Behavioral Research
Chapter 10
Complex Experimental Designs
Simple designs
• Composed of one indep var that is
manipulated with two levels and one dep
var which is measured.
– Example: IV: Stress vs. no stress (control)
• Both measured by a test of cognitive function
• Hypothesis: The affects of stress impair cognitive
function stress as well as cognitive function
would have to be operationally defined as to what
was used as a stressor (IV) and what
measurement did one use to measure cognitive
function (DV)
Factorial design
• Designs with more than one indep var or
factor .
– all levels of each indep var are combined with
all levels of the other indep var
– The simplest type of factorial design is a 2 X
2—has two indep var, each having two levels.
Example: 2 X 2
– Indep var 1: difficulty of the task—easy or
hard
– Indep var 2: attitude of the confederate—
helpful or mocking
– Dep var: performance on a cognitive task
Four Experimental Conditions for
2 X 2 Factorial Design
–
–
–
–
Easy task – helpful confederate
Easy task – mocking confederate
Hard task – helpful confederate
Hard task – mocking confederate
Interpretation of Factorial Designs
•
Main Effect The impact of each IV on
the DV.
-The number of main effects depends on the
number of Independent Variables.
•
InteractionThe effect of any combination
of two or more IV. on the D.V..
– The effect that an independent variable has
on the dependent variable depends on the
level of the other independent variable.
Example: Factorial design
Examining the after-effects of exposure to an irritating
noise on several behavioral measures as a measure
of frustration: Two levels of each independent variable
• Hypothesis: IV one: Irritating noise: loud vs. soft
IV two: Predictability: predictable vs. non
predicable
DV: Number of attempts at difficult
puzzle during different noise levels
Noise intensity vs. Predictability
Loud
Soft
Group 1
Group 2
Unpredictable Group 3
Group 4
Predictable
Noise intensity vs.
Predictability
Loud
Soft
7
8
Unpredictable 3
5
Predictable
Calculating Main Effects:
Comparing Row and Column
Means
Column Means: Loud= 5
Soft = 6.5
Row Means: Predictable =
7.5
Unpredictable = 4
Interpretation of Main Effects
– A reliable difference in the column means
would indicate an effect of noise intensity,
independent of noise predictability
– A reliable difference in the row means would
indicate an effect of noise predictability,
independent of noise intensity
Interactions
• Number of attempts to solve the difficult
puzzle was greater when the noise was
soft than when it was loud.
• However, this relationship was dependent
on whether the noise was unpredictable
Effects of Predictable vs. Unpredictable
Loud vs. Soft Noise on Puzzle Attempts
Number of puzzle
attempts
10
8
6
Predictable
4
Unpredictable
2
0
Loud VS. Soft
Factorial Designs With Manipulated and
Nonmanipulated Variables: IV X PV Designs
– allow researchers to investigate how different types
of individuals respond to the same manipulated
variable
– E.g., of Participant variables – gender, age, ethnic
group, personality characteristics
– The simplest IV X PV design includes one
manipulated independent variable with at least two
levels and one Participant variable with at least two
levels
– E.g., Participant variable – two different age groups;
or males vs. females
IV X PV design, Furnham, Gunter,
Peterson (1994)
– Showed that the ability to study with a distracting
task in the room is affected by whether you are
more extraverted or introverted
– Manipulated var—distraction
– Subject var—extroversion or introversion
– Measured var—reading comprehension
– A repeated measures design was used  college
students read material in silence and within hearing
range of a TV program
Results
– Overall, students had higher comprehension
scores when they studied in silence
– Interaction between extraversion and
distraction
– Without a distraction, the performance of
extraverts and introverts was the same
– However, extraverts performed better than
introverts when the TV was on.
Further Considerations in Factorial Designs
• If you were to have a 2 x 2 x 2 factorial design,
you could look at it as two 2 x 2 designs.
– E.g., 2 (instruction method: lecture or discussion) x
2 (class size: 10 or 40) x 2 (gender)
– Divide 2 x 2s by gender—2x2 for males and 2x2 for
females
– Could then look at the main effects and interactions
within each of these 2 x 2s )(three main effects)
– gender
– lecture vs. discussion
– class size (small=10; large= 40)
Interactions in a 2 X 2 X 2
– Could also look at the interaction in the 2 x 2 x
2 design—have the possibility of 3 simple
interactions
– Instruction method and class size
– Instruction method and gender
– Class size and gender
» Could also have a three-way interaction, where the
effect of the interaction b/t two of the variables differs
depending on the particular level of the third variable
» Three-way interactions are complicated and hard to
interpret
F-Statistic
• Used in Factorial Designs
• Is an extension of the t-test.
• It is an analysis of variance that is a more general
procedure than the t-test.
• When a study has only one independent variable and only
two groups using an F or a t makes no difference.
• However analysis of variance (ANOVA) is conducted when
there are more than two levels of an independent variable
and when a factorial design with two or more independent
variables is used.
• Therefore, the f-test is appropriate for the simplest
experimental design as well as more complex.
• T-test demonstrates the relationship between two
groups and the within group variability
• F test is the ratio of two types of variance:
– Sytematic variance: deviation of the group means from
the grand man which is the mean score of all individuals
in all groups. (Grand mean-5.75: Loud=5, Soft = 6.5)
• Is small when the differences between group means is small and
increases as the group mean differences increase
– Error variance: the deviation of the individual scores in
each group from their respective group mean.
F-Significance
• Ratio of Systematic variance over Error
Variance.
• Therefore you want systematic variance
(difference between groups as shown by
comparing grand mean to group means) to
be high.
• Error variance to be low (comparison of
individual scores against the group mean)
• Low error variance indicates homogeneity
within your groups which will increase your
F statistic and be more likely to show
significant results.