to standard of excellence (good/better/best); negative reactions to failure; winning competition/succeeding

Rorschach Inkblot

•  show these symmetric, complex blots to their clients and asked them what they saw 

  • Particularly valid for predicting certain outcomes such as suicide

Why use projective tests?

• Projective tests have generally not been shown to be reliable or valid diagnostic tools, cannot be sure what they mean, hard to standardize

• Clinicians still find them useful as a springboard to open up conversation with clients

  • more enjoyable than a checklist

• Some validity?

  • May assess the readiness and frequency with which certain thoughts come to mind (chronic accessibility)

Rational Method

• Create items based on theory or definition of a construct, items that seem direction and obviously related to what want to measure

• Relies on subjective judgments of test writer

  • E.g: What would a shy person say?

• Four conditions for rational to work: each item must mean same thing to person who takes test as psychologist who wrote it, accurate self-assessment, must be willing to report accurately, items must be valid indicators

• Self-report questionnaires most common form of measurement even if don’t fit all of criteria

Factor Analytic

• Identify groups of items that “hang together”

  • Factors- groups of highly correlated items

• Humans interpret factors

  • Label in a meaningful way

• “Garbage in, garbage out”

  • Ideally use a large set of representative items

• Begin with list of objective items, administer to large number of participants, then do factor analysis by calculating correlation coefficients → consider what items have in common and name factor

Empirical

• Create scales based on how pre-existing, well- understood groups differ

  • e.g., diagnosed depressed vs. not depressed

• Content of items often ignored

  • e.g., “I like hopscotch”

• Gather lots of items, then have samples of participants who have been divided into groups interested in, then administer a test, then cross-validate the scale by using it to predict behavior, diagnosis, or category membership in new samples of participants. 

Null Hypothesis Significance Testing

• How slim is the probability that you would have gotten this result by chance?

• If the result is significant, the statistic probably did not arise by chance so we can reject null hypothesis

• Interpretation is frequently wrong - does not give probability that hypothesis is true, instead gives probability of getting result found if null were true

• A more obvious difficulty with NHST is that the criterion for a significant result is little more than a traditional rule of thumb (0.05)

• Nonsignificant does not mean “no result” → leads to Type I errors

P-Values

• p-values/level are the probability that a difference of that size (or larger) would be found, if the actual size of the difference were zero.

• 0.05 = threshold, can tolerate

• if >0.05, high probability of chance

• if <0.05, low probability of chance, what you want, ___ important for _

Effect Size

• Magnitude or strength of relationship/correlation

• More meaningful than significance level

• Most commonly used measure is correlation coefficient

• Can use to judge practical significance: how useful to know ___ to determine __, how much it matters  

Correlation Coefficient ( r )

• measure of effect size

• can be computed in experiments 

• formulas to convert other statistics (e.g., F, t, d) into r

  • standard metric to compare strength of variables across different studies

• Direction

  • Negative: -1 = Perfect Negative Correlation

    • as one goes up, the other tends to go down

  • Positive: +1 = Perfect Positive Correlation

    • as one goes up, the other tends to go up too

  • 0= no correlation (should be, but not always--vary by chance)

• Strength:

  • weak: 0.10-0.29

  • medium: 0.30-0.49

  • strong: 0.50+

  • Why so low? humans are very complex- leftover variants are not explained

• Interpreting: cannot just asses statistical significance–depends on how many participants

  • Squaring, strong/weak scales are vague and make research seem trivial

  • use Binomial Effect Size Display (BESD)

Type I Error

• investigator rejects a null hypothesis that is actually true in the population

• Deciding that one variable has an effect on, or a relationship with, another variable, when really it does not. 

Type II Error

• occurs if the investigator fails to reject a null hypothesis that is actually false in the population (false negative)

• involves deciding that one variable does not have an effect on, or relationship with, another variable, when it really does.  

Statistical Power

• Measure of the likelihood to reject the null hypothesis, \n given that the null is false

  • Probability that we will reject the null when we should

  • Probability that we will avoid a Type II error

• More is better!

  • Detect even tiny effects or relationships

• To increase power:

  • Large N (sample size)

  • More reliable measures (good personality tests, less error)

Practical Significance

• Being statistically significant does NOT necessarily mean that a result is strong or important

• Should also examine effect size

• Tells us if results are actually meaningful and useful in the real world

• You can still consider practical significance even if a result is not statistically significant

  • e.g., taking aspirin & preventing heart attack

Research Ethics

• Not all questions can be addressed with all designs

  • ex: tattoos and accidents

• Personality and other tests function as a part of society’s mechanism for controlling people, by rewarding the “right” kind and punishing the “wrong” kind. 

• There is something undignified or degrading about submitting oneself to a test and having personality described by a set of scores

• Concern that psychological research might be used for harmful purposes - who decides what behaviors to create and whose behavior to control 

  • Will findings do more harm than good? Do we want to know racial and sex differences?

• Deception is frequently used in psychology research, such as cover story

• The experience-sampling methods that gather real-world B data, offer the possibility of violating the privacy of the people who are participants in the studies, or even bystanders who never agreed to participate. 

• Honesty- researchers fabricate their data or only report the most interesting, misleading

Personality Trait

• A personality trait is a particular pattern of thinking, feeling, or behaving that is generally consistent over time and across relevant situations

  • A specific piece of an individual’s personality

• Trait approach: relies on correlational designs, focuses exclusively on individual differences, no zero point on scale–ordinal rather than ratio scales

• Traits are characteristic patterns of thinking, feeling, and behaving that generalize across similar situations, differ systematically between individuals, and remain rather stable across time. 

States

States are characteristic patterns of thinking, feeling, and behaving in a concrete situation at a specific moment in time.

Behavioral Consistency

• Classifying people according to traits raises an important problem: people are inconsistent. Some psychologists have suggested that people are so inconsistent in their behavior from one situation to the next that it is not worthwhile to characterize them in terms of personality traits.

• Older people are more consistent, stable, seem to believe more in personality versus young people who believe more in situation

• Behavioral change and behavioral consistency can and do exist simultaneously

  • Situations have an important influence on behavior but people still tend to be consistent

• The effect of personality on behavior shows up in relative consistency, the maintenance of individual differences; it does not imply that people act the same way regardless of the situation. 

Personality Paradox

• people usually agree to both:

  • People have patterns of behavior that are consistent across situations.

  • People adjust their behavior to the specific situation that they are in–behave differently in different situations.

  • Personality is defined as being stable across situations → is personality real?

The Person-Situation Debate

Which is more important for determining what people do: the person or situation?

• For any given behavior at any given time, personality and situation both matter

• People perceive personality traits in themselves and others because such perceptions are often valid and useful.

• The large number of personality-trait terms also implies that traits are a useful way for predicting behavior and understanding personality. 

• Situationism implies that outcomes are due to circumstances, and under the right ones, anyone can be rich/successful etc – more pleasant and attractive thought than trait view, can also help to absolve people from blame

• The debate may really be arguing about their fundamental values which is why the controversy refuses to go away

• Resolution: consistent personality traits and adaptation to situations are not in conflict after all, do not need to choose between these values

• Central lesson: people are different from each other, and these differences matter

Interactionism

• persons and situations are constantly interacting to produce behavior together, interact in 3 major ways

  • The effect of a personality variable may depend on the situation, or vice versa - neither has an effect by itself, they work together

  • Situations are not randomly populated - certain types of people go to/find themselves in different types of situations

  • Process by which people change situations, and then react to those changes, can accelerate quickly

Mischel’s Critique of Traits

• People don’t behave very consistently across situations.

• Personality measures don’t predict behavior very well.

• Therefore, behavior must be primarily caused by situational factors.

• Therefore, personality traits don’t matter.

• Correlations between personality and behavior or between behavior in different situations seldom exceeded 0.30

  • Led some psychologists to conclude that personality did not exist

•  Mischel’s (1968) critique got a lot of attention.  

  • It challenged everyday intuitions about personality 

  • It implied that personality psychology was useless 

  • It led to the decades-long person-situation debate