Doing Mixed Effects Models
Qn1. Recall our file websearch3.csv. If you have not done so already, please download if from the course materials. This file describes a study of the number of searches people did with various search engines to successfully find 100 facts on the web. You originally analyzed this data with a one-way repeated measures ANOVA. Now you will use a linear mixed model (LMM). Let’s refresh our memory: How many subjects took part in this study?
Qn2. To the nearest hundredth (two digits), how many searches on average did subjects require with the Google search engine?
Qn3. Conduct a linear mixed model (LMM) on Searches by Engine. To the nearest ten-thousandth (four digits), what is the p-value of such a test? Hint: use the lme4 library and its lmer function with the subject as random effect. The use the car library and its Anova function with type = 3 and test.statistic = “f”. Prior to either, set sum-to-zero contrasts for engine.
Qn4. In light of your p-value result, are post hoc pairwise comparisons among levels of Engine justified, strictly speaking?
Qn5. Regardless of your answer to the previous question, conduct simultaneous pairwise comparisons among all levels of Engine. Correct your p-values with Holm’s sequential Bonferroni procedure. To the nearest ten-thousandth (four digits), what is the lowest corredted p-value resulting from such tests? Hint: use the multcomp library and its mcp function from within a call to its glht function.
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