Experimental and Correlational
research designs
Author: Owen Emlen [Email]
Copyright © 2006 Owen Emlen, All Rights Reserved

These slides were extracted from a series of two
PowerPoint presentations created by the author

Owen works as an independent consultant and is a
member of BrainTech, LLC, where he creates online
applications that analyze data that look for trends in
behavior. Here is an example model
Target Audience

The target audience consisted of undergraduate
students enrolled in a 300-level research design
and statistics course offered through the
Psychology Department at Western Washington
University

The course instructor was Professor David
Sattler, who was out of the country doing
research for the week
The Importance of Research Designs
and Methods
Statistics cannot salvage a poorly designed experiment!
1.
What is wrong with the question below?
“I believe that homosexual marriage is wrong and
therefore they should not be allowed to adopt children,
live together, or hold hands in public.”
Strongly Agree - - - - - - - - Strongly Disagree
1
2
3
4
5
A poorly designed experimental manipulation
2.
“We showed participants angry or clown faces
in an attempt to make them angry or happy:”
More Pitfalls: Confounds

A researcher is measuring differences in
talking speed between men and women
1.
Participants are asked to converse in
separate waiting areas…

2.
One of the waiting areas has a coffee vendor!
Women are observed in the morning, and
men at night
Two types of research designs

Experimental designs involve manipulation
of one or more Independent Variables (IV)


Causality can potentially be inferred
Correlational designs examine the
relationship between two or more existing
(non-manipulated) variables
Experimental Design


Example study: Level of Arousal and Perceived
Attractiveness*
Method
1.
2.
3.
Participants are asked to meet a confederate (of the
opposite sex) in a park at one of two locations: (1)
on a garden path, or (2) past the garden path,
across a rickety old suspension bridge.
Participants were asked some standard
demographic questions by the confederate.
Participants returned to their car, where they were
asked to rate the attractiveness of the confederate.
*Hypothetical study based on a prior research by Dutton and Aron (1974)
Experimental Design, cont.
First, is this an experimental design?
 What are we measuring / What is the IV, DV?
 What was the manipulation?

(N=40)
Low arousal
(Met on the garden
path)
High arousal
(Walked across the rickety
suspension bridge)
Attractiveness
rating
M=7.1
M=8.4

This is a 1x2 design (rows x columns)
Low arousal
(Met on the garden
path)
High arousal
(Walked across the rickety
suspension bridge)
Attractiveness
rating by Female
participants
(n=20)
M=7.0
M=7.1
Attractiveness
rating by Male
participants
(n=20)
M=7.2
M=9.5
• Now this is a 2x2 design
• 1 true independent variable, 1 status (quasi-independent) variable
• 1 Dependent Variable (DV): Attractiveness
Low arousal
(Met on the
garden path)
Medium arousal
High arousal
(Passed a barking (Walked across the rickety
dog)
suspension bridge)
Attractiveness
rating by Female
participants
(n=20)
M=7.0
M=7.1
M=6.7
Attractiveness
rating by Male
participants
(n=20)
M=7.2
M=8.5
M=9.3
Now what? A 2x3 design
• Note, there are still 2 IVs and 1 DV
• One participant (quasi-independent) variable (Sex)
• One manipulated IV (arousal level), with 3 levels
• Attractiveness rating is still our DV
Adding more “dimensions” to the table

A 2x2x2 design would indicate 3 independent (or
quasi-independent) variables


A 2x2x2 design:
1.
2.
3.

You can picture this as a cube, or a 3-D table
Quasi-IV (Participant Variable): Sex (male/female)
True IV: Arousal level: (low/high - path or bridge)
Quasi-IV (Participant variable):
Introversion/Extroversion
There is still only one DV (attractiveness)
Clarification: Participant (or status) Variables

What is a participant (or status) variable?




Hint: If we recruited participants only from an
acrophobia (fear of heights) support group, how
aroused might they be when walking across the bridge?
If we recruited participants only from a climbing club,
how aroused might they be in the same situation?
Random selection and Random assignment of
participants is important
How can we be sure that having participants walk over the bridge had
any effect?
Manipulation Checks
Were participants really more aroused
after walking over the bridge?
 A manipulation check allows a researcher
to confirm that his/her manipulation was
successful
 Ideas?

 Measure
pulse and blood pressure
 Give participants a survey – ask them
Correlational Design

Correlational research examines the
relationship between two or more non
manipulated variables.

What is the relationship between:
1.
2.
3.
4.
Height and weight?
Birth order and years of education?
Cigarettes smoked per day and health care costs?
How close to the front you sit in a classroom and
your grade in a class?
What can correlational research tell us?

Imagine that researchers find an
association between sitting in the front of
the classroom and receiving good grades

You promptly move to the front of the
classroom, and expect your grade will
improve

Don’t bet money on it…
Correlation and Causality
With correlational research designs,
causality cannot be inferred
 Example: Researchers want to investigate
the link between religious affiliation and
alcohol consumption*

 They
measure the number of bars and churches
in randomly-selected towns
# of Bars
# of Churches ?
* Example by Professor Kristi Lemm of Western Washington University
Pitfalls of correlational research designs

The researchers find that towns with more bars
also have more churches

Therefore, religious persons tend to drink
more, or perhaps alcohol consumption is a
reason people attend church
…what is wrong with these conclusions?
We cannot infer causation!

Larger towns tend to have more bars and
more churches. Therefore, a third (and
more likely) explanation:
# of Bars
Town Population
# of Churches
A more realistic example

Example:
 Researchers
find that people who had a
psychotic episode tended to report high levels
of marijuana use just prior to the episode
 Does this imply that marijuana causes
psychotic episodes?
 It may, but be careful! It is difficult to
establish causality via observation alone
Example, cont.
1.
There may be a “third” explanation:

2.
High levels of stress may be a reason for increased
drug use, and high levels of stress are associated
with psychotic episodes
Which came first, the chicken or the egg?


Perhaps some people self-medicate to deal with the
early symptoms associated with a psychotic episode
Does this imply that the onset of a psychotic episode
leads to increased drug use?
Trying to guess what causes what gets messy fast! Don’t do it!
But what if there really is a cause?

How would we confirm causality?
 Simply
 By

use an experimental design
manipulating a variable can we infer causality
So, the solution is simple:
 Take
10,000 people, randomly assign half of
them to consume large amounts of marijuana
each day, and measure how many
… people
from each group have a psychotic episode
Ethics and practicality

Ethics:
 Should
researchers create Tsunamis by
detonating sub-oceanic nuclear bombs in order
to study the effects of “natural” disasters?

Practicality:
 Randomly
assign half of your participants to cut
out all caffeine intake for the next year…
Critical Reading: Correlational Research Designs

Creative interpretation of data is not limited to
statisticians, politicians, and lawyers! 

“Headline: Children who sit in the back of the
classroom receive lower grades than those
who sit in the front.”
Are there implied overgeneralizations?
2. Does the use of present tense affect your
interpretation?
3. Would a parent whose child is doing poorly be
more or less likely to assume causality?
1.
Critical Evaluation, cont.
 Possibly
implied: “[All] Children who sit
in the back of the classroom [always]
receive worse grades than [each and
every child] who sits in the front.”
 Better:
“Researchers X and Y found that
children who sat in the back of the
classroom were more likely to receive
lower grades than those who sat in the
front.”
Critical Evaluation: Summary
Be objective and know your biases;
emotion affects interpretation
2. Is there mention of the research
design?
3. Look for the Manipulated Variable. If
nothing was manipulated, consider
carefully before assuming causality
1.
For Discussion

Headline: “People who swim in the ocean
have five times the average level of toxic
mercury in their bloodstream!”

Time to cancel your Snorkeling Adventure?
Swimming example, cont.

What groups were the researchers comparing?


People who swim in the ocean (how often?)
The “average level” of mercury may include people
who live in the Midwest who rarely eat seafood
Perhaps people who swim in the ocean tend to
live near the ocean and eat more fish, and the
fish have high levels of mercury
 Still, this relationship between ocean swimmers
and high blood mercury levels contains
important information!

Critical Evaluation, cont.

The data are still extremely valuable and
may indeed hint at some underlying cause

Research design is cyclic in nature;
observation can lead to deeper
investigation and refined theories and
hypotheses
Critical Evaluation: Summary
Remain aware of the research design
(correlational vs. experimental) before
assuming causality
 What was experimentally manipulated?

 If
nothing was manipulated, what might be some
other explanations for the relationship?
# of Bars
 Look for more
Population
parsimonious hypotheses
# of Churches
About the Author

These slides were extracted from a series
of two PowerPoint presentations created by
Owen Emlen [Email]

Owen works as an independent consultant
and is a member of BrainTech, LLC, where he
creates online applications that analyze data
that look for trends in behavior. Here is an
example model