Dataset description

Our fake study has 3 primary independent variables:

• prime: What kind of primes was the participant given? neutral means neutral primes, elderly means elderly primes.
• prime.duration: How long (in minutes) were primes displayed to participants? There were 4 conditions: 1, 5, 10 or 30.
• grandparents: Did the participant have a close relationship with their grandparents? yes means yes, no means no, none means they never met their grandparents.

There was one primary dependent variable

• walk: How long (in seconds) did participants take to walk down the hallway?

There were 4 additional variables:

• id: The order in which participants completed the study
• age: Participants’ age
• sex: Participants’ sex
• attention: Did the participant pass an attention check? 0 means they failed the attention check, 1 means they passed.

Load the data

1. The text file containing the data is called priming.txt. It is available at http://nathanieldphillips.com/wp-content/uploads/2016/10/priming.txt. Load the data into R by running the following:

priming <- read.table("http://nathanieldphillips.com/wp-content/uploads/2016/10/priming.txt")

Here is how the data should look:

Understanding and cleaning the data

2. Get to know the data using View(), summary(), head() and str().

3. Look at the names of the dataframe with names(). Those aren’t very informative are they? Change the names to the correct values (make sure to use the naming scheme I describe in the dataset description).

Applying functions to columns

4. What was the mean participant age?

4b. Create a new vector object called age.v that contains the age data. Calculate the mean age from this vector. Do you get the same result as you did in the previous question?

5. What was the median walking time?

6. How many females were there? How many males?

7. What percent of participants passed the attention check (Hint: To calculate a percentage from a 0, 1 variable, use mean())

8. Walking time is currently in seconds. Add a new column to the dataframe called walk.m That shows the walking time in minutes rather than seconds.

Indexing and subsettting dataframes

Try to split your answers to these problems into two steps

Step 1: Index or subset the original data and store as a new object with a new name.

Step 2: Calculate the appropriate summary statistic using the new, subsetted object you just created.

9. What were the sexes of the first 10 participants?

10. What was the data for the 50th participant?

11. What was the mean walking time for the elderly prime condition?

12. What was the mean walking time for the neutral prime condition?

13. What was the mean walking time for participants less than 23 years old?

14. What was the mean walking time for females with a close relationship with their grandparents?

15. What was the mean walking time for males over 21 years old without a close relationship with their grandparents?

Checkpoint! If you got this far you are doing great!

Creating new dataframe objects

16. Create a new dataframe called priming.simple that only contains the columns id, prime, and walk

17. Some of the data don’t make any sense. For example, some walking times are negative, some prime values aren’t correct, and some prime.duration values weren’t part of the original study plan. Create a new dataframe called priming.c (aka., priming clean) that only includes rows with valid values for each column – do this by looking for an few strange values in each column, and by looking at the original dataset description. Additionally, only include participants who passed the attention check. Here’s a skeleton of how your code should look

# Create priming.c, a subset of the original priming data
#  (replace __ with the appropriate values)
priming.c <- subset(priming,
                    subset = sex %in% c(_____) & 
                             age > ____ &
                             attention == ___ &
                             prime %in% c(___) &
                             prime.duration %in% c(___) &
                             grandparents %in% c(___) &
                             walk > ___ )

18. How many participants gave valid data and passed the attention check? (Hint: Use the result from your previous answer!)

19. Of those participants who gave valid data and passed the attention check, what was the mean walking time of those given the elderly and neutral prime (calculate these separately).

Challenges

The following questions apply to your cleaned dataframe (priming.c)

20. Did the effect of priming condition on walking times differ between the first 50 and the last 50 participants. That is, what was the difference in the mean walking time between the two priming conditions for the first 50 participants? What about the last 50 participants? (Hint: Make sure to index the data using id!)?

21. Do you find evidence that a participant’s relationship with their grandparents affects how they responded to the primes?

22. Due to a computer error, the data from every participant with an even id number is invalid. Remove these data from your priming.c dataframe.

Submit!

Save and email your wpa_X_LastFirst.R file to me at nathaniel.phillips@unibas.ch. Then, go to https://goo.gl/forms/8pKm39PMS29JoLjI2 to complete the WPA submission form.