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Main effects are any differences between the levels of one independent variable. An interaction effect occurs when the influence of an independent variable on a given dependent variable depends on the level of other factors being examined. It might be found, for instance, that the impact of salary on worker productivity may be more pronounced for entry level as opposed to experienced employees. This type of analysis allows researchers to gain a deeper view of patterns that may emerge in the data set. Remember, an interaction occurs when the effect of one independent variable depends on the level of the other independent variable.
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It’s the difference between performance in the Distraction and No-Distraction conditions. In general, it is very common to use the word effect to refer to the differences caused by the manipulation. We manipulated distraction, it caused a difference, so we call this the “distraction effect”. The number of interactions in the design also depend on the number of IVs. This occurs when the effect of say IV2 (whether there is a difference between the levels of IV2) changes across the levels of IV1.
Contrasts, main effects and interactions
As an exercise toward this goal, we will first take a closer look at extracting main effects and interactions from tables. This exercise will how the condition means are used to calculate the main effects and interactions. Instead of using tables to show the data, let’s use some bar graphs. First, we will plot the average heights in all four conditions.
1.1 2x2 Designs
Although she found that creativity was unaffected by the ambient odor, she found that people’s moods were lower in the dimethyl sulfide condition, and that their perceived health was greater in the lemon condition. When we use a 2×2 factorial design, we often graph the means to gain a better understanding of the effects that the independent variables have on the dependent variable. In simulation research, we are often interested in comparing the effects of more than one independent variable. To continue with more examples, let’s consider an imaginary experiment examining what makes people hangry.
Main Effects
This could be because being in a happy or sad mood has no effect on memories for childhood events. But it could also be that the music was ineffective at putting participants in happy or sad moods. A manipulation check, in this case, a measure of participants’ moods, would help resolve this uncertainty. If it showed that you had successfully manipulated participants’ moods, then it would appear that there is indeed no effect of mood on memory for childhood events. But if it showed that you did not successfully manipulate participants’ moods, then it would appear that you need a more effective manipulation to answer your research question.
Confidence Interval for the Difference in Proportions
Results could potentially reveal that high productivity found in entry level employees may or may not apply to those who are more experienced. Likewise, low productivity that may be found in low salaried employees may or may not be evident with increased wages. The ability to recognize if results can be generalized to different circumstances or group features thus serves as another advantage for this type of design.
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Non-Experimental Studies With Factorial Designs
Since the high and low levels for each factor may not be known when the design is first created, it is convenient to be able to define them later. Factors A - D can be renamed to represent the actual factors of the system. Additionally, a low and high value are initially listed as -1 and 1, where -1 is the low and 1 is the high value. The low and high levels for each factor can be changed to their actual values in this menu. Ignoring the first row, look in the last stage and find the variable that has the largest relative number, then that row indicates the MAIN TOTAL EFFECT.
In essence, factor analysis organizes the variables into a smaller number of clusters, such that they are strongly correlated within each cluster but weakly correlated between clusters. Each cluster is then interpreted as multiple measures of the same underlying construct. The Big Five personality factors have been identified through factor analyses of people’s scores on a large number of more specific traits.
Introduction to Statistics is our premier online video course that teaches you all of the topics covered in introductory statistics. For example, this means the effect that sunlight has on plant growth depends on the watering frequency. In the previous plot, the two lines were roughly parallel so there is likely no interaction effect between watering frequency and sunlight exposure.
A take-home message before we begin is that some independent variables (like shoes and hats) do not interact; however, there are many other independent variables that do. Manipulation checks are usually done at the end of the procedure to be sure that the effect of the manipulation lasted throughout the entire procedure and to avoid calling unnecessary attention to the manipulation. Manipulation checks become especially important when the manipulation of the independent variable turns out to have no effect on the dependent variable.
Importantly, the effect of the gas variable on driving depends on the levels of having a key. Or, to state it in reverse, the effect of the key variable on driving depends on the levesl of the gas variable. As expected, we the average height is 6 inches taller when the subjects wear a hat vs. do not wear a hat. So, the main effect of wearing hats is to add 1 inch to a person’s height. We can see from the figure the direction of this difference, but let’s look at the table to see the numbers more clearly.
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Furthermore, collecting data for different combinations of conditions enables researchers to make a variety of assessments, including main and interaction effects. Just as including multiple levels of a single independent variable allows one to answer more sophisticated research questions, so too does including multiple independent variables in the same experiment. As we will see, interactions are often among the most interesting results in empirical research. In Chapter 1 we briefly described a study conducted by Simone Schnall and her colleagues, in which they found that washing one’s hands leads people to view moral transgressions as less wrong [SBH08]. In a different but related study, Schnall and her colleagues investigated whether feeling physically disgusted causes people to make harsher moral judgments [SHCJ08]. In this experiment, they manipulated participants’ feelings of disgust by testing them in either a clean room or a messy room that contained dirty dishes, an overflowing wastebasket, and a chewed-up pen.
For example, people are either low in self-esteem or high in self-esteem; they cannot be tested in both of these conditions. Recall that in a between-subjects single factor design, each participant is tested in only one condition. This would mean that each participant would be tested in one and only one condition. Just as including multiple dependent variables in the same experiment allows one to answer more research questions, so too does including multiple independent variables in the same experiment.
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