End Chapter 7

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CHAPTER 7
Fundamental Ideas in
Experimental Methods

Goals of experimental research
Only type of research that explains and controls
Explain behavior by finding out what caused it to happen
Control behavior by manipulating causes
Text example – Elderly people with more responsibility are
healthier…don’t know if having responsibility makes you healthy, or
being healthy allows you to be more responsible

Causation – experiments allow researchers to draw
conclusions about causation
Causation
(Controlled Experiments)

Experiments control other things so researcher can be sure
the effect is the result of the cause
Text example…plant’s water sits under magic pyramid and then
plant grows better…is the pyramid the cause?
What else could cause the plant to grow better?
Have not controlled for changes in the water due to evaporation of
chemicals
Have not controlled for other changes (more light, cooler)

Controlled experiment allows researcher to control these
other factors
THREE CANONS OF CAUSATION
(These must be met to establish cause and effect)

The cause must come before the effect
 A before B to cause B

A change in the cause must be related to a change in the effect
 A and B are correlated

There is nothing else that could have caused the change in the
effect (rule out all other causes…good experiments rule out rival
hypotheses)
 Rule out all other causes of B
Experimental Overview
Basic Experimental Design
Assign
subjects
randomly
to groups
Group A is
Experimen
tal Group
→ Pretest →
Group B is
Control
Group
→ Pretest →
Give
Experime
ntal
Treatment
Maintain
Control
Condition
→
Posttest
→
Posttest
RO X O
RO O
Elements of Experiments



Random selection of subjects from population
Random assignment into two or more groups
Random assignment of independent variable to one group
Note: Independent variable is the cause, while the dependent
variable is the effect

Measure results
Experiments Focus on
Key Variables

Independent variable (IV) – the variable that makes the effect
happen (the cause)
 This variable should be the only difference between the treatment and
control groups


Dependent variable (DV) – the effect which depends on the
independent variable
Extraneous variables – all the other potential variables that might
interfere in the relationship between the IV and DV (Three types:
subject, experimenter, situation)
 All extraneous variables must be considered and dealt with
Controlling Extraneous Variables
(Random assignment is best way to do so)


Subject variables – characteristics of subjects (sex, age,
health) that might interfere
Experimenter variables – characteristics and behaviors of
the researcher
Experimenter expectancy effects (keep subjects blind)
Experimenter bias (keep researcher blind)

Situational variables – qualities of the experiment
(temperature, time of day, location)
Confounding Variables
(Variables that differ in the experimental and control groups)



Avoid confounded experiments
Confound variables are the source of rival hypotheses
Random selection and assignment are the keys to avoiding
confounds
Text example on nursing home responsibility…what are some
confounding variables to avoid?
Complex
Experimental Designs



Simple experiments have one IV and one DV
Complex experiments often involve different levels of IV or multiple
IVs or DVs
Factor designs manipulate more than one IV at a time
 Examine the relationship between the IV and DV at different levels of
another IV
 Look for main effects and interaction effects
 Ex: Look for differences in performance under an incentive program and check to see if
it differs for older and younger workers

Multivariate designs involve more than one DV
 Ex: See how incentive program affects attendance, attitude, etc.
End Chapter 7

See Exercises
CHAPTER 8
Control of Extraneous Variables

Equivalent groups must be used for treatment and control
 Does not mean identical or equal just that any differences are either
random or due to the IV
 Never know for sure, only know the probability that the difference is
random (or due to the IV)
 Must ensure there is no SYSTEMATIC DIFFERENCE (aka BIAS) in the two
groups
 Any systematic differences undermine the random sampling distribution
that is assumed to underlie the two groups, so cannot estimate accuracy if
this is not present
Simple Subject Variables


Subject variables are major problem in behavior sciences
because people have so much variation
Random assignment of subjects controls for it
Don’t do assignment arbitrarily and don’t let subjects decide
which group to be in
Sometimes get uneven seeming groups, but can account for that
statistically

Random assignment is the key characteristic of a true
experiment
Related v. Independent Groups
(SUBJECT VARIABLES)


Random assignment into one of two groups results in independent
samples
Sometimes (in ex post facto) must pair scores…this results in
related groups
 Matched groups design – each person in the treatment group is match on
related extraneous variables to another person in the control group (yoked
design)
 Within subjects design – each person is matched on one variable with
another variable for that person (pretest and posttest)
 Reduced variability with related groups, so is a more powerful experiment,
BUT due to matching have reduced number of observations and subjects
Reactivity Effects
EXPERIMENTAL SITUATION VARIABLES
(interactions between subjects and IV)

Placebo effect – when the subject has an expectation of an
effect
Placebo cannot be the same as the treatment
Text example on stress causing herpes and relaxation therapy as
a treatment
treatment and control group must both receive some sort of group social
interaction to rule out it as the cause
treatment group receives relaxation techniques in a group setting;
control receives group discussion (it gets social interaction also, but not
the relaxation techniques)
Control Group Effects
EXPERIMENTAL SITUATION VARIABLES
(things that happen to modify CG’s behavior)


Demoralization – control group members are not getting the
special treatment so they essentially pout and do not perform well
Overachievement/over compensation – control group members try
to prove they are as good as the treatment group
 Ex: teachers threatened by computer based instruction (CBI) do a super
job so their students perform better than those getting CBI
 Best prevention is ignorance of the treatment (blind designs)
Response Style Effects
EXPERIMENTAL SITUATION VARIABLES
(when respondents generally provide a certain response)




Self-inflated ratings (people tend to rate themselves highly)
Global tendency bias (people tend to be either a yes or a no type
person)
Social desirability bias (people give what they believe are socially
acceptable answers)
To overcome these
 Use direct observation or observation by others
 Mix up positive and negative answers on surveys
 Don’t invite self ratings or socially desirable ones
EXPERIMENTER VARIABLES
(things the research does or is that affect the results)

Simple experimenter variables
Unchanging characteristics of the researcher (age, sex, looks,
etc.)
Biggest problem occurs when more than one person collects
data-rotate them between EG & CG

Experimenter expectations
Researcher records results because of his/her expectations
Double blinds eliminate this problem
End Chapter 8

See Exercises
CHAPTER 9
Quasi-Experimental Research

(do this when cannot use true experimental)
Allows partial control of extraneous variables
 Quasi is compromise between field research and laboratory
 Field research is real world conditions (cannot control every variable)
 Laboratory research allows control over most variables

Quasi is done when cannot (effects of race or socioeconomic
status) or should not (effects of smoking) manipulate the
independent variable
 Use quasi-experimental designs to achieve external validity
 Use true experimental designs for internal validity
 In critical cases do lab experiments to demonstrate high internal validity then quasi
(field trials) to demonstrate high external validity
Basic Before-After Design
(also known as one-group pre-test post-test design)

Measure before, apply treatment, measure after
 This is an O X O design (no random assignment or R)


Example: Take a group of people with headaches, give them all
aspirin, measure again to see if the headaches are gone
Are there any rival explanations for the headaches being gone?
 Problems with too many extraneous variables such as effects from history,
maturation, testing, instrumentation, mortality, and regression toward the
mean
Part I
THREATS TO INTERNAL VALIDITY
(These effects contaminate experiments)

History effect – a common event everyone experiences
 Ex: Measure TV viewing during 9/11 attacks

Maturation effect – internal changes within the subjects that occur
over the course of time
 Ex: New employees get better at job w/out training

Testing effect – pretest can cause sensitization; sometimes makes
little sense to measure twice
 Ex: Pretest before training makes employees pay attention to the tested
sections so do better on those on posttest
Part II
THREATS TO INTERNAL VALIDITY
(These effects contaminate experiments)

Instrumentation effect – measurement instruments that work
differently from pre to post testing
 Ex: A subjective test is not consistent when repeated

Mortality effect – subjects drop out of the experiment or cannot be
found for posttest
 Ex: Weight loss study, those on exercise routine drop at a higher
rate…only those who stuck are there for post test

Regression toward the mean – extreme measures on pretest will
naturally move toward the mean on post
 Ex: If a group has really high (or low) achievement and you introduce a
treatment, probability is that group will move down (or up) because little
room to move further up (or down)
Controlling for Threats to Internal
Validity

A randomized pretest/posttest control group design addresses each
threat except testing
 Controls for history because both groups should experience the event
 Controls for maturation because both groups should be maturing
 Controls for instrumentation because both groups will experience any instrumentation
effects
 Controls for mortality because both groups should lose at the same rate (otherwise
there’s a problem)
 Controls for regression because both groups should experience it

Only way to control for testing is to use 4 groups (two without
pretest) in a Solomon 4-Group Design
Variations on the
One Group Pretest/Postest

Static group comparison – use two groups that already differ on
the IV and compare their posttest scores (no random assignment)
 Ex: Nurse burnout in overtime hospital


Before-after non-equivalent groups (take static groups and add a
pretest)
Simulated before-after (take one group, divide in half randomly
and assign half to pretest
and half to posttest)
Expanded Variations of Before/After Design

Interrupted time-series designs (tell if a treatment effect
lasts)
History/maturation problems

Multiple time-series designs (aka counterbalanced designs)
Adds comparison group

Regression-discontinuity designs
Predicts DV for treatment group if there was no intervention
SINGLE-SUBJECT DESIGNS
(Subjects are a person, business, department, etc.)


Reversal designs (treatment, then take it away…ABA
designs)
Reversal is important due to Hawthorne effect
Increased light, productivity increased, but when reversed and
decreased light, productivity increased again!

Reversals are often done with multiple variables and with
multiple baselines (to guard against cumulative effects)
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