CHAPTER 2

Question 1

goal of research

Answer 1

continuously improve on tentative answers to questions

• think and seek new knowledge
• question what we know, explore unknown

Question 2

research

Answer 2

exploration of the unknown thru data gathering

Question 3

personality data

Answer 3

applies to psych triad using personality clues that we search for

Question 4

funder’s second law

Answer 4

there aren’t any perfect indicators of personality, only ambiguous clues

must put tgt clues
- realize clues may be misleading bcs ambig

Question 5

funder’s third law

Answer 5

smth beats nothing, 2/3 times

Question 6

ways personality data can be collected

Answer 6

• s data
• i data
• l data
• b data

Question 7

S data

Answer 7

self reports, usually surveys
- has high face validity

pros:
1. large amounts info: bcs know self best
2. access to psych triad
3. definitional truth: correct bcs you say it is i.e. i’m smart
4. causal force: self-efficacy—you become what you say you are

self-verification: try to make others see how you see yourself

cons:
1. bias: overly pos, desire privacy
2. error: active mem distortion, lack insight
3. too simple = careless

Question 8

fish and water effect

Answer 8

an error w s data

ppl don’t notice constants in their personality i.e. always grumpy so don’t realize it

Question 9

i data

Answer 9

informant report data: collected by informants i.e. coworkers, psychs, acquaintances

may be more accurate to ID -ve traits than S data

pros:
1. lots of info i.e. many informants
2. real-world basis: not controlled environ, more likely to be relevant
3. common sense: accounts for CONTEXT i.e. screamed at elevator vs thief
4. definitional truth: if others think it, it’s true
5. causal force: reputation affects expectations and opps

cons:
1. limited behav info
2. lack priv exp
3. error: likely to remember unusual behav
4. bias: prej and stereotype

Question 10

judgments

Answer 10

based on observing ppl in context they know them from

collected via i data

Question 11

letter of recommendation effect

Answer 11

bias in i data bcs ppl only offer informants w pos views of them

Question 12

expectancy effect

Answer 12

causal force in i data

become the person others expect
- aka behavioural confirmation

Question 13

L data

Answer 13

life outcomes data: verifiable, concrete, real-life evidence
- facts that may hold sig i.e. school records, med files, soc med

pros:
1. verifiable events
2. intrinsic importance
3. psych relevance

cons:
1. multideterminism: many reasons for evidence i..e recession causes unemployment, messy bcs of guests

Question 14

B data

Answer 14

behavioural data: obs of daily life or in lab
- visible indication of personality
- can also be seen thru some personality tests

Question 15

natural B data

Answer 15

natural B data: from daily life…diary and exp sampling methods

• ear: electronically activated recorder
• wearable cameras
• soc med

ambulatory assessment: uses comp methods to access psych triad in daily activiites

pros: realistic
cons: difficult, desire contexts rarely happen

Question 16

laboratory B data

Answer 16

experiments that make situation and record behav
- examine rxns, may measure phys behav
- represents diff to observe contexts

pros:
1. inc range of contexts
2. appearance of objectivity: still subjective judgments made

cons: diff and expensive, uncertain interpretation

Question 17

mixed data types

Answer 17

wide range of types of data relevant to personality

each w dis/advantages

Question 18

reliability

Answer 18

tendency of measurement instrument to provide the SAME INFO mult times

Question 19

measurement error

Answer 19

cumulative effect of extraneous influences on a test score

Question 20

states vs traits

Answer 20

traits more stable across situations w little variation

Question 21

factors undermining reliability

Answer 21

1. low precision of measurement
2. state of participant
3. state of experimenters

some participants respond w/o thought, etc.

Question 22

ways to enhance reliability

Answer 22

1. carefully
2. use constant, structured procedures
3. measure smth imp that engages participants

Question 23

aggregation

Answer 23

allow random influences to cancel each other out

• esp imp for predicting behav

Question 24

spearman brown formula

Answer 24

formula in psychometrics

predicts deg to which a test’s reliability can be inc by adding more items

Question 25

construct validation

Answer 25

gather as many constructs as possible…compare one measure to many

Question 26

generalizability

Answer 26

deg to which test can be found in diverse circumstances

ethical and cultural diversity

shows vs no shows (ppl who don’t arrive may be for a reason)

Question 27

case method

Answer 27

can explain events, gen lessons, scientific principles
- detailed descs, collect history and biographical info

pros:
1. source for ideas i.e. theories
2. desc whole phenomenon
3. sometimes necessary to understand individ

cons: unknown generalizability

Question 28

experimental method

Answer 28

establishes CAUSAL RELATIONSHIP b/w indep vari and dep vari

requires random assignment to experi and control groups

test differs b/w groups to determine if difference is significant

Question 29

between subject variable

Answer 29

ONE lvl of IV is gen to some participants, not to others

i.e. bobo doll…cartoon model, filmed model, adult model

Question 30

subject variables

Answer 30

pertains to the indiviudal

subjects are classified and compared based on personality variables that are NOT manipulated

i.e. perfectionists vs non-perfectionists

Question 31

correlational method

Answer 31

establishes a relationship b/w 2 varis but DOES NOT say if causal
- just measures amount of variable

Question 32

why is correlational method bad

Answer 32

problems of causality: only says if correlation exists, not why

directionality problem: can’t tell which is cause, which is effect

Question 33

how to fix correlational method

Answer 33

longitudinal method

Question 34

third variable problem

Answer 34

unforeseen factor actually CAUSES the correlation

Question 35

experimental vs correlational methods

Answer 35

both try to assess relationship b/w varis

experimental MANIPULATES
correlational MEASURES

only experiments can determine causality

Question 36

complications with experiments

Answer 36

• not always possible
• artificial
• often deceptive

Question 37

mediator effect

Answer 37

link b/w varis exists mainly bcs of MUTUAL LINK with an intervening vari

mediated is caused by IV and causes DV

i.e. verbal abuse in childhood assoc w -ve self esteem, which is assoc w depression

third variable is a mediator

Question 38

demand characteristics

Answer 38

cues in an experiment that allow participant to figure out hypothesis and try to act accordingly

please experimenter

Question 39

cut-off scores

Answer 39

sometimes used to assign participants to groups
- must use pre-established scores to determine traits i.e. introversion

median is not good idea…cannot assume ppl in middle will have linear trend in personality