For example, in the interaction plot above we saw that the effect of partner_status appeared weak among the factory = high group, but it was larger for the other two fcategory levels. The next step is to determine the nature of the interaction. We will want to determine when each factor is significant. This tells us that the effect of partner status will depend on levels of F-category, or vice versa. We indeed find that \(p = 0.023\) for fcategory*partner_status. However, if the interaction is significant, the main effects will not be very helpful, as we will need to explore when each factor is significant given levels of the other factor. If it is non-significant, we can proceed to looking at the main effects. The first thing to investigate is the significance of the interaction. First, let’s look at the interaction plot. We will get two tables with class level information, three tables with the ANOVA results, and an interaction plot. Finally, the model statement specifies that conformity is the outcome, and fcategory | partner_status means we want both main effects and the interaction for our treatment variables. If we were to interpret the results as a regression model, the ref options specify which level of the categorical variables we would treat as the reference value. The ref options are not required but are specified here so that the figures that are output from the procedure are ordered from low to high. This syntax specifies that we have two categorical variables, fcategory and partner_status. Model conformity = fcategory | partner_status proc glm data=moore Ĭlass fcategory (ref = "high") partner_status (ref = "high") When we calculate the two-way ANOVA using the glm procedure, it will generate an interaction plot. In other words, the mean differences on the first factor depend on the the level of the second factor. Non-parallel lines mean that an interaction is likely present. Parallel lines indicate that no interaction is present, because the mean differences in the first factor are the same regardless of the level of the other factor. An interaction plot shows the means for the outcome within each level of one of the factors, with separate lines for the other factor. We can also get a sense of whether an interaction is present by looking at an interaction plot. Vbox conformity / category=fcategory group=partner_status We will use the following code: proc sgplot data=moore The following boxplot shows the distribution of scores on the conformity variable within each combination of partner_status and fcategory. First, let’s inspect the data for outliers or funky distributions. We are also interested in exploring whether their F-score category (a measure of authoritarianism) affects outcomes or interacts with partner status. We are interested in exploring an individuals conformity based on their partner’s status: carData: Companion to Applied Regression Data Sets. The subjects could either conform to the partner’s judgment or stick with their own judgment.” (John Fox, Sanford Weisberg and Brad Price (2018). This data frame consists of subjects in a “social-psychological experiment who were faced with manipulated disagreement from a partner of either of low or high status. We will be using the Moore dataset, which can be downloaded from our GitHub repository.
How to perform two way anova in excel how to#
This tutorial is going to take the theory learned in our Two-Way ANOVA tutorial and walk through how to apply it using SAS.
0 kommentar(er)
