Analysis of Experimental Designs II: Factorial ANOVA and Statistical Interpretation
An assignment solution covering factorial ANOVA and statistical data interpretation.
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WattsSEDU7006-8-4 1
Experimental Designs II
Experimental Designs II
Analysis of Experimental Designs II: Factorial ANOVA and Statistical
Interpretation
Jackson (2012) Chapter Exercises
#2.A 4 x 6 factorial design has two independent variables; the first with four levels and
the second with six. A 4 x 6 factorial design has 24 conditions.
#4.A cell mean represents the average score of participants in a condition, where a
specific value of each independent variable interacts. Main effect means represent the average
score of participants for a single independent variable, where no interaction with other
independent variables are considered.
#6.In a complete factorial design comparison is made between each level of all
independent variables with each level of every other independent variable. An incomplete
factorial design is considered incomplete because comparisons are not made between each level
of all independent variables with each level of every other independent variable; some
comparisons are not made (Jackson, 2012).
#8.The number associated with the way of the ANOVA identifies the number of
independent variables. A two-way ANOVA is an analysis of variance with two independent
variables. A three-way ANOVA is an analysis of variance comparing three independent
variables.
#10.
Experimental Designs II
Experimental Designs II
Analysis of Experimental Designs II: Factorial ANOVA and Statistical
Interpretation
Jackson (2012) Chapter Exercises
#2.A 4 x 6 factorial design has two independent variables; the first with four levels and
the second with six. A 4 x 6 factorial design has 24 conditions.
#4.A cell mean represents the average score of participants in a condition, where a
specific value of each independent variable interacts. Main effect means represent the average
score of participants for a single independent variable, where no interaction with other
independent variables are considered.
#6.In a complete factorial design comparison is made between each level of all
independent variables with each level of every other independent variable. An incomplete
factorial design is considered incomplete because comparisons are not made between each level
of all independent variables with each level of every other independent variable; some
comparisons are not made (Jackson, 2012).
#8.The number associated with the way of the ANOVA identifies the number of
independent variables. A two-way ANOVA is an analysis of variance with two independent
variables. A three-way ANOVA is an analysis of variance comparing three independent
variables.
#10.
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Statistics