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Chapter 10

PSYB01 Textbook Notes - Chapter 10

6 Pages
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Department
Psychology
Course Code
PSYB01H3
Professor
Anna Nagy

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Chapter 10 – Complex Experimental Design
Increasing the Number of Levels of an Independent Variable
In the simplest experimental design there are only two levels of the
independent variable
However, a researcher may want to design an experiment with three or more
levels for several reasons:
1. A design with only two levels cannot provide very much info about the
exact relationship. Eg look at example 10.1 on page 187. It illustrates
how a relationship can go from a positive linear relationship to a
monotonic positive relationship by adding levels.
2. an experimental design with only two levels of the independent
variable cannot detect curvilinear relationships (recall form chapter 4 –
in a curvilinear relationship the relationship between variables changes
and sot he graph changes direction at least once). If a curvilinear
relationship is predicted, at least three levels must be used. For
example, the relationship between fear arousal and performance – may
such relationship exist in psychology!
3. Researchers are often interested in comparing more than two groups.
For example, when comparing the effect of playing with animals on
elderly people, they may want to test the difference between playing
with a dog, playing with a cat, playing with a bird, or playing with no
animal at all.
Increasing the Number of Independent Variables: Factorial Designs
Researchers often more than one independent variable in a single experiment, -
typically 2 or 3 independent variables are operating simultaneously, which is a
closer approximation of real-world conditions in which independent variables do
not exist by themselves
In any given situation a number of variables are operating to affect behaviour – eg
the experiment in which both the crowding and the windows were effecting the
cognitive performance of participants
It is possible to design an experiment with more than one independent variable
Factorial Designs are designs with more than one independent variable or factor
In a factorial design, all levels of each independent variable are combined with all
levels of the other independent variables
In the simplest factorial design, known as a 2 x 2 (two by two) factorial design –
there are two independent variables each with two levels
In a study by Ellesworth, a 2 x 2 design was used. They studied the effects of
asking misleading questions on the accuracy of eyewitness testimony. The second
independent variable was the questioner’s knowledge of the crime: either they
were knowledgeable or naïve. This 2 x 2 design resulted in 4 experimental
conditions:
www.notesolution.com
1. knowledgeable questioner – misleading questions
2. knowledgeable questioner – honest questions
3. naïve questioner – misleading questions
4. naïve questioner – honest questions
the general format for describing a factorial design is:
Nuber of levels x Number of levels x Number of levels
of first IV of second IV of third IV
and so on
a design with three two independent variables, one with two levels and one with
three levels would have a 2 x 3 factorial design. There are therefore six conditions
in the design.
Interpretation of Factorial Designs
Factorial designed yield two types of info:
1. the effect of each independent variable taken by itself. This is known as
the main effect of an independent variable. In a design with two
independent variables, there are two main effects, one for each
independent variable
2. interaction – if there is interaction between two independent variables,
the effect of one independent variable depends of the particular level of
the other variable. In other words, the effect that one independent variable
has on a dependent variable depends on the level of the other independent
variable. To illustrate main effect and interaction, look at table 10.1 on
page 190 which illustrates a common method of presenting both outcomes
– the number in each cell is the mean percent of errors made in the four
conditions of the experiment
Main Effects
the main effect is the effect each variable has by itself.
The main effect of each independent variable is the overall relationship between
the independent variable and the dependent variable.
Good explanation of chart 10.1 on pg 190
Interactions
These main effects tell us that overall there are more errors when the questioner is
knowledgeable and when the questions are misleading, but there is also the
possibility that an interaction exists; if so, the main effects of the independent
variables must be qualified because an interaction between independent variables
indicated that the effect of one independent variable is different at different at
different levels of the other independent variable
www.notesolution.com

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Description
Chapter 10 Complex Experimental Design Increasing the Number of Levels of an Independent Variable In the simplest experimental design there are only two levels of the independent variable However, a researcher may want to design an experiment with three or more levels for several reasons: 1. A design with only two levels cannot provide very much info about the exact relationship. Eg look at example 10.1 on page 187. It illustrates how a relationship can go from a positive linear relationship to a monotonic positive relationship by adding levels. 2. an experimental design with only two levels of the independent variable cannot detect curvilinear relationships (recall form chapter 4 in a curvilinear relationship the relationship between variables changes and sot he graph changes direction at least once). If a curvilinear relationship is predicted, at least three levels must be used. For example, the relationship between fear arousal and performance may such relationship exist in psychology! 3. Researchers are often interested in comparing more than two groups. For example, when comparing the effect of playing with animals on elderly people, they may want to test the difference between playing with a dog, playing with a cat, playing with a bird, or playing with no animal at all. Increasing the Number of Independent Variables: Factorial Designs Researchers often more than one independent variable in a single experiment, - typically 2 or 3 independent variables are operating simultaneously, which is a closer approximation of real-world conditions in which independent variables do not exist by themselves In any given situation a number of variables are operating to affect behaviour eg the experiment in which both the crowding and the windows were effecting the cognitive performance of participants It is possible to design an experiment with more than one independent variable Factorial Designs are designs with more than one independent variable or factor In a factorial design, all levels of each independent variable are combined with all levels of the other independent variables In the simplest factorial design, known as a 2 x 2 (two by two) factorial design there are two independent variables each with two levels In a study by Ellesworth, a 2 x 2 design was used. They studied the effects of asking misleading questions on the accuracy of eyewitness testimony. The second independent variable was the questioners knowledge of the crime: either they were knowledgeable or nave. This 2 x 2 design resulted in 4 experimental conditions: www.notesolution.com
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