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PSYB01 - Chapter 10 notes.doc

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Anna Nagy

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: 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 • That is, an interaction tells us that the effect of one independent variable depends on the particular level of another • We can see an interaction in the Ellsworthy study: the effect of the type of question is different depending on whether the questioner is knowledgeable or not • Thus, the relationship between type of question and recall error is best understood by looking at both independent variebles. • Interactions can be seen easily when the means for all conditions are presented in a graph • The concept of interaction is a simple one that we use often, for example, when we say “it depends” because “it depends” on some other variable. • When graphing the experimental results, the dependent variable is always on the vertical (y) axis and one independent variable is placed on the horizontal (x) axis. Bars are then drawn (and usually colour coded) to represent each of the levels of the other independent variable. Factorial Designs with Manipulated and Non-manipulated Variables • one common type of factorial design includes both experimental (manipulated) and non-experimental (measured or non-manipulated) variables • they are often called IV x PV designs (independent variable by participant variable) and they allow researchers to investigate how different types of individuals/participants respond to the same manipulated variable. • Theses participant variables are personal attributes of the participants, such as age, gender, ethnicity, personality characteristics, etc. They are sometimes called subject variables or attribute variables • The simplest IV X PV design includes one manipulated independent variable with at least 2 levels and one participant variable that has at least 2 levels (eg two age groups) • Peterson conducted an experiment in which he found that the degree to which we are able to focus/pay attention when we are trying to read when there are
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