# PSYC 305 Chapter Notes -Dependent And Independent Variables, Statistical Significance

38 views2 pages

Chapter 2-Methods in the Study of Personality

Gathering Information

-look at yourself/introspection or others (problems: distort what you see, can’t get

into their head)

Seeking Depth: Case Study

Personology-study whole person, not just one aspect (Henry Murray)

-observe person in natural environment, unstructured interviews

-many case studies are also clinical studies (person has problem)

Seeking Generality: Studying many people

-generality (breadth of applicability) is a continuum

Establishing Relationships among Variables

-to see if relationship exists btwn variables, must look at more than 1 variable (eg.

not enough to say low self-esteem=poor GPA…what does high self-esteem

mean?)-can’t do this in case studies

Correlation:

-in many examples, variables/dimensions go together in a systematic way (look at

strength + direction)

Correlation coefficient-shows how strong correlation is (1.0 means perfect positive

correlation)

-negative 1.0 is perfect inverse correlation

0.6-0.8 is strong, 0.3-0.5 is moderately strong, below 0.3 or 0.2 is weak (more

scatter)

Statistical Significance-the likelihood of an obtained effect occurring when there is

no true effect

-when probability is small enough, the correlation is said to be statistically

significant

Clinical/Practical Significance-when correlation is believable (statistical significance)

and large enough to have practical importance (eg. can have high s.s. but only

account for tiny bit of behavior-low p.s.)

Causality-variation in one dimension causes variation in another (explains the why)

3rd-variable problem-possibility that an unmeasured variable caused variations in 2

correlated variables

Search for Causality: Experimental Research

Independent Variable-manipulated to see if it’s the cause

Experimental Control-trying to hold all other variables (except independent)

constant

Random Assignment-how to treat variables that can’t be controlled so in a large

group, any significant differences will balance out (race, physique, depressed,

etc.)

-if do all this, and groups differ in dependent variable at end, must have been b/c of

IV (only difference)

Problem: don’t know what aspect of IV caused difference

-short events in carefully controlled conditions (whereas correlational studies

let you examine elaborate events over a long period)

-correlation studies let you look at eg. effects of divorced parents on smoking

(unethical in experiment)

Multifactor Study/Experimental Personality Research-2 or more predictor variables,

varied separately

eg. People come in with low/high self-esteem (personality variable), success/failure

on test is manipulated (experiment variable)-> dependent variable is

performance on 2nd task

Main Effect-effect of 1 predictor variable independent of other variables (fail=both

esteems do worse)

Interaction-effect of 1 predictor variable differs depending on level of other

predictor variable

eg. failure has different effects on high/low esteems

## Document Summary

Look at yourself/introspection or others (problems: distort what you see, can"t get into their head) Personology-study whole person, not just one aspect (henry murray) Many case studies are also clinical studies (person has problem) In many examples, variables/dimensions go together in a systematic way (look at. Correlation coefficient-shows how strong correlation is (1. 0 means perfect positive. 0. 6-0. 8 is strong, 0. 3-0. 5 is moderately strong, below 0. 3 or 0. 2 is weak (more strength + direction) correlation) scatter) Statistical significance-the likelihood of an obtained effect occurring when there is no true effect. When probability is small enough, the correlation is said to be statistically significant. Clinical/practical significance-when correlation is believable (statistical significance) and large enough to have practical importance (eg. can have high s. s. but only account for tiny bit of behavior-low p. s. ) Causality-variation in one dimension causes variation in another (explains the why) 3rd-variable problem-possibility that an unmeasured variable caused variations in 2 correlated variables.