# Copyright 2005, Prentice Hall, Sarafino

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CHAPTER 9 Factorial Designs Copyright 2005, Prentice Hall, Sarafino

What is a Factorial Design?
Factorial designs are common in psychology They are unique because they employ more that one independent variable. Because at least two IV are involved factorial designs generally produce two types of effects: Main Effects: Changes in the dependent variable due only to one IV. There is one main effect per IV. Interaction Effects: Changes in the dependent variable that result from a combination of the IVs. The results obtained due to an interaction often are not anticipated or could not be predicted if the IVs were presented individually. In other words, the influence of an independent variable changes across levels of another independent variable. Copyright 2005, Prentice Hall, Sarafino

Factorial Design Terminology
Factorial designs are typically written as: 2 X 2 ; 2 X 3 X 4 ; 3 X 6 X 2 X 2. What does this mean? 2 X 2 means that there are 2 IVs, and each has 2 levels or conditions. 2 X 3 X 4 means there are 3 IVs, one with 2 levels, another 3 levels, and one 4 levels 3 X 6 X 2 X 2 means there are 4 IVs, one with 3 levels, one with 6 levels, one with 2 levels, and another with 2 levels. Copyright 2005, Prentice Hall, Sarafino

Why Use Factorial Designs?
Factorials designs are more complex and require more participants than single-factor designs, but are beneficial because: They enhance external validity They are more efficient – it is easier to do one factorial design than two or more single-factor designs. They allow for an interaction effect! They can better control for both systematic and nonsystematic variance. Copyright 2005, Prentice Hall, Sarafino

The 2 X 2 Factorial Design Drug Dose Zero High Drug History User Cell 1 2 NonUser 3 4 The simplest factorial design is a 2 X 2 factorial design; 2 IVs each with 2 levels. In a 2 X 2 design there are 4 cells: See example of drug dose (Zero, High) X drug history (User, Non-User) Copyright 2005, Prentice Hall, Sarafino

2 X 2 : Effects With any 2 X 2 Factorial design there are 3 null hypotheses that state: There is no difference for the interaction. There is no main effect for one IV There is no main effect for the other IV. When trying to make sense of the results of a 2 X 2 design – always look for an interaction first, then look for main effects. Copyright 2005, Prentice Hall, Sarafino

How to Spot an Interaction
The easiest way to spot an interaction is to graph your results. The results need be graphed as followed: The y-axis: position the dependent variable here, the scale represents scores on DV. The x-axis: position one of your IVs here, the levels of the IV will be points on the axis. Plot the appropriate values (usually the mean) for each level (or cell). You should have 4 points plotted. Now join the two points together that represent the levels of the other IV (the one not on the x-axis). You should have 2 lines. Each line representing a level of the IV not on the x-axis. Copyright 2005, Prentice Hall, Sarafino

How to Spot an Interaction
If your lines on the graph are parallel then you do not have an interaction. If the lines are not parallel (they look like they converge) then you likely have an interaction. Copyright 2005, Prentice Hall, Sarafino

Composing a Survey Describe, in detail, your measure and refer to this description often. Decide on who you will measure. Why? Design items to gather demographic data. Design numerous items only on the topic you are interested in – do not try to measure everything. Rework your items – e.g., make them as simple and straightforward as possible. Avoid jargon, technical terms, negative wording, and double-barreled items. If using closed-ended items, be sure your response sets cover the complete range of possible answers in equal increments. Why? And be sure you response sets match the question. Why? Decide on the statistics you are likely to use. Pretest the survey on a group of people who will not be in your actual study. Tell them you want constructive criticism. Revise the test and make it look professional. Now the fun part begins – giving the survey and deciding if your survey is reliable and valid. This comes in later chapters. Copyright 2005, Prentice Hall, Sarafino

Other Types of Data: Remnant
Remnant data refers to the use of existing remains, products, or evidence of behavior to infer or explain past events. Some examples include: Physical Traces E.g., Graffiti, garbage. Archival Data E.g., birth weights and rates, weather reports and crime. Copyright 2005, Prentice Hall, Sarafino

How to Identify Pseudoscience?
The “Trappings” of Science Pseudoscience tries to be like real science. Pseudoscience makes predictions about phenomenon, but rarely tests them. Data is Often Based on Testimonials Data like this can be easily manipulated. Evasion of Disproof Explanations given by the pseudoscience to account for data that disprove the pseudoscience are difficult/impossible to test (e.g., the phenomenon can’t be measure by conventional means) Copyright 2005, Prentice Hall, Sarafino

Naturally Occurring Behavior
Sometimes psychologists want to study ongoing or naturally occurring behavior. This is unique because researchers not only have to be concerned about what they are observing, but also when and where the behavioral observations will be made. To deal with this, psychologists often employ: Time sampling technique Taking samples of behavior only at certain times. Event or Situation sampling technique Taking samples only in a predetermined situation. Copyright 2005, Prentice Hall, Sarafino

Types of Data The are two main categories of data: Quantitative Is numerical or can easily be converted to numerical form. 2. Qualitative Usually narrative in nature and difficult if not impossible to convert to numerical. Copyright 2005, Prentice Hall, Sarafino

Scales of Measurement Once you have determined the type of data you will generate, you now need to coordinate this with statistics procedures. The first step in doing this is to determine your data’s “scale of measurement”. There are 4 Different Scales of Measurement Nominal Ordinal Interval Ratio Let’s consider each one. Copyright 2005, Prentice Hall, Sarafino

Nominal Scale Most basic level of measurement. Numbers represent simple qualitative differences in your variables. E.g., 1 = group_1; 2 = group_2, etc. Numbers are not intended for numerical calculations, but to classify data. General rule: Similar objects or events are assigned similar numbers, and different objects get different numbers. Statistical procedures: Frequency counts; Chi-Square. Copyright 2005, Prentice Hall, Sarafino

Goals of Scientific Research
There are 4 main goals of scientific research: Description Prediction Understanding Application Copyright 2005, Prentice Hall, Sarafino