PSYC30013: Research Methods For Human Inquiry

title: ‘PSYC30013: Research Methods for Human Inquiry ‘
author: ‘Your name, your student id [Word count: XX]’
output:
word_document: default
—
# details
**Available:** 15 June 2020 at 3pm
**Due:** 19 June 2020 at 5pm
**Questions:** 30
**Word count:** 2000
**Total marks:** 100
**Instructions:** Complete instructions found on Canvas
# The story
For the assignment, we return to Bunnyland and Otherland one year later! When we last
saw them things had come to a tentative conclusion but substantial challenges remained.
Could people from both lands manage to work together to solve their food problem? Would
tensions flare? Would the fragile peace get disrupted? Would they be able to grow enough
food that nobody went hungry?
You’ll find answers to these questions in the vignettes below. We’ll visit each of our friends
and see what they’re doing and you’ll be able to help them with their statistics one last
time. Enjoy!
“`r setup, include=FALSE
# We’ll begin by loading up the libraries and data we need, as always.
# You don’t need to change anything in this code chunk.
knitr::opts_chunk$set(echo = TRUE)
# loading the libraries
library(tidyverse)
library(knitr)
library(here)
library(ggplot2)
library(lsr)
library(car)
library(RColorBrewer)
dm <- read_csv(file=here(“meatballs.csv”))
dl <- read_csv(file=here(“leaders.csv”))
da <- read_csv(file=here(“art.csv”))
dp <- read_csv(file=here(“punk.csv”))
db <- read_csv(file=here(“bunny.csv”))
dd <- read_csv(file=here(“adventures.csv”))
dm$person <- as.factor(dm$person)
dl$person <- as.factor(dl$person)
da$id <- as.factor(da$id)
da$art <- as.factor(da$art)
da$month <- as.factor(da$month)
dp$concert <- as.factor(dp$concert)
dp$time <- as.factor(dp$time)
dp$ad <- as.factor(dp$ad)
db$patient <- as.factor(db$patient)
dd$adventure <- as.factor(dd$adventure)
“`
# Meatballs
The opening up of relations between Bunnyland and Otherland has been great for Gladly.
He hadn’t realised how hard he found it as one of the few bears in a land full of bunnies
until things changed. He ended up making fast friends with Super Size, who also had
struggled with a similar alienation being so large and different. He was still large and
different, but with Gladly that didn’t seem to matter, and the two bonded over one of their
favourite topics: food.
Now that they were growing so much more food, both Gladly and Super Size’s persistent
hunger was assuaged, and the two friends turned their thoughts to how they could share
their love of food with others. One day Gladly had an idea: they could start a restaurant! It
was ideal for both of them. They could be around friends all day, and make use of all of the
fantastic new foods being grown in their land.
Before long, their restaurant was the social hub linking Bunnyland and Otherland. Located
on the border, Meatball Cafe was a vibrant place of food, conversation, and laughter. No
longer did Gladly wonder what he wanted to do with his life: he knew! And best of all, there
were meatballs every day.
One day Gladly and Super Size started wondering which of them tended to eat the most
meatballs. Luckily, they were meticulous business owners and kept track of such things. This
data can be found in the `dm` tibble, which has been loaded for you. Each row corresponds
to one day for either Gladly or Super Size (they worked different numbers of days so there
are different numbers of rows for each). There are three columns:
*person*: Gladly or Super Size.
*day*: A number indicating the day that the data was recorded.
*meatballs*: The number of meatballs eaten by that person on that day.
Their research question is whether Gladly and Super Size eat the same average number of
meatballs as each other. Your job in the next few problems is to use your R and statistics
knowledge to find the answer to this question.
## Q1 [2 marks]
First, as always, let’s visualise the data. Make a density plot with meatballs on the x axis.
Gladly and Shadow’s meatball consumption should be shown with two different densities
on the same facet, in different colours, semi-transparent. Make sure to have a title, an
informative axis label on the x axis, and a nice theme.
“`r q1
“`
## Q2 [2 marks]
There is one assumption you should check given this research question. Check it in the code
chunk below. In 50 words or less, indicate what assumption you are checking and what the
results of the code indicate. Was the assumption violated? How can you tell?
“`r q2
“`
*ANSWER: [Word count: ]*
## Q3 [4 marks]
Run the appropriate statistical test to evaluate the research hypothesis, given the results of
Q2. Report on the results in 90 words or less. In your report, don’t worry about including
descriptive statistics or the results of the assumption check from Q2 but do include an
explanation of which statistical test you used and why, what the predictor and outcome
variables were, the appropriate stats block, and the interpretation of this data in terms of
the research question (refer to the figure if you need to use information from it as well). Do
not worry about calculating or reporting effect size here.
“`r q3
“`
*ANSWER: [Word count: ]*
# Art
Not much has changed for Cuddly Paws, although all the drama made her think even more
about what she wanted out of life. She hadn’t enjoyed all of the tension and worry but had
enjoyed how everything had forced her out of her shell a bit and deepened her interactions
with people. Although she still preferred the comforts of art over people, she did expand
out of her comfort zone enough to begin selling her art.
To her surprise, business was very lucrative. There was a large audience for many of her
creations and before long, Cuddly Paws was able to set up her own studio. In it she
displayed her three main kinds of artwork: oil paints, watercolours, and pencil sketches. She
kept track of how many of each kind of artwork were sold each month and eventually grew
curious about whether there were any patterns in the data.
This data can be found in the `da` tibble, which has been loaded for you. Each row
corresponds to a single piece of artwork. The tibble contains three columns:
*id*: A unique identifier for that specific piece of artwork
*month*: The month the artwork was sold (December or January)
*art*: What kind of art the artwork was (oil, watercolour, or sketch)
Cuddly Paws would like to know if the distribution of kinds of artworks sold was similar in
December and January.
## Q4 [1.5 marks]
First let’s visualise the data. Make a table whose rows are the months, columns are the
kinds of artwork, and cells indicate the number of each kind sold that month. Use kable() to
display it and be sure to include an informative caption.
“`r q4
“`
## Q5 [5 marks]
For the sake of simplicity, let’s not worry about checking assumptions for this test and just
assume that all of them have been met. Run the appropriate statistical test to evaluate the
research hypothesis and report on the results in 90 words or less. In your report, don’t
worry about including descriptive statistics but do include an explanation of which statistical
test you used and why, the appropriate stats block, and the interpretation of this data in
terms of the research question. Be sure to also calculate effect size and report your answer
along with its interpretation.
“`r q5
“`
*ANSWER: [Word count: ]*
## Q6 [5 marks]
Suppose in addition that Cuddly Paws wants to determine whether the three kinds of
artwork differ from each other in terms of number of units sold overall. Run the appropriate
statistical test to answer that question (again not worrying about assumptions) and report
on the results in 90 words or less. In your report, don’t worry about including descriptive
statistics but do include an explanation of which statistical test you used and why, the
appropriate stats block, and the interpretation of this data in terms of the research
question. Be sure to also calculate effect size and report your answer along with its
interpretation.
“`r q6
“`
*ANSWER: [Word count: ]*
# New leaders
The path toward integrating the people of Bunnyland and Otherland was not easy, but was
helped a lot by the burgeoning friendship between LFB and Rainbow the unicorn. LFB will
always remember Rainbow as her first friend in Otherland. For her part, Rainbow has found
LFB to be a delightful companion and loves her fresh views and honest, straightforward
manner. The trust between the two of them carried the day several times when tensions
were high.
As a result, when it came time for the next elections, there was a groundswell of support for
the idea of electing LFB and Rainbow *together* to be leaders of their respective lands. LFB
and Rainbow were more surprised than anyone, but found that they truly enjoyed being
able to guide their people and work together to make things better.
It has now been five months since they were elected and Rainbow and LFB would like to
know how their citizens feel things are going. They therefore commission a poll which asks a
representative sample of people from both lands the following question: “How would you
rate how things are going relative to this time last year? Answer on a scale from 0 to 100,
where 0 indicates ‘very badly’, 100 indicates `very well’, and 50 indicates ‘neither better nor
worse.’”
This data can be found in the `dl` tibble, which has been loaded for you. The tibble contains
two columns:
*person*: ID of the person being polled
*answer*: answer given by that person in response to the poll question
Rainbow and LFB want to know if, on balance, most people think that things are better,
worse, or the same as they were last year.
## Q7 [1.5 marks]
First let’s visualise the data. Make a histogram of the *answer* variable using a bin width of
5. Make the outlines of the bars black and the insides the colour of your choice. Make sure
to have a title, an informative axis label on the x axis, and a nice theme.
“`r q7
“`
## Q8 [5 marks]
For the sake of simplicity, let’s not worry about checking assumptions for this test and just
assume that all of them have been met. Run the appropriate statistical test to evaluate the
research hypothesis and report on the results in 90 words or less. In your report, include the
mean for the *answer* variable as well as an explanation of which statistical test you used
and why, the appropriate stats block, and the interpretation of this data in terms of the
research question. Be sure to also calculate effect size and report your answer along with its
interpretation.
“`r q8
“`
*ANSWER: [Word count: ]*
# Punk music
The last year has been a good one for Flopsy. They have embraced their non-binary identity
and decided to change their name to something that felt more like “them”. They are now
known as Flaye and have formed a goth punk band along with Sissily the snake and Yellow
Quackers the duck. They’ve spent much of the last year touring around Bunnyland and
Otherland with their band, which is called Mental Limit Theorem.
Flaye is curious about what factors most affect attendance at their concerts. They have kept
copious data about various possible factors, contained in the `dp` tibble which has been
loaded for you. Each row in the tibble corresponds to one concert and the columns are the
following:
*concert*: a unique id for that concert
*time*: whether the concert was held in the afternoon, evening or at night
*ad*: whether the concert was advertised by word of mouth or posters in the local pubs
*attendance*: number of people attending that concert
Flaye’s first question is about their poster advertising. Considering only the concerts that
were advertised by posters, they wonder whether attendance varied depending on when
the concert was held.
## Q9 [3 marks]
First let’s visualise the data. Since Flaye is only interested in concerts that were advertised
by posters, you’ll first have to create a new tibble called `dp_post` which contains just that
data. Then use it make a bar plot showing how the attendance varies by time (attendance
should be on the y axis and time on the x axis). Your figure should have bars with nontransparent black outlines and error bars, a semi-transparent colour scheme for the bar
colours, the individual data points in the same colour as the bar colours (and not
transparent), and of course a title informative axis labels, and a nice theme.
“`r q9
“`
## Q10 [4 marks]
Let us evaluate Flaye’s research question about whether for the concerts that were
advertised by posters, attendance varied depending on when it was held. In order to do so,
there are two assumptions you should test the data in `dp_post` on. Run both of them and
report in 75 words or less what the results show (i.e., what each assumption is, whether it is
violated, and how you know that).
“`r q10
“`
*ANSWER: [Word count: ]*
## Q11 [4 marks]
Taking the results of Q10 into account, run the appropriate statistical test to evaluate
Flaye’s research hypothesis and report on the results in 90 words or less. In your report,
don’t worry about the descriptive statistics but do include an explanation of which statistical
test you used and why, the appropriate stats block, and the interpretation of this data in
terms of the research question. Don’t worry about effect size for this question.
“`r q11
“`
*ANSWER: [Word count: ]*
## Q12 [3 marks]
Next Flaye wants to evaluate all of the data, not just the concerts that were advertised by
posters. They are curious about the effect of both *time* and *ad* on attendance. Does
each predict attendance? Do they interact with each other in doing so? First, let’s visualise
the data (use `dp` now). This time your figure should be a violin plot with time on the x axis,
# of people attending on the y axis, and the ad type on two different facets. The boxes
should be coloured according to ad type, with the redundant legend removed. Superimpose
the actual data points on the violins, coloured whatever colour you like. As always your plot
should have an informative title and axis labels and a nice theme.
“`r q12