Chapter 10&11 PsyB01.doc

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Connie Boudens

Chapter 10 Complex Experimental Designs Increasing the number of levels of an independent variable - A researcher might want to design an experiment with three or more levels for several reasons: o A design with only two levels of the independent variable cannot provide very much information about the exact form of the relationship between the independent and dependent variable o An experimental design with only two levels of the independent variable cannot detect curvilinear relationships (the direction of the relationship changes, like an inverted-U) between variables o Finally, researchers frequently are interested in comparing more than two groups Increasing the number of independent variables: Factorial designs - This type of experimental design is a closer approximation of real-world conditions, in which independent variables do not exist by themselves; researchers recognize that in any situation a number of variables are operating to affect behaviour - Factorial designs are designs with more than one independent variable (or fact); in a factorial design, all levels of each independent variable are combined with all levels of the other independent variables o The simplest factorial design known as a 2 x 2 (two by two) factorial design has two independent variables, each having two levels Interpretation of Factorial Designs - Factorial designs yield two kinds of information: o Information about the effect of each independent variable taken by itself: the main effect of an independent variable In a design with two independent variables, there are two main effects one for each independent variable o The second type of information is called an interaction; if there is an interaction between two independent variables, the effect of one independent depends on the particular level of the other variable In other words, the effect that an independent variable has on the dependent variable depends on the level of the other independent variable Main Effects - A 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 Interactions - An interaction between independent variables indicates that the effects of one independent variable is different at different levels of the other independent variable o That is, an interaction tells us that the effect of one independent variable depends on the particular level of the other - Interactions can be seen easily when the means for all conditions are presented in a graph o The dependent variable is always placed on the vertical axis o One independent variable is placed on the horizontal axis o Bars are then drawn to represent each of the levels of the other independent variable - The concept of interaction is a relatively simple one that you probably use all the time; when we say it depends, we are actually indicating that some sort of interaction is operating it depends on some other variable Factorial Designs with Manipulated and Nonmanipulated Variables - One common type of factorial design includes both experimental (manipulated) and nonexperimental (measured or nonmanipulated) variables - These designs sometimes called IV x PV designs (independent variable by participant variable) allow researchers to investigate how different types of individuals respond to the same manipulated variable - Participant variables are personal attributes such as gender, age, ethnic group, and personality characteristics o Participant variables are also called subject variables or attribute variables - The simples IV x PV design includes one manipulated independent variable that has at least two levels and one participant variable with at least two levels o The two levels of the subject variable might be two different age groups, two different gender groups, etc. Interactions and Moderator Variables - In many research studies, interactions are discussed in terms of the operations of a moderator variable - A moderator variable influences the relationship between two other variables Outcomes of a 2 x 2 Factorial Design - A 2 x 2 factorial design has two independent variables, each with two levels - When analyzing results, there are several possibilities: o There may or may not be a significant main effect for independent variable A o There may or may not be a significant main effect for independent variable B o There may or may not be a significant interaction between the independent variables - In a 2 x 2 factorial design, there are eight possible outcomes Interactions and Simple Main Effects - Analysis of variance is used to assess the statistical significance of the main effects and the interaction in a factorial design
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