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

True
False

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.

if you'd like someone to help you with r statistics assignments, then you can send us your files with the assignments instructions, and we'll get back to you with the solutions on time.

Planned comparisons between domestic and international bookings

Qn11. Download the file bookflights.csv from the course materials. This file describes a survey in which website visitors books a flight on either Expedia, Orbitz, or Priceline. Whether they booed a domestic or international flight was recorded. The survey response was 1-7 rating for Ease on a Likert-type scale, with “7” being easiest. The research question is which site felt easiest to use overall, and specifically for domestic vs. international bookings. How many subjects took part in this study?

Qn12. Create an interaction plot with Website on the X-axis and International as the traces. How many times, if ay, do the two traces cross? Hint: if you already recoded Ease as an ordinal response, you must use as.numeric when passing it to interaction.plot.

Qn13. Use ordinal logistic regression to examine Ease by Website and International. To the nearest ten-thousandth (four digits), what is the p-value of the website main effect? Hint: Use the MASS library and its polr function with Hess = TRUE to create the ornidal logistic model. Then use the car library and its Anova function with type = 3. Prior to either, set sum-to-zero contrasts for both website and international.

Qn14. Conduct three planned comparisons between domestic and international bookings for each website. Adjust for multiple comparisons using Holm’s sequential Bonferroni procedure. What is the highest p-value from such tests? Hint: use the multcomp and lsmeans libraries and the lsmeans, pairs, and as.glht functions. (The lsm formulation from within glht will not work in this case.) Because we only have three planned pairwise comparisons, use “none” for the multiple comparisons adjustment to avoid correcting for all possible pairwise comparisons. Instead, just find the three planned and as-yet uncorrected p-values and pass them manually to p.adjust with method=”holm”. Since the formulation for simultaneous comparisons is a bit different, we place the code for those aspects of this questions here:

Summary(glht(m,lsm(pairwise ~ website * International)), test = adjusted (type = “non”)) 

Qn15. Which of the following conclusions are supported by the analyses we performed on bookflights.csv?

There was a significant main effect of website on Ease
There was a significant main effect of International on Ease
There was significant website*international interaction
Expedia was perceived as significantly easier for booking international flights than domestic
Orbitz was perceived as significantly easier for booking domestic flights than international flights
Priceline was perceived as significantly easier for booking domestic flights than interanion flights.

COnducting Planned Comparisons Coursera Quiz
Qn5. Download the file hwreco.csv from the course materials. This file describes a study of three handwriting recognizers (A,B,C) and subjects who were either right-handed or left-handed. The response is the number of incorrectly recognized handwriting words out of every 100 handwritten words. The research questions are how each recognizer fared overall and whether a given recognizer performed better for right-handed or left-handed writers. How many subjects took part in this study?

Qn6. Create an interaction plot with Recognizer on the X-axis and Hand as the traces. How many times, if any, do the two traces cross?

Qn7. Fit Poisson distributions to the Errors of each of the three Recognizer levels and test those fits with goodness-of-fit tests, To the nearest ten-thousandth (four digits), what is the lowest p-value produced by these tests? Hint: To fit a Poisson distribution, use the fitdistrplus library and its fitdist function. Then test the fit with the gotstat function.

Qn8. Use Poisson regression to examine Errors by Recognizer and Hand. To the nearest ten-thousandth (four digits), What is the p-value of the recognizer * hand interaction? Hint: Create a model with glm using family – poisson. Then use the car library and its Anova function with type = 3. Prior to either, set sum-to-zero contrasts for bot recognizer and Hand.

Qn9. Conduct three planned comparisons between left-and right-handed recognition errors within each recognizer, Adjust for multiple comparisons using Holm’s sequential Bonferroni procedure. What is the lowest corrected p-value form such tests? Hint: use the multcomp and lsmeans libraries and the lsm formulation of the right glht function. Because we only have three planned pairwise comparisons, use “non” for the initial multiple comparisons adjustment to avoid correcting for all possible pairwise comparisons. Instead, just find the three planned and as=ye uncorrected p-values and pass them manually to p.adjust with method=”holm”.

Qn10. Which of the following conclusions are supported by the analyses we performed on hwreco.csv? (Mark all that apply)

The handwriting counts seemed to be Poisson-distributed.
There was a significant main effect of Recognizer on Errors
There was a significant main effect of hands on Errors
There was a significant Recognizer * Hand interaction
For recognizer ‘a’ there were significantly more errors for right-handed writers that left handed writers.
For recognizer B, there were significantly more errors for left-handed writers than left-handed writers.
For recognizer C, there were significantly more errors for right-handed writers than left-handed writers. 

Doing Factorial ANOVAs

Qn20. Download the file socialvalue.csv from 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. Examine the data and indicate what kind of experiment design this was.

- A 2x2 between-subjects design with factors for clip (positive, negative) and social (Facebook, Twitter).
-A 2x2 within-subjects design with factors for clip(positive, negative) and social (facebook, Twitter).
-A 2x2 mixed factorial design with a between-subjects factor for clip (positive, negative) and a within-subjects factor for social (Facebook, Twitter).
- None of the above

Qn21. How many subjects took part in this experiment?
Qn22. To the nearest hundredth (two digits), on average how many posts out of 100 were valued for the most combination of clip and social?

Qn23. Create an interaction plot with social on the X-axis and clip as the traces. Do the lines cross?

Yes
No

Qn24. Create an interaction plot with clip on the X-axis and social as the traces. Do the lines cross?

Yes
No

Qn25. Conduct a factorial ANOVA to test for any order effects that the presentation order of the clip factor and/or the social factor may have had. To the nearest ten-thousandth (four digits), what is the p-value for the ClipOrder main effect? Hint: Use the ez library and its ezANOVA function. Pass both ClipOrder and Socialorder as the within parameter using a vector created with the “c” function.

Qn26. Conduct a factorial ANOVA on valued by clip and social. To the nearest hundredth (two digits), what is the largest F statistic produced by such a test? Hint: use the ez library and its function. Pass both clip and social as the within parameter using a vector created with the “c” function.

Qn27. Conduct two planned pairwise comparison using paired-samples t-tests. The first question is whether on Facebook, the number of valued posts was different after people saw a positive fil 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. Assuming equal variances and using Holm’s sequential Bonferroni procedure to correct for multiple comparisons, what to within a ten-thousandth (four digits) is the lowest p-value from these tests? Hint: use the reshape2 library and its dcast function to make a wide-format table with columns for subject and the combination of social* clip, and then do a paired-samples t-test between columns with the same social level.

Qn28. Which of the following conclusions are supported by the planned pairwise comparisons just conducted? (Mark all that apply)

On Facebook, people valued significantly more posts after seeing a positive film clip than a negative film clip
On Facebook, people valued significantly more posts after seeing a negative film clip than a positive film clip.
On Twitter, people valued significantly more posts after seeing a positive film clip than a negative film clip,
On Twitter, people valued significantly more posts after seeing a negative film clip than a positive film clip.

Qn29. Continue using the file socialvalue.csv from the course materials. Conduct a nonparametric Aligned Rank Transform procedure on Valued by Clip and Social. To the nearest hundredth (two digits). What is the largest F statistic produced by this procedure?

Hint: use the ARTOOL library and its art function with the formula.
Valued ~ Clip * Social + (1|Subject)

The above formular expression indicates that subject is to be treated as a random effect.

Qn30. Pairwise comparisons among levels of clip and among levels of social could be conducted using the following code, but these are unnecessary after our main effects tests because each of these factors only has two levels.

*library(lsmeans)
lsmeans(artlm(m,”clip”), pairwise ~ Clip)
lsmenas(artlm(m, “social”), pairwise ~ social)*

True
False

Qn31. Conduct interaction contrasts (i.e difference-of-differences) to discover whether the difference in the number of valued posts after viewing a negative clip vs. a positive clip on Facebook was itself different that that same difference on Twitter. To the nearest hundredth (two digits), what is the chi-square statistic from such a test? Hint: use the phia library and its testInteractions function with the artlm function.

Qn32. The difference in the number of valued posts after people saw negative film clip vs positive film clips in the Facebook condition is significantly different from that difference in the Twitter condition. An interaction plot makes it clear that the difference in valued posts was much greater in the Facebook condition than in the Twitter condition, with positive film clips resulting in more valued posts.

Qn1. Download the file avatars.csv from the course materials. This file describes a study in which men and women were shown a virtual human avatar that was itself either male of female, and asked to craft a persona and write a day-in-th0life scenario for that avatar. The number of positive sentiments in either description were summed by a blind panel of judges. Examine the data and indicate what kind of experiment design this was.

• A 2 x 2 between-subjects design with factors for sex (M,F) and Avatar (M,F)
• A 2 x2 within-subjects design with factors for ex (M,F) and Avatar (M,F).
• A 2 x 2 mixed factorial design with a between-subjects factor for sex (M,F) and a within-subjects factor for Avatar (M,F).
• None of the above

Qn2. How many subjects took part in this experiment?

Qn3. To the nearest hundredth (two digits), on average how many positive sentiments were expressed for the most positive combination of sex and avatar?
Qn4. Create an interaction plot with Sex on the X-axis and Avatar as the traces. Do the lines cross?

Yes
No

Qn5. Create an interaction plot with Avatar on the X-axis and Sex as the traces. Do the line cross?

Yes,
No

Qn6. Conduct a factorial ANOVA on positives by sex avatar. To the nearest hundredth (two digits), what is the largest F statistic from such a test? Hint: Use the ez library and its exANOVA function. Pass both Sex and Avatar as the between parameter using a vector created with the “c” function.

Qn7. Which effects are statistically significant in the factorial ANOVA of positives by sex and avatar? (Mark all that apply)

Main effect of sex
Main effect of Avatar
Sex * Avatar interaction
None of the above

Planned Pairwise Comparisons

Qn8. Conduct two planned pairwise comparisons using independent samples t-tests. The first question is whether women produced different numbers of positive sentiments for male avatars versus female avatars. The second question is whether men produced different numbers of positive sentiments for male avatars versus female avatars. Assuming equal variances and using Holm’s sequential Bonferroni procedure to correct for multiple comparisons, what to within a ten-thousandth (four digits) is the lowest corrected p-value from these tests? Hint: You will need conjunctions with ampersands (&) to select the necessary rows for your t.test functions.

Qn9. Which of the following conclusions are supported by the planned pairwise comparisons just conducted? (Mark all that apply.)

Women made significantly more positive sentiments about male avatars that they did female avatars
Women made significantly more positive sentiments about female avatars than they did male avatars
Men made significantly more positive sentiments about male avatars than they did female avatars
Men made significantly more positive sentiments about female avatars than they did male avatars
None of the above

Qn10. Download the file notes.csv from the course materials. This file describes a study in which iphone and Android smartphone owners used their phone’s built-in note-taking app and then switched to an add-on third-party app, or vice-versa. The number of words they wrote in their notes apps over the course of the week was recorded. Examine the data and indicate what kind of experiment design this was

A 2 x 2 between-subjects design with factors for phone (iPhone, Android) and Notes (Built-in, Add-on).
A 2 x 2 **within-subjects design with factors** for Phone(iPhone, Android) and Notes (Built-in, Add-on)
A 2 x2 mixed factorial design with a between-subjects factor for Phone (iPhone, Andoid) and a within-subjects factor for Notes (Built-in, Add-on).
None of the above

Qn11. How many subjects took part in this experiment?

Qn12. To the nearest hundredth (two digits) on average how many words were record with the most heavily used combination of phone and notes?

Qn13. Create an interaction plot with Phone on the X-axis and Notes as the traces, Do the lines cross?

Yes
No

Qn14. Create an interaction plot with notes on the X-axis and Phones as the traces. Do the lines cross?

Yes
No

Qn15. Conduct a factorial ANOVA to test for any order effect that the presentation order of the Notes factor may have had. To the nearest ten-thousandth (four digits), what is the p-value for the order factor from such a test? Hint: use the ez library and its ezANOVA function, passing one between parameter and Order as the withing parameter.

Qn16. In our test of possible order effects, Mauchly’s test of sphericity is irrelevant because our within-subjects factor only has two levels, which cannot present a sphericity violation.

True
False

Qn17. Conduct a factorial ANOVA on words by phone and Notes. To the nearest hundredth (two digits), what is the largest F statistic produced by such a test? Hint: use the ez library and its ezANOVA function, passing one between parameter and on within parameter.

Qn18. Conduct two planned pairwise comparisons using paired-samples t-tests. The first question is whether iPhone users entered different numbers of words using built-in notes apps versus the add-on notes app. The second question is whether Android users entered different numbers of words using the built-in notes app versus the add-on notes app. Assuming equal variances and using Holm’s sequential Bonferroni procedure to correct for multiple comparisons, what to within a ten-thousandth (four digits) is the lowest p-value from these tests? Hint: use the reshape2 library and its dcast function to make a wide-format table with columns for subject, phone, Add-on, and Built-in, and then within each phone type, do a paired-samples t-test between the Add-on and built-in columns.

Qn19. Which of the following conclusions are supported by the planned pairwise comparisons just conducted? (Mark all that apply)

Android users entered significantly more words using the built-in notes app than theadd-on notes app.
Android users entered significantly more words using the add-on notes app than the built-in notes app.
iPhone users entered significantly more words using the add-on notes app than the built-in notes app.
None of the above