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CHAPTER 7
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
(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
(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

Assign
Group A is
Experimen → Pretest →
Give
Experime → Posttest subjects tal Group ntal randomly Treatment to groups
Group B is Maintain
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Control → Pretest → Control → Posttest
Group Condition
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R O X O
R O O
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
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:

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 subject, experimenter, situation)
 All extraneous variables must be considered and dealt with
(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)
(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?
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.

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See Exercises
CHAPTER 8
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
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
(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)

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 Reduced variability with related groups, so is a more powerful experiment,
BUT due to matching have reduced number of observations and subjects
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)
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)
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

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(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
See Exercises
CHAPTER 9
(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
(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

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Part I
(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
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Part II
(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)
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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)

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 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
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)
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Simulated before-after (take one group, divide in half randomly and assign half to pretest and half to posttest)
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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
(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)