* Definitions

* Examples

* Comparisons

* Conclusions

* Designing our own studies

**Correlational Method**

__Definition__:

* A technique in which two or more variables are measured so that the
relationship between them can be assessed statistically

* In other words, are X and Y associated?

* Can we use X to predict the level of Y or Y to predict the level
of X?

* Random sampling is necessary to make results generalizable

**Correlational Method (cont'd)**

* Correlations are quantified using a scale from -1 to +1

* A positive correlation means that X and Y are associated such that
the higher X is, the higher Y is and vice versa

* A negative correlation means that the higher X is, the lower Y is
and vice versa

* The closer a correlation is to zero, the weaker the association is
(when __r__ = 0, there is no association at all between X and Y)

* Correlational Examples

The correlation of the size
of a baseball teamís payroll with the number of games it wins is + .77

The correlation of how confident
an eyewitness is and her accuracy is + .03

The correlation of how important
school performance is to a student and his self-esteem on days when he
is rejected from

graduate school is - .42

**Correlation and Causation**

* Correlational designs and analyses tell us about association (whether
X is related to Y), but not causation (whether X causes Y)

* At least three possibilities when X is correlated with Y:

X causes Y

Y causes X

Z causes both X and Y

**Interpret These Correlations**

* Owning pets is positively correlated with good
health

* Pepsi consumption in any given month is positively
correlated with street violence

* A realistic view of the self is negatively correlated
with happiness

* Hours spent watching violent TV is positively
correlated with violent behavior on the playground

**Experimental Method**

__Definition__:

* A technique in which participants are randomly assigned to different
conditions so that the causal influence of some variable(s) on

other variable(s) can be examined.

* In other words, does X cause changes in Y?

* Does W cause changes in Y?

* Do W and X interact to cause changes in Y?

**Experimental Method (cont'd)**

* __Independent Variable (IV)__: the variable a researcher manipulates
to see the effect this has on other variables

* __Dependent Variable (DV)__: the variable measured by a researcher
to see the effect of manipulating the IV

* Probability level (p value) is used to determine the likelihood that
changes in the IV influenced the DV

**Experimental Design Considerations**

* __Internal Validity__: Everything except the IV must be exactly
the same between conditions to conclude that the IV caused changes

in the DV

* __External Validity__: Extent to which the results of a study
can be applied and generalized to other people, settings, and situations

* Random assignment to conditions is necessary, otherwise IV is not
the only difference between groups

**Experimental Examples**

To test the effect that happiness has on helping behavior:

* IV: compliment one group of subjects and insult
the other group of subjects

* DV: measure how many papers they pick up when
a confederate walks by them in the diag and drops an entire binder on the
ground

Experiments often have more than one IV and/or more than one DV

**Importance of Manipulation**

* We can infer causality from experiments because the only difference
between groups is the IV we have manipulated

* If the IV is not manipulated by the experimenter, the study is not
a true experiment

* What does that mean for studies investigating gender, race, socioeconomic
status, self-esteem, etc?

**Advantages**

**Correlational Method**

* Allows for the study of variables we simply canít manipulate (race,
gender)

* Allows for the study of variables we ethically canít manipulate (effects
of smoking on health)

* Allows us to examine the relationship between several variables at
once

**Experimental Method**

* Allows us to draw conclusions about causality

* Affords us a great deal of control over the variables being studied

* Allows us to look at the __interaction__ of 2 or more IVs on the
DV

**Disadvantages**

**Correlational Method**

* Tells us nothing about the direction of causality

**Experimental Method**

* We might be tempted to simply correlate a slew of variables instead
of planning more controlled studies with specific hypotheses

* Laboratory studies can be artificial and lacking in mundane realism

* Lab studies can require elaborate cover stories and may require us
to deceive participants

**Checklist for a "Good" Design**

* specific hypotheses

* operational and conceptual variable definitions

* random sampling/assignment

* internal and external validity

* mundane and experimental realism

* informed consent

* replication (and eventually meta-analysis)

* use of various methods, including correlational, experimental, and
observational

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