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Chapter 9

PSYC 201W Chapter 9: Psyc 201W - Chapter 9 Notes

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A.George Alder

Chapter 9 – Factorial Designs Introduction • Factorial design – includes two or more independent variables and crosses (i.e., combines) every level of each independent variable with every level of all the other independent variables Basic Characteristics of Factorial Designs • Most basic factorial design possible is combining two independent variables, which have two levels each, creating an experimental design that has four conditions Describing A Factorial Design • Between-subjects factorial design – a factorial design in which each subject engages in only one condition • Within-subjects factorial design – a factorial design in which each subject engages in every condition • Mixed-factorial design – a factorial design that includes at least one between- subjects variable and at least one within-subjects variable Advantages of Factorial Designs • Factorial designs are better able to capture multiple factors operating simultaneously than designs that manipulate only one independent variable • Main effect – occurs when an independent variable has an overall effect on a dependent variable • Example  A main effect for humidity would mean that overall, as humidity changes, this influences task performance Examining Interactions Between Independent Variables • Studying both variables in the same experiment allows us to determine whether the way one independent variable influences behaviour differs, depending on the level of a second independent variable • Interaction (or interaction effect) – occurs when the way in which an independent variable influences behaviour differs, depending on the level of another independent variable • Example  the effect of one drug differs, depending on whether a second drug is also being used • Most important advantage of factorial designs: o Ability to test whether unique combinations of two or more independent variables affect our behaviour in ways that cannot be predicted simply by knowing how each variable individually affects behaviour Examining Moderator Variables • Moderator variable – a variable that alters the strength or direction of the relation between an independent and dependent variable • The effect of the independent variable on behaviour depends on the level of the moderator variable, and this is precisely what an interaction involves Other Advantages • More efficient to conduct, as compared to conducting a series of separate experiments, each of which examines only one independent variable o Example  rather than setting up equipment and initiating procedures for recruiting and scheduling participants for each individual experiment, all these administrative and procedural aspects of conducting an experiment can be done just once when a factorial design is used • Researchers frequently wish to examine whether situational factors have different effects on different types of people o Example  would the effects of room temperature and humidity on task performance be the same for women and men, or older versus younger adults? Limitations of Factorial Designs • As the number of independent variables increases, and as the number of levels within each independent variable increases, the total number of conditions in an experiment can rapidly increase beyond manageable proportions o Practical issues may make it difficult to conduct the experiment: ▪ Examples: • Number of participants that need to be recruited • Amount of time that each participant must devote to the experiment • The amount of time and effort that the researchers need to expend • As the number of independent variables and conditions increases, interpreting the results often becomes more difficult and mentally taxing, because of all the possible outcomes that can occur Designing A Factorial Experiment • Practical limitations often necessitate a choice between adding independent variables to a factorial design or adding more levels to a smaller number of independent variable Examining Non-Linear Effects • When a researcher seeks to determine whether an independent variable has a nonlinear influence on behaviour, that variable must be designed to have three or more levels • Example  errors increase at higher temperature levels more rapidly when humidity is high than when it is low Incorporating Subject Variables • Subject variables – represent characteristics of the people or non-human animals who are being studied • Scientific and practical questions:
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