Whipps, J., Byra, M., Gerow, K. G., & Guseman, E. H. (2018). Evaluation of nighttime media use and sleep patterns in first-semester college students. American Journal of Health Behavior, 42(3), 47-55. DOI: 10.5993/AJHB.42.3.5 In this second portion of the Final Exam, you will critically evaluate a quantitative research study on a social science topic. Your instructor will post an announcement with the reference for the article assigned for the exam. The study will be from a peer-reviewed journal and published within the last 10 years. In the body of your critique, describe the statistical approaches used, the variables included, the hypothesis(es) proposed, and the interpretation of the results. In your conclusion, suggest other statistical approaches that could have been used and, if appropriate, suggest alternative interpretations of the results. This process will allow you to apply the concepts learned throughout the course in the interpretation of actual scientific research. Your critique must include the following sections and information: Introduction: This section will introduce the assigned peer-reviewed quantitative study. Identify clearly the research questions and/or hypothesis(es) as well as the purpose of the study. Methods: Describe the procedures and methods of data collection, measures/instruments used, the participants and how they were selected, and the statistical techniques used. Results: Summarize in this section the results presented in the study. Discussion: Evaluate the efficacy of the research study by discussing the following: Address the strengths, weaknesses, and limitations of the study and suggest future research directions. Include additional forms of statistical analyses as part of the suggestions for future research. Conclusion: Summarize the main points of your evaluation of the study. Explain how the statistical test used in the study could be applied to your future career. Give one example. Discuss how your ability to critique quantitative research could impact your future career.

#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt in table form:#2) For this dataset, what is our dependent variable? #3) How many independent variables do we have? #4) How many levels does each independent variable have (use the function unique(x) to check)? #5) Make a boxplot for this set of data (submit the image). What problem do you see?#6) What is our null hypothesis?#7) Now use the classic method to analyze this dataset using the format aov(x~factor(g)). # Save this as an object called hw1.anova. #NOTE: MAKE SURE TO USE factor() AROUND YOUR GROUPING VARIABLE SO IT IS TREATED AS A FACTOR, NOT AS A NUMERIC VARIABLE. # Then summarize these results using summary(hw1.anova). #8) Do we reject or do we fail to reject the null hypothesis?#9) Now let's use the t1way() function, which is based on trimmed means and can deal with heteroscedasticity.#Hint 1: First, reorganize your data using fac2list(x, g). Save your new list as hw1.list.#Hint 2: You will need to have loaded in the source code to use the t1way function.#10) Do we reject or do we fail to reject the null hypothesis from 1.9?----------------------------------------------------------------------------------------------------------------------------------------------------------Lab 10 lecture notes:#Lab 10#Lab 10-Contents#1. One-Way Independent Groups ANOVA (Equal Variance)#2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#---------------------------------------------------------------------------------# 1. One-Way Independent Groups ANOVA (Equal Variance)#--------------------------------------------------------------------------------- #Scenario for first exercise: # A professor is interested in the effect of visualization strategies#on test performance. In order to study this, he tells students in#his statistics class that they will have a 15 question exam in #two weeks. Then, he randomly assigns students to three groups. # # The first group is told to spend 15 min each day vizualizing #the outcome of getting an A on the test to vividly imagine #the exam with an "A" written on it and how great it will feel. # # The second group is a control group that does no visualization. ## The third group is told to spend 15 min each day visualizing#the process of studying for the exam: imagine the hours of studying,#reviewing their chapters, working through chapter problems, # quizzing themeselves, etc. # Two weeks later, the students take the exam and the professor # records how many questions the students answer correctly out of 15.#So, the groups are:#Group 1: Visualize Outcome (Grade)#Group 2: No visualization (Control)#Group 3: Visiualize Process (Studying)#######################################################Question: Are the groups here Independent?#######################################################We'll instroduce a few new terms: #Factor: A variable that consists of categories. #Levels: The categories of the Factor variable. #In our example above, the variable that contains#the groups is called "Group". #So, our factor is the variable "Group"#How many levels are there for the Group Factor?#Let's read in LAB10A.txtlab10a=read.table('LAB10A.txt', header=T)#While we can easily see the levels for the Group #factor we could also use a new command to figure out #the number of unique levels.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Number of Unique Levels: unique(data$variable)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#unique(lab10a$Group) #As we can see, there are 3 levels. 1, 2, and 3#Look at boxplot of each group using #boxplot(y~group, data=data)par(mfrow=c(1,1))boxplot(Score~Group, data=lab10a)#Do you think the means will be different (statistically)#between the groups?#Before we begin to test for differences between #the means, let's wrtie out our NUll #and Alternative Hyhpotheses#H0: The means are equal (mu1=mu2=mu3)#HA: At least one mean is different. #(eg. mu1 != mu2 OR mu1 != mu3 OR mu2 != mu3 )#To test the Hypothesis we can use the ANOVA function aov():#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## One-Way ANOVA: aov(y~factor(g), data)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##The aov() function assumes that the #variance is the same within each of the groups.mod1=aov(Score ~ factor(Group), data=lab10a)summary(mod1)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Do we Reject or Fail to Reject the Null?#Reject 0.00129 < .05 then Reject H0#What does this tell us? That the groups are different?#If so, how do we know which groups?#P-value we just got is called the Omnibus P-value, #which tells us that there are differences somewhere#With this P-value we often use the term #"Main Effect" to say that there is an effect of the#factor on the outcome.#In this instance we'd say that there is a Main Effect #of Group on the Score.#To Answer which groups are different, we need to first#conver the data into List Mode (a different way #of storing the data). We can convert the factor Group #to a list using the function fac2list(y, g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Convert Factors to List Data: fac2list(data$y, data$g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#listA=fac2list(lab10a$Score, lab10a$Group)listA #Once the data is in List Mode we have to use the#lincon() command from Dr. Wilcox's source code.#The lincon() package is used to compare the groups while#controlling for the experimentwise Type 1 error rate.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Compare Groups: lincon(list_name, tr=0.2)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##By default lincon() compares groups using 20% trimming. #We will set this to 0 for now:lincon(listA, tr=0) #result:# H0_1: mu1=mu2 --- p=0.32 ---Fail to reject# H0_2: mu1=mu3 --- p=0.0009 ---Reject# H0_3: mu2=mu3 --- p=0.008 ---Reject#---------------------------------------------------------------------------------# 2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#--------------------------------------------------------------------------------- # We just learned how to conduct a One-Way ANOVA # when the variances are equal within each group. # Now, we will learn how to conduct a One-Way ANOVA #for then the variance is not equal.# Let's start by reading in the LAB10B.txt datafile.lab10b=read.table('LAB10B.txt', header=T)# Then examine a boxplot of all of it.boxplot(Score~factor(Group), data=lab10b)# What do we notice about this boxplot?#-----# Let's start by running the equal variance ANOVA#on the data (which of course is WRONG!)mod2=aov(Score ~ factor(Group), data=lab10b) #---DON'Tsummary(mod2)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis# Do we Reject or Fail to Reject the Null?#Fail to reject: p-value=0.0895 > .05 !!!INCORRECT----#----# Now let's try to run the correct test that assumes #unequal variance. #We call this the Welch's test (just like in the t-test)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Welch's One-Way ANOVA: t1way(list_name, tr=0.20)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##In order to use this t1way function, #we will first need to convert the data to #List Mode using fac2list()listB=fac2list(lab10b$Score, lab10b$Group)t1way(listB, tr=0.2)# Do we Reject or Fail to Reject the Null?#Reject: p-value:0.04966583 <.05#Again, we can use the lincon() command to #find out Where the group differences are.#This time we will use the 20% trimming.lincon(listB, tr=0.2)# G1 and G2: p-value=0.92210409 > .05 Fail to reject# G1 and G3: p-value=0.19451518 > .05 Fail to reject#G2 and G3: p-value=0.03227316 < .05 Reject#