Consider the research question, what is the mean heartrate for adults who have been using aromatherapy foot massage for an extended period of time?

Question 1.

Note: this will be a tough question. If you get stuck, consider moving on to the other questions and then returning to it later. 

The dataset we are working with is in the long format (as opposed to wide). This format is useful for many reasons (and is the preferred format for R), but can make it challenging in repeated measures studies like the one we are working with, when we want to compare “before” and “after” measurements on the same individual, or an individual’s response under two conditions. This question asks you to do just that. 

Each participant’s level of anxiety was recorded using the State-Trait Anxiety Index at each of the three time points in the study. One of the questions the researchers for the study asked is whether aromatherapy foot massage has a positive impact on anxiety. To address this question, we would need to see whether the anxiety scores observed improved from baseline to after the intervention. The researchers defined an improvement as any change in anxiety score from baseline to after the intervention that was at least 3 points (e.g. moving from a score of 65 to 62 would be considered an improvement).

 Use R to create a new vector (i.e. variable) called change that characterizes whether each study participant experienced an improvement or not in their anxiety score from baseline to after the intervention.

 Report your complete R code (with #comments to help us understand the steps in your method) that is used to create this new vector, change. As well, use R to generate a table that summarizes the distribution of the change variable. Copy and paste the table into your answer.

Question 2.

For all parts of this question, write your answers in the language of the Problem. 

a) Consider the research question, what is the mean heartrate for adults who have been using aromatherapy foot massage for an extended period of time?

 Which of the following inference procedures would be most appropriate to address this research question? (simply copy/paste your selection as your answer)

·         t confidence interval for the population mean

·         large sample confidence interval for the population proportion

·         large sample test for the population proportion

·         t test for the population mean

 b) Is it valid to use the inference procedure you selected in part a for our data? To answer this question,

·         write each condition required for the confidence interval to be valid using the language of the Problem

·         specify explicitly whether the condition is valid or not (i.e. yes or no?);

·         provide a justification for your decision on its validity (i.e. explain how you came to your

conclusion about whether the condition is valid or not…remember, show us you understand!).

·         include any R code and associated Output you used as part of your justification (if applicable).

 Question 3.

Regardless of your answer to Question 2 (i.e. whether you determined the model fit our data or not), use R to conduct the inference procedure you identified for Question 2.

 a)  Restate the inference procedure that you chose in Question 2. Simply copy/paste your answer from Question 2 part a.

 b)  Provide the complete R code and associated Output you used to conduct the inference procedure. It should be clear from the code you report where any values/vectors you use as arguments come from (i.e. we should be able to figure out what you did based on your code).

 c)  Provide a proper scientific conclusion for the results of your analysis.

 Question 4.

Consider the following research question, Does engaging in aromatherapy foot massage have a positive therapeutic impact on blood pressure in healthy adults? This question could be addressed using a hypothesis test based on our dataset.

Describe what you would do with our dataset to address this research question. Your description should include mention of which all variables in the dataset you would need to work with, any sub-setting or data transformation you would need to complete, and specifically what type of comparisons (if any) you would be doing/focused on. Wherever possible, provide a brief explanation of why you are using those variables/subsets/comparisons (i.e. justify/explain your approach). Essentially, this question is asking you to “plan out” an analysis approach for this research question, based on our particular data set. Note that there is NO requirement for use of R (or even referencing R functions) for this question. For reference, a moderate length paragraph (or a series of numbered “steps” with explanation if you prefer) should be quite sufficient to address this question.