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Correlational method

*Used in describing the relationships among naturally occurring variables ie. without imposing manipulations

*Commonly used in personality research to explain:
~ the relationships among various personality traits (eg are aggressive people more egocentric? --> co-vary)
~ the relationships between personality and some specific behaviour (eg are anxious people more likely to plan for retirement?)

*interrelated statistical analyses (Pearson correlation, partial correlation, regression analysis, structural equation modeling, & factor analysis)


Correlation & the Scatter plot

Distributions that show the relationship between two variables (x & y) are called bivariate distributions typically displayed in a scatter plot.

Scatter plot --> indicates strength and direction of relationship


Strength of relationship

Defined by degree of linearity

Positive (bottom L to top R)
Negative (top L to bottom R)


Correlation coefficient

Strength of the relationship between two variables is indicated by the size of the correlation coefficient (rxy).

Cohen & Cohen: (r .10 = sml, r .30 = med, r .50+ = large)

R2 = the proportion of variance in y scores that can be accounted for by variation in x scores (r2=.5x.5=.25 --> 25% of variance in y scores can be accounted for by variation in x scores ie common variance; 75% = unique or specific variance)

-1= perfect negative; +1= perfect positive; 0= no relationship

P = stat meaningful
R= practically meaningful


Correlation limitations

Direction of causation --> with Correlational research it is not always clear to what extent personality traits can be said to have cause primacy.

Descriptive and incapable of isolating causal action

Third variable problem: the possibility exisits, pending further investigation, that some third, as yet unknown or unmeasured variable has causal primacy.


Experimental method

Hypothesised cause-effect relationships are put to a direct test.

Independent variable - cause, situational
Dependent variable - effect, behaviour

IV, believed to be the cause is manipulated to see weather it has the hypothesised effect of the DV.

Statistical tests - t-test, ANOVA, etc

Eg bandurra & bobo doll


Experimental limitations

Ecological validity --> to carry out manipulations of the IV, experiments have to be performed in labs or artificial settings therefore taking behaviour out of context & raises questions about legitimacy.

Sometimes experiments are simply not possible --> ethical reasons! technical reasons.


Experimental strengths

Has the ability to manipulate variables of interest - establish cause-effect relationships.

Independent replications


Mixed methods

Non-manipulated IV (ie participant variables) --> any time the IV groupings reflect naturally occurring differences