Chapter 13: Methods in the Study of Personality Flashcards
Exner Scoring System for the Rorshach Inkblot Test
average p (population responses)
- M=6.28
- conventional, well-adjusted
high p
- M<7
- conventional, overconforming, frequently depressed
- anxiety related to fear of making mistakes
- conforms to achieve approval
low p
- M>4
- poorly adjusted, detached, unable to see world as others do, rejection of conventionality
- possible character disorder
4 Steps of the Scientific Method
- observe and describe
- form hypothesis
- use hypothesis to predict something
- test prediction using experimental methods
Personology
- case study
- method of examining the whole person at once
- provides in-depth perspective
- gather information over a long period of time through various modalities
Bias
- observational methods are influenced by the observer
- observations within case studies lack generalizability
How can Generality be provided?
- testing assumptions with as many subjects as possible
- testing with a large variety of groups
- diversity
Variables
- concept or dimension of interest
- at least two levels: sex - male/female
- can have two types of relationship: correlational, causal
Correlation
- tendency of two variables to go together
- direction: positive/negative
- strength: degree of accuracy with which you can predict the values of each variable
Correlation Coefficient
- r = 1.0 perfect
- 0.6-0.8: strong
- 0.3-0.6: moderatly strong
- <0.3: forget about it
- tells the direction and strength of the relationship
- positive relationship: no sign
- negative relationship: minus sign
Significant Relationship
result is large enough to be unlikely a product of chance
Common Standard of a Significant Relationship
- <5% or <1%
- p value: < 0.05 or 0.01
Clinical Significance
result is both statistically and practically significant
Causality
relationship between cause and effect
Difference Causality and Correlation
Correlation: tells that there is a relationship
Causality: tells why
Dangers of interpreting Correlation Coefficients
- variable could have a causal relationship, or a third variable not measured could influence both variables
- confound or “third variable problem”
How to determine Causality
experimental research with IV and DV
Eliminate Confounds
- increase experimental control
- standardize procedures across all subjects
- random assignment to groups: selection bias, individual differences, large sample size and random assignment
Benefits of Correlational Studies
- allow study of naturally occurring differences and variables that would be unethical to manipulate
- in new areas: provide preliminary information about a potential causal relationship
Benefits of Causal Studies
- allow conclusions about direct causal relationships between variables
- exceptionally important with treatment outcome research
Limitation of Causal Studies
- expensive
- high validity, but low generalizability
Multivariate Research
- examines relationship between 2 or more IV’s and 2 or more DV’s at the same time
- allow examination of: main effects (a change in the IV leads to the change in the DV, regardless of another IV), interaction effects (the effect of one IV depends on the value of another IV)
- provide information about the amount of variance in DV accounted for by each IV