# Coursera conducting linear mixed model using social sav data

Conducting Linear Mixed Model using Social.sav Data

Qn6. Because the omnibus linear mixed model (LMM) did not result in a significant main effect of Engine on Searches, post hoc pairwise comparisons were not justified. As a result, despite one such comparison having p < 0.05, strictly speaking this “finding” must be disregarded

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Qn7. Recall our file socialvalue.cv. If you have not done so already, please download it form the course materials. This file describes a study of people viewing a positive or negative film clip before going onto social media and then judging the value of the first 100 posts they see there. The number of valued posts was recorded. You originally analyzed this data with a 2x2 within subjects ANOVA. Now you will use a linear mixed model (LMM). Let’s refresh our memory: How many subjects took part in this study?

Qn8. To the nearest whole number, how many more posts were valued of Facebook than Twitter after seeing a positive film clip?

Qn9. Conduct a linear mixed model (LMM) on valued by social and Clip. To the nearest ten-thousandth (four digits), what is the p-value of the interaction effect? Hint: use the lme4 library and its lmer function with subject as a random effect. Then use the car library and its Anova function with type = 3 and test.statistic = “F”. Prior to either, set sum-to-zero contrasts for both social and clip.

Planned Pairwise comparisons of the data
Q10. Conduct two planned pairwise comparisons of how the film clips may have influenced judgements about the vale of social media. The first question is whether on Facebook, the number of valued posts was different after people saw a positive film clip versus a negative film clip. The second question is whether on Twitter, the number of valued posts was different after people saw a positive film clip versus a negative film clip. Correcting for these two planned comparisons using Holm’s sequential Bonferroni procedure, to the nearest ten-thousandth (four digits), what is the lowers corrected p-value of the two tests? Hint: use the multcomp and lsmeans libraries and the lsm function within the glht function. Do not correct for multiple comparisons yet as only two planned comparisons will be regarded. After retrieving the two as-yet uncorrected p-values of interest manually pass them to p.adjust for correction.

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