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Increasing the Number of Levels of an Independent Variable In the simplest experimental design, there are only levels of the independent variable. However, researchers design experiments with three or more levels for several reasons. |
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One reason is that a design with only two levels cannot provide very much information about the exact of the relationship between the independent and dependent variable. |
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For instance, when there are only two levels, a relationship cannot be detected. |
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Another reason is that researchers are often interested in comparing more than groups. |
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Another reason is that researchers are often interested in comparing more than groups. |
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Increasing the Number of Independent Variables: Factorial Designs In addition to increasing the levels of an independent variable, researchers also manipulate more than one variable in a single experiment. |
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Researchers know that in any given situation, a number of variables are operating on behavior . |
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Therefore, they design experiments with more than one independent variable that will more closely resemble -world conditions. |
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designs are designs with more than one independent variable. |
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In a factorial design, levels of each independent variable are combined with all of the other independent variables. |
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The simplest factorial design is known as a factorial design. |
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It has independent variables, each having levels.Factorial designs may be more complex, such as a 2 x 2 x 4. |
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Regardless of the complexity, all factorial design yields kinds of information. |
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The effect each independent variable has one the dependent variable and the effect of one independent variable depending on the levels of the other independent variable. |
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One common type of factorial design includes both manipulated and variables. This design is sometimes called a design. |
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The IV refers to variable and the PV refers to variable. |
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This design allows researchers to investigate how different types of individuals respond to the same variable. |
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There are several ways of assigning participants to conditions of each independent variable: groups design and measures design. |
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It is also possible to use a factorial combined assignment procedure in which participants are assigned to the conditions of one independent variable using the independent groups procedure and assigned to the conditions of another independent variable using the repeated measures procedure. |
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When the data from factorial designs are analyzed, there are possible outcomes. |
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There may or may not be a significant effect for each independent variable and there may or may not be a significant between the independent variables. |
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To assess the statistical significance of the main effects and the interaction(s), a procedure called of Variance is used. |
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