contrasts can be applied only to factors with 2 or more levels,

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ice cream, dessert, food @ Pixabay

The order in which factors are evaluated can have a significant impact on the results. Consider this example: If we want to find out how much people like chocolate, and ask them “Do you prefer chocolate or vanilla?” then their answer will depend on whether they were asked first about their preference for chocolate or vanilla ice cream. The same goes for many other types of research experiments where participants are randomly assigned to different groups. People who prefer chocolate (e.g., group A) might rate it as significantly better than those who prefer vanilla (e.g., group B). But if you reverse the order so that people in Group A are asked first about their preference for chocolate, and then Group B is asked about its preference for chocolate, the results may be reversed. In some cases this order reversal can make a huge difference in the final result. But before relying on such an analysis it is important to ensure that your use of contrasts was appropriate and not biased by factors other than those you are testing for. -The Order Factor (A/B) -Factor #X -The Contrasts or Main Effect(Y) A vs B -Main Effects Y:x variations (n=number of different x’s used) -Model Summary Output: Estimate Std Error t value Pr(t|T)*Hypothesis df*Pseudo R^R^(-value)–>(A)\par \text{or}\par

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