Transcript Research in Psychology: Methods & Design

Chapter 7:
Single Factor Designs
Exam I Results
Problems with Biasing
• Subject bias
• Hawthorne effect
• Effect of knowing one is in a study
• “Good” subjects
• Participants tend to be cooperative, to please the researcher
• Evaluation apprehension
• Participants tend to behave in ideal ways so as not to be evaluated negatively
• Demand characteristics
• Cues giving away true purpose and study’s hypothesis
• Controlling for participant bias
• Effective deception
• Use of manipulation checks
• Field research
Ethical Responsibilities of Participants
• Be responsible
• Show up for scheduled appointments, or inform research of cancellation
• Be cooperative
• Behave professionally when participating in research
• Listen carefully
• Ask questions if unsure of your rights or of what you are asked to do
• Respect the researcher
• Do not discuss study with others
• Be actively involved in debriefing
• Help the researcher understand your experience
Notes about counterbalancing
Reverse counterbalancing, the experimenter simply presents the
conditions in one order, and then presents them again in the reverse order.
ABCDDCBA
Block Counterbalance: every condition occurs once before any condition is
repeated a second time.
CDABACBD
Chapter 7. Experimental Design I: Single-Factor
Designs
Chapter Objectives
• Identify and understand the defining features of the four
varieties of single-factor designs
• Know when to use an independent samples t test and
when to use a dependent samples t test
• Describe two different reasons for using more than two
levels of an independent variable
• Decide when to use a bar graph versus a line graph to
present data
Chapter Objectives
• Understand why a 1-way ANOVA is the appropriate
analysis when examining data from single-factor,
multilevel studies
• Know when to use a one-way ANOVA for
independent groups versus a one-way ANOVA for
repeated measures
• Construct an ANOVA source table for a 1-factor
ANOVA for an independent groups design
Chapter Objectives
• Understand why post hoc statistical analyses
typically accompany 1-factor ANOVAs for singlefactor, multilevel studies
• Understand the logic behind the use of three special
types of control groups: placebo, wait list, yoked
• Understand the ethical issues involved when using
certain types of control groups
Single-Factor—Two Levels
• Between-subjects, single factor designs
• Independent groups designs
• Manipulated independent variable (separate groups)
• Random assignment to create equivalent groups

Matched Groups Designs
• Manipulated independent variable (separate groups)
• Matching to produce equivalent groups

Nonequivalent groups design (ex post facto designs)
• Subject variable as an independent variable
• Deliberate attempts to select Ss to reduce nonequivalence
Single-Factor—Two Levels
• Within-subjects, single factor designs
• Also called repeated measures designs
• Manipulated independent variable
(all Ss participate in all levels of the independent variable)
• Famous historical example  Stroop
Single-Factor—Two Levels
Single-Factor—Two Levels
• Analyzing single-factor, two level designs
• t test assumptions
• Interval or ratio scale data
• Data normally distributed
• Homogeneity of variance
• t test for independent samples, for
• Independent groups designs
• Nonequivalent groups designs
• t test for dependent samples (paired, repeated measures) for
• Matched groups designs
• Repeated measures designs
Single-Factor—More Than Two Levels
• Between-subjects,
multilevel designs
• Advantage #1  ability
to discover nonlinear
effects
• RT study with 2 levels (1
and 3 mg of caffeine)
• Adding levels (2 and 4
mg)  possible nonlinear
effect
Another nonlinear example…
Single-Factor—More Than Two Levels
• Between-subjects, multilevel designs
• Advantage #2  ability to rule out alternative explanations
Multilevel Designs
• If the balloons popped, the sound wouldn’t be able to carry,
since everything would be too far away from the correct floor.
A closed window would also prevent the sound from carrying,
since most buildings tend to be well insulated. Since the whole
operation depends on a steady flow of electricity, a break in the
middle of the wire would also cause problems. Of course, the
fellow could shout, but the human voice is not loud enough to
carry that far. An additional problem is that a string could
break on the instrument. Then there could be no
accompaniment to the message. It is clear that the best situation
would involve less distance. Then there would be fewer
potential problems. With face to face contact, the least number
of things could go wrong. (Bransford & Johnson, 1972, p. 392)
Single-Factor—More Than Two Levels
• Left: context sketch
• Right: partial context sketch
Single-Factor—More Than Two Levels
• Between-subjects, multilevel designs
• Effects of practice ruled out (1 rep = 2 reps)
• Context has to accurately reflect content (“partial context”
condition poor)
• Context must be there when studying content (“context after”
condition poor)
Single-Factor—More Than Two Levels
• Within-subjects, multilevel designs
• Research Example: Debunking the Mozart effect
• Multilevel repeated measures
• IV  listening experience
• Listening to Mozart
• Listening to gentle rainstorm
• Control – no listening
• DV  recall of digits
• Results 
• No “Mozart” effect
• Significant practice effect instead
Single-Factor—More Than Two Levels
• Presenting the data
• Sentence and paragraph form
• Table form (e.g., for the balloon study)
Single-Factor—More Than Two Levels
• Presenting the data
• Graph form
• Continuous variable – unlimited intermediate values exist
• e.g., drug dosage level
• Line graph preferred, but bar graph OK
• Discrete variable – no intermediate values
• e.g., the five levels of the context experiment
• Use a bar graph, line graph inappropriate, for example:
Single-Factor—More Than Two Levels
• Analyzing single-factor, multilevel designs
• Multiple t tests inappropriate
• Increases chances of Type I error
• 1-factor Analysis of Variance (ANOVA)
• “1-factor” = 1 IV
• “2-factor” = 2 IVs (factorial design – Chapter 8)
• Once overall significant effect found, then post hoc testing
• Comparing each level of IV against each other level
Special-Purpose Control Group Designs
• Placebo control groups
• Placebo – inactive substance
• Ss think they are being treated but they are not
• Placebo effect
• When performance of placebo group = experimental group, Ss expectations
explain the effect of treatment
• Wait list control groups
• To insure equivalent groups in a study of program
effectiveness
• Wait list group later administered treatment only if shown to
be effective (unethical to deny treatment)
Special-Purpose Control Group Designs
• Research example 15:
Placebo + Wait List
• IV  exposure to subliminal
recordings
• Experimental  weight loss
recording
• Placebo control  dental pain
recording (but told was weight
loss tape)
• Waiting list control  no tape
until wait was over
• Double blind procedure used
• DV  weight loss
• Results  equal amounts of weight
loss for all three groups
Special-Purpose Control Group Designs
• Yoked control groups
• Each control group subject “yoked” to an experimental group
subject.
• In experimental designs in which members of an experimental group and a control
group are paired, the yoked control group members receive the same stimuli,
reinforcements, or punishments as the experimental group members but without the
possibility of influencing these effects through their own behavior.
Example: Reward and test performance.
Summary
• Depending on your empirical question, you may choose which type of
design to use:
• Between-subjects vs. within-subjects
• Single factor, two level vs. Single factor, multi-level
• Special-purpose control group designs
• Depending on the type of design, you will choose the appropriate
statistical test to test your hypothesis
• e.g., independent samples t-test, dependent samples t-test, 1-way ANOVA +
post-hoc tests
• Once you have your results, you share them with others both in
writing and in visual form (tables, graphs)